Preserving high-resolution surface and rainfall data in operational-scale basin hydrology: a fully-distributed physically-based approach

Abstract This study presents various aspects of the continuous simulation capabilities of a fully-distributed, triangulated irregular network (TIN) hydrologic model. The TIN-based Real-time Integrated Basin Simulator (tRIBS) is calibrated and verified for the Baron Fork at Eldon, Illinois River at Watts, and Blue River at Blue over the period 1993–2000. Computational effort is significantly reduced by simulating complex watersheds using a multiple resolution mesh to represent terrain. Model performance is assessed by comparing streamflow predictions to observations at the basin outlet and interior gauging stations. In addition, simulation results describing the distributed basin response to atmospheric forcing are discussed, including the spatial and temporal variability of runoff, surface soil moisture, evaporative flux, and groundwater table position. By modeling the land-surface water and energy states and fluxes over the computational domain in an efficient manner, the potential for utilizing fully-distributed models at the scales of operational hydrologic forecasting is realized. Through the spatially-explicit approach, high-resolution remote sensing data describing surface properties, topography, rainfall, and soil moisture can be integrated directly into a predictive hydrologic model. A greater degree of physical interpretation of hydrological estimation can thus be added to existing methods of operational forecasting.

[1]  Rafael L. Bras,et al.  Use of Weather Radar for Flood Forecasting in the Sieve River Basin: A Sensitivity Analysis , 1993 .

[2]  Dara Entekhabi,et al.  Hillslope and Climatic Controls on Hydrologic Fluxes , 1995 .

[3]  Niko E. C. Verhoest,et al.  The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for small-scale basins through data assimilation , 2001 .

[4]  Keith Beven,et al.  Changing ideas in hydrology — The case of physically-based models , 1989 .

[5]  J. Schellekens,et al.  Modelling rainfall interception by a lowland tropical rain forest in northeastern Puerto Rico , 1999 .

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

[7]  E. Vivoni,et al.  Catchment hydrologic response with a fully distributed triangulated irregular network model , 2004 .

[8]  R. Bras,et al.  Hydrologic modeling Of New England river basins using radar rainfall data , 1990 .

[9]  S. Sorooshian,et al.  Measurement and analysis of small-scale convective storm rainfall variability , 1995 .

[10]  Walter J. Rawls,et al.  Green‐ampt Infiltration Parameters from Soils Data , 1983 .

[11]  Günter Blöschl,et al.  Spatial Patterns of Catchment Hydrology: Observations and Modelling , 2000 .

[12]  L. B. Leopold,et al.  The hydraulic geometry of stream channels and some physiographic implications , 1953 .

[13]  K. A. Poiani,et al.  A Spatial Simulation Model of Hydrology and Vegetation Dynamics in Semi-Permanent Prairie Wetlands. , 1993, Ecological applications : a publication of the Ecological Society of America.

[14]  R. Bras Hydrology : an introduction to hydrologic science , 1990 .

[15]  Dong-Jun Seo,et al.  Scale dependencies of hydrologic models to spatial variability of precipitation , 1999 .

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

[17]  A. Rutter,et al.  A predictive model of rainfall interception in forests, 1. Derivation of the model from observations in a plantation of Corsican pine , 1971 .

[18]  Nicole M. Gasparini,et al.  The Channel-Hillslope Integrated Landscape Development Model (CHILD) , 2001 .

[19]  G. Salvucci,et al.  Equilibrium analysis of groundwater–vadose zone interactions and the resulting spatial distribution of hydrologic fluxes across a Canadian Prairie , 1999 .

[20]  V. Klemeš,et al.  Operational Testing of Hydrological Simulation Models , 2022 .

[21]  K. Mitchell,et al.  Simple water balance model for estimating runoff at different spatial and temporal scales , 1996 .

[22]  Dara Entekhabi,et al.  Generation of triangulated irregular networks based on hydrological similarity , 2004 .

[23]  Shafiqul Islam,et al.  Prediction of Ground Surface Temperature and Soil Moisture Content by the Force‐Restore Method , 1995 .

[24]  J. D. Lin On the force-restore method for prediction of ground surface temperature , 1980 .

[25]  M. Wigmosta,et al.  A distributed hydrology-vegetation model for complex terrain , 1994 .

[26]  D. L. Fread,et al.  National threshold runoff estimation utilizing GIS in support of operational flash flood warning systems , 1999 .

[27]  H. L. Penman Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[28]  Keith Beven,et al.  On hydrologic similarity: 2. A scaled model of storm runoff production , 1987 .

[29]  A. Taylor,et al.  Fire regimes and stand dynamics in an upper montane forest landscape in the southern Cascades, Caribou Wilderness, California1 , 2001 .

[30]  Dong-Jun Seo,et al.  The distributed model intercomparison project (DMIP): Motivation and experiment design , 2004 .

[31]  Peter Troch,et al.  Assimilation of active microwave observation data for soil moisture profile estimation , 2000 .

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

[33]  J. Pelletier Scale-invariance of soil moisture variability and its implications for the frequency-size distribution of landslides , 1997, physics/9705035.

[34]  Dong-Jun Seo,et al.  Space-time scale sensitivity of the Sacramento model to radar-gage precipitation inputs , 1997 .

[35]  J. Monteith Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[36]  Thomas A. McMahon,et al.  Physically based hydrologic modeling: 1. A terrain‐based model for investigative purposes , 1992 .

[37]  A. Rutter,et al.  A Predictive Model of Rainfall Interception in Forests. II. Generalization of the Model and Comparison with Observations in Some Coniferous and Hardwood Stands , 1975 .

[38]  Konstantine P. Georgakakos,et al.  The distributed model intercomparison project (DMIP) , 2004 .

[39]  D. Seo,et al.  Overall distributed model intercomparison project results , 2004 .

[40]  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 .

[41]  P. E. O'connell,et al.  An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system , 1986 .

[42]  Thom Bogaard,et al.  The role of the soil moisture balance in the unsaturated zone on movement and stability of the Beline landslide, France , 2002 .

[43]  T. Schmugge,et al.  Remote sensing in hydrology , 2002 .

[44]  Garry R. Willgoose,et al.  Three‐dimensional soil moisture profile retrieval by assimilation of near‐surface measurements: Simplified Kalman filter covariance forecasting and field application , 2002 .

[45]  D. McLaughlin,et al.  Downscaling of radio brightness measurements for soil moisture estimation: A four‐dimensional variational data assimilation approach , 2001 .

[46]  Dara Entekhabi,et al.  Basin hydrologic response relations to distributed physiographic descriptors and climate , 2001 .

[47]  K. Georgakakos,et al.  On the parametric and NEXRAD-radar sensitivities of a distributed hydrologic model suitable for operational use , 2001 .

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

[49]  George Kuczera,et al.  Parameterisation of a simple semi-distributed model for assessing the impact of land-use on hydrologic response , 2001 .

[50]  Dennis McLaughlin,et al.  Recent developments in hydrologic data assimilation , 1995 .