Availability of satellite remote sensing dataset for Shihuiyao basin of Nenjing River
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Abstract:
The Shihuiyao basin is located in the upper of the Nenjiang River,with basin area of 17,205 km2.Fewer rainfall stations within or near the basin lead to insufficient rainfall data.It is difficult to accurately predict the flooding process at the outlet section of the basin using existing rainfall data,which brings certain difficulties to the downstream river flood control and the Nierji Reservoir′s forecast flood control operations.Based on the characteristic of flood forecasting,the near real-time satellite remote sensing global precipitation products GSMaP_NRT and GSMaP_Gauge_NRT are selected to supplement the Shihuiyao basin,aiming to solve the problem of scarcely gauged precipitation. The two products accuracy on the rainfall stations grid is evaluated based on the precipitation within or near the basin.Three flood forecasting input schemes are designed:scheme I precipitation of rainfall stations within or near the basin by Tyson polygon weighted method),scheme Ⅱ(average precipitation of GSMaP_NRT in the basin),scheme Ⅲ (average precipitation of GSMaP_Gauge_NRT in the basin),and combined with the Xin′anjiang model for flood forecasting to verify their availability. On monthly and daily scales for flood season,GSMaP_NRT and GSMaP_Gauge_NRT show high accuracy,but both overestimate the rain gauge observation precipitation,and the accuracy of GSMaP_Gauge_NRT is generally better than that of GSMaP_NRT.The results of 8 flood forecasts during the training and validation stages show that the forecast effect of scheme Ⅲ is the best,and the runoff and confluence results both have higher accuracies. Results indicated that the GSMaP_Gauge_NRT has high availability in flood forecasting in the Shihuiyao basin,and the GSMaP_Gauge_NRT can be used as a source of rainfall data in ungauged basins.In future,more near real-time satellite remote sensing global precipitation products can be applied for flood forecasting to further improve flood forecasting accuracy.
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