An enhanced and automated approach for deriving a priori SAC-SMA parameters from the soil survey geographic database

This paper presents an automated approach for processing the Soil Survey Geographic (SSURGO) Database and the National Land Cover Database (NLCD), and deriving gridded a priori parameters for the National Weather Service (NWS) Sacramento Soil Moisture Accounting (SAC-SMA) model from these data sets. Our approach considerably extends methods previously used in the NWS and offers automated and geographically invariant ways of extracting soil information, interpreting soil texture, and spatially aggregating SAC-SMA parameters. The methodology is composed of four components. The first and second components are SSURGO and NLCD preprocessors. The third component is a parameter generator producing SAC-SMA parameters for each soil survey area on an approximately 30-m grid mesh. The last component is a postprocessor creating parameters for user-specified areas of interest on the Hydrologic Rainfall Analysis Project (HRAP) grid. Implemented in open-source software, this approach was employed by creating a set of SAC-SMA parameter and related soil property grids spanning 25 states, wherein it was shown to greatly reduce the derivation time and meanwhile yield results comparable to those based on the State Soil Geographic Database (STATSGO). The broad applicability of the methodologies and associated intermediate products to hydrologic modeling is discussed.

[1]  D. Maidment,et al.  Coordinate Transformations for Using NEXRAD Data in GIS-Based Hydrologic Modeling , 1999 .

[2]  Richard H. McCuen,et al.  A guide to hydrologic analysis using SCS methods , 1982 .

[3]  Dara Entekhabi,et al.  Preserving high-resolution surface and rainfall data in operational-scale basin hydrology: a fully-distributed physically-based approach , 2004 .

[4]  Ben Chie Yen,et al.  Hydraulics and hydrology , 1981 .

[5]  Nws.  Distributed Model Intercomparison Project , 2005 .

[6]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[7]  G. Hornberger,et al.  Empirical equations for some soil hydraulic properties , 1978 .

[8]  Victor Koren,et al.  Use of Soil Property Data in the Derivation of Conceptual Rainfall-Runoff Model Parameters , 2000 .

[9]  Assefa M. Melesse,et al.  EFFECTS OF STATSGO AND SSURGO AS INPUTS ON SWAT MODEL'S SNOWMELT SIMULATION 1 , 2006 .

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

[11]  Michael Smith,et al.  Hydrology laboratory research modeling system (HL-RMS) of the US national weather service , 2004 .

[12]  Vijay P. Singh,et al.  The NWS River Forecast System - catchment modeling. , 1995 .

[13]  E. E. Hardy,et al.  A land-use classification system for use with remote sensor data , 1972 .

[14]  J. Schaake,et al.  A distributed hydrologic model and threshold frequency-based method for flash flood forecasting at ungauged locations , 2007 .

[15]  Jeffrey G. Arnold,et al.  Formulation of a hybrid calibration approach for a physically based distributed model with NEXRAD data input , 2004 .

[16]  Victor Koren,et al.  Using SSURGO data to improve Sacramento Model a priori parameter estimates , 2006 .

[17]  V. Singh,et al.  Computer Models of Watershed Hydrology , 1995 .

[18]  Seann Reed,et al.  Deriving flow directions for coarse‐resolution (1–4 km) gridded hydrologic modeling , 2003 .

[19]  Dong Jun Seo,et al.  Fast and efficient optimization of hydrologic model parameters using a priori estimates and stepwise line search , 2008 .

[20]  David G. Tarboton,et al.  Application of TOPNET in the distributed model intercomparison project , 2004 .

[21]  Douglas A. Miller,et al.  A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling , 1998 .

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

[23]  Fred L. Ogden,et al.  GSSHA: Model To Simulate Diverse Stream Flow Producing Processes , 2004 .

[24]  Fred L. Ogden,et al.  Gridded Surface Subsurface Hydrologic Analysis (GSSHA) User’s Manual; Version 1.43 for Watershed Modeling System 6.1 , 2006 .

[25]  J. Chambers,et al.  The New S Language , 1989 .

[26]  Gilbert T. Bernhardt,et al.  A comprehensive surface-groundwater flow model , 1993 .

[27]  Qingyun Duan,et al.  Use of a Priori Parameter Estimates in the Derivation of Spatially Consistent Parameter Sets of Rainfall‐Runoff Models , 2013 .

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

[29]  Limin Yang,et al.  COMPLETION OF THE 1990S NATIONAL LAND COVER DATA SET FOR THE CONTERMINOUS UNITED STATES FROM LANDSAT THEMATIC MAPPER DATA AND ANCILLARY DATA SOURCES , 2001 .

[30]  G. Hornberger,et al.  A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils , 1984 .