Modeling the potential impacts of climate change on streamflow in agricultural watersheds of the Midwestern United States

The ability to predict spatial variation in streamflow at the watershed scale is essential to understanding the potential impacts of projected climate change on aquatic systems in this century. However, problems associated with single outlet-based model calibration and validation procedures can confound the prediction of spatial variation in streamflow under future climate change scenarios. The goal of this study is to calibrate and validate a distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), using distributed streamflow data (1978–2009), and to assess the potential impacts of climate change on future streamflow (2051–2060 and 2086–2095) for the Rock River (RRW), Illinois River (IRW), Kaskaskia River (KRW), and Wabash River (WRW) watersheds in the Midwestern United States, primarily in Illinois. The potential impacts of climate change on future water resources are assessed using SWAT streamflow simulations driven by projections from nine global climate models (GCMs) under a maximum of three SRES scenarios (A1B, A2, and B1). Results from model validation indicate reasonable spatial and temporal predictions of streamflow, suggesting that a multi-site calibration strategy is necessary to accurately predict spatial variation in watershed hydrology. Compared with past streamflow records, predicted future streamflow based on climate change scenarios will tend to increase in the winter but decrease in the summer. According to 26 GCM projections, annual streamflows from 2051 – 2060 (2086–2095) are projected to decrease up to 45.2% (61.3%), 48.7% (49.8%), 48.7% (56.6%), and 41.1% (44.6%) in the RRW, IRW, KRW, and WRW, respectively. In addition, under the projected changes in climate, intra- and inter-annual streamflow variability generally does not increase over time. Results suggest that increased temperature could change the rate of evapotranspiration and the form of precipitation, subsequently influencing monthly streamflow patterns. Moreover, the spatially varying pattern of streamflow variability under future climate conditions suggests different buffering capabilities among regions. As such, regionally specific management strategies are necessary to mitigate the potential impacts of climate change and preserve aquatic ecosystems and water resources.

[1]  E. F. Roots.  Climate change: High-latitude regions , 1989 .

[2]  J. Meyer,et al.  Leaf breakdown in streams differing in catchment land use , 2006 .

[3]  Igor G Dubus,et al.  Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO. , 2002, Journal of environmental quality.

[4]  J. Arnold,et al.  VALIDATION OF THE SWAT MODEL ON A LARGE RWER BASIN WITH POINT AND NONPOINT SOURCES 1 , 2001 .

[5]  J. Wheaton,et al.  Assessing streamflow sensitivity to temperature increases in the Salmon River Basin, Idaho , 2012 .

[6]  Jean Poesen,et al.  Spatial evaluation of a physically-based distributed erosion model (LISEM) , 1999 .

[7]  J. Houghton,et al.  Climate change 1995: the science of climate change. , 1996 .

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

[9]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[10]  Jing Yang,et al.  Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China , 2008 .

[11]  The basic science of anthropogenic climate change , 1995 .

[12]  Brian L. McGlynn,et al.  Landscape structure and climate influences on hydrologic response , 2011 .

[13]  A. Bárdossy,et al.  Robust estimation of hydrological model parameters , 2008 .

[14]  Henrik Madsen,et al.  An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation , 2004 .

[15]  Eugene S. Takle,et al.  Hydrological cycle in the upper Mississippi River basin: 20th century simulations by multiple GCMs , 2005 .

[16]  Xuesong Zhang,et al.  Multi-Site Calibration of the SWAT Model for Hydrologic Modeling , 2008 .

[17]  R. Sparks,et al.  THE NATURAL FLOW REGIME. A PARADIGM FOR RIVER CONSERVATION AND RESTORATION , 1997 .

[18]  Keith Beven,et al.  Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system , 2002 .

[19]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[20]  Rollin H. Hotchkiss,et al.  Water yield responses to high and low spatial resolution climate change scenarios in the Missouri River Basin , 2003 .

[21]  Richard N. Palmer,et al.  Water Resources Implications of Global Warming: A U.S. Regional Perspective , 1999 .

[22]  Xuesong Zhang,et al.  On the use of multi‐algorithm, genetically adaptive multi‐objective method for multi‐site calibration of the SWAT model , 2010 .

[23]  Alexei G. Sankovski,et al.  Special report on emissions scenarios : a special report of Working group III of the Intergovernmental Panel on Climate Change , 2000 .

[24]  W. Cully Hession,et al.  Influence of stream geomorphic condition on fish communities in Vermont, U.S.A. , 2006 .

[25]  E. Eltahir,et al.  On the asymmetric response of aquifer water level to floods and droughts in Illinois , 1999 .

[26]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[27]  Dennis P. Lettenmaier,et al.  Spatial Patterns in Catchment Hydrology: Observations and Modeling , 2004 .

[28]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[29]  Elfatih A. B. Eltahir,et al.  Hydroclimatology of Illinois: A comparison of monthly evaporation estimates based on atmospheric water balance and soil water balance , 1998 .

[30]  M. A. Nearinga,et al.  Modeling response of soil erosion and runoff to changes in precipitation and cover , 2005 .

[31]  L. Hay,et al.  Watershed-Scale Response to Climate Change through the Twenty-First Century for Selected Basins across the United States , 2011 .

[32]  J. Famiglietti,et al.  Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data , 2010 .

[33]  J. C. Abanades,et al.  Summary for Policymakers A Special Report of Working Group III of the Intergovernmental Panel on Climate Change , 2006 .

[34]  E. Maurer,et al.  Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods , 2007 .

[35]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[36]  D. Lettenmaier,et al.  A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States* , 2002 .

[37]  C. T. Haan,et al.  An approach to estimating uncertainties in modeling transport of solutes through soils , 1993 .

[38]  Martijn Gough.  Climate change , 2009, Canadian Medical Association Journal.

[39]  A. Protopapas,et al.  Uncertainty Propagation in Layered Unsaturated Soils , 2000 .

[40]  Keith A. Cherkauer,et al.  Hydrologic Impacts of Projected Future Climate Change in the Lake Michigan Region , 2009 .

[41]  J. Arnold,et al.  SWAT2000: current capabilities and research opportunities in applied watershed modelling , 2005 .

[42]  D. Leenhardt,et al.  Errors in the estimation of soil water properties and their propagation through a hydrological model , 1995 .

[43]  B. Kang,et al.  Response of Streamflow to Weather Variability under Climate Change in the Colorado Rockies , 2007 .

[44]  Bellie Sivakumar,et al.  Dominant processes concept in hydrology: moving forward , 2004 .

[45]  Dan Rosbjerg,et al.  Modelling of hydrologic processes and potential response to climate change through the use of a multisite SWAT , 2010 .

[46]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

[47]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[48]  Causes of the northern high‐latitude land surface winter climate change , 2007 .

[49]  A. Islam,et al.  Streamflow Response to Climate Change in the Brahmani River Basin, India , 2012, Water Resources Management.

[50]  Keith Beven,et al.  Dalton Medal Lecture: How far can we go in distributed hydrological modelling? , 2001 .

[51]  Keith Beven,et al.  Distributed Hydrological Modelling , 1998 .

[52]  F. Mutua,et al.  Climate change impact on SWAT simulated streamflow in western Kenya , 2009 .

[53]  Hamid Moradkhani,et al.  Statistical Comparisons of Watershed-Scale Response to Climate Change in Selected Basins across the United States , 2011 .

[54]  M. Jha,et al.  CLIMATE CHHANGE SENSITIVITY ASSESSMENT ON UPPER MISSISSIPPI RIVER BASIN STREAMFLOWS USING SWAT 1 , 2006 .

[55]  Patrick Willems,et al.  Parameter estimation in semi‐distributed hydrological catchment modelling using a multi‐criteria objective function , 2007 .

[56]  M. Jha,et al.  Impacts of Climate Change on Stream Flow in the Upper Mississippi River Basin: A Regional Climate Model Perspective, The , 2003 .

[57]  Regional terrestrial water storage change and evapotranspiration from terrestrial and atmospheric water balance computations , 2008 .

[58]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[59]  T. Hawkins,et al.  Simulating streamflow and the effects of projected climate change on the Savage River, Maryland, USA , 2011 .

[60]  J. Famiglietti,et al.  Regional Groundwater Evapotranspiration in Illinois , 2009 .

[61]  Paul Houser,et al.  Runoff, Precipitation, and Soil Moisture at Walnut Gulch , 2004 .

[62]  Vijay P. Singh,et al.  Historical temporal trends of hydro-climatic variables and runoff response to climate variability and their relevance in water resource management in the Hanjiang basin , 2007 .

[63]  J. Finn,et al.  Streamflow regulation and fish community structure , 1988 .

[64]  K. Abbaspour,et al.  Modeling blue and green water availability in Africa , 2008 .

[65]  C. Yoder,et al.  The biotic integrity of streams in urban and suburbanizing landscapes , 2004 .

[66]  George Kuczera,et al.  Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .

[67]  W. Cornelis,et al.  Influence of landuse on soil erosion risk in the Cuyaguateje watershed (Cuba) , 2008 .

[68]  A van Griensven,et al.  Methods to quantify and identify the sources of uncertainty for river basin water quality models. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[69]  M. Kemp,et al.  Potential climate-change impacts on the Chesapeake Bay , 2008 .

[70]  M. Dettinger,et al.  Downscaling With Constructed Analogues: Daily Precipitation and Temperature Fields Over The United States , 2008 .

[71]  Gerard Govers,et al.  Erosion models: quality of spatial predictions , 2003 .

[72]  J. Famiglietti,et al.  Constraining water table depth simulations in a land surface model using estimated baseflow , 2008 .

[73]  D. Lettenmaier,et al.  Hydrologic Sensitivity of Global Rivers to Climate Change , 2001 .

[74]  D. Roy,et al.  Conterminous United States crop field size quantification from multi-temporal Landsat data , 2015 .

[75]  Murugesu Sivapalan,et al.  Downward approach to hydrological prediction , 2003 .

[76]  R. Woods,et al.  Catchment Classification and Hydrologic Similarity , 2006, Geography Compass.

[77]  M. Dettinger,et al.  The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California , 2010 .

[78]  Bellie Sivakumar,et al.  Dominant processes concept, model simplification and classification framework in catchment hydrology , 2008 .

[79]  K. Abbaspour,et al.  Modelling blue and green water resources availability in Iran , 2009 .

[80]  John R. Williams,et al.  EPIC-erosion/productivity impact calculator: 1. Model documentation. , 1990 .

[81]  W. Cao,et al.  Multi‐variable and multi‐site calibration and validation of SWAT in a large mountainous catchment with high spatial variability , 2006 .

[82]  K. Beven.  Rainfall-Runoff Modelling: The Primer , 2012 .

[83]  Heejun Chang,et al.  Spatial Patterns of March and September Streamflow Trends in Pacific Northwest Streams, 1958–2008 , 2012 .

[84]  Indrajeet Chaubey,et al.  SENSITIVITY ANALYSIS, CALIBRATION, AND VALIDATIONS FOR A MULTISITE AND MULTIVARIABLE SWAT MODEL 1 , 2005 .

[85]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[86]  Jeffrey G. Arnold,et al.  Soil and Water Assessment Tool Theoretical Documentation Version 2009 , 2011 .

[87]  K. Abbaspour,et al.  Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure , 2004 .

[88]  J. Hansen,et al.  Global temperature change , 2006, Proceedings of the National Academy of Sciences.

[89]  Arnold Schwarzenegger.  DOWNSCALING WITH CONSTRUCTED ANALOGUES: DAILY PRECIPITATION AND TEMPERATURE FIELDS OVER THE UNITED STATES , 2008 .

[90]  Wade T. Crow,et al.  A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations , 2006, IEEE Transactions on Geoscience and Remote Sensing.