Insights on geologic and vegetative controls over hydrologic behavior of a large complex basin – Global Sensitivity Analysis of an integrated parallel hydrologic model

Summary This study demonstrated the first application of a GSA technique to a transient ISSHM–LSM application developed for a large-scale river basin. The Morris method was used to identify the spatially and temporally variable sensitivity amongst a large number of model parameters to provide insights on hydrologic processes dominating behavior in the basin and to identify a small subset of parameters that should be evaluated in subsequent, more computationally intensive quantitative GSA and parameter estimation techniques. Results showed that in the upper region of the basin, evapotranspiration (ET), total streamflow and peak streamflow were less sensitive to surficial aquifer system characteristics, but highly sensitive to the hydraulic conductivity of the confining unit separating the surficial aquifer and the regional aquifer system and leaf area index of near stream vegetation. In the lower region of the basin, hydraulic conductivity of the regional aquifer system was found to have a significant effect on ET, total stream flow, and groundwater contributions to streamflow while surface–groundwater dynamics during storm events was most sensitive to storage properties of the regional aquifer system. Peak streamflow in the lower basin was most sensitive to the hydraulic conductivity of the confining unit in the upper basin, and the Manning’s coefficient of upper basin streams, indicating that all peak storm flows originate in the upper basin. Throughout the basin ET was sensitive to soil/geologic properties and vegetation properties, with unsaturated zone processes and relevant parameters gaining importance in moisture limited conditions existing in the lower regions of the basin.

[1]  B. A. Meyer,et al.  Modeling Karstic Controls on Watershed-Scale Groundwater Flow in the Floridan Aquifer of North Florida , 2008 .

[2]  Jeroen P. van der Sluijs,et al.  What do I make of your Latinorum? Sensitivity auditing of mathematical modelling. , 2012, 1211.2668.

[3]  E. Screaton,et al.  Interactions of diffuse and focused allogenic recharge in an eogenetic karst aquifer (Florida, USA) , 2012, Hydrogeology Journal.

[4]  Fotini Katopodes Chow,et al.  Coupling groundwater and land surface processes: Idealized simulations to identify effects of terrain and subsurface heterogeneity on land surface energy fluxes , 2010 .

[5]  André Revil,et al.  Characterization of groundwater and surface water mixing in a semiconfined karst aquifer using time‐lapse electrical resistivity tomography , 2014 .

[6]  G. Panagopoulos Application of MODFLOW for simulating groundwater flow in the Trifilia karst aquifer, Greece , 2012, Environmental Earth Sciences.

[7]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[8]  R. Maxwell,et al.  A comparison of two physics-based numerical models for simulating surface water–groundwater interactions , 2010 .

[9]  L. Florea,et al.  Eogenetic Karst Hydrology: Insights from the 2004 Hurricanes, Peninsular Florida , 2007, Ground water.

[10]  Vibhava Srivastava Geologic, vegetative and climatic controls on coupled hydrologic processes in a complex river basin: Lessons learned from a fully integrated hydrologic model , 2013 .

[11]  P. Huyakorn,et al.  A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow , 2004 .

[12]  R. Srinivasan,et al.  A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .

[13]  Willy Bauwens,et al.  Multi-variable sensitivity and identifiability analysis for a complex environmental model in view of integrated water quantity and water quality modeling. , 2012, Water science and technology : a journal of the International Association on Water Pollution Research.

[14]  Laura E. Condon,et al.  Implementation of a linear optimization water allocation algorithm into a fully integrated physical hydrology model , 2013 .

[15]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

[16]  J. D. Tarpley,et al.  Real‐time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project , 2003 .

[17]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[18]  S. Ashby,et al.  A parallel multigrid preconditioned conjugate gradient algorithm for groundwater flow simulations , 1996 .

[19]  J. Dehotin,et al.  Which spatial discretization for distributed hydrological models? Proposition of a methodology and illustration for medium to large-scale catchments , 2008 .

[20]  R. Maxwell,et al.  Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model , 2008 .

[21]  R. Maxwell,et al.  Integrated surface-groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model , 2006 .

[22]  K. Nagarajan,et al.  Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions , 2011 .

[23]  K. Loague,et al.  Hydrologic‐Response simulations for the R‐5 catchment with a comprehensive physics‐based model , 2001 .

[24]  Vedat Batu Aquifer Hydraulics: A Comprehensive Guide to Hydrogeologic Data Analysis , 1998 .

[25]  Olaf Kolditz,et al.  Surface‐subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks , 2014 .

[26]  Stefan Finsterle,et al.  Making sense of global sensitivity analyses , 2014, Comput. Geosci..

[27]  Reed M. Maxwell,et al.  Groundwater-fed irrigation impacts spatially distributed temporal scaling behavior of the natural system: a spatio-temporal framework for understanding water management impacts , 2014 .

[28]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[29]  Reed M. Maxwell,et al.  Development of a Coupled Land Surface and Groundwater Model , 2005 .

[30]  Martin Sauter,et al.  Simulation of flow processes in a large scale karst system with an integrated catchment model (Mike She) – Identification of relevant parameters influencing spring discharge , 2012 .

[31]  R. Maxwell,et al.  Visualization of conduit‐matrix conductivity differences in a karst aquifer using time‐lapse electrical resistivity , 2012 .

[32]  B. Croke,et al.  Addressing ten questions about conceptual rainfall–runoff models with global sensitivity analyses in R , 2013 .

[33]  R. Freeze,et al.  Blueprint for a physically-based, digitally-simulated hydrologic response model , 1969 .

[34]  Jasmeet Judge,et al.  Effect of simultaneous state–parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF , 2010 .

[35]  Peter Bayer,et al.  The Influence of Rain Sensible Heat and Subsurface Energy Transport on the Energy Balance at the Land Surface , 2009 .

[36]  Rafael Muñoz-Carpena,et al.  GLOBAL SENSITIVITY AND UNCERTAINTY ANALYSES OF THE WATER QUALITY MODEL VFSMOD-W , 2007 .

[37]  R. Maxwell,et al.  Demonstrating fractal scaling of baseflow residence time distributions using a fully‐coupled groundwater and land surface model , 2008 .

[38]  Reed M. Maxwell,et al.  The impact of subsurface conceptualization on land energy fluxes , 2013 .

[39]  Craig T. Simmons,et al.  On the testing of fully integrated surface–subsurface hydrological models , 2013 .

[40]  Jim E. Jones,et al.  Approved for Public Release; Further Dissemination Unlimited Newton-krylov-multigrid Solvers for Large-scale, Highly Heterogeneous, Variably Saturated Flow Problems , 2022 .

[41]  R. Dickinson,et al.  The Common Land Model , 2003 .

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

[43]  Mary C. Hill,et al.  Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function , 2009 .

[44]  Rafael Muñoz-Carpena,et al.  Hydrologic Modeling, Uncertainty, and Sensitivity in the Okavango Basin: Insights for Scenario Assessment , 2013 .

[45]  Evapotranspiration over spatially extensive plant communities in the Big Cypress National Preserve, southern Florida, 2007-2010 , 2011 .

[46]  A. Saltelli,et al.  Sensitivity analysis: Could better methods be used? , 1999 .

[47]  H. Fowler,et al.  Large scale surface – subsurface hydrological model to assess climate change impacts on groundwater reserves , 2009 .

[48]  Young-Jin Park,et al.  Simulating the pre-development hydrologic conditions in the San Joaquin Valley, California , 2011 .

[49]  Young-Jin Park,et al.  Simulating complex flow and transport dynamics in an integrated surface-subsurface modeling framework , 2008 .

[50]  Wei Gong,et al.  Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis , 2013 .

[51]  Jing Yang,et al.  Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis , 2011, Environ. Model. Softw..

[52]  R. Confalonieri,et al.  Sensitivity Analysis and Investigation of the Behaviour of the UTOPIA Land-Surface Process Model: A Case Study for Vineyards in Northern Italy , 2012, Boundary-Layer Meteorology.

[53]  Reed M. Maxwell,et al.  Influences of subsurface heterogeneity and vegetation cover on soil moisture, surface temperature and evapotranspiration at hillslope scales , 2011 .

[54]  Marie Larocque,et al.  A modeling study of heterogeneity and surface water-groundwater interactions in the Thomas Brook catchment, Annapolis Valley (Nova Scotia, Canada) , 2009 .

[55]  S. Brouyère,et al.  Assessing the effects of spatial discretization on large-scale flow model performance and prediction uncertainty , 2014 .

[56]  Mary C. Hill,et al.  Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models , 2014 .

[57]  J. Vanderkwaak Numerical simulation of flow and chemical transport in integrated surface-subsurface hydrologic systems , 1999 .

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

[59]  Reed M. Maxwell,et al.  Feedbacks between managed irrigation and water availability: Diagnosing temporal and spatial patterns using an integrated hydrologic model , 2014 .

[60]  Andrea Saltelli,et al.  An effective screening design for sensitivity analysis of large models , 2007, Environ. Model. Softw..

[61]  Patrick M. Reed,et al.  Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models , 2013 .

[62]  H. Vacher,et al.  Eogenetic karst from the perspective of an equivalent porous medium , 2002, Carbonates and Evaporites.

[63]  Reed M. Maxwell,et al.  Human impacts on terrestrial hydrology: climate change versus pumping and irrigation , 2012 .

[64]  H. Riedwyl Goodness of Fit , 1967 .

[65]  C. Simmons,et al.  Vegetation controls on variably saturated processes between surface water and groundwater and their impact on the state of connection , 2011 .

[66]  C. Simmons,et al.  Hydrogeologic controls on disconnection between surface water and groundwater , 2009 .

[67]  D. L. Brakensiek,et al.  Estimation of Soil Water Properties , 1982 .

[68]  Reed M. Maxwell,et al.  Role of groundwater in watershed response and land surface feedbacks under climate change , 2010 .

[69]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[70]  Silvio Funtowicz,et al.  Are all models wrong? , 2020, Computational and systems oncology.

[71]  David R. Montgomery,et al.  Physics‐based continuous simulation of long‐term near‐surface hydrologic response for the Coos Bay experimental catchment , 2007 .

[72]  G. Panagopoulos,et al.  The contribution of time series analysis to the study of the hydrodynamic characteristics of the karst systems: Application on two typical karst aquifers of Greece (Trifilia, Almyros Crete) , 2006 .

[73]  Stefan Finsterle,et al.  Modeling the performance of large-scale CO2 storage systems: A comparison of different sensitivity analysis methods , 2012 .

[74]  E. Sudicky,et al.  Simulating the multi-seasonal response of a large-scale watershed with a 3D physically-based hydrologic model , 2008 .

[75]  Edward A. Sudicky,et al.  Application of a fully‐integrated surface‐subsurface flow model at the watershed‐scale: A case study , 2008 .