Phenology-adjusted dynamic curve number for improved hydrologic modeling

JOURNAL OF ENVIRONMENTAL MANAGEMENT

卷:235

Article

Muche, Muluken E.^1,2  Hutchinson, Stacy L.²  Hutchinson, J. M. Shawn³  Johnston, John M.¹ 

[1] US EPA, Off Res & Dev, Natl Exposure Res Lab, Athens, GA USA

[2] Kansas State Univ, Dept Biol & Agr Engn, Manhattan, KS 66506 USA

[3] Kansas State Univ, Dept Geog, Manhattan, KS 66506 USA

关键词: Curve number (CN);    Normalized difference vegetation index (NDVI);    Surface runoff;    Surface water hydrology;    Watershed modeling;    Spatiotemporal modeling;   

DOI  :  10.1016/j.jenvman.2018.12.115

来源: Elsevier

【 摘 要 】

The Soil Conservation Service Curve Number (SCS-CN, or CN) is a widely used method to estimate runoff from rainfall events, It has been adapted to many parts of the world with different land uses, land cover types, and climatic conditions and successfully applied to situations ranging from simple runoff calculations and land use change assessment to comprehensive hydrologic/water quality simulations. However, the CN method lacks the ability to incorporate seasonal variations in vegetated surface conditions, and unnoticed landuse/landcover (LULC) change that shape infiltration and storm runoff. Plant phonology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and eva-potranspiration. This study used regression analysis to develop a dynamic CN (CNNDVI) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phonological development. A time series of 16-day MODIS NDVI (MOD13Q1 Collection 5) images were used to monitor vegetation development and provide NDVI data necessary for CNNDVI model calibration and validation. Twelve years of rainfall and runoff data (2001-2012) from four small watersheds located in the Konza Prairie Biological Station, Kansas were used to develop, calibrate, and validate the method. Results showed CNNDVI performed significantly better in predicting runoff with calibrated CN(NDVI )runoff increasing by approximately 0.74 for every unit increase in observed runoff compared to 0.46 for SCS-CN runoff and was more highly correlated to observed runoff (r = 0.78 vs. r = 0.38). In addition, CN(NDVI )runoff had better NSE (0.53) and PBLAS (4.22) compared to the SCS-CN runoff ( - 0.87 and -94.86 respectively). In the validated model, CN(NDVI )runoff increased by approximately 0.96 for every unit of observed runoff, while SCS-CN runoff increased by 0.49. Validated runoff was also better correlated to observed runoff than SCS-CN runoff (r = 0.52 vs. r = 0.33). These findings suggest that the CN(NDVI )can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions.

【 授权许可】

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