Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia

Yang

et al. 2018

The China Meteorological Assimilation Driving Datasets for the Soil and Water AssessmentTool model (CMADS) have been widely applied in recent years because of their accuracy. An evaluation of the accuracy and efficiency of the Soil and Water Assessment Tool (SWAT) model and CMADS for simulating hydrological processes in the fan-shaped Lijiang River Basin, China, was carried out. The Sequential Uncertainty Fitting (SUFI-2) algorithm was used for parameter sensitivity and uncertainty analysis at the daily scale. The pair-wise correlation between parameters and the uncertainties associated with equifinality in model parameter estimation were investigated. The results showed that the SWAT model performed well in predicting daily streamflow for the calibration period (2009)(2010). The correlation coefficient (R 2 ) was 0.92, and the Nash-Sutcliffe model efficiency coefficient (NSE) was 0.89. For the validation period (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018), R 2 = 0.89, NSE = 0.88, and reasonable values for the P-factor, R-factor, and percent bias (PBIAS) were obtained. In addition, the spatial and temporal variation of evapotranspiration (ET), surface runoff, and groundwater discharge were analyzed. The results clearly showed that spatial variation in surface runoff and groundwater discharge are strongly related to precipitation, while ET is largely controlled by land use types. The contributions to the water budget by surface runoff, groundwater discharge, and lateral flow were very different in flood years and dry years.

Section: Introductionmentioning

confidence: 99%

Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China

Cao

Yang

et al. 2018

“…In order to better reflect the spatial distribution of precipitation, the China Meteorological Assimilation Driving Datasets for the SWAT model version 1.0 (CMADS V1.0) was introduced in this study [50]. The CMADS was developed by Dr. Xianyong Meng from the China Institute of Water Resources and Hydropower Research (IWHR) and has received worldwide attention [28][29][30]. In the CMADS, the integration of air temperature, air pressure, humidity, and wind velocity data were mainly achieved through the LAPS (Local Analysis and Prediction System)/STMAS (Space-Time Multiscale Analysis System).…”

Section: Data Collectionmentioning

confidence: 99%

“…Based on this method, several products have been developed and widely applied in hydrological modeling [26,27]. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), which was developed by Dr. Xianyong Meng from the China Institute of Water Resources and Hydropower Research (IWHR), is one such product and has been widely applied in numerous studies [28][29][30][31]. For example, Liu [28] found that CMADS had unique advantages in hydrological simulations compared with observed data and Climate Forecast System Reanalysis (CFSR).…”

Section: Introductionmentioning

confidence: 99%

The Impacts of Climate Variability and Land Use Change on Streamflow in the Hailiutu River Basin

Guang-hua

Guan

et al. 2018

Abstract:The Hailiutu River basin is a typical semi-arid wind sandy grass shoal watershed in northwest China. Climate and land use have changed significantly during the period 1970-2014. These changes are expected to impact hydrological processes in the basin. The Mann-Kendall (MK) test and sequential t-test analysis of the regime shift method were used to detect the trend and shifts of the hydrometeorological time series. Based on the analyzed results, seven scenarios were developed by combining different land use and/or climate situations. The Soil Water Assessment Tool (SWAT) model was applied to analyze the impacts of climate variability and land use change on the values of the hydrological components. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) was applied to enhance the spatial expressiveness of precipitation data in the study area during the period 2008-2014. Rather than solely using observed precipitation or CMADS precipitation, the precipitation values of CMADS and the observed precipitation values were combined to drive the SWAT model for better simulation results. From the trend analysis, the annual streamflow and wind speed showed a significant downward trend. No significant trend was found for the annual precipitation series; however, the temperature series showed upward trends. With the change point analysis, the whole study period was divided into three sub-periods (1970-1985, 1986-2000, and 2001-2014). The annual precipitation, mean wind speed, and average temperature values were 316 mm, 2.62 m/s, and 7.9 • C, respectively, for the sub-period [1970][1971][1972][1973][1974][1975][1976][1977][1978][1979][1980][1981][1982][1983][1984][1985] 272 were the increased area of shrub and grass land and decreased area of sandy land. In the simulation it was shown that these changes caused the mean annual streamflow to decrease by 0.23 mm/year and 0.68 mm/year during the periods 1986-2000 and 2001-2014, respectively. Thus, the impact of climate variability on the streamflow was more profound than that of land use change. Under the impact of coupled climate variability and land use change, the mean annual streamflow decreased by 2.45 mm/year during the period 1986-2000, and the contribution of this variation to the decrease in observed streamflow was 27.8%. For the period 2001-2014, the combined climate variability and land use change resulted in an increase of 0.84 mm/year in annual streamflow. The results obtained in this study could provide guidance for water resource management and planning in the Erdos plateau.

“…We examined the performance of TRMA-3B42V7 and CMADS using the six statistical measures (PC, POD, FBI, FAR, CSI and HSS) through the pixel-to-point comparison with the ground-based data. Following the studies of Dai [67] and Vu et al [24], a minimum precipitation threshold of 1.0 mm per day was used for the precipitation and non-precipitation event for ground observation and satellite/reanalysis estimate. The contingency statistics of TRMA-3B42V7 and CMADS were evaluated each year through the precipitation datasets from 2008 to 2016.…”

Section: Precipitation Detectionmentioning

confidence: 99%

Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models

Guo

et al. 2018

Hydrologic models are essential tools for understanding hydrologic processes, such as precipitation, which is a fundamental component of the water cycle. For an improved understanding and the evaluation of different precipitation datasets, especially their applicability for hydrologic modelling, three kinds of precipitation products, CMADS, TMPA-3B42V7 and gauge-interpolated datasets, are compared. Two hydrologic models (IHACRES and Sacramento) are applied to study the accuracy of the three types of precipitation products on the daily streamflow of the Lijiang River, which is located in southern China. The models are calibrated separately with different precipitation products, with the results showing that the CMADS product performs best based on the Nash-Sutcliffe efficiency, including a much better accuracy and better skill in capturing the streamflow peaks than the other precipitation products. The TMPA-3B42V7 product shows a small improvement on the gauge-interpolated product. Compared to TMPA-3B42V7, CMADS shows better agreement with the ground-observation data through a pixel-to-point comparison. The comparison of the two hydrologic models shows that both the IHACRES and Sacramento models perform well. The IHACRES model however displays less uncertainty and a higher applicability than the Sacramento model in the Lijiang River basin.are frequently missing because of malfunctioning of devices [3]. Remote sensing [4] and modelling [5] of precipitation have become viable approaches to address these problems effectively and are often used as input data to hydrologic models.With regard to the development of remote-sensing technology, satellite-derived precipitation data are an attractive alternative in data-sparse regions because of the relatively high resolution and complete spatial coverage.