Hydrological Processes and Model Representation: Impact of Soft Data on Calibration

Hydrologic and water quality models are increasingly used to determine the environmental impacts of climate variability and land management. Due to differing model objectives and differences in monitored data, there are currently no universally accepted procedures for model calibration and validation in the literature. In an effort to develop accepted model calibration and validation procedures or guidelines, a special collection of 22 research articles that present and discuss calibration strategies for 25 hydrologic and water quality models was previously assembled. The models vary in scale temporally as well as spatially from point source to the watershed level. One suggestion for future work was to synthesize relevant information from this special collection and to identify significant calibration and validation topics. The objective of this article is to discuss the importance of accurate representation of model processes and its impact on calibration and scenario analysis using the information from these 22 research articles and other relevant literature. Models are divided into three categories: (1) flow, heat, and solute transport, (2) field scale, and (3) watershed scale. Processes simulated by models in each category are reviewed and discussed. In this article, model case studies are used to illustrate situations in which a model can show excellent statistical agreement with measured stream gauge data, while misrepresented processes (water balance, nutrient balance, sediment source/sinks) within a field or watershed can cause errors when running management scenarios. These errors may be amplified at the watershed scale where additional sources and transport processes are simulated. To account for processes in calibration, a diagnostic approach is recommended using both hard and soft data. The diagnostic approach looks at signature patterns of behavior of model outputs to determine which processes, and thus parameters representing them, need further adjustment during calibration. This overcomes the weaknesses of traditional regression-based calibration by discriminating between multiple processes within a budget. Hard data are defined as long-term, measured time series, typically at a point within a watershed. Soft data are defined as information on individual processes within a budget that may not be directly measured within the study area, may be just an average annual estimate, and may entail considerable uncertainty. The advantage of developing soft data sets for calibration is that they require a basic understanding of processes (water, sediment, nutrient, and carbon budgets) within the spatial area being modeled and constrain the calibration.

[1]  Xiuying Wang,et al.  A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling , 2014, Environ. Model. Softw..

[2]  H. Gavin,et al.  Modelling the hydrological impacts of climate change on UK lowland wet grassland , 2009, Wetlands Ecology and Management.

[3]  Raghavan Srinivasan,et al.  Simultaneous calibration of surface flow and baseflow simulations: a revisit of the SWAT model calibration framework , 2011 .

[4]  D. Higdon,et al.  Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling , 2009 .

[5]  Kevin Wagner,et al.  Calibration of SWAT2009 Using Crop Biomass, Evapotranspiration, and Deep Recharge: Calera Watershed in Zacatecas, Mexico Case Study , 2012 .

[6]  R. W. Skaggs,et al.  DRAINMOD-GIS : A lumped parameter watershed scale drainage and water quality model , 2006 .

[7]  R. W. Skaggs,et al.  DRAINMOD: Model Use, Calibration, and Validation , 2012 .

[8]  J. Nieber,et al.  Dynamic Capillary Pressure Mechanism for Instability in Gravity-Driven Flows; Review and Extension to Very Dry Conditions , 2005 .

[9]  Robert W. Malone,et al.  SIMULATING THE LONG-TERM PERFORMANCE OF DRAINAGE WATER MANAGEMENT ACROSS THE MIDWESTERN UNITED STATES , 2008 .

[10]  Luis A. Bastidas,et al.  Estimating Parameters and Structure of a Hydrochemical Model Using Multiple Criteria , 2013 .

[11]  J. Mullins,et al.  PRZM-2, a model for predicting pesticide fate in the crop root and unsaturated soil zones: User's manual for release 2. 0 , 1993 .

[12]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[13]  Peter Droogers,et al.  Calibration of a distributed hydrological model based on satellite evapotranspiration , 2008 .

[14]  John R. Williams,et al.  The EPIC crop growth model , 1989 .

[15]  R. W. Skaggs,et al.  THE NITROGEN SIMULATION MODEL, DRAINMOD-N II , 2005 .

[16]  Liwang Ma,et al.  Root Zone Water Quality Model (RZWQM2): Model Use, Calibration, and Validation , 2012 .

[17]  Shiying Tian,et al.  DRAINMOD-FOREST: Integrated Modeling of Hydrology, Soil Carbon and Nitrogen Dynamics, and Plant Growth for Drained Forests. , 2012, Journal of environmental quality.

[18]  M. S. Moran,et al.  Soil water evaluation using a hydrologic model and calibrated sensor network , 2000 .

[19]  Gerald N. Flerchinger,et al.  Simultaneous Heat and Water Model of a Freezing Snow-Residue-Soil System I. Theory and Development , 1989 .

[20]  M. Watkins,et al.  The gravity recovery and climate experiment: Mission overview and early results , 2004 .

[21]  R. W. Skaggs,et al.  Evaluation of a watershed scale forest hydrologic model , 1997 .

[22]  R. W. Skaggs,et al.  APPLICATION OF DRAINMOD-GIS TO A LOWER COASTAL PLAIN WATERSHED , 2007 .

[23]  Knut Alfredsen,et al.  Sensitivity-guided evaluation of the HBV hydrological model parameterization , 2013 .

[24]  Joop G Kroes,et al.  User's guide of SWAP version 2.0 : Simulation of water flow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment , 1997 .

[25]  Jaehak Jeong,et al.  EPIC and APEX: Model Use, Calibration, and Validation , 2012 .

[26]  Douglas Helms,et al.  Hugh Hammond Bennett and the creation of the Soil Erosion Service , 2009, Journal of Soil and Water Conservation.

[27]  K. Beven,et al.  On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration process , 1998 .

[28]  Adel Shirmohammadi,et al.  Atrazine movement in soil: Comparison of field observations and PRZM simulations , 1995 .

[29]  Carl C. Trettin,et al.  Effect of Assessment Scale on Spatial and Temporal Variations in CH4, CO2, and N2O Fluxes in a Forested Wetland , 2011, Water, Air, & Soil Pollution.

[30]  A. B. Bottcher,et al.  Watershed Assessment Model (WAM): Model Use, Calibration, and Validation , 2012 .

[31]  R. Wooding,et al.  Steady Infiltration from a Shallow Circular Pond , 1968 .

[32]  W. G. Knisel,et al.  CREAMS/GLEAMS: model use, calibration, and validation. , 2012 .

[33]  David Pimentel,et al.  Rates of Soil Erosion , 1999, Science.

[34]  Devendra M. Amatya,et al.  DRAINWAT--Based Methods For Estimating Nitrogen Transport in Poorly Drained Watersheds , 2004 .

[35]  Rui Ma,et al.  MT3DMS: Model Use, Calibration, and Validation , 2012 .

[36]  J. Philip,et al.  Theory of Infiltration , 1969 .

[37]  A. Challinor,et al.  Design and optimisation of a large-area process-based model for annual crops , 2004 .

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

[39]  Devendra M. Amatya,et al.  DEVELOPMENT AND TESTING OF WATERSHED-SCALE MODELS FOR POORLY DRAINED SOILS , 2005 .

[40]  Hoshin Vijai Gupta,et al.  A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model , 2008 .

[41]  Jeffrey G. Arnold,et al.  Spatial Calibration and Temporal Validation of Flow for Regional Scale Hydrologic Modeling 1 , 2008 .

[42]  Liwang Ma,et al.  DRAINMOD-N II: Evaluated for an Agricultural System in Iowa and Compared to RZWQM-DSSAT , 2009 .

[43]  Raghavan Srinivasan,et al.  SWAT: Model Use, Calibration, and Validation , 2012 .

[44]  Michael Rode,et al.  Multi-objective calibration and fuzzy preference selection of a distributed hydrological model , 2008, Environ. Model. Softw..

[45]  R. L. Johnson,et al.  User's guide to the Variably Saturated Flow (VSF) process to MODFLOW , 2006 .

[46]  Devendra M. Amatya,et al.  Hydrologic Effects of Size and Location of Fields Converted from Drained Pine Forest to Agricultural Cropland , 2013 .

[47]  Michael J White,et al.  SWAT Check: A Screening Tool to Assist Users in the Identification of Potential Model Application Problems. , 2014, Journal of environmental quality.

[48]  John R. Williams,et al.  A general, process-oriented model for two competing plant species , 1992 .

[49]  Yan Zhang,et al.  A general predictive model for estimating monthly ecosystem evapotranspiration , 2011 .

[50]  Hubert H. G. Savenije,et al.  On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information , 2009 .

[51]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[52]  Hoshin Vijai Gupta,et al.  Model identification for hydrological forecasting under uncertainty , 2005 .

[53]  H. Wheater,et al.  Application of the Kineros2 rainfall-runoff model to an arid catchment in Oman , 2008 .

[54]  Nicholas Jarvis,et al.  MACRO (v5.2): Model Use, Calibration, and Validation , 2012 .

[55]  David D. Bosch,et al.  Review of nitrogen fate models applicable to forest landscapes in the Southern U.S. , 2013 .

[56]  Jan Feyen,et al.  Rainfall-runoff modelling of a rocky catchment with limited data availability: Defining prediction limits , 2010 .

[57]  Per-Erik Jansson,et al.  Coupmodel: Model use, calibration, and validation , 2012 .

[58]  Jeffrey J. McDonnell,et al.  The Quest for an Improved Dialog Between Modeler and Experimentalist , 2013 .

[59]  R. W. Skaggs,et al.  HYDROLOGIC EFFECTS OF GLOBAL CLIMATE CHANGE ON A LARGE DRAINED PINE FOREST , 2006 .

[60]  Prasanna H. Gowda,et al.  ADAPT: Model Use, Calibration, and Validation , 2012 .

[61]  M. Arabi,et al.  The role of interior watershed processes in improving parameter estimation and performance of watershed models. , 2014, Journal of environmental quality.

[62]  P. Gassman,et al.  History of model development at Temple, Texas , 2008 .

[63]  Eligius M. T. Hendrix,et al.  A method for robust calibration of ecological models under different types of uncertainty , 1994 .

[64]  A. Thomson,et al.  Simulating long-term and residual effects of nitrogen fertilization on corn yields, soil carbon sequestration, and soil nitrogen dynamics. , 2006, Journal of environmental quality.

[65]  Keith Beven,et al.  Bayesian estimation of uncertainty in land surface‐atmosphere flux predictions , 1997 .

[66]  Ge Sun,et al.  Linkage of MIKE SHE to Wetland-DNDC for carbon budgeting and anaerobic biogeochemistry simulation , 2005 .

[67]  Stefan Finsterle,et al.  TOUGH: Model use, calibration and validation , 2012 .

[68]  Søren Hansen,et al.  Daisy: Model Use, Calibration, and Validation , 2012 .

[69]  S. Sorooshian,et al.  Calibration of watershed models , 2003 .

[70]  Anthony S. Donigian,et al.  BASINS/HSPF: MODEL USE, CALIBRATION, AND VALIDATION , 2012 .

[71]  K. R. Douglas-Mankin,et al.  Evaluating, interpreting, and communicating performance of hydrologic/water quality models considering intended use: A review and recommendations , 2014, Environ. Model. Softw..

[72]  Christina Tonitto,et al.  Modeling denitrification in a tile-drained, corn and soybean agroecosystem of Illinois, USA , 2009 .

[73]  R. W. Skaggs,et al.  Comparison of Two Drainage Simulation Models Using Field Data , 1989 .

[74]  K. Schilling,et al.  Chemical transport from paired agricultural and restored prairie watersheds. , 2002, Journal of environmental quality.

[75]  Changsheng Li,et al.  Bi-criteria evaluation of the MIKE SHE model for a forested watershed on the South Carolina coastal plain , 2010 .

[76]  L. A. Richards.  Capillary conduction of liquids through porous mediums , 1931 .

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

[78]  Paul G. Constantine,et al.  Accelerating Markov Chain Monte Carlo with Active Subspaces , 2016, SIAM J. Sci. Comput..

[79]  James C. Ascough,et al.  WEPP: Model Use, Calibration, and Validation , 2012 .

[80]  John L. Nieber,et al.  Stability analysis of gravity‐driven infiltrating flow , 2003 .

[81]  Jirka Simunek,et al.  HYDRUS: Model Use, Calibration, and Validation , 2012 .

[82]  S. Trimble.  Fluvial processes, morphology and sediment budgets in the Coon Creek Basin, WI, USA, 1975–1993 , 2009 .

[83]  J. Philip,et al.  What happens near a quasi-linear point source? , 1992 .

[84]  Matthew Lorber,et al.  The Pesticide Root Zone Model (PRZM): A procedure for evaluating pesticide leaching threats to groundwater , 1985 .

[85]  Leslie M. Reid,et al.  Rapid Evaluation of Sediment Budgets , 1996 .

[86]  Bryan A. Tolson,et al.  Dynamically dimensioned search algorithm for computationally efficient watershed model calibration , 2007 .

[87]  Hans M. Gregersen,et al.  Integrated Watershed Management: Connecting people to their land and water , 2007 .

[88]  Mark D. Tomer,et al.  Evaluating hydrology of the Soil and Water Assessment Tool (SWAT) with new tile drain equations , 2012, Journal of Soil and Water Conservation.

[89]  Thomas C. Winter,et al.  THE CONCEPT OF HYDROLOGIC LANDSCAPES 1 , 2001 .

[90]  R. W. Skaggs,et al.  A Coupled, Field Hydrology – Open Channel Flow Model: Theory , 1992 .

[91]  Heikki Haario,et al.  DRAM: Efficient adaptive MCMC , 2006, Stat. Comput..

[92]  Jean L Steiner,et al.  Sediment measurement and transport modeling: impact of riparian and filter strip buffers. , 2011, Journal of environmental quality.

[93]  Peter M. Allen,et al.  Automated Base Flow Separation and Recession Analysis Techniques , 1995 .

[94]  Jirka Simunek,et al.  STANMOD: Model Use, Calibration, and Validation , 2012 .

[95]  John M. Stockie,et al.  Numerical simulations of gravity‐driven fingering in unsaturated porous media using a nonequilibrium model , 2010, 1005.3297.

[96]  N. I. Huth,et al.  SWIM3: Model Use, Calibration, and Validation , 2012 .

[97]  Yuqiong Liu,et al.  Reconciling theory with observations: elements of a diagnostic approach to model evaluation , 2008 .

[98]  Douglas Helms.  Hugh Hammond Bennett and the creation of the Soil Conservation Service , 2010, Journal of Soil and Water Conservation.

[99]  J. R. Philip.  Flows satisfying mixed no-slip and no-shear conditions , 1972 .

[100]  Mark A. Nearing,et al.  DWEPP: a dynamic soil erosion model based on WEPP source terms , 2007 .

[101]  B. Sohngen,et al.  Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools 1 , 2011 .

[102]  Craig T Simmons,et al.  The compleat Darcy: New lessons learned from the first English translation of les fontaines publiques de la Ville de Dijon , 2005, Ground water.

[103]  D. Boyle.  Multicriteria Calibration of Hydrologic Models , 2013 .

[104]  Kellie B. Vaché,et al.  A process‐based rejectionist framework for evaluating catchment runoff model structure , 2006 .

[105]  Sanjay Shukla,et al.  MIKE SHE: model use, calibration, and validation. , 2012 .

[106]  Matthew D. Wallenstein,et al.  Bridging the gap between modelers and experimentalists , 2012 .

[107]  James W. Jones,et al.  DRAINMOD-DSSAT model for simulating hydrology, soil carbon and nitrogen dynamics, and crop growth for drained crop land , 2014 .

[108]  R. W. Skaggs,et al.  WATGIS: A GIS–BASED LUMPED PARAMETER WATER QUALITY MODEL , 2002 .

[109]  Richard W. Healy,et al.  VS2DI: Model Use, Calibration, and Validation , 2012 .

[110]  Stuart P. Hardegree,et al.  Simultaneous heat and water (SHAW) model: model use, calibration, and validation. , 2012 .

[111]  J. Thompson,et al.  Application of the coupled MIKE SHE/MIKE 11 modelling system to a lowland wet grassland in southeast England , 2004 .

[112]  Raghavan Srinivasan,et al.  Regional estimation of base flow and groundwater recharge in the Upper Mississippi river basin , 2000 .

[113]  Jens Christian Refsgaard,et al.  A good-looking catchment can turn into a modeller's nightmare , 2010 .

[114]  Devendra M. Amatya,et al.  Modeling water, carbon, and nitrogen dynamics for two drained pine plantations under intensive management practices , 2012 .

[115]  Thibault Mathevet,et al.  Discrete parameterization of hydrological models: Evaluating the use of parameter sets libraries over 900 catchments , 2008 .

[116]  Cajo J. F. ter Braak,et al.  Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation , 2008 .

[117]  D. Goodrich,et al.  KINEROS2/AGWA: Model use, calibration and validation , 2012 .

[118]  Randy L. Raper,et al.  Simulating Field-Scale Soil Organic Carbon Dynamics Using EPIC , 2007 .

[119]  J. Arnold,et al.  Advances in the application of the SWAT model for water resources management , 2005 .