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Assessment of Three Long-Term Gridded Climate Products for Hydro-Climatic Simulations in Tropical River Basins

机译:热带河流域水文气候模拟的三种长期网格化气候产品评估

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Gridded climate products (GCPs) provide a potential source for representing weather in remote, poor quality or short-term observation regions. The accuracy of three long-term GCPs (Asian Precipitation—Highly-Resolved Observational Data Integration towards Evaluation of Water Resources: APHRODITE, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record: PERSIANN-CDR and National Centers for Environmental Prediction Climate Forecast System Reanalysis: NCEP-CFSR) was analyzed for the Kelantan River Basin (KRB) and Johor River Basin (JRB) in Malaysia from 1983 to 2007. Then, these GCPs were used as inputs into calibrated Soil and Water Assessment Tool (SWAT) models, to assess their capability in simulating streamflow. The results show that the APHRODITE data performed the best in precipitation estimation, followed by the PERSIANN-CDR and NCEP-CFSR datasets. The NCEP-CFSR daily maximum temperature data exhibited a better correlation than the minimum temperature data. For streamflow simulations, the APHRODITE data resulted in strong results for both basins, while the NCEP-CFSR data showed unsatisfactory performance. In contrast, the PERSIANN-CDR data showed acceptable representation of observed streamflow in the KRB, but failed to track the JRB observed streamflow. The combination of the APHRODITE precipitation and NCEP-CFSR temperature data resulted in accurate streamflow simulations. The APHRODITE and PERSIANN-CDR data often underestimated the extreme precipitation and streamflow, while the NCEP-CFSR data produced dramatic overestimations. Therefore, a direct application of NCEP-CFSR data should be avoided in this region. We recommend the use of APHRODITE precipitation and NCEP-CFSR temperature data in modeling of Malaysian water resources.
机译:网格气候产品(GCP)提供了一个潜在的来源,可以表示偏远,质量欠佳或短期观测区域的天气。三个长期GCP的准确性(亚洲降水—高度集成的观测数据对水资源评估的准确性:APHRODITE,使用人工神经网络从遥感信息中进行降水估算-气候数据记录:PERSIANN-CDR和国家环境预测中心气候预测系统的再分析:对1983年至2007年马来西亚吉兰丹河流域(KRB)和柔佛河流域(JRB)的NCEP-CFSR进行了分析。然后,这些GCP被用作经过校准的土壤和水评估工具(SWAT)的输入。 )模型,以评估其模拟流量的能力。结果表明,APHRODITE数据在降水估计方面表现最佳,其次是PERSIANN-CDR和NCEP-CFSR数据集。 NCEP-CFSR每日最高温度数据比最低温度数据表现出更好的相关性。对于流量模拟,APHRODITE数据在两个盆地中均获得了不错的结果,而NCEP-CFSR数据却显示出不令人满意的性能。相比之下,PERSIANN-CDR数据显示了KRB中观察到的流量的可接受表示,但是未能跟踪JRB观察到的流量。 APHRODITE降水量和NCEP-CFSR温度数据的结合导致了精确的水流模拟。 APHRODITE和PERSIANN-CDR数据经常低估了极端降水和径流,而NCEP-CFSR数据却产生了高估。因此,应避免在该区域直接应用NCEP-CFSR数据。我们建议在马来西亚水资源建模中使用APHRODITE降水量和NCEP-CFSR温度数据。

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