Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model

Guo, Dandan; Wang, Hantao; Zhang, Xiaoxiao; Liu, Guodong

doi:10.3390/w11020253

Cited by 17 publications

(17 citation statements)

References 39 publications

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“…The spatial resolution of the CMADS data grid is 0.25°× 0.25°, and the data relate to the years spanning from 2008 to 2016. CMADS data were obtained over a nine-year period from 1 January 2008 to 31 December 2016 and they include many watersheds throughout East Asia [15,[27][28][29]. Studies have also used CMADS data and the Penman- Monteith method to calculate potential evapotranspiration (PET) across China, and a reliable performance has been noted [29,30].…”

Section: Cmads Datamentioning

confidence: 99%

Hydrological process simulation in Manas River Basin using CMADS

Yang

et al. 2020

Open Geosciences

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The inability to conduct hydrological simulations in areas that lack historical meteorological data is an important factor limiting the development of watershed models, understanding of watershed water resources, and ultimate development of effective sustainability policies. This study focuses on the Manas River Basin (MRB), which is a high-altitude area with no meteorological stations and is located on the northern slope of the Tianshan Mountains, northern China. The hydrological processes were simulated using the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) using the Soil and Water Assessment Tool (SWAT) model. Simulated runoff was corrected using calibration/uncertainty and sensitivity program for the SWAT. Through parameter sensitivity analysis, parameter calibration, and verification, the Nash–Sutcliffe efficiency (NSE), adjusted R-square ({R}_{\text{adj}}^{2}), and percentage bias (\text{PBIAS}) were selected for evaluation. The results were compared with statistics obtained from Kenswat Hydrological Station, where the monthly runoff simulation efficiency was \text{NSE}\hspace{.25em}=0.64, {R}_{\text{adj}}^{2}\hspace{.25em}=0.69, and \text{PBIAS}\hspace{.25em}=\mbox{--}0.9, and the daily runoff simulation efficiency was \text{NSE}\hspace{.25em}=0.75, {R}_{\text{adj}}^{2} = 0.75, \text{PBIAS} = −1.5. These results indicate that by employing CMADS data, hydrological processes within the MRB can be adequately simulated. This finding is significant, as CMADS provide continuous temporal, detailed, and high-spatial-resolution meteorological data that can be used to build a hydrological model with adequate accuracy in areas that lack historical meteorological data.

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Section: Cmads Datamentioning

confidence: 99%

Hydrological process simulation in Manas River Basin using CMADS

Yang

et al. 2020

Open Geosciences

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“…9. Guo et al [18] evaluated the accuracy and hydrological simulation utility of CMADS, TMPA 3B42 V7, and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-F) products in the Jinsha River, which is a complex terrain area. The results of statistical analysis showed that the three types of datasets had relatively high accuracy on the average grid scale with R 2 values greater than 0.8, at 0.86, 0.81, and 0.88, respectively.…”

Section: Contributionsmentioning

confidence: 99%

“…The CMADS can be used efficiently without any adjustments and treatments to the SWAT model, which will save at least 90% of the time spent by SWAT users on meteorological data preparation. From the perspective of data precision, the CMADS has been validated by users worldwide in combination with various international reanalysis data, such as Climate Forecast System Reanalysis (CFSR) and Tropical Rainfall Measuring Mission (TRMM) in various river basins in East Asia, with satisfactory results [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. These verification results demonstrate that the performance of CMADS products in East Asia can be trusted, especially in mountainous and highland areas with high altitudes and large differences of land use and geography, where meteorological stations are sparse.…”

Section: Introductionmentioning

confidence: 99%

“…This special issue provides a platform for researchers by assisting with the use of CMADS to conduct in-depth research on water quality and quantity modeling in East Asia in order to improve the research in the atmosphere, hydrology, and water environment in East Asia. The papers included in this special issue fall into eight broad categories: Meteorological verification and analysis [3,11], non-point source pollution [12], water resource modeling and parameter uncertainty analysis [13][14][15][16], comparison of reanalysis products [17][18][19][20], optimal operational of reservoirs [21,22], water footprint assessment [23], changes in water resources under climate and land use change [24], hydrological simulation in cold area [25,26], and CMADS-Soil Temperature (ST) application [27]. The following section summarizes the individual research within each application.…”

Section: Introductionmentioning

confidence: 99%

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Profound Impacts of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)

Meng

Wang

2019

Water

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As global warming continues to intensify, the problems of climate anomalies and deterioration of the water environment in East Asia are becoming increasingly prominent. In order to assist decision-making to tackle these problems, it is necessary to conduct in-depth research on the water environment and water resources through applying various hydrological and environmental models. To this end, the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) has been applied to East Asian regions where environmental issues are obvious, but the stations for monitoring meteorological variables are not uniformly distributed. The dataset contains all of the meteorological variables for SWAT, such as temperature, air pressure, humidity, wind, precipitation, and radiation. In addition, it includes a range of variables relevant to the Earth’s surface processes, such as soil temperature, soil moisture, and snowfall. Although the dataset is used mainly to drive the SWAT model, a large number of users worldwide for different models have employed CMADS and it is expected that users will not continue to limit the application of CMADS data to the SWAT model only. We believe that CMADS can assist all the users involved in the meteorological field in all aspects. In this paper, we introduce the research and development background, user group distribution, application area, application direction, and future development of CMADS. All of the articles published in this special issue will be mentioned in the contributions section of this article.

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“…There are several types of grid precipitation products. Many studies have discussed the accuracy of precipitation products and their applicability in different regions and used them as data sources for data-driven methods [21,23], but the effect on using different data sources for modeling the relationship of precipitation and water level has not been discussed [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

et al. 2020

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Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

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Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model

Cited by 17 publications

References 39 publications

Hydrological process simulation in Manas River Basin using CMADS

Hydrological process simulation in Manas River Basin using CMADS

Profound Impacts of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)

Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

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