Clustering of concurrent flood risks via Hazard Scenarios

The study of multiple effects of a number of variables, and the assessment of the corresponding environmental risks, may require the adoption of suitable multivariate models when the variables at play are dependent, as it often happens in environmental studies. In this work, the flood risks in a given region are investigated, in order to identify specific spatial sub-regions (clusters) where the floods show a similar behavior with respect to suitable multivariate) criteria. The reason of the work is three-fold, and the outcomes have deep implications in the hydrological practice: (i) such a regionalization (as it is called in hydrology) may provide useful indications for deciding which gauge stations have a similar (stochastic) behavior; (ii) the spatial clustering may represent a valuable tool for investigating ungauged basins present in a given ‘‘homogeneous’’ Region; (iii) the estimate of extreme design values may be improved by using all the observations collected in a cluster (instead of only single-station data). For this purpose, a Copulabased Agglomerative Hierarchical Clustering algorithm – a key tool in geosciences for the analysis of the dependence information – is proposed. The procedure is illustrated via a case study involving the Po river basin, the largest Italian one. A comparison with a previous attempt to cluster the gauge stations present in the same spatial region is also carried out. The sub-regions picked out by the clustering procedure outlined here agree with previous results obtained via heuristic hydrological and meteorological reasonings, and identify spatial areas characterized by similar flood regimes

A review of applied methods in Europe for flood-frequency analysis in a changing environment

The report presents a review of methods used in Europe for trend analysis, climate change projections and non-stationary analysis of extreme precipitation and flood frequency. In addition, main findings of the analyses are presented, including a comparison of trend analysis results and climate change projections. Existing guidelines in Europe on design flood and design rainfall estimation that incorporate climate change are reviewed. The report concludes with a discussion of research needs on non-stationary frequency analysis for considering the effects of climate change and inclusion in design guidelines. Trend analyses are reported for 21 countries in Europe with results for extreme precipitation, extreme streamflow or both. A large number of national and regional trend studies have been carried out. Most studies are based on statistical methods applied to individual time series of extreme precipitation or extreme streamflow using the non-parametric Mann-Kendall trend test or regression analysis. Some studies have been reported that use field significance or regional consistency tests to analyse trends over larger areas. Some of the studies also include analysis of trend attribution. The studies reviewed indicate that there is some evidence of a general increase in extreme precipitation, whereas there are no clear indications of significant increasing trends at regional or national level of extreme streamflow. For some smaller regions increases in extreme streamflow are reported. Several studies from regions dominated by snowmelt-induced peak flows report decreases in extreme streamflow and earlier spring snowmelt peak flows. Climate change projections have been reported for 14 countries in Europe with results for extreme precipitation, extreme streamflow or both. The review shows various approaches for producing climate projections of extreme precipitation and flood frequency based on alternative climate forcing scenarios, climate projections from available global and regional climate models, methods for statistical downscaling and bias correction, and alternative hydrological models. A large number of the reported studies are based on an ensemble modelling approach that use several climate forcing scenarios and climate model projections in order to address the uncertainty on the projections of extreme precipitation and flood frequency. Some studies also include alternative statistical downscaling and bias correction methods and hydrological modelling approaches. Most studies reviewed indicate an increase in extreme precipitation under a future climate, which is consistent with the observed trend of extreme precipitation. Hydrological projections of peak flows and flood frequency show both positive and negative changes. Large increases in peak flows are reported for some catchments with rainfall-dominated peak flows, whereas a general decrease in flood magnitude and earlier spring floods are reported for catchments with snowmelt-dominated peak flows. The latter is consistent with the observed trends. The review of existing guidelines in Europe on design floods and design rainfalls shows that only few countries explicitly address climate change. These design guidelines are based on climate change adjustment factors to be applied to current design estimates and may depend on design return period and projection horizon. The review indicates a gap between the need for considering climate change impacts in design and actual published guidelines that incorporate climate change in extreme precipitation and flood frequency. Most of the studies reported are based on frequency analysis assuming stationary conditions in a certain time window (typically 30 years) representing current and future climate. There is a need for developing more consistent non-stationary frequency analysis methods that can account for the transient nature of a changing climate

Clustering of concurrent flood risks via Hazard Scenarios

The study of multiple effects of a number of variables, and the assessment of the corresponding environmental risks, may require the adoption of suitable multivariate models when the variables at play are dependent, as it often happens in environmental studies. In this work, the flood risks in a given region are investigated, in order to identify specific spatial sub-regions (clusters) where the floods show a similar behavior with respect to suitable multivariate) criteria. The reason of the work is three-fold, and the outcomes have deep implications in the hydrological practice: (i) such a regionalization (as it is called in hydrology) may provide useful indications for deciding which gauge stations have a similar (stochastic) behavior; (ii) the spatial clustering may represent a valuable tool for investigating ungauged basins present in a given \u2018\u2018homogeneous\u2019\u2019 Region; (iii) the estimate of extreme design values may be improved by using all the observations collected in a cluster (instead of only single-station data). For this purpose, a Copulabased Agglomerative Hierarchical Clustering algorithm \u2013 a key tool in geosciences for the analysis of the dependence information \u2013 is proposed. The procedure is illustrated via a case study involving the Po river basin, the largest Italian one. A comparison with a previous attempt to cluster the gauge stations present in the same spatial region is also carried out. The sub-regions picked out by the clustering procedure outlined here agree with previous results obtained via heuristic hydrological and meteorological reasonings, and identify spatial areas characterized by similar flood regimes

MODELLING DRY AND WET EXTREMES OVER THE CANADIAN PRAIRIE PROVINCES BASED ON THE DYNAMICAL DOWNSCALING AND MULTIVARIATE FREQUENCY ANALYSIS APPROACHES

The primary tools to assess climate change are the Atmosphere–Ocean General Circulation Model (AOGCM) or Regional Climate Model (RCM) transient climate change simulations. Currently, RCMs offer higher spatial resolution than AOGCMs and therefore are preferred for assessing impact of climate change on different components of the hydrological cycle at regional domains of interest. The overall purpose of this research was to evaluate the impact of climate change on dry and wet climate extremes over the Canadian Prairie Provinces of Alberta, Saskatchewan and Manitoba using a multi–RCM ensemble from the North American Regional Climate Change Assessment Program (NARCCAP). This region of Canada is characterized by highly variable hydroclimate, with recurrent droughts and floods and localized summer convective storm activity often resulting in heavy precipitation events and thus poses many challenges for water managers. At first, the Saskatchewan River Basin, the largest river in the study area, was evaluated and drought vulnerable parts of the basin were identified based on historical data and multivariate frequency analysis approaches. For the development of projected changes to drought characteristics, the research effort was extended over the entire study area and changes to various return levels of drought severity, duration and maximum severity were developed based on NARCCAP RCM simulations and multivariate frequency analysis approaches. It was found that the southern and south-western parts of the study area will experience increased drought severity in the future. Based on the projected bi- and trivariate joint occurrence probabilities of drought characteristics, southern parts along with the central parts of the study area were found to be highly drought vulnerable, whereas the southwestern and southeastern parts were found less vulnerable. Though producing reliable estimates of changes in precipitation extremes remains an important challenge under climate change, this study attempted to develop projected changes to April–October short- and long-duration precipitation extremes based on the NARCCAP RCM simulations and regional frequency analysis approach. Projected changes to selected regional return levels of precipitation extremes were found mostly statistically significant, with relatively larger changes noted for the southeastern regions and smaller for the southwestern and western regions of the study area

MULTIVARIATE MULTISITE STATISTICAL DOWNSCALING OF ATMOSPHERE-OCEAN GENERAL CIRCULATION MODEL OUTPUTS OVER THE CANADIAN PRAIRIE PROVINCES

Atmosphere-Ocean General Circulation Models (AOGCMs) are the primary tool for modelling global climate change in the future. However, their coarse spatial resolution does not permit direct application for local scale impact studies. Therefore, either dynamical or statistical downscaling techniques are used for translating AOGCM outputs to local scale climatic variables. The main goal of this study was to improve our understanding of the historical and future climate change at local-scale in the Canadian Prairie Provinces (CPPs) of Alberta, Saskatchewan and Manitoba, comprising 47 diverse watersheds. Given the vast nature of the study area and paucity of recorded data, a novel approach for identifying homogeneous regions for regionalization of precipitation characteristics for the CPPs was proposed. This approach incorporated information about predictors ― large-scale atmospheric covariates from the National Center for Environmental Prediction (NCEP) Reanalysis-I, teleconnection indices and geographical site attributes that impact spatial patterns of precipitation in order to delineate homogeneous precipitation regions using a combination of multivariate approaches. This resulted in the delimitation of five homogeneous climatic regions which were validated independently for homogeneity using statistics computed from observations recorded at 120 stations across the CPPs. For multisite multivariate statistical downscaling, an approach based on the Generalized Linear Model (GLM) framework was developed to downscale daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the CPPs. First, the aforementioned predictors and observed daily precipitation and temperature records were used to calibrate GLMs for the 1971–2000 period. Then the calibrated GLMs were used to generate daily sequences of precipitation and temperatures for the 1962–2005 historical (conditioned on NCEP predictors), and future period (2006–2100) using outputs from six CMIP5 (Coupled Model Intercomparison Project Phase-5) AOGCMs corresponding to Representative Concentration Pathway (RCP): RCP2.6, RCP4.5, and RCP8.5 scenarios. The results indicated that the fitted GLMs were able to capture spatiotemporal characteristics of observed climatic fields. According to the downscaled future climate, mean precipitation is projected to increase in summer and decrease in winter while minimum temperature is expected to warm faster than the maximum temperature. Climate extremes are projected to intensify with increased radiative forcing

Systemic risk diagnostics: coincident indicators and early warning signals

We propose a novel framework to assess financial system risk. Using a dynamic factor framework based on state-space methods, we construct coincident measures (‘thermometers’) and a forward looking indicator for the likelihood of simultaneous failure of a large number of financial intermediaries. The indicators are based on latent macro-financial and credit risk components for a large data set comprising the U.S., the EU-27 area, and the respective rest of the world. Credit risk conditions can significantly and persistently de-couple from macro-financial fundamentals. Such decoupling can serve as an early warning signal for macro-prudential policy. JEL Classification: G21, C33credit portfolio models, financial crisis, frailty-correlated defaults, state space methods, systemic risk

Predicting water quality and ecological responses

Abstract Changes to climate are predicted to have effects on freshwater streams. Stream flows are likely to change, with implications for freshwater ecosystems and water quality. Other stressors such as population growth, community preferences and management policies can be expected to interact in various ways with climate change and stream flows, and outcomes for freshwater ecosystems and water quality are uncertain. Managers of freshwater ecosystems and water supplies could benefit from being able to predict the scales of likely changes. This project has developed and applied a linked modelling framework to assess climate change impacts on water quality regimes and ecological responses. The framework is designed to inform water planning and climate adaptation activities. It integrates quantitative tools, and predicts relationships between future climate, human activities, water quality and ecology, thereby filling a gap left by the considerable research effort so far invested in predicting stream flows. The modelling framework allows managers to explore potential changes in the water quality and ecology of freshwater systems in response to plausible scenarios for climate change and management adaptations. Although set up for the Upper Murrumbidgee River catchment in southern NSW and ACT, the framework was planned to be transferable to other regions where suitable data are available. The approach and learning from the project appear to have the potential to be broadly applicable. We selected six climate scenarios representing minor, moderate and major changes in flow characteristics for 1oC and 2oC temperature increases. These were combined with four plausible alternative management adaptations that might be used to modify water supply, urban water demand and stream flow regimes in the Upper Murrumbidgee catchment. The Bayesian Network (BN) model structure we used was developed using both a ‘top down’ and ‘bottom up’ approach. From analyses combined with expert advice, we identified the causal structure linking climate variables to stream flow, water quality attributes, land management and ecological responses (top down). The ‘bottom up’ approach focused on key ecological outcomes and key drivers, and helped produce efficient models. The result was six models for macroinvertebrates, and one for fish. In the macroinvertebrate BN models, nodes were discretised using statistical/empirical derived thresholds using new techniques. The framework made it possible to explore how ecological communities respond to changes in climate and management activities. Particularly, we focused on the effects of water quality and quantity on ecological responses. The models showed a strong regional response reflecting differences across 18 regions in the catchment. In two regions the management alternatives were predicted to have stronger effects than climate change. In three other regions the predicted response to climate change was stronger. Analyses of water quality suggested minor changes in the probability of water quality exceeding thresholds designed to protect aquatic ecosystems. The ‘bottom up’ approach limited the framework’s transferability by being specific to the Upper Murrumbidgee catchment data. Indeed, to meet stakeholder questions models need to be specifically tailored. Therefore the report proposes a general model-building framework for transferring the approach, rather than the models, to other regions.  Please cite this report as: Dyer, F, El Sawah, S, Lucena-Moya, P, Harrison, E, Croke, B, Tschierschke, A, Griffiths, R, Brawata, R, Kath, J, Reynoldson, T, Jakeman, T 2013 Predicting water quality and ecological responses, National Climate Change Adaptation Research Facility, Gold Coast, pp. 110 Changes to climate are predicted to have effects on freshwater streams. Stream flows are likely to change, with implications for freshwater ecosystems and water quality. Other stressors such as population growth, community preferences and management policies can be expected to interact in various ways with climate change and stream flows, and outcomes for freshwater ecosystems and water quality are uncertain. Managers of freshwater ecosystems and water supplies could benefit from being able to predict the scales of likely changes. This project has developed and applied a linked modelling framework to assess climate change impacts on water quality regimes and ecological responses. The framework is designed to inform water planning and climate adaptation activities. It integrates quantitative tools, and predicts relationships between future climate, human activities, water quality and ecology, thereby filling a gap left by the considerable research effort so far invested in predicting stream flows. The modelling framework allows managers to explore potential changes in the water quality and ecology of freshwater systems in response to plausible scenarios for climate change and management adaptations. Although set up for the Upper Murrumbidgee River catchment in southern NSW and ACT, the framework was planned to be transferable to other regions where suitable data are available. The approach and learning from the project appear to have the potential to be broadly applicable. We selected six climate scenarios representing minor, moderate and major changes in flow characteristics for 1oC and 2oC temperature increases. These were combined with four plausible alternative management adaptations that might be used to modify water supply, urban water demand and stream flow regimes in the Upper Murrumbidgee catchment. The Bayesian Network (BN) model structure we used was developed using both a ‘top down’ and ‘bottom up’ approach. From analyses combined with expert advice, we identified the causal structure linking climate variables to stream flow, water quality attributes, land management and ecological responses (top down). The ‘bottom up’ approach focused on key ecological outcomes and key drivers, and helped produce efficient models. The result was six models for macroinvertebrates, and one for fish. In the macroinvertebrate BN models, nodes were discretised using statistical/empirical derived thresholds using new techniques. The framework made it possible to explore how ecological communities respond to changes in climate and management activities. Particularly, we focused on the effects of water quality and quantity on ecological responses. The models showed a strong regional response reflecting differences across 18 regions in the catchment. In two regions the management alternatives were predicted to have stronger effects than climate change. In three other regions the predicted response to climate change was stronger. Analyses of water quality suggested minor changes in the probability of water quality exceeding thresholds designed to protect aquatic ecosystems. The ‘bottom up’ approach limited the framework’s transferability by being specific to the Upper Murrumbidgee catchment data. Indeed, to meet stakeholder questions models need to be specifically tailored. Therefore the report proposes a general model-building framework for transferring the approach, rather than the models, to other regions.&nbsp