Integration of a Three-Dimensional Process-Based Hydrological Model into the Object Modeling System

The integration of a spatial process model into an environmental modeling framework can enhance the model’s capabilities. This paper describes a general methodology for integrating environmental models into the Object Modeling System (OMS) regardless of the model’s complexity, the programming language, and the operating system used. We present the integration of the GEOtop model into the OMS version 3.0 and illustrate its application in a small watershed. OMS is an environmental modeling framework that facilitates model development, calibration, evaluation, and maintenance. It provides innovative techniques in software design such as multithreading, implicit parallelism, calibration and sensitivity analysis algorithms, and cloud-services. GEOtop is a physically based, spatially distributed rainfall-runoff model that performs three-dimensional finite volume calculations of water and energy budgets. Executing GEOtop as an OMS model component allows it to: (1) interact directly with the open-source geographical information system (GIS) uDig-JGrass to access geo-processing, visualization, and other modeling components; and (2) use OMS components for automatic calibration, sensitivity analysis, or meteorological data interpolation. A case study of the model in a semi-arid agricultural catchment is presented for illustration and proof-of-concept. Simulated soil water content and soil temperature results are compared with measured data, and model performance is evaluated using goodness-of-fit indices. This study serves as a template for future integration of process models into OMS.

[1]  Timothy R. Green,et al.  Measurement and inference of profile soil‐water dynamics at different hillslope positions in a semiarid agricultural watershed , 2011 .

[2]  Riccardo Rigon,et al.  A robust and energy-conserving model of freezing variably-saturated soil , 2011 .

[3]  Olaf David,et al.  The AgroEcoSystem (AgES) Response‐Function Model Simulates Layered Soil‐Water Dynamics in Semiarid Colorado: Sensitivity and Calibration , 2015 .

[4]  James C. Ascough,et al.  Spatial Interrelationships between Wheat Phenology, Thermal Time, and Terrain Attributes , 2012 .

[5]  Olaf David,et al.  A software engineering perspective on environmental modeling framework design: The Object Modeling System , 2013, Environ. Model. Softw..

[6]  Peter Krause,et al.  Environmental modeling framework invasiveness: Analysis and implications , 2011, Environ. Model. Softw..

[7]  Toshio Koike,et al.  Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin , 2009 .

[8]  Jose D. Salas,et al.  Fractal Analyses of Steady Infiltration and Terrain on an Undulating Agricultural Field , 2009 .

[9]  Sven Kralisch,et al.  DEVELOPMENT AND APPLICATION OF A MODULAR WATERSHED-SCALE HYDROLOGIC MODEL USING THE OBJECT MODELING SYSTEM: RUNOFF RESPONSE EVALUATION , 2012 .

[10]  S. Gruber,et al.  Sensitivities and uncertainties of modeled ground temperatures in mountain environments , 2013 .

[11]  Olaf David,et al.  The Object Modeling System , 2016 .

[12]  David C. Goodrich,et al.  An integrated modelling framework of catchment‐scale ecohydrological processes: 1. Model description and tests over an energy‐limited watershed , 2014 .

[13]  R. Rigon,et al.  GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets , 2006 .

[14]  R. Rigon,et al.  An energy-conserving model of freezing variably-saturated soil , 2010 .

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

[16]  Olaf David,et al.  Modeling shortwave solar radiation using the JGrass-NewAge system , 2012 .

[17]  Klemen Zaksek,et al.  Sky-View Factor as a Relief Visualization Technique , 2011, Remote. Sens..

[18]  R. Hunt,et al.  Approaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration , 2010 .

[19]  Olaf David,et al.  Development of the Land-use and Agricultural Management Practice web-Service (LAMPS) for generating crop rotations in space and time , 2016 .

[20]  T. Green,et al.  Comparison of grid‐based algorithms for computing upslope contributing area , 2006 .

[21]  Hoshin Vijai Gupta,et al.  Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .

[22]  E. Poeter,et al.  Documentation of UCODE; a computer code for universal inverse modeling , 1998 .

[23]  Martyn P. Clark,et al.  STEP WISE, MULTIPLE OBJECTIVE CALIBRATION OF A HYDROLOGIC MODEL FOR A SNOWMELT DOMINATED BASIN 1 , 2006 .

[24]  George H. Leavesley,et al.  USGS Modular Modeling System (MMS) - Precipitation-Runoff Modeling System (PRMS) , 2006 .

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

[26]  Riccardo Rigon,et al.  GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects , 2013 .

[27]  P. E. O'connell,et al.  An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 2: Structure of a physically-based, distributed modelling system , 1986 .

[28]  Mike Schwank,et al.  Laboratory Characterization of a Commercial Capacitance Sensor for Estimating Permittivity and Inferring Soil Water Content , 2006 .

[29]  Andrea Antonello,et al.  The JGrass-NewAge system for forecasting and managing the hydrological budgets at the basin scale: models of flow generation and propagation/routing , 2011 .

[30]  Mary C. Hill,et al.  JUPITER: Joint Universal Parameter Identification and Evaluation of Reliability ? An Application Programming Interface (API) for Model Analysis , 2014 .

[31]  Giuseppe Formetta,et al.  Hydrological modelling with components: A GIS-based open-source framework , 2014, Environ. Model. Softw..

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

[33]  Mario Putti,et al.  Newtonian nudging for a Richards equation-based distributed hydrological model , 2003 .

[34]  Liwang Ma,et al.  Optimizing Soil Hydraulic Parameters in RZWQM2 under Fallow Conditions , 2010 .

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

[36]  Giacomo Bertoldi,et al.  Impact of Watershed Geomorphic Characteristics on the Energy and Water Budgets , 2006 .

[37]  Luis Garrote,et al.  A distributed model for real-time flood forecasting using digital elevation models , 1995 .

[38]  A. Furman Modeling Coupled Surface–Subsurface Flow Processes: A Review , 2008 .

[39]  Erwin Zehe,et al.  Predictability of hydrologic response at the plot and catchment scales: Role of initial conditions , 2004 .