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Precipitation estimated from different measuring techniques such as rain gauge, radar and satellite have some similarities, but there are also differences among them. For example, techniques based on radar and satellite data underestimate rainfall than those using rain gauge data. In addition, many different interpolation techniques have been used to measure spatial pattern of precipitation but it is still difficult to have an accurate pattern by any one of them. The differences between the rainfall estimates from different techniques vary seasonally as well as regionally so that the radar or satellites are not directly applied into hydrologic analysis. In this regard, a main objective of this study is to develop a systematic way to interpolate ground rain gauge using discharge data from distributed rainfall-runoff model The spatial rainfall patterns estimated from the interpolation methods will be evaluated with the object function to minimize the difference between observed and estimated discharge. In other words, this study seeks to identify the optimal spatial pattern in rain field that can generate a similar pattern of observed discharge through the distributed rainfall-runoff model. This study will compare the spatial pattern from different types of climate systems and different seasons derived from different interpolation methods may help to validate the proposed algorithms.


Session R45, Remote Sensing and LiDAR Data: Precipitation Products



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