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Prediction of storm‐based nutrient loss incorporating the estimated runoff and soil loss at a slope scale on the Loess Plateau
Author(s) -
Shi Wenhai,
Huang Mingbin,
Wu Lianhai
Publication year - 2018
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.3028
Subject(s) - surface runoff , environmental science , nutrient , hydrology (agriculture) , sediment , storm , soil science , soil carbon , soil conservation , erosion , soil retrogression and degradation , phosphorus , particulates , soil water , agronomy , geology , chemistry , ecology , paleontology , oceanography , geotechnical engineering , organic chemistry , biology , agriculture
Soil nutrient loss driven by soil erosion and runoff leads to land degradation, thereby decreasing soil productivity and crop yield. Nutrient loss modelling is an important tool for designing efficient soil and water conservation practices that alleviate land degradation. We proposed a framework to integrate newly developed models for sediment‐bound nutrient loss and runoff‐associated nutrient loss with the modified storm‐based Chinese soil loss equation model and modified Soil Conservation Service curve number model to predict particulate nutrient losses of nitrogen (N), carbon (C), and phosphorus (P) and soluble nutrient losses of P and nitrate. The data collected from the literature included 330 storm events with experimental plot measurements of runoff, sediment, and particulate and soluble nutrient losses conducted on slopes in Loess Plateau; and these data were used to calibrate and assess model performances. The accuracies of the model estimations were examined with the Nash–Sutcliffe efficiency ( NSE ). The proposed models with optimized parameters showed high NSE for particulate N (98.5%), C (98.9%), and P (99.8%) and soluble P (95.8%) and nitrate (85.4%) when compared with the literature‐reported measured losses. To test the models, independent literature data were compared with model‐estimated values. The comparison showed agreement between the estimated and observed data with NSE s of 74.4%, 63.7%, 86.3%, 63.6%, and 66.7% for particulate N, C, P, soluble P, and nitrate, respectively.