U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Athens, GA, USA; Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, USA.
Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, USA.
J Environ Manage. 2019 Apr 1;235:403-413. doi: 10.1016/j.jenvman.2018.12.115. Epub 2019 Jan 30.
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 phenology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and evapotranspiration. This study used regression analysis to develop a dynamic CN (CN) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phenological 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 CN 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 CN performed significantly better in predicting runoff with calibrated CN 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 runoff had better NSE (0.53) and PBIAS (4.22) compared to the SCS-CN runoff (-0.87 and -94.86 respectively). In the validated model, CN 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 can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions.
土壤保持局的水文曲线数（SCS-CN 或 CN）是一种广泛用于估算降雨事件径流量的方法。它已被应用于世界上许多具有不同土地利用、土地覆盖类型和气候条件的地区，并成功应用于从简单的径流量计算和土地利用变化评估到综合水文/水质模拟等各种情况。然而，CN 方法缺乏纳入植被表面条件季节性变化的能力，也无法察觉塑造入渗和暴洪径流量的土地利用/土地覆盖（LULC）变化。植物物候是通过影响地表粗糙度和蒸散作用来改变季节间水文过程和水量平衡的主要决定因素。本研究使用回归分析，基于遥感归一化差异植被指数（NDVI）的季节性变化，开发了一个动态 CN（CN），以监测年内植物物候发育。使用 16 天的 MODIS NDVI（MOD13Q1 Collection 5）时间序列图像来监测植被发育，并提供 CN 模型校准和验证所需的 NDVI 数据。来自堪萨斯州 Konza 草原生物站的四个小流域的 12 年降雨和径流水文数据（2001-2012 年）用于开发、校准和验证该方法。结果表明，CN 在预测径流量方面表现更好，与 SCS-CN 径流量相比，经过校准的 CN 径流量每增加一个单位，预测的径流量增加约 0.74，而 SCS-CN 径流量增加 0.46，与观测径流量的相关性更高（r=0.78 与 r=0.38）。此外，CN 径流量的 NSE（0.53）和 PBIAS（4.22）分别优于 SCS-CN 径流量的（-0.87 和-94.86）。在验证模型中，CN 径流量每增加一个单位观测径流量，增加约 0.96，而 SCS-CN 径流量增加 0.49。验证后的径流量与观测径流量的相关性也优于 SCS-CN 径流量（r=0.52 与 r=0.33）。这些发现表明，CN 可以提高对降水事件引起的地表径流量的估算，从而做出更明智的水和土地管理决策。
