diff --git a/methodology/PhysicalRiskMethodology.pdf b/methodology/PhysicalRiskMethodology.pdf index e754a2e9..3951ef2f 100644 Binary files a/methodology/PhysicalRiskMethodology.pdf and b/methodology/PhysicalRiskMethodology.pdf differ diff --git a/methodology/PhysicalRiskMethodology.tex b/methodology/PhysicalRiskMethodology.tex index c78d7b12..c114e90e 100644 --- a/methodology/PhysicalRiskMethodology.tex +++ b/methodology/PhysicalRiskMethodology.tex @@ -23,7 +23,6 @@ \usepackage{framed} \usepackage{glossaries} \usepackage{graphicx} -\usepackage[colorlinks,citecolor=blue,urlcolor=black,linkcolor=black,bookmarks=false,hypertexnames=true]{hyperref} \usepackage{numprint} %\usepackage{physics} % causing problems; using different notation for bra-ket %\usepackage{sfmath}[cmbright] @@ -33,9 +32,35 @@ \usepackage{sistyle} \usepackage{subcaption} -\usepackage{bookmark} +%\usepackage{bookmark} +% Define hyperlink colors +\usepackage{xcolor} +\usepackage[linkcolor=blue, colorlinks=true, citecolor=blue, urlcolor=blue]{hyperref} \usepackage[normalem]{ulem} +\usepackage{mathtools} + +% Theorem environments +\usepackage{amsthm} +\newtheorem{theorem}{Theorem}[section] +\newtheorem{lemma}[theorem]{Lemma} +\newtheorem{corollary}[theorem]{Corollary} +\newtheorem{proposition}[theorem]{Proposition} + + +% Definition environments +\theoremstyle{definition} +\newtheorem{definition}[theorem]{Definition} +\newtheorem{example}[theorem]{Example} +\newtheorem{assumption}{Assumption} +\newtheorem{error}{Error} + +%% Remark environment +%% Define a 'remark' environment, numbered in sequence with theorem +\newtheorem{remarkx}[theorem]{Remark} +\newenvironment{remark} +{\pushQED{\qed}\renewcommand{\qedsymbol}{$\diamond$}\begin{remarkx}} + {\popQED\end{remarkx}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % General settings @@ -94,19 +119,23 @@ \title{Physical Climate Risk Methodology} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -\author{Joe Moorhouse\thanks{\textit{E-mail}: Joe.Moorhouse@gmail.com} +\author{Joe Moorhouse\thanks{\textit{E-mail}: joe.Moorhouse@gmail.com} \and - Florian Gallo\thanks{} + Florian Gallo + \and + Álvaro Romaniega + \and + Michael Levin \and Mariem Bouchaala\thanks{{E-mail}: mariem.bouchaala@essec.edu} \and - Davide Ferri\thanks{\textit{E-mail}:davide.ferri.94@gmail.com + Davide Ferri\thanks{\textit{E-mail}: davide.ferri.94@gmail.com \smallskip \newline% \indent The views expressed in this paper are those of the authors and do not necessarily reflect the views and policies of their respective employers.} } -\date{May 2023 [Draft]} +\date{Feb 2024 [Draft]} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -353,25 +382,76 @@ \subsubsection{Return-period-based approach} $f_{S_i}(s_i)$ is usually inferred from a type of hazard indicator data set which are known as \emph{hazard maps}. -\paragraph{Hazard maps.} Hazard maps are three-dimensional data sets from which intensities of hazard indicators can be looked up for different locations and different return periods, i.e. $H(x, y, \tau)$ provides hazard indicator intensity at location $(x, y)$ for return period $\tau$. That is, $H$ is the hazard indicator intensity such that the average time between events with intensity higher than $H$ is $\tau$. +\paragraph{Hazard maps.} Hazard maps are three-dimensional data sets from which intensities of hazard indicators can be looked up for different locations and different return periods, i.e., $h(x, y, \tau)$ provides hazard indicator intensity at location $(x, y)$ for return period $\tau$. That is, $h$ is the hazard indicator intensity such that the average time between events with intensity higher than $h$ is $\tau$. -In order to use hazard maps to derive probabilities, we must, strictly, specify the model of probability of occurrence of events with intensity higher than $H$ assumed by the data set. Occurrence may be modelled by a Poisson distribution as in Equation~\ref{Eq:Poisson}. This gives the probability of $k$ occurrences in time interval $t$ where $\tau$ is the return period. +\begin{remark} + More precisely, if we define $X^h_t$ as the number of events in a given period of length $t$ such that the intensity is greater than $h$, then, the return period (in years) satisfies \cite{RaschkeEtAl:2022}, + \begin{equation}\label{Eq:ReturnPeriodDef} + \mathbb{E}\left( X_t^{h(x,y, \tau)}\right)=\frac{t}{\tau}\,. + \end{equation} +For instance, assuming that $X_t^h$ follows a Poisson distribution with parameter $\lambda^h$ (see below), that is, $X_t^h\sim\text{Poi}\left(\lambda^H_t\right)$, then, +$$ +\mathbb{E}\left( X_t^{h(x,y, \tau)}\right)=\lambda^h_t\,, +$$ +where $\lambda^h$ is the intensity. Thus, $\lambda^h_t = t/\tau .$ By definition, we set $X_1^h\coloneqq X^h,$ that is, if there is no subindex we are assuming one year. +\end{remark} - \begin{equation} - \label{Eq:Poisson} - \mathbb{P}[X = k] = \frac{(t / \tau)^k}{k!} e^{-\frac{t}{\tau}} +In order to use hazard maps to derive probabilities, we must specify the model of probability of occurrence of events with intensity higher than $h$ assumed by the data set. Occurrence may be modelled by a Poisson distribution as in Equation~\ref{Eq:Poisson}. This gives the probability of $k$ occurrences in time interval $t$ where $\tau$ is the return period. + +\begin{equation} +\label{Eq:Poisson} +\mathbb{P}[{X^h_t} = k] = \frac{(t / \tau)^k}{k!} e^{-\frac{t}{\tau}} \end{equation} -Alternatively, the number of occurrences can be modelled as a Binomial distribution as in Equation~\ref{Eq:Binomial}, which provides the probability that $k$ occurrences occur in $n$ years, assuming that $\tau$ is specified in years. -According to Equation~\ref{Eq:Binomial}, \emph{the probability that in a single year there is at least one event with intensity of $H$ or higher is $1/\tau$}. Unless otherwise specified, this is the interpretation used for $\tau$. Note that for Equation~\ref{Eq:Poisson} this relationship only applies approximately. +\begin{remark} + It can be noted that by Taylor's Theorem, +\begin{equation}\label{Eq:PoissonApprox} + \mathbb{P}(X_t^h\ge1)=1-e^{-t/\tau}=t/\tau+o\left(t/\tau\right)\approx t/\tau\,, +\end{equation} + where the last approximation is valid if $t/\tau$ is ``small''. $\mathbb{P}(X_t^h\ge1)$ is also known as the \emph{occurrence exceedance probability}: the probability that the intensity of the highest-intensity event in $t$ exceeds $h$. Using this notation, $F'_S$ is more precisely defined: $F'_S(h) = \mathbb{P}(X_1^h\ge1)$. That is, $F'_S$ is the occurrence exceedance probability. +\end{remark} - \begin{equation} +Alternatively, the number of occurrences can be modelled as a Binomial distribution as in Equation~\ref{Eq:Binomial}, which provides the probability that $k$ years have at least one occurrence, for a period of $n$ years, assuming that $\tau$ is specified in years. This is given by +\begin{equation} \label{Eq:Binomial} - \mathbb{P}[X = k] = \binom{n}{k} (1/\tau)^k (1-1/\tau)^{n - k} + \binom{n}{k} (1/\tau)^k (1-1/\tau)^{n - k}. \end{equation} -$F_{S_i}$ can then be inferred from the hazard map for point-like assets. The curve $H(x_i, y_i, \tau)$ is looked up, providing $\tau$ and thereby annual exceedance probabilities for different intensities, $H$. In the case of a point-like asset, the look up is from spatial coordinates ($x_i$, $y_i$). A hazard map will have an associated co-ordinate reference system (CRS). For example the CRS of whole-globe maps is often the WGS84 World Geodetic System (EPSG:4326). In this case ($x_i$, $y_i$) represent longitude and latitude under that CRS. +According to Equation~\ref{Eq:Binomial}, \emph{the probability that in a single year there is at least one event with intensity of $h$ or higher is $1/\tau$}. Unless otherwise specified, this is the interpretation used for $\tau$. Note that for Equation~\ref{Eq:Poisson} this relationship only applies approximately, see \eqref{Eq:PoissonApprox}. + + +$F_{S_i}$ can then be inferred from the hazard map for point-like assets. The curve $h(x_i, y_i, \tau)$ is looked up, providing $\tau$ and thereby annual (occurrence) exceedance probabilities for different intensities, $h$. + +\begin{remark}\label{rem:OEPvsFS} + More precisely, let us define occurrence exceedance probability, $F'_{S,t}(\cdot)$, as + \begin{equation}\label{Eq:DefOEP} + F'_{S,t}(s)\coloneqq \mathbb{P}\left(\exists~S~\text{in the period }t/~S>s\right)=\mathbb{P}\left(X_t^s\ge 1\right)\,. + \end{equation} + How does this relate to the cumulative probability of each individual event, $F^{(e)}_S$? As before, $F'_S\coloneqq F'_{S,1}$. Then, if we consider a collection of identically distributed variables for each event $\{S^k\}_{k\in \mathbb{N}}$: + \begin{align*} + 1-F'_S(s)&=\sum_{k=0}^{\infty} \mathbb{P}\left(S^1\le s,\ldots, S^k\le s\right)\mathbb{P}\left(X^{H=0}=k\right)\\ + &=\sum_{k=0}^{\infty} \mathbb{P}\left(S\le s,\ldots, S\le s\right)\mathbb{P}\left(X^{H=0}=k\right)\,. + \end{align*} +Note that $X^{H=0}$ represents the number of events in the given period, no matter the intensity. Using Sklar's Theorem, there exist copulas $C_k$ such that + \begin{equation*} + 1-F'_S(s)=\sum_{k=0}^{\infty} C_k\left(F^{(e)}_S(s),\ldots, F^{(e)}_S(s)\right)\mathbb{P}\left(X^{H=0}=k\right)=:G(F^{(e)}_S(s))\,. + \end{equation*} +By the rectangle inequality for copulas, we know that $\tilde{C}_k(F^{(e)}_S(s))\coloneqq C_k\left(F^{(e)}_S(s),\ldots, F^{(e)}_S(s)\right)$ is non-decreasing. If furthermore, it is strictly increasing, it is sufficient\footnote{Consider the case of $\mathbb{P}(N=2)=1$ and the countermonotonicity copula $C_2(u,v)=(u+v-1)_+$, then $G\mid_{[0,1]}$ is not invertible, so $F^{(e)}_S$ cannot be always recovered from $O$.} to guarantee that $G$ is increasing. So we can recover $F^{(e)}_S=G^{-1}\circ O$. For instance, if we assume independence and a Poisson process, using the Taylor series of the exponential function, +\begin{equation} + 1-F'_S(s) = \sum_{k=0}^{\infty}F^{(e)}_S(s)^k \frac{\lambda^k}{k!}e^{-\lambda}=e^{F^{(e)}_S(s)\lambda -\lambda}\,, +\end{equation} +where $\lambda\coloneqq \lambda^{H=0}$. Thus, the relation between exceedance probability and occurrence exceedance probability is the following +$$ +F^{(e)}_S(s)=\frac1\lambda\log\left(1-F'_S(s)\right)+1\,. +$$ +Note that in this case, by \eqref{Eq:PoissonApprox}, +$$ +F'_S(s)\approx\frac1{\tau_s}\,. +$$ +\end{remark} + +In the case of a point-like asset, the look up is from spatial coordinates ($x_i$, $y_i$). A hazard map will have an associated co-ordinate reference system (CRS). For example the CRS of whole-globe maps is often the WGS84 World Geodetic System (EPSG:4326). In this case ($x_i$, $y_i$) represent longitude and latitude under that CRS. \paragraph{Effective impact.} Once $F_{S_i}$ and thereby $f_{S_i}$ is obtained, Equation~\ref{Eq:ImpactEffective} can be applied to obtain the impact distributions for each location $i$: @@ -614,27 +694,37 @@ \subsubsection{Probability bins from hazard maps} As an example, suppose that we have a hazard map for flood which contains return periods of 2, 5, 10, 25, 50, 100, 250, 500 and 1000 years. For a certain latitude/longitude the flood depths corresponding to the 9 return periods are, in metres: 0.06, 0.33, 0.51, 0.72, 0.86, 1.00, 1.15, 1.16 and 1.16. The data is shown together with the exceedance probability in Table~\ref{Table:HazardData}. \begin{table}[ht] - \caption{Example hazard event data.} + \caption{Example hazard event data. The exceedance probability calculated via $1 / \tau$ is provided as well as the value assuming a Poisson distribution. There is a significant difference for short return periods only. } \centering \begin{tabular}{c c c c} \hline - Return period (years) & Flood depth (m) & Exceedance probability \\ [0.5ex] + Return period (years) & Flood depth (m) & $F'_S(s)$ & Poisson $F'_S(s)$ \\ [0.5ex] \hline - 2 & 0.06 & 0.5 \\ - 5 & 0.33 & 0.2 \\ - 10 & 0.51 & 0.1 \\ - 25 & 0.72 & 0.04 \\ - 50 & 0.86 & 0.02 \\ - 100 & 1.00 & 0.01 \\ - 250 & 1.15 & 0.004 \\ - 500 & 1.16 & 0.002 \\ - 1000 & 1.16 & 0.001 \\ +2 & 0.06 & 0.5 & 0.39347 \\ +5 & 0.33 & 0.2 & 0.18127 \\ +10 & 0.51 & 0.1 & 0.09516 \\ +25 & 0.72 & 0.04 & 0.03921 \\ +50 & 0.86 & 0.02 & 0.01980 \\ +100 & 1.00 & 0.01 & 0.00995 \\ +250 & 1.15 & 0.004 & 0.00399 \\ +500 & 1.16 & 0.002 & 0.00199 \\ +1000 & 1.16 & 0.001 & 0.00099 \\ \hline \end{tabular} \label{Table:HazardData} \end{table} -The flood depths become the bin edges of the probability distribution and the probabilities are calculated from Equation~\ref{Eq:DiscreteExceed2}. For example, the probability of occurrence of a flood with depth in the range (0.86m, 1.00m] is $0.02 - 0.01 = 0.01$\footnote{Care is needed at either end of the curve. There is a 0.001 probability that flood depth exceeds 1.16m in this example; should this be included in the (point-like) 1.16m bin?}. Note that in defining a set of bins in this way, no assumption about the interpolation between the flood depths is required. However, if we assume this to be linear then this implies that the probability density is constant across each bin since $f_S = \frac{dF_S(s)}{ds}$. +The flood depths become the bin edges of the probability distribution and the probabilities are calculated from Equation~\ref{Eq:DiscreteExceed2}. +\begin{remark} + As before, $F'_S$ is the \emph{occurrence} exceedance probability. Similarly to Remark \ref{rem:OEPvsFS}, for $u>l$, + \begin{align*} + \mathbb{P}\left(\exists~S~/~S\in(l,u]\right)&=\mathbb{P}\left(\exists~S~/~S> l\right) - \mathbb{P}\left(\exists~S~/~S> u\right) \\ + &= + \mathbb{P}\left(X^l\ge 1\right) - \mathbb{P}\left(X^u\ge 1\right) = + F'_S(l)-F'_S(u)\,, + \end{align*} +\end{remark} +For example, the probability of occurrence of a flood with depth in the range (0.86m, 1.00m] is $0.02 - 0.01 = 0.01$\footnote{Care is needed at either end of the curve. There is a 0.001 probability that flood depth exceeds 1.16m in this example; should this be included in the (point-like) 1.16m bin?}. Note that in defining a set of bins in this way, no assumption about the interpolation between the flood depths is required. However, if we assume this to be linear then this implies that the probability density is constant across each bin since $f_S = \frac{dF_S(s)}{ds}$. \subsubsection{Vulnerability distributions and heuristics} @@ -786,13 +876,11 @@ \subsubsection{Data availability} - - %\section{Hazard and vulnerability models} \section{Inundation} \subsection{Hazard models} -Inundation is modelled as an acute risk using the approach of Section~\ref{SubSec:AcuteAssetImpactModel}. Hazard event models compatible with this method provide inundation depths for different annual probabilities of occurrence -- or equivalently return periods. The need for sufficient granularity in the set of return periods is discussed in \cite{WardEtAl:2011}. +Inundation is modelled as an acute risk using the approach of Section~\ref{Sec:MathematicalDescriptionOfAssetImpactModel}. Hazard event models compatible with this method provide inundation depths for different annual probabilities of occurrence -- or equivalently return periods. The need for sufficient granularity in the set of return periods is discussed in \cite{WardEtAl:2011}. Inundation hazards are incorporated into physical risk calculations using the World Resource Institute (WRI) Aqueduct flood model \cite{WardEtAl:2020} which has relatively high return-period granularity. This is based on the global modelling approach of \cite{WardEtAl:2013}. @@ -822,24 +910,19 @@ \subsubsection{Acute hazard models} -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -%%%%%%%%%%%%%%%%%%%%% Changes - Heat Vulnerability Model %%%%%%%%%%%%%%%%%%%%% -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - - \subsection{Heat Vulnerability Model} \label{SubSec:HeatVulnerabilityModel} \subsubsection{Impact of Temperature on labour productivity} -The Heat vulnerability model presented in this section is based on the approach introduced in 'Temperature And Work: Time allocated to work under varying climate and labor market conditions' (2021)\cite{TemperatureAndWork:2021}. The paper uses survey data to estimate labour allocation decisions in the United States (US) based on temperature. It does not extend the analysis beyond the US. It replicates previous research done (original GZN method \cite{TemperatureAndTheAllocationofTime:2014}.) whilst extending the period of data used and adding an assessment based on the economic cycle: the main innovation is that it includes the economic cycle by splitting out the 2008 financial crisis first through segmented regressions and following them by using an indicator variable: the non-recession period is 2003-2007 and 2015-2018. The Great Recessions period is 2008-2014. The methodology in only applied to climate exposed sectors: agriculture, forestry, fishing and hunting, construction, mining, and transportation and utilities. +The heat vulnerability model presented in this section is based on the approach introduced in \cite{NeidellEtAl:2021}. The paper uses survey data to estimate labour allocation decisions in the United States (US) based on temperature. It does not extend the analysis beyond the US. It replicates previous research done (original GZN method \cite{NeidellEtAl:2014}.) whilst extending the period of data used and adding an assessment based on the economic cycle: the main innovation is that it includes the economic cycle by splitting out the 2008 financial crisis first through segmented regressions and following them by using an indicator variable: the non-recession period is 2003-2007 and 2015-2018. The `Great Recession' period is 2008-2014. The methodology in only applied to climate exposed sectors: agriculture, forestry, fishing and hunting, construction, mining, and transportation and utilities. -The paper main conclusions are: +The paper's main conclusions are: \begin{itemize} - \item A statistically significant impact of 2.6 minutes lost per degree of temperature above $90^\circ F$ during normal economic periods and no relationship during a recession. This result is converted into the Celsius scale as \emph{physrisk} decided to take that scale as a reference. The 2.6 minutes is multiplied by a scaling factor of 1.8, which returns \textbf{an impact of 4.7 minutes lost per degree of temperature above $32.2^\circ C$}. - \item When using an indicator variable and linear regression the estimated impact was 5.6 minutes under Fahrenheit scale (respectively 10.08 minutes under Celsius scale) during normal economic periods, but the parameter was insignificant. + \item A statistically significant impact of 2.6 minutes lost per degree of temperature above $90^\circ$F during normal economic periods and no relationship during a recession. This result is converted into the Celsius scale as \emph{physrisk} decided to take that scale as a reference. The 2.6 minutes is multiplied by a scaling factor of 1.8, which returns \textbf{an impact of 4.7 minutes lost per degree of temperature above $\mathbf{32.2^\circ}$C}. + \item When using an indicator variable and linear regression the estimated impact was 5.6 minutes under Fahrenheit scale (respectively 10.08 minutes under Celsius scale) during normal economic periods, but the parameter was not significant at the 90\% level. \item No relationship between temperature and work allocation with temperatures below $32.2^\circ C$. \item Focus on labour allocation decisions, it does not account for other impacts such as reducing productivity. \end{itemize} @@ -850,7 +933,7 @@ \subsubsection{Impact of Temperature on labour productivity} \item The conclusion holds for US (so maybe also for the EU as well other developed countries), but not for developing countries which experience more economic turmoil periods. \end{itemize} -The paper attempts to estimate economic cost (assuming the impact of 4.7 minutes lost per degree of temperature above $32.2^\circ C$ during normal economic periods) however it only focuses on the direct costs and does not account for feedback effects (reducing the labour productivity will result in a decrease of the products available, wages, demand, etc.). Figure \ref{fig:economiccost} provides the results, as extracted from the paper: +The paper attempts to estimate economic cost (assuming the impact of 4.7 minutes lost per degree of temperature above $32.2^\circ $C during normal economic periods) however it only focuses on the direct costs and does not account for feedback effects (reducing the labour productivity will result in a decrease of the products available, wages, demand, etc.). Figure \ref{fig:economiccost} provides the results, as extracted from the paper: \begin{figure}[h] \centering \includegraphics[scale = 0.6]{plots/economic cost.png} @@ -858,17 +941,18 @@ \subsubsection{Impact of Temperature on labour productivity} \label{fig:economiccost} \end{figure} -Last but not least, it is worth to note that the results of this paper are interpreted as the impact of \textbf{chronic increase in temperature}. +Last but not least, it is worth noting that the results of this paper are interpreted as the impact of \textbf{chronic increase in temperature}. %NEW %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -The GZN hazard model is based on the projections of the climate variable 'daily maximum temperature'. The projected data spans for 20 years. Then, the following statistical processing is used to compute the daily cooling degree days: if the daily number of degrees is higher than $32.2^\circ C$ then the cooling degree days is equal to the number of degrees, and 0 if not. Once the 20-year projected time series of daily cooling degree days is computed, the indicator of the GZN hazard data is calculated as an annual average of the cooling degree days (over all the days within the 20-year period). That indicator will be used afterwards as an input in the impact function to compute the number of minutes of labour productivity loss. Figure \ref{fig:GZL-Hazard} provides a summary of the methodology: +The GZN hazard model is based on the projections of the climate variable `daily maximum temperature'. The projected data spans for 20 years. Then, the following statistical processing is used to compute the daily cooling degree days: if the daily number of degrees is higher than $32.2^\circ C$ then the cooling degree days is equal to the number of degrees, and 0 if not. Once the 20-year projected time series of daily cooling degree days is computed, the indicator of the GZN hazard data is calculated as an annual average of the cooling degree days (over all the days within the 20-year period). That indicator will be used afterwards as an input in the impact function to compute the number of minutes of labour productivity loss. Figure \ref{fig:GZL-Hazard} provides a summary of the methodology: + +$T^\text{max}$ is the daily maximum near-surface temperature. This is a point of attention, given that heating and cooling degree day indicators are often calculated using the daily \emph{average} near-surface temperature. + +\begin{equation} + \label{Eq:degree_days} + I^\text{dd} = \frac{365}{n_y} \sum_{i = 1}^{n_y} | T^\text{max}_i - T^\text{ref} | +\end{equation} -\begin{figure}[h] - \centering - %\includegraphics[scale = 0.8]{plots/GZL-Hazard.PNG} - \caption{Cooling degree days and labour availability} - \label{fig:GZL-Hazard} -\end{figure} \subsubsection{Uncertainty around the vulnerability Heat model} @@ -917,7 +1001,7 @@ \subsubsection{Uncertainty around the vulnerability Heat model} The paper shows that labour allocation decisions are sensitive to where in the economic cycle the US is; during a recession there does not appear to be a relationship between labour allocation and temperature. In order to measure the uncertainty explained by the economic cycle, one might consider the probability of a recession as a Bernoulli random variable with a probability p. Based on this there are two possibly approaches. A first approach is a Monte-Carlo like approach where one can randomly sample the 1, 0 value whether a recession occurs at each period on a time path. A second approach would be to use the expected value of the probability of the recession and estimate the impact as: \begin{equation} \label{Eq:economiccycle} - Forecasted \ Minutes \ Lost = p \times 0 + (1-p) \times EV(X) + \mathrm{Forecast \ minutes \ lost} = p \times 0 + (1-p) \times EV(X) \end{equation} Where $X$ is the variable that refers to Minutes Lost during normal economic cycle The second approach is attractive in its simplicity and ensuring the model does not lose its focus. @@ -932,15 +1016,15 @@ \subsubsection{Uncertainty around the vulnerability Heat model} % publisher={University of Oregon} % year={2022}, %} -Instead, we focus on the \textbf{model parameter uncertainty approach}. In \cite{ZhangAndShindell:2021}, there is a linear relationship between temperature and work minutes lost ($\beta$),and a constraint is applied to ensure that total time allocation sums to 24 hours, which returns a non-linear regression model at the end. Given that the main coefficient of interest is denoted $\beta$, an inference is applied assuming that $\beta$ follows a $Student-T$ distribution: +Instead, we focus on the \textbf{model parameter uncertainty approach}. In \cite{ZhangAndShindell:2021}, there is a linear relationship between temperature and work minutes lost ($\beta$),and a constraint is applied to ensure that total time allocation sums to 24 hours, which returns a non-linear regression model at the end. Given that the main coefficient of interest is denoted $\beta$, an inference is applied assuming that $\beta$ follows a Student's-t distribution: \begin{equation} \label{Eq:uncertaintyStudentT} - d\beta \sim T(\beta, SE, N-K) + \delta \beta \sim T(\beta, \mathrm{SE}, N-K) \end{equation} Where $SE$ is the Standard Error of the coefficient and $(N-K)$ is the number of degrees of freedom, $N$ is the number of observations and $K$ is the number of model parameters. Hence, one can estimate the coefficients of the confidence interval (CI) at a given level of confidence $CI\%$: \begin{equation} \label{Eq:CIStudent} - d\beta_{CI\%} = \beta \pm T(p) \times SE + \beta_{\mathrm{CI\%}} = \beta \pm T(p) \times SE \end{equation} Where $T(p)$ donates the probability density function (pdf) of the student $T$ distribution with probability $p$ which corresponds to the $CI\%$, the standard error $SE = 2.23 \ min$ and $\beta = - 4.68$. diff --git a/methodology/PhysicalRiskMethodologyBibliography.bib b/methodology/PhysicalRiskMethodologyBibliography.bib index 7bad0134..ea499e7e 100644 --- a/methodology/PhysicalRiskMethodologyBibliography.bib +++ b/methodology/PhysicalRiskMethodologyBibliography.bib @@ -202,7 +202,6 @@ @book{Nelsen:2007 address = {New York~(NY)} } -% TemperatureAndTheAllocationofTime @article{NeidellEtAl:2014, title={Temperature and the Allocation of Time: Implications for Climate Change}, author={Matthew Neidell, Joshua Graff Zivin}, @@ -214,12 +213,15 @@ @article{NeidellEtAl:2014 publisher={The University of Chicago Press on behalf of the Society of Labor Economists and the NORC at the University of Chicago} } -% TemperatureAndWork @article{NeidellEtAl:2021, title={Temperature and work: Time allocated to work under varying climate and labor market conditions}, - author={Matthew Neidell, Joshua Graff Zivin, Megan Sheahan, Jacqueline Willwerth, Charles Fant, Marcus Sarofim, Jeremy Martinich}, - journal={PLOS ONE}, + author={Neidell, Matthew and Graff Zivin, Joshua and Sheahan, Megan and Willwerth, Jacqueline and Fant, Charles and Sarofim, Marcus and Martinich, Jeremy}, + journal={PloS one}, + volume={16}, + number={8}, + pages={e0254224}, year={2021}, + publisher={Public Library of Science San Francisco, CA USA} } @misc{OasisFinancialModule, @@ -256,6 +258,17 @@ @article{RangerEtAl:2022 publisher={Washington, DC: World Bank} } +@article{RaschkeEtAl:2022, + title={About the return period of a catastrophe}, + author={Raschke, Mathias}, + journal={Natural Hazards and Earth System Sciences}, + volume={22}, + number={1}, + pages={245--263}, + year={2022}, + publisher={Copernicus GmbH} +} + @book{ReisingerEtAl:2020, title={The Concept of Risk in the IPCC Sixth Assessment Report: A Summary of Cross-Working Group Discussions}, author={Reisinger, Andy and Howden, Mark and Vera, Carolina and others}, diff --git a/src/physrisk/api/v1/common.py b/src/physrisk/api/v1/common.py index 639fecdf..f753f026 100644 --- a/src/physrisk/api/v1/common.py +++ b/src/physrisk/api/v1/common.py @@ -69,10 +69,25 @@ class Countries(BaseModel): class IntensityCurve(BaseModel): - """Intensity curve of an acute hazard.""" + """Hazard indicator intensity curve. Acute hazards are parameterized by event intensities and + return periods in years. Chronic hazards are parameterized by a set of index values. + Index values are defined per indicator.""" - intensities: List[float] - return_periods: List[float] + intensities: List[float] = Field([], description="Hazard indicator intensities.") + return_periods: Optional[List[float]] = Field( + [], description="[Deprecated] Return period in years in the case of an acute hazard." + ) + index_values: Optional[List[float]] = Field( + [], + description="Set of index values. \ + This is return period in years in the case of an acute hazard or \ + a set of indicator value thresholds in the case of a multi-threshold chronic hazard.", + ) + index_name: str = Field( + "", + description="Name of the index. In the case of an acute hazard this is 'return period'; \ + for a multi-threshold chronic hazard this is 'threshold'.", + ) class ExceedanceCurve(BaseModel): diff --git a/src/physrisk/data/image_creator.py b/src/physrisk/data/image_creator.py index 271a695b..8cbba013 100644 --- a/src/physrisk/data/image_creator.py +++ b/src/physrisk/data/image_creator.py @@ -1,4 +1,5 @@ import io +import logging from functools import lru_cache from pathlib import PurePosixPath from typing import Callable, List, NamedTuple, Optional @@ -10,6 +11,8 @@ from physrisk.data import colormap_provider from physrisk.data.zarr_reader import ZarrReader +logger = logging.getLogger(__name__) + class Tile(NamedTuple): x: int @@ -46,7 +49,11 @@ def convert( Returns: bytes: Image data. """ - image = self._to_image(path, colormap, tile=tile, min_value=min_value, max_value=max_value) + try: + image = self._to_image(path, colormap, tile=tile, min_value=min_value, max_value=max_value) + except Exception as e: + logger.exception(e) + image = Image.fromarray(np.array([[0]]), mode="RGBA") image_bytes = io.BytesIO() image.save(image_bytes, format=format) return image_bytes.getvalue() diff --git a/src/physrisk/data/static/hazard/inventory.json b/src/physrisk/data/static/hazard/inventory.json index ecd772dc..c7a1f688 100644 --- a/src/physrisk/data/static/hazard/inventory.json +++ b/src/physrisk/data/static/hazard/inventory.json @@ -10,7 +10,7 @@ "params": {}, "display_name": "Flood depth/baseline (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods baseline riverine model using historical data.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods baseline riverine model using historical data.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -63,7 +63,7 @@ "params": {}, "display_name": "Flood depth/NorESM1-M (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods riverine model using GCM model from\nBjerknes Centre for Climate Research, Norwegian Meteorological Institute.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods riverine model using GCM model from\nBjerknes Centre for Climate Research, Norwegian Meteorological Institute.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -128,7 +128,7 @@ "params": {}, "display_name": "Flood depth/GFDL-ESM2M (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nGeophysical Fluid Dynamics Laboratory (NOAA).\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nGeophysical Fluid Dynamics Laboratory (NOAA).\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -193,7 +193,7 @@ "params": {}, "display_name": "Flood depth/HadGEM2-ES (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model:\nMet Office Hadley Centre.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model:\nMet Office Hadley Centre.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -258,7 +258,7 @@ "params": {}, "display_name": "Flood depth/IPSL-CM5A-LR (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nInstitut Pierre Simon Laplace\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods riverine model using GCM model from\nInstitut Pierre Simon Laplace\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -323,7 +323,7 @@ "params": {}, "display_name": "Flood depth/MIROC-ESM-CHEM (WRI)", "display_groups": [], - "description": "World Resource Institute Aqueduct Floods riverine model using\n GCM model from Atmosphere and Ocean Research Institute\n (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency\n for Marine-Earth Science and Technology.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "World Resource Institute Aqueduct Floods riverine model using\n GCM model from Atmosphere and Ocean Research Institute\n (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency\n for Marine-Earth Science and Technology.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -388,7 +388,7 @@ "params": {}, "display_name": "Flood depth/baseline, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resources Institute Aqueduct Floods baseline coastal model using historical data. Model excludes subsidence.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resources Institute Aqueduct Floods baseline coastal model using historical data. Model excludes subsidence.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -443,7 +443,7 @@ "params": {}, "display_name": "Flood depth/95%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 95th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 95th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -508,7 +508,7 @@ "params": {}, "display_name": "Flood depth/5%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 5th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods coastal model, excluding subsidence; 5th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -573,7 +573,7 @@ "params": {}, "display_name": "Flood depth/50%, no subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; 50th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; 50th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -638,7 +638,7 @@ "params": {}, "display_name": "Flood depth/baseline, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; baseline (based on historical data).\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, excluding subsidence; baseline (based on historical data).\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -693,7 +693,7 @@ "params": {}, "display_name": "Flood depth/95%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 95th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 95th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -758,7 +758,7 @@ "params": {}, "display_name": "Flood depth/5%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 5th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 5th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -823,7 +823,7 @@ "params": {}, "display_name": "Flood depth/50%, with subsidence (WRI)", "display_groups": [], - "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 50th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00c3\u2014 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", + "description": "\nWorld Resource Institute Aqueduct Floods model, including subsidence; 50th percentile sea level rise.\n\n \nThe World Resources Institute (WRI) [Aqueduct Floods model](https://www.wri.org/aqueduct) is an acute riverine and coastal flood hazard model with a spatial resolution of 30 \u00d7 30 arc seconds (approx. 1 km at the equator). Flood intensity is provided as a _return period_ map: each point comprises a curve of inundation depths for 9 different return periods (also known as reoccurrence periods). If a flood event has depth $d_i$ with return period of $r_i$ this implies that the probability of a flood event with depth greater than $d_i$ occurring in any one year is $1 / r_i$; this is the _exceedance probability_. Aqueduct Floods is based on Global Flood Risk with IMAGE Scenarios (GLOFRIS); see [here](https://www.wri.org/aqueduct/publications) for more details.\n\nFor more details and relevant citations see the\n[OS-Climate Physical Climate Risk Methodology document](https://github.com/os-climate/physrisk/blob/main/methodology/PhysicalRiskMethodology.pdf).\n", "map": { "colormap": { "min_index": 1, @@ -899,7 +899,7 @@ "display_groups": [ "Mean degree days" ], - "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00c2\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", + "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", "map": { "colormap": { "min_index": 1, @@ -1572,7 +1572,7 @@ "display_groups": [ "Days with average temperature above" ], - "description": "Days per year for which the average near-surface temperature 'tas' is above a threshold specified in \u00c2\u00b0C.\n\n$$\nI = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^{avg}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the daily average near-surface temperature for day index $i$ in \u00c2\u00b0C, $n_y$ is the number of days in the year\nand $T^\\text{ref}$ is the reference temperature.\nThe OS-Climate-generated indicators are inferred from downscaled CMIP6 data. This is done for 6 Global Circulation Models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nIndicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", + "description": "Days per year for which the average near-surface temperature 'tas' is above a threshold specified in \u00b0C.\n\n$$\nI = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{\\; \\, T^{avg}_i > T^\\text{ref}} \\nonumber\n$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the daily average near-surface temperature for day index $i$ in \u00b0C, $n_y$ is the number of days in the year\nand $T^\\text{ref}$ is the reference temperature.\nThe OS-Climate-generated indicators are inferred from downscaled CMIP6 data. This is done for 6 Global Circulation Models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nIndicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", "map": { "colormap": { "min_index": 1, @@ -1731,7 +1731,7 @@ "display_groups": [ "Mean degree days" ], - "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00c2\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", + "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", "map": { "colormap": { "min_index": 1, @@ -1823,7 +1823,7 @@ "display_groups": [ "Mean degree days" ], - "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00c2\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", + "description": "Degree days indicators are calculated by integrating over time the absolute difference in temperature\nof the medium over a reference temperature. The exact method of calculation may vary;\nhere the daily maximum near-surface temperature 'tasmax' is used to calculate an annual indicator:\n\n$$\nI^\\text{dd} = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} | T^\\text{max}_i - T^\\text{ref} | \\nonumber\n$$\n\n$I^\\text{dd}$ is the indicator, $T^\\text{max}$ is the daily maximum near-surface temperature, $n_y$ is the number of days in the year and $i$ is the day index.\nand $T^\\text{ref}$ is the reference temperature of 32\u00b0C. The OS-Climate-generated indicators are inferred\nfrom downscaled CMIP6 data, averaged over 6 models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nThe indicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050)\nand 2050 (2041-2060).\n", "map": { "colormap": { "min_index": 1, @@ -1910,11 +1910,11 @@ "NorESM" ] }, - "display_name": "Weeks with average temperature above threshold in degrees celsius/{gcm}", + "display_name": "Weeks with average water temperature above threshold in \u00b0C/{gcm}", "display_groups": [ - "Weeks with average temperature above threshold in degrees celsius" + "Weeks with average water temperature above threshold in \u00b0C" ], - "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00c2\u00b0C:\n\n$$I = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^{avg}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00c2\u00b0C, $n_y$ is the number of weeks in the sample\nand $T^\\text{ref}$ is the reference temperature.\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP5 data. This is done for 5 Global Circulation Models: GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M.\nThe downscaled data is sourced from the [Futurestreams dataset](https://geo.public.data.uu.nl/vault-futurestreams/research-futurestreams%5B1633685642%5D/original/waterTemp/) on the data publication platform of Utrecht University.\nIndicators are generated for periods: 'historical' (averaged over 1976-2005), 2020 (2006-2030), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", + "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00b0C:\n\n$$I = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^{avg}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00b0C, $n_y$ is the number of weeks in the sample\nand $T^\\text{ref}$ is the reference temperature.\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP5 data. This is done for 5 Global Circulation Models: GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M.\nThe downscaled data is sourced from the [Futurestreams dataset](https://geo.public.data.uu.nl/vault-futurestreams/research-futurestreams%5B1633685642%5D/original/waterTemp/) on the data publication platform of Utrecht University.\nIndicators are generated for periods: 'historical' (averaged over 1976-2005), 2020 (2006-2030), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", "map": { "colormap": { "min_index": 1, @@ -2033,11 +2033,11 @@ "indicator_model_id": null, "indicator_model_gcm": "E2O", "params": {}, - "display_name": "Weeks with average temperature above threshold in degrees celsius/E2O", + "display_name": "Weeks with average water temperature above threshold in \u00b0C/E2O", "display_groups": [ - "Weeks with average temperature above threshold in degrees celsius" + "Weeks with average water temperature above threshold in \u00b0C" ], - "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00c2\u00b0C:\n\n$$I = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^{avg}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00c2\u00b0C, $n_y$ is the number of weeks in the sample\nand $T^\\text{ref}$ is the reference temperature.\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP5 data. This is done for 5 Global Circulation Models: GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M.\nThe downscaled data is sourced from the [Futurestreams dataset](https://geo.public.data.uu.nl/vault-futurestreams/research-futurestreams%5B1633685642%5D/original/waterTemp/) on the data publication platform of Utrecht University.\nIndicators are generated for periods: 'historical' (averaged over 1979-2005), 2020 (2006-2030), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", + "description": "Weeks per year for which the average water temperature is above a threshold specified in \u00b0C:\n\n$$I = \\frac{52}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^{avg}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $T^\\text{avg}_i$ is the weekly average water temperature for week index $i$ in \u00b0C, $n_y$ is the number of weeks in the sample\nand $T^\\text{ref}$ is the reference temperature.\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP5 data. This is done for 5 Global Circulation Models: GFDL-ESM2M, HadGEM2-ES, ISPL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M.\nThe downscaled data is sourced from the [Futurestreams dataset](https://geo.public.data.uu.nl/vault-futurestreams/research-futurestreams%5B1633685642%5D/original/waterTemp/) on the data publication platform of Utrecht University.\nIndicators are generated for periods: 'historical' (averaged over 1979-2005), 2020 (2006-2030), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", "map": { "colormap": { "min_index": 1, @@ -2113,11 +2113,11 @@ "NorESM2-MM" ] }, - "display_name": "Days with wet-bulb globe temperature above threshold in degrees celsius/{gcm}", + "display_name": "Days with wet-bulb globe temperature above threshold in \u00b0C/{gcm}", "display_groups": [ - "Days with wet-bulb globe temperature above threshold in degrees celsius" + "Days with wet-bulb globe temperature above threshold in \u00b0C" ], - "description": "Days per year for which the 'Wet Bulb Globe Temperature' indicator is above a threshold specified in \u00c2\u00b0C:\n\n$$I = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^\\text{WBGT}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $n_y$ is the number of days in the sample and $T^\\text{ref}$ is the reference temperature. \n\nThe 'Wet-Bulb Globe Temperature' (WBGT) indicator is calculated from both the average daily near-surface surface temperature in \u00c2\u00b0C denoted $T^\\text{avg}$ and the water vapour partial pressure in kPa denoted $p^\\text{vapour}$:\n\n$$\nT^\\text{WBGT}_i = 0.567 \\times T^\\text{avg}_i + 0.393 \\times p^\\text{vapour}_i + 3.94\n$$\n\nThe water vapour partial pressure $p^\\text{vapour}$ is calculated from relative humidity $h^\\text{relative}$:\n\n$$\np^\\text{vapour}_i = \\frac{h^\\text{relative}_i}{100} \\times 6.105 \\times \\exp \\left( \\frac{17.27 \\times T^\\text{avg}_i}{237.7 + T^\\text{avg}_i} \\right)\n$$\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP6 data, averaged over for 6 Global Circulation Models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nIndicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", + "description": "Days per year for which the 'Wet Bulb Globe Temperature' indicator is above a threshold specified in \u00b0C:\n\n$$I = \\frac{365}{n_y} \\sum_{i = 1}^{n_y} \\boldsymbol{\\mathbb{1}}_{T^\\text{WBGT}_i > T^\\text{ref}}$$\n\n$I$ is the indicator, $n_y$ is the number of days in the sample and $T^\\text{ref}$ is the reference temperature. \n\nThe 'Wet-Bulb Globe Temperature' (WBGT) indicator is calculated from both the average daily near-surface surface temperature in \u00b0C denoted $T^\\text{avg}$ and the water vapour partial pressure in kPa denoted $p^\\text{vapour}$:\n\n$$\nT^\\text{WBGT}_i = 0.567 \\times T^\\text{avg}_i + 0.393 \\times p^\\text{vapour}_i + 3.94\n$$\n\nThe water vapour partial pressure $p^\\text{vapour}$ is calculated from relative humidity $h^\\text{relative}$:\n\n$$\np^\\text{vapour}_i = \\frac{h^\\text{relative}_i}{100} \\times 6.105 \\times \\exp \\left( \\frac{17.27 \\times T^\\text{avg}_i}{237.7 + T^\\text{avg}_i} \\right)\n$$\n\nThe OS-Climate-generated indicators are inferred from downscaled CMIP6 data, averaged over for 6 Global Circulation Models: ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1, MPI-ESM1-2-LR, MIROC6 and NorESM2-MM.\nThe downscaled data is sourced from the [NASA Earth Exchange Global Daily Downscaled Projections](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6).\nIndicators are generated for periods: 'historical' (averaged over 1995-2014), 2030 (2021-2040), 2040 (2031-2050), 2050 (2041-2060), 2060 (2051-2070), 2070 (2061-2080), 2080 (2071-2090) and 2090 (2081-2100).\n", "map": { "colormap": { "min_index": 1, @@ -2233,7 +2233,7 @@ "display_groups": [ "Gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year)." ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u00e2\u20ac\u2122s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u00e2\u20ac\u201dthe total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u00e2\u20ac\u201dincludes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", "map": { "colormap": { "min_index": 1, @@ -2312,7 +2312,7 @@ "display_groups": [ "Available blue water \u2014 the total amount of renewable freshwater available to a sub-basin with upstream consumption removed \u2014 includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year)." ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u00e2\u20ac\u2122s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u00e2\u20ac\u201dthe total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u00e2\u20ac\u201dincludes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", "map": { "colormap": { "min_index": 1, @@ -2391,7 +2391,7 @@ "display_groups": [ "Water stress is an indicator of competition for water resources and is defined informally as the ratio of demand for water by human society divided by available water." ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u00e2\u20ac\u2122s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u00e2\u20ac\u201dthe total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u00e2\u20ac\u201dincludes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", "map": { "colormap": { "min_index": 1, @@ -2470,7 +2470,7 @@ "display_groups": [ "Water depletion measures the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only." ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u00e2\u20ac\u2122s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u00e2\u20ac\u201dthe total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u00e2\u20ac\u201dincludes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", "map": { "colormap": { "min_index": 1, @@ -2549,7 +2549,7 @@ "display_groups": [ "Discrete measure of the ratio of total water withdrawals to available renewable surface and ground water supplies:\n-1: Arid and low water use, 0 : Low (<10%), 1: Low-medium (10-20%), 2 : Medium-high (20-40%), 3: High (40-80%), 4 : Extremely high (>80%)." ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u00e2\u20ac\u2122s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u00e2\u20ac\u201dthe total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u00e2\u20ac\u201dincludes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", "map": { "colormap": { "min_index": 1, @@ -2628,7 +2628,7 @@ "display_groups": [ "Discrete measure of the ratio of total water consumption to available renewable water supplies:\n-1: Arid and low water use, 0 : Low (<5%), 1: Low-medium (5-25%), 2 : Medium-high (25-50%), 3: High (50-75%), 4 : Extremely high (>75%)." ], - "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u00e2\u20ac\u2122s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u00e2\u20ac\u201dthe total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u00e2\u20ac\u201dincludes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00c3\u2014 5 arc minutes which equates roughly to 10 kilometer (km) \u00c3\u2014 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", + "description": "The World Resources Institute (WRI) [Aqueduct 4.0](https://www.wri.org/data/aqueduct-global-maps-40-data) is the latest iteration of [WRI\u2019s water risk framework](https://www.wri.org/data/aqueduct-water-risk-atlas) designed to translate complex \nhydrological data into intuitive indicators of water-related risk:\n\n* **Water demand**: gross demand is the maximum potential water required to meet sectoral demands. Sectoral water demand includes: domestic, industrial, irrigation, and livestock. Demand is displayed as a flux (cm/year).\n\n* **Water supply**: available blue water\u2014the total amount of renewable freshwater available to a sub-basin with upstream consumption removed\u2014includes surface flow, interflow, and groundwater recharge. Available blue water is displayed as a flux (cm/year).\n\n* **Water stress**: an indicator of competition for water resources defined informally as the ratio of demand for water by human society divided by available water.\n\n* **Water depletion**: the ratio of total water consumption to available renewable water supplies. Total water consumption includes domestic, industrial, irrigation, and livestock consumptive uses. Available renewable water supplies include the impact of upstream consumptive water users and large dams on downstream water availability. Higher values indicate larger impact on the local water supply and decreased water availability for downstream users. Water depletion is similar to water stress; however, instead of looking at total water demand, water depletion is calculated using consumptive withdrawal only.\n\n* **Interannual variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\n* **Seasonal variability**: the average within-year variability of available water supply, including both renewable surface and groundwater supplies. Higher values indicate wider variations of available supply within a year.\n\nThe spatial resolution is 5 \u00d7 5 arc minutes which equates roughly to 10 kilometer (km) \u00d7 10 km pixels. \nThe future projections were created using CMIP6 climate forcings based on three future scenarios: optimistic (ssp126), business-as-usual (ssp370), and pessimistic (ssp585) available at [HYPFLOWSCI6](https://public.yoda.uu.nl/geo/UU01/YM7A5H.html). WRI's original data are presented at the HydroBASINS Level 6 scale. Indicators are available for periods: 'historical' (averaged over 1979-2019), 2030 (2015-2045), 2050 (2035-2065) and 2080 (2065-2095).", "map": { "colormap": { "min_index": 1, @@ -2694,6 +2694,77 @@ } ], "units": "" + }, + { + "hazard_type": "Drought", + "group_id": "", + "path": "drought/osc/v1/months_spei12m_below_index_{gcm}_{scenario}_{year}", + "indicator_id": "months/spei12m/below/index", + "indicator_model_id": null, + "indicator_model_gcm": "{gcm}", + "params": { + "gcm": [ + "MIROC6" + ] + }, + "display_name": "Drought SPEI index", + "display_groups": [ + "Drought SPEI index" + ], + "description": "", + "map": { + "colormap": { + "min_index": 1, + "min_value": 0.0, + "max_index": 255, + "max_value": 12.0, + "name": "heating", + "nodata_index": 0, + "units": "months/year" + }, + "path": "drought/osc/v1/months_spei12m_below_index_{gcm}_{scenario}_{year}_map", + "bounds": [ + [ + -180.0, + 85.0 + ], + [ + 180.0, + 85.0 + ], + [ + 180.0, + -60.0 + ], + [ + -180.0, + -60.0 + ] + ], + "index_values": [ + 0, + -1, + -1.5, + -2, + -2.5, + -3, + -3.6 + ], + "source": "map_array_pyramid" + }, + "scenarios": [ + { + "id": "ssp585", + "years": [ + 2005, + 2030, + 2040, + 2050, + 2080 + ] + } + ], + "units": "months/year" } ] } diff --git a/src/physrisk/kernel/vulnerability_model.py b/src/physrisk/kernel/vulnerability_model.py index f4a3474e..a6cc8feb 100644 --- a/src/physrisk/kernel/vulnerability_model.py +++ b/src/physrisk/kernel/vulnerability_model.py @@ -71,6 +71,13 @@ def get_data_requests( ) -> Union[HazardDataRequest, Iterable[HazardDataRequest]]: ... +class EventBased(Protocol): + def impact_samples(self, asset: Asset, data_responses: Iterable[HazardDataResponse]) -> np.ndarray: + # event-based models generate impact samples based on events received by the hazard model + # the events may be in the form of an array of severities in the form of return periods. + ... + + class VulnerabilityModelBase(ABC, DataRequester): def __init__(self, indicator_id: str, hazard_type: type, impact_type: ImpactType): self.indicator_id = indicator_id @@ -112,12 +119,12 @@ def get_distributions( """ ... - def get_impact(self, asset: Asset, event_data_responses: Iterable[HazardDataResponse]): - impact, _, _ = self.get_impact_details(asset, event_data_responses) + def get_impact(self, asset: Asset, data_responses: Iterable[HazardDataResponse]): + impact, _, _ = self.get_impact_details(asset, data_responses) return impact def get_impact_details( - self, asset: Asset, event_data_responses: Iterable[HazardDataResponse] + self, asset: Asset, data_responses: Iterable[HazardDataResponse] ) -> Tuple[ImpactDistrib, VulnerabilityDistrib, HazardEventDistrib]: """Return impact distribution along with vulnerability and hazard event distributions used to infer this. @@ -125,7 +132,7 @@ def get_impact_details( asset: the asset. event_data_responses: the responses to the requests made by get_data_requests, in the same order. """ - vulnerability_dist, event_dist = self.get_distributions(asset, event_data_responses) + vulnerability_dist, event_dist = self.get_distributions(asset, data_responses) impact_prob = vulnerability_dist.prob_matrix.T @ event_dist.prob return ( ImpactDistrib( diff --git a/src/physrisk/requests.py b/src/physrisk/requests.py index dcad2f50..8421bdd6 100644 --- a/src/physrisk/requests.py +++ b/src/physrisk/requests.py @@ -219,12 +219,22 @@ def _get_hazard_data(request: HazardDataRequest, hazard_model: HazardModel): resps = (response_dict[req] for req in requests) intensity_curves = [ ( - IntensityCurve(intensities=list(resp.intensities), return_periods=list(resp.return_periods)) + IntensityCurve( + intensities=list(resp.intensities), + index_values=list(resp.return_periods), + index_name="return period", + return_periods=[], + ) if isinstance(resp, hmHazardEventDataResponse) else ( - IntensityCurve(intensities=list(resp.parameters), return_periods=list(resp.param_defns)) + IntensityCurve( + intensities=list(resp.parameters), + index_values=list(resp.param_defns), + index_name="threshold", + return_periods=[], + ) if isinstance(resp, HazardParameterDataResponse) - else IntensityCurve(intensities=[], return_periods=[]) + else IntensityCurve(intensities=[], index_values=[], index_name="", return_periods=[]) ) ) for resp in resps