Cross‐scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity to climate variability and climate change is comparable for impact models designed for either scale. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a better reproduction of reference conditions. However, the sensitivity of the two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases, but have distinct differences in other cases, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability. Whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models calibrated and validated against observed discharge should be used.

[1]  Martin C. Thoms,et al.  Water resource development and hydrological change in a large dryland river: the Barwon–Darling River, Australia , 2000 .

[2]  F. Piontek,et al.  A trend-preserving bias correction – the ISI-MIP approach , 2013 .

[3]  Z. Kundzewicz,et al.  Brief Communication: An update of the article "Modelling flood damages under climate change conditions - a case study for Germany" , 2015 .

[4]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[5]  T. Okruszko,et al.  Effect of climate change on environmental flow indicators in the narew basin, poland. , 2014, Journal of environmental quality.

[6]  S. Kanae,et al.  An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing , 2008 .

[7]  M. Wattenbach,et al.  Runoff simulations on the macroscale with the ecohydrological model SWIM in the Elbe catchment–validation and uncertainty analysis , 2005 .

[8]  K. Verzano,et al.  Climate change impacts on flood related hydrological processes: Further development and application of a global scale hydrological model , 2009 .

[9]  S. Eisner Comprehensive evaluation of the WaterGAP3 model across climatic, physiographic, and anthropogenic gradients , 2016 .

[10]  R. Betts,et al.  Comparing projections of future changes in runoff from hydrological and biome models in ISI-MIP , 2013 .

[11]  Vincent R. Gray Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[12]  S. Attinger,et al.  Improving the realism of hydrologic model functioning through multivariate parameter estimation , 2016 .

[13]  Z. Kundzewicz,et al.  Model-Supported Impact Assessment for the Water Sector in Central Germany Under Climate Change—A Case Study , 2011 .

[14]  J. Randerson,et al.  Technical Description of version 4.0 of the Community Land Model (CLM) , 2010 .

[15]  Nigel W. Arnell,et al.  Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis , 2011 .

[16]  Naota Hanasaki,et al.  Incorporating anthropogenic water regulation modules into a land surface model , 2012 .

[17]  W. J. Shuttleworth,et al.  Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century , 2011 .

[18]  Axel Bronstert,et al.  Integrating wetlands and riparian zones in river basin modelling , 2006 .

[19]  Martina Flörke,et al.  Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study , 2013 .

[20]  T. Stacke,et al.  Multimodel projections and uncertainties of irrigation water demand under climate change , 2013 .

[21]  S. Kanae,et al.  The Influence of Precipitation Variability and Partial Irrigation within Grid Cells on a Hydrological Simulation , 2007 .

[22]  Valentina Krysanova,et al.  Development of the ecohydrological model SWIM for regional impact studies and vulnerability assessment , 2005 .

[23]  M. Bierkens,et al.  Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources , 2013 .

[24]  Petra Döll,et al.  Impact of climate change on renewable groundwater resources: assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections , 2013 .

[25]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[26]  Anthony J. Jakeman,et al.  Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) I: Model intercomparison with current land use , 2009 .

[27]  S. Hagemann,et al.  Climate change impact on available water resources obtained using multiple global climate and hydrology models , 2012 .

[28]  S. Kanae,et al.  An integrated model for the assessment of global water resources – Part 2: Applications and assessments , 2008 .

[29]  S. Hagemann,et al.  Development and validation of a global dynamical wetlands extent scheme , 2012 .

[30]  F. Ludwig,et al.  Global water resources affected by human interventions and climate change , 2013, Proceedings of the National Academy of Sciences.

[31]  S. Hagemann,et al.  Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment , 2013, Proceedings of the National Academy of Sciences.

[32]  Richard T. Kingsford,et al.  Ecological impacts of dams, water diversions and river management on floodplain wetlands in Australia , 2000 .

[33]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[34]  Hyungjun Kim,et al.  First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble , 2013, Proceedings of the National Academy of Sciences.

[35]  S. Kotlarski,et al.  Quantifying uncertainty sources in an ensemble of hydrological climate‐impact projections , 2013 .

[36]  F. Piontek,et al.  The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): Project framework , 2013, Proceedings of the National Academy of Sciences.

[37]  Nigel W. Arnell,et al.  A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models , 2010 .

[38]  Tao Yang,et al.  Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents , 2014 .

[39]  H Koch,et al.  Integrating water resources management in eco-hydrological modelling. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[40]  Sabine Attinger,et al.  Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins , 2016 .

[41]  Günter Blöschl,et al.  Time stability of catchment model parameters: Implications for climate impact analyses , 2011 .

[42]  P. Döll,et al.  Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration , 2014 .

[43]  J. Friesen,et al.  Adjustment of global precipitation data for enhanced hydrologic modeling of tropical Andean watersheds , 2017, Climatic Change.

[44]  Hans Joachim Schellnhuber,et al.  The elephant, the blind, and the intersectoral intercomparison of climate impacts , 2013, Proceedings of the National Academy of Sciences.

[45]  Berit Arheimer,et al.  Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe , 2016 .

[46]  K. Abbaspour,et al.  Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT , 2007 .

[47]  Y. Hundecha,et al.  Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide , 2017, Climatic Change.

[48]  Chong-Yu Xu,et al.  Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows , 2012, Water Resources Management.

[49]  Qiuhong Tang,et al.  Multi-model assessment of water scarcity under climate change , 2013 .

[50]  R. Betts,et al.  Comparing projections of future changes in runoff and water resources from hydrological and ecosyste , 2013 .

[51]  L. Feyen,et al.  Fluvial flood risk in Europe in present and future climates , 2012, Climatic Change.

[52]  P. Döll,et al.  Impact of climate change on renewable groundwater resources : assessing the benefits of avoided greenhouse gas emissions using selected CMIP 5 climate projections , 2013 .

[53]  M. Büchner,et al.  Modelling flood damages under climate change conditions – a case study for Germany , 2014 .

[54]  Felipe J. Colón-González,et al.  Multimodel assessment of water scarcity under climate change , 2013, Proceedings of the National Academy of Sciences.

[55]  Dipan Kundu,et al.  A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C , 2016, Climatic Change.