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http://dx.doi.org/10.9717/JMIS.2018.5.4.235

A Study of Soil Moisture Retention Relation using Weather Radar Image Data  

Choi, Jeongho (Dept. of Mechatronics Engineering, Chosun College of Science & Technology)
Han, Myoungsun (Dept. of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology)
Lim, Sanghun (Dept. of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Donggu (Dept. of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology)
Jang, Bong-joo (Dept. of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Multimedia Information System / v.5, no.4, 2018 , pp. 235-244 More about this Journal
Abstract
Potential maximum soil moisture retention (S) is a dominant parameter in the Soil Conservation Service (SCS; now called the USDA Natural Resources Conservation Service (NRCS)) runoff Curve Number (CN) method commonly used in hydrologic modeling for event-based flood forecasting (SCS, 1985). Physically, S represents the depth [L] soil could store water through infiltration. The depth of soil moisture retention will vary depending on infiltration from previous rainfall events; an adjustment is usually made using a factor for Antecedent Moisture Conditions (AMCs). Application of the method for continuous simulation of multiple storms has typically involved updating the AMC and S. However, these studies have focused on a time step where S is allowed to vary at daily or longer time scales. While useful for hydrologic events that span multiple days, this temporal resolution is too coarse for short-term applications such as flash flood events. In this study, an approach for deriving a time-variable potential maximum soil moisture retention curve (S-curve) at hourly time-scales is presented. The methodology is applied to the Napa River basin, California. Rainfall events from 2011 to 2012 are used for estimating the event-based S. As a result, we derive an S-curve which is classified into three sections depending on the recovery rate of S for soil moisture conditions ranging from 1) dry, 2) transitional from dry to wet, and 3) wet. The first section is described as gradually increasing recovering S (0.97 mm/hr or 23.28 mm/day), the second section is described as steeply recovering S (2.11 mm/hr or 50.64 mm/day) and the third section is described as gradually decreasing recovery (0.34 mm/hr or 8.16 mm/day). Using the S-curve, we can estimate the hourly change of soil moisture content according to the time duration after rainfall cessation, which is then used to estimate direct runoff for a continuous simulation for flood forecasting.
Keywords
Weather Radar; Soil Moisture Retention; SCS-CN; Direct Runoff Estimation; S-Curve Recovery;
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