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- ItemPerformance evaluation of global hydrological models in six large Pan-Arctic watersheds(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) Gädeke, Anne; Krysanova, Valentina; Aryal, Aashutosh; Chang, Jinfeng; Grillakis, Manolis; Hanasaki, Naota; Koutroulis, Aristeidis; Pokhrel, Yadu; Satoh, Yusuke; Schaphoff, Sibyll; Müller Schmied, Hannes; Stacke, Tobias; Tang, Qiuhong; Wada, Yoshihide; Thonicke, KirstenGlobal Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds. © 2020, The Author(s).
- ItemHow evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) Krysanova, Valentina; Zaherpour, Jamal; Didovets, Iulii; Gosling, Simon N.; Gerten, Dieter; Hanasaki, Naota; Müller Schmied, Hannes; Pokhrel, Yadu; Satoh, Yusuke; Tang, Qiuhong; Wada, YoshihideImportance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections. © 2020, The Author(s).
- ItemUnderstanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication(Katlenburg-Lindau : Copernicus, 2021-6-24) Telteu, Camelia-Eliza; Müller Schmied, Hannes; Thiery, Wim; Leng, Guoyong; Burek, Peter; Liu, Xingcai; Boulange, Julien Eric Stanislas; Andersen, Lauren Seaby; Grillakis, Manolis; Gosling, Simon Newland; Satoh, Yusuke; Rakovec, Oldrich; Stacke, Tobias; Chang, Jinfeng; Wanders, Niko; Shah, Harsh Lovekumar; Trautmann, Tim; Mao, Ganquan; Hanasaki, Naota; Koutroulis, Aristeidis; Pokhrel, Yadu; Samaniego, Luis; Wada, Yoshihide; Mishra, Vimal; Liu, Junguo; Döll, Petra; Zhao, Fang; Gädeke, Anne; Rabin, Sam S.; Herz, FlorianGlobal water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.
- ItemState-of-the-art global models underestimate impacts from climate extremes([London] : Nature Publishing Group UK, 2019) Schewe, Jacob; Gosling, Simon N.; Reyer, Christopher; Zhao, Fang; Ciais, Philippe; Elliott, Joshua; Francois, Louis; Huber, Veronika; Lotze, Heike K.; Seneviratne, Sonia I.; van Vliet, Michelle T. H.; Vautard, Robert; Wada, Yoshihide; Breuer, Lutz; Büchner, Matthias; Carozza, David A.; Chang, Jinfeng; Coll, Marta; Deryng, Delphine; de Wit, Allard; Eddy, Tyler D.; Folberth, Christian; Frieler, Katja; Friend, Andrew D.; Gerten, Dieter; Gudmundsson, Lukas; Hanasaki, Naota; Ito, Akihiko; Khabarov, Nikolay; Kim, Hyungjun; Lawrence, Peter; Morfopoulos, Catherine; Müller, Christoph; Müller Schmied, Hannes; Orth, René; Ostberg, Sebastian; Pokhrel, Yadu; Pugh, Thomas A. M.; Sakurai, Gen; Satoh, Yusuke; Schmid, Erwin; Stacke, Tobias; Steenbeek, Jeroen; Steinkamp, Jörg; Tang, Qiuhong; Tian, Hanqin; Tittensor, Derek P.; Volkholz, Jan; Wang, Xuhui; Warszawski, LilaGlobal impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.
- ItemEvapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets(Bristol : IOP Publ., 2018) Wartenburger, Richard; Seneviratne, Sonia I; Hirschi, Martin; Chang, Jinfeng; Ciais, Philippe; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Gosling, Simon N; Gudmundsson, Lukas; Henrot, Alexandra-Jane; Hickler, Thomas; Ito, Akihiko; Khabarov, Nikolay; Kim, Hyungjun; Leng, Guoyong; Liu, Junguo; Liu, Xingcai; Masaki, Yoshimitsu; Morfopoulos, Catherine; Müller, Christoph; Müller Schmied, Hannes; Nishina, Kazuya; Orth, Rene; Pokhrel, Yadu; Pugh, Thomas A M; Satoh, Yusuke; Schaphoff, Sibyll; Schmid, Erwin; Sheffield, Justin; Stacke, Tobias; Steinkamp, Joerg; Tang, Qiuhong; Thiery, Wim; Wada, Yoshihide; Wang, Xuhui; Weedon, Graham P; Yang, Hong; Zhou, TianActual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%–40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.
- ItemProjecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales(Hoboken, NJ : Wiley-Blackwell, 2020) Lange, Stefan; Volkholz, Jan; Geiger, Tobias; Zhao, Fang; Vega, Iliusi; Veldkamp, Ted; Reyer, Christopher P.O.; Warszawski, Lila; Huber, Veronika; Jägermeyr, Jonas; Schewe, Jacob; Bresch, David N.; Büchner, Matthias; Chang, Jinfeng; Ciais, Philippe; Dury, Marie; Emanuel, Kerry; Folberth, Christian; Gerten, Dieter; Gosling, Simon N.; Grillakis, Manolis; Hanasaki, Naota; Henrot, Alexandra-Jane; Hickler, Thomas; Honda, Yasushi; Ito, Akihiko; Khabarov, Nikolay; Koutroulis, Aristeidis; Liu, Wenfeng; Müller, Christoph; Nishina, Kazuya; Ostberg, Sebastian; Müller Schmied, Hannes; Seneviratne, Sonia I.; Stacke, Tobias; Steinkamp, Jörg; Thiery, Wim; Wada, Yoshihide; Willner, Sven; Yang, Hong; Yoshikawa, Minoru; Yue, Chao; Frieler, KatjaThe extent and impact of climate-related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross-category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. ©2020. The Authors.
- ItemWFDE5: Bias-adjusted ERA5 reanalysis data for impact studies(Katlenburg-Lindau : Copernics Publications, 2020) Cucchi, Marco; Weedon, Graham P.; Amici, Alessandro; Bellouin, Nicolas; Lange, Stefan; Müller Schmied, Hannes; Hersbach, Hans; Buontempo, CarloThe WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5 spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data - allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020). © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
- ItemRisks for the global freshwater system at 1.5 °c and 2 °c global warming(Bristol : IOP Publishing, 2018) Döll, Petra; Trautmann, Tim; Gerten, Dieter; Müller Schmied, Hannes; Ostberg, Sebastian; Saaed, Fahad; Schleussner, Carl-FriedrichTo support implementation of the Paris Agreement, the new HAPPI ensemble of 20 bias-corrected simulations of four climate models was used to drive two global hydrological models, WaterGAP and LPJmL, for assessing freshwater-related hazards and risks in worlds approximately 1.5 °C and 2 °C warmer than pre-industrial. Quasi-stationary HAPPI simulations are better suited than transient CMIP-like simulations for assessing hazards at the two targeted long-term global warming (GW) levels. We analyzed seven hydrological hazard indicators that characterize freshwater-related hazards for humans, freshwater biota and vegetation. Using a strict definition for significant differences, we identified for all but one indicator that areas with either significantly wetter or drier conditions (calculated as percent changes from 2006–2015) are smaller in the 1.5 °C world. For example, 7 day high flow is projected to increase significantly on 11% and 21% of the global land area at 1.5 °C and 2 °C, respectively. However, differences between hydrological hazards at the two GW levels are significant on less than 12% of the area. GW affects a larger area and more people by increases—rather than by decreases—of mean annual and 1-in-10 dry year streamflow, 7 day high flow, and groundwater recharge. The opposite is true for 7 day low flow, maximum snow storage, and soil moisture in the driest month of the growing period. Mean annual streamflow shows the lowest projected percent changes of all indicators. Among country groups, low income countries and lower middle income countries are most affected by decreased low flows and increased high flows, respectively, while high income countries are least affected by such changes. The incremental impact between 1.5 °C and 2 °C on high flows would be felt most by low income and lower middle income countries, the effect on soil moisture and low flows most by high income countries.
- ItemAssessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)(München : European Geopyhsical Union, 2017) Frieler, Katja; Lange, Stefan; Piontek, Franziska; Reyer, Christopher P.O.; Schewe, Jacob; Warszawski, Lila; Zhao, Fang; Chini, Louise; Denvil, Sebastien; Emanuel, Kerry; Geiger, Tobias; Halladay, Kate; Hurtt, George; Mengel, Matthias; Murakami, Daisuke; Ostberg, Sebastian; Popp, Alexander; Riva, Riccardo; Stevanovic, Miodrag; Suzuki, Tatsuo; Volkholz, Jan; Burke, Eleanor; Ciais, Philippe; Ebi, Kristie; Eddy, Tyler D.; Elliott, Joshua; Galbraith, Eric; Gosling, Simon N.; Hattermann, Fred; Hickler, Thomas; Hinkel, Jochen; Hof, Christian; Huber, Veronika; Jägermeyr, Jonas; Krysanova, Valentina; Marcé, Rafael; Müller Schmied, Hannes; Mouratiadou, Ioanna; Pierson, Don; Tittensor, Derek P.; Vautard, Robert; van Vliet, Michelle; Biber, Matthias F.; Betts, Richard A.; Bodirsky, Benjamin Leon; Deryng, Delphine; Frolking, Steve; Jones, Chris D.; Lotze, Heike K.; Lotze-Campen, Hermann; Sahajpal, Ritvik; Thonicke, Kirsten; Tian, Hanqin; Yamagata, YoshikiIn Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5°C, including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).
- ItemHistorical and future changes in global flood magnitude - evidence from a model-observation investigation(Munich : EGU, 2020) Do, Hong Xuan; Zhao, Fang; Westra, Seth; Leonard, Michael; Gudmundsson, Lukas; Boulange, Julien Eric Stanislas; Chang, Jinfeng; Ciais, Philippe; Gerten, Dieter; Gosling, Simon N.; Müller Schmied, Hannes; Stacke, Tobias; Telteu, Camelia-Eliza; Wada, YoshihideTo improve the understanding of trends in extreme flows related to flood events at the global scale, historical and future changes of annual maxima of 7 d streamflow are investigated, using a comprehensive streamflow archive and six global hydrological models. The models' capacity to characterise trends in annual maxima of 7 d streamflow at the continental and global scale is evaluated across 3666 river gauge locations over the period from 1971 to 2005, focusing on four aspects of trends: (i) mean, (ii) standard deviation, (iii) percentage of locations showing significant trends and (iv) spatial pattern. Compared to observed trends, simulated trends driven by observed climate forcing generally have a higher mean, lower spread and a similar percentage of locations showing significant trends. Models show a low to moderate capacity to simulate spatial patterns of historical trends, with approximately only from 12 % to 25 % of the spatial variance of observed trends across all gauge stations accounted for by the simulations. Interestingly, there are statistically significant differences between trends simulated by global hydrological models (GHMs) forced with observational climate and by those forced by bias-corrected climate model output during the historical period, suggesting the important role of the stochastic natural (decadal, inter-annual) climate variability. Significant differences were found in simulated flood trends when averaged only at gauged locations compared to those averaged across all simulated grid cells, highlighting the potential for bias toward well-observed regions in our understanding of changes in floods. Future climate projections (simulated under the RCP2.6 and RCP6.0 greenhouse gas concentration scenarios) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or decrease; at 10 % significance level) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of locations consistently projected with increased flood hazards under the RCP6.0 greenhouse gas concentration scenario by more than an order of magnitude (0.9 % compared to 11.7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change. © Author(s) 2020.