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Special Issue "Application of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in East Asia"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management and Governance".

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Prof. Dr. Hao Wang

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin & China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Beijing, 100038, China
Website | E-Mail
Interests: allocation of water resources; water resource management
Guest Editor
Prof. Dr. Xianyong Meng

Research Associate, Department of Civil Engineering​​, The University of Hong Kong (HKU), Hong Kong
Website | E-Mail
Interests: atmospheric data assimilation; hydrological modeling; land surface model assimilation

Special Issue Information

Dear Colleagues,

Due to spatial differences of climate conditions and the lack of meteorological data in East Asia, there are many challenges to conducting research on surface water in the hydrologic cycle. In addition, East Asia is facing pressure from both water resource scarcity and water pollution. The consequences of water pollution problems have been attracting public conerns in recent years. Due to the low frequencies and difficulty in monitoring non-point source pollutions, it becomes challenging to understand the continuous spatial distributions of non-point source pollution mechanisms in East Asia. China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) were developed and provided high resolution and quality meteorological data for the community. Applying CMADS can significantly reduce the meteorological input uncertainty and improve the performance of non-point source pollution modeling, since water resources and non-point source pollution can be more accurately localized. In addition, researchers can make use of high resolution time series data from CMADS for spatial and temporal scale analysis of meteorological data. Over the past few years, the CMADS data set has received worldwide attention from applicants such as the United States, Germany, Russia, Italy, India, South Korea, etc.

This Special Issue on “Application of the China Meteorological Assimilation Driving Datasets  for the SWAT model (CMADS) in East Asia” invites papers that report recent advances in the modeling of water quality and quantity in watersheds using CMADS and the hydrological model on a wide range of topics. These include, but are not limited to, water resource modeling, hydrological ecology, evolution and regulation of water ecological footprint, evolution of water resources and insurance, non-point source pollution, meteorological analysis, meteorological verification, atmospheric and hydrological coupling studies, changes in water resources under climate change, optimal operational  of reservoirs, water footprint assessment  and water cycle in arid and cold regions. We encourage submissions based on theoretical, computational and field studies that involve multiple hydrologic domains and interactions, as well as contributions that demonstrate novel applications.

Prof. Dr. Hao Wang
Prof. Dr. Xianyong Meng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CMADS
  • Climate Change
  • Ecology
  • Hydrology
  • Meteorology
  • Natural Hazard
  • Pollution
  • SWAT
  • Sustainability

Published Papers (9 papers)

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Editorial

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Open AccessEditorial Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia
Water 2017, 9(10), 765; https://doi.org/10.3390/w9100765
Received: 26 August 2017 / Revised: 16 September 2017 / Accepted: 30 September 2017 / Published: 8 October 2017
Cited by 11 | PDF Full-text (187 KB) | HTML Full-text | XML Full-text
Abstract
The high degree of spatial variability in climate conditions, and a lack of meteorological data for East Asia, present challenges to conducting surface water research in the context of the hydrological cycle. In addition, East Asia is facing pressure from both water resource
[...] Read more.
The high degree of spatial variability in climate conditions, and a lack of meteorological data for East Asia, present challenges to conducting surface water research in the context of the hydrological cycle. In addition, East Asia is facing pressure from both water resource scarcity and water pollution. The consequences of water pollution have attracted public concern in recent years. The low frequency and difficulty of monitoring water quality present challenges to understanding the continuous spatial distributions of non-point source pollution mechanisms in East Asia. The China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) was developed to provide high-resolution, high-quality meteorological data for use by the scientific community. Applying CMADS can significantly reduce the meteorological input uncertainty and improve the performance of non-point source pollution models, since water resources and non-point source pollution can be more accurately localised. In addition, researchers can make use of high-resolution time series data from CMADS to conduct spatial- and temporal-scale analyses of meteorological data. This Special Issue, “Application of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in East Asia”, provides a platform to introduce recent advances in the modelling of water quality and quantity in watersheds using CMADS and hydrological models, and underscores its application to a wide range of topics. Full article

Research

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Open AccessArticle Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China
Water 2018, 10(9), 1225; https://doi.org/10.3390/w10091225
Received: 14 June 2018 / Revised: 5 September 2018 / Accepted: 8 September 2018 / Published: 11 September 2018
PDF Full-text (7749 KB) | HTML Full-text | XML Full-text
Abstract
Satellite-based and reanalysis precipitation products provide a practical way to overcome the shortage of gauge precipitation data because of their high spatial and temporal resolution. This study compared two reanalysis precipitation datasets (the China Meteorological Assimilation Driving Datasets for the Soil and Water
[...] Read more.
Satellite-based and reanalysis precipitation products provide a practical way to overcome the shortage of gauge precipitation data because of their high spatial and temporal resolution. This study compared two reanalysis precipitation datasets (the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), the National Centers for Environment Prediction Climate Forecast System Reanalysis (NCEP-CFSR)) and two satellite-based datasets (the Tropical Rainfall Measuring Mission 3B42 Version 7 (3B42V7) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR)) with observed precipitation in the Xiang River basin in China at two spatial (grids and the whole basin) and two temporal (daily and monthly) scales. These datasets were then used as inputs to a SWAT model to evaluate their usefulness in hydrological prediction. Bayesian model averaging was used to discriminate dataset performance. The results show that: (1) for daily timesteps, correlations between reanalysis datasets and gauge observations are >0.55, better than satellite-based datasets; The bias values of satellite-based datasets are <10% at most evaluated grid locations and for the whole baseline. PERSIANN-CDR cannot detect the spatial distribution of rainfall events; the probability of detection (POD) of PERSIANN-CDR at most evaluated grids is <0.50; (2) CMADS and 3B42V7 are better than PERSIANN-CDR and NCEP-CFSR in most situations in terms of correlation with gauge observations; satellite-based datasets are better than reanalysis datasets in terms of bias; and (3) CMADS and 3B42V7 simulate streamflow well for both daily (The Nash-Sutcliffe coefficient (NS) > 0.70) and monthly (NS > 0.80) timesteps; NCEP-CFSR is worst because it substantially overestimates streamflow; PERSIANN-CDR is not good because of its low NS (0.40) during the validation period. Full article
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Open AccessArticle Investigating the Dynamic Influence of Hydrological Model Parameters on Runoff Simulation Using Sequential Uncertainty Fitting-2-Based Multilevel-Factorial-Analysis Method
Water 2018, 10(9), 1177; https://doi.org/10.3390/w10091177
Received: 26 June 2018 / Revised: 22 July 2018 / Accepted: 23 July 2018 / Published: 3 September 2018
PDF Full-text (4106 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Hydrological model parameters are generally considered to be simplified representations that characterize hydrologic processes. Therefore, their influence on runoff simulations varies with climate and catchment conditions. To investigate the influence, a three-step framework is proposed, i.e., a Latin hypercube sampling (LHS-OAT) method multivariate
[...] Read more.
Hydrological model parameters are generally considered to be simplified representations that characterize hydrologic processes. Therefore, their influence on runoff simulations varies with climate and catchment conditions. To investigate the influence, a three-step framework is proposed, i.e., a Latin hypercube sampling (LHS-OAT) method multivariate regression model is used to conduct parametric sensitivity analysis; then, the multilevel-factorial-analysis method is used to quantitatively evaluate the individual and interactive effects of parameters on the hydrologic model output. Finally, analysis of the reasons for dynamic parameter changes is performed. Results suggest that the difference in parameter sensitivity for different periods is significant. The soil bulk density (SOL_BD) is significant at all times, and the parameter Soil Convention Service (SCS) runoff curve number (CN2) is the strongest during the flood period, and the other parameters are weaker in different periods. The interaction effects of CN2 and SOL_BD, as well as effective hydraulic channel conditions (CH_K2) and SOL_BD, are obvious, indicating that soil bulk density can impact the amount of loss generated by surface runoff and river recharge to groundwater. These findings help produce the best parameter inputs and improve the applicability of the model. Full article
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Open AccessArticle Evaluation of Potential Evapotranspiration Based on CMADS Reanalysis Dataset over China
Water 2018, 10(9), 1126; https://doi.org/10.3390/w10091126
Received: 11 July 2018 / Revised: 21 August 2018 / Accepted: 21 August 2018 / Published: 23 August 2018
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Abstract
Potential evapotranspiration (PET) is used in many hydrological models to estimate actual evapotranspiration. The calculation of PET by the Food and Agriculture Organization of the United Nations (FAO) Penman–Monteith method requires data for several meteorological variables that are often unavailable in remote areas.
[...] Read more.
Potential evapotranspiration (PET) is used in many hydrological models to estimate actual evapotranspiration. The calculation of PET by the Food and Agriculture Organization of the United Nations (FAO) Penman–Monteith method requires data for several meteorological variables that are often unavailable in remote areas. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) reanalysis datasets provide an alternative to the use of observed data. This study evaluates the use of CMADS reanalysis datasets in estimating PET across China by the Penman–Monteith equation. PET estimates from CMADS data (PET_cma) during the period 2008–2016 were compared with those from observed data (PET_obs) from 836 weather stations in China. Results show that despite PET_cma overestimating average annual PET and average seasonal in some areas (in comparison to PET_obs), PET_cma well matches PET_obs overall. Overestimation of average annual PET occurs mainly for western inland China. There are more meteorological stations in southeastern China for which PET_cma is a large overestimate, with percentage bias ranging from 15% to 25% for spring but a larger overestimate in the south and underestimate in the north for the winter. Wind speed and solar radiation are the climate variables that contribute most to the error in PET_cma. Wind speed causes PET to be underestimated with percentage bias in the range −15% to −5% for central and western China whereas solar radiation causes PET to be overestimated with percentage bias in the range 15% to 30%. The underestimation of PET due to wind speed is offset by the overestimation due to solar radiation, resulting in a lower overestimation overall. Full article
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Open AccessArticle The Impacts of Climate Variability and Land Use Change on Streamflow in the Hailiutu River Basin
Water 2018, 10(6), 814; https://doi.org/10.3390/w10060814
Received: 6 March 2018 / Revised: 16 June 2018 / Accepted: 17 June 2018 / Published: 20 June 2018
Cited by 2 | PDF Full-text (10512 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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 1970–1985, 272 mm, 2.58 m/s, and 8.4 °C, respectively, for the sub-period 1986–2000, and 391 mm, 2.2 m/s, and 9.35 °C, respectively, for the sub-period 2001–2014. The simulated mean annual streamflow was 35.09 mm/year during the period 1970–1985. Considering the impact of the climate variability, the simulated mean annual streamflow values were 32.94 mm/year (1986–2000) and 36.78 mm/year (2001–2014). Compared to the period 1970–1985, the simulated mean annual streamflow reduced by 2.15 mm/year for the period 1986–2000 and increased by 1.69 mm/year for the period 2001–2014. The main variations of land use from 1970 to 2014 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. Full article
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Open AccessArticle 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
Water 2018, 10(6), 742; https://doi.org/10.3390/w10060742
Received: 29 March 2018 / Revised: 2 June 2018 / Accepted: 4 June 2018 / Published: 7 June 2018
Cited by 3 | PDF Full-text (4887 KB) | HTML Full-text | XML Full-text
Abstract
The China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool 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
[...] Read more.
The China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool 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 (R2) was 0.92, and the Nash-Sutcliffe model efficiency coefficient (NSE) was 0.89. For the validation period (2011–2018), R2 = 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. Full article
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Open AccessArticle Parameter Uncertainty Analysis of the SWAT Model in a Mountain-Loess Transitional Watershed on the Chinese Loess Plateau
Water 2018, 10(6), 690; https://doi.org/10.3390/w10060690
Received: 12 April 2018 / Revised: 22 May 2018 / Accepted: 22 May 2018 / Published: 25 May 2018
Cited by 5 | PDF Full-text (3588 KB) | HTML Full-text | XML Full-text
Abstract
Hydrological models play an important role in water resource management, but they always suffer from various sources of uncertainties. Therefore, it is necessary to implement uncertainty analysis to gain more confidence in numerical modeling. The study employed three methods (i.e., Parameter Solution (ParaSol),
[...] Read more.
Hydrological models play an important role in water resource management, but they always suffer from various sources of uncertainties. Therefore, it is necessary to implement uncertainty analysis to gain more confidence in numerical modeling. The study employed three methods (i.e., Parameter Solution (ParaSol), Sequential Uncertainty Fitting (SUFI2), and Generalized Likelihood Uncertainty Estimation (GLUE)) to quantify the parameter sensitivity and uncertainty of the SWAT (Soil and Water Assessment Tool) model in a mountain-loess transitional watershed—Jingchuan River Basin (JCRB) on the Loess Plateau, China. The model was calibrated and validated using monthly observed streamflow at the Jingchuan gaging station and the modeling results showed that SWAT performed well in the study period in the JCRB. The parameter sensitivity results demonstrated that any of the three methods were capable for the parameter sensitivity analysis in this area. Among the parameters, CN2, SOL_K, and ALPHA_BF were more sensitive to the simulation of peak flow, average flow, and low flow, respectively, compared to others (e.g., ESCO, CH_K2, and SOL_AWC) in this basin. Although the ParaSol method was more efficient in capturing the most optimal parameter set, it showed limited ability in uncertainty analysis due to the narrower 95CI and poor P-factor and R-factor in this area. In contrast, the 95CIs in SUFI2 and GLUE were wider than ParaSol, indicating that these two methods can be promising in analyzing the model parameter uncertainty. However, for the model prediction uncertainty within the same parameter range, SUFI2 was proven to be slightly more superior to GLUE. Overall, through the comparisons of the proposed evaluation criteria for uncertainty analysis (e.g., P-factor, R-factor, NSE, and R2) and the computational efficiencies, SUFI2 can be a potentially efficient tool for the parameter optimization and uncertainty analysis. This study provides an insight into selecting uncertainty analysis method in the modeling field, especially for the hydrological modeling community. Full article
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Open AccessArticle Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia
Water 2018, 10(5), 642; https://doi.org/10.3390/w10050642
Received: 27 March 2018 / Revised: 9 May 2018 / Accepted: 13 May 2018 / Published: 16 May 2018
Cited by 5 | PDF Full-text (8982 KB) | HTML Full-text | XML Full-text
Abstract
The accuracy and sufficiency of precipitation data play a key role in environmental research and hydrological models. They have a significant effect on the simulation results of hydrological models; therefore, reliable hydrological simulation in data-scarce areas is a challenging task. Advanced techniques can
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The accuracy and sufficiency of precipitation data play a key role in environmental research and hydrological models. They have a significant effect on the simulation results of hydrological models; therefore, reliable hydrological simulation in data-scarce areas is a challenging task. Advanced techniques can be utilized to improve the accuracy of satellite-derived rainfall data, which can be used to overcome the problem of data scarcity. Our study aims to (1) assess the accuracy of different satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Climate Data Record (PERSIANN-CDR), and China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) by comparing them with gauged rainfall data; and (2) apply them for runoff simulations for the Han River Basin in South Korea using the SWAT model. Based on the statistical measures, that is, the proportion correct (PC), the probability of detection (POD), the frequency bias index (FBI), the index of agreement (IOA), the root-mean-square-error (RMSE), the mean absolute error (MAE), the coefficient of determination (R2), and the bias, the rainfall data of the TRMM and CMADS show a better accuracy than those of PERSIANN and PERSIANN-CDR when compared to rain gauge measurements. The TRMM and CMADS data capture the spatial rainfall patterns in mountainous areas as well. The streamflow simulated by the SWAT model using ground-based rainfall data agrees well with the observed streamflow with an average Nash-Sutcliffe efficiency (NSE) of 0.68. The four satellite rainfall products were used as inputs in the SWAT model for streamflow simulation and the results were compared. The average R2, NSE, and percent bias (PBIAS) show that hydrological models using TRMM (R2 = 0.54, NSE = 0.49, PBIAS = [−52.70–28.30%]) and CMADS (R2 = 0.44, NSE = 0.42, PBIAS = [−29.30–41.80%]) data perform better than those utilizing PERSIANN (R2 = 0.29, NSE = 0.13, PBIAS = [38.10–83.20%]) and PERSIANN-CDR (R2 = 0.25, NSE = 0.16, PBIAS = [12.70–71.20%]) data. Overall, the results of this study are satisfactory, given that rainfall data obtained from TRMM and CMADS can be used to simulate the streamflow of the Han River Basin with acceptable accuracy. Based on these results, TRMM and CMADS rainfall data play important roles in hydrological simulations and water resource management in the Han River Basin and in other regions with similar climate and topographical characteristics. Full article
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Open AccessArticle Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the Qinghai-Tibet Plateau
Water 2018, 10(4), 513; https://doi.org/10.3390/w10040513
Received: 11 March 2018 / Revised: 9 April 2018 / Accepted: 10 April 2018 / Published: 20 April 2018
Cited by 6 | PDF Full-text (3460 KB) | HTML Full-text | XML Full-text
Abstract
Multisource reanalysis datasets provide an effective way to help us understand hydrological processes in inland alpine regions with sparsely distributed weather stations. The accuracy and quality of two widely used datasets, the China Meteorological Assimilation Driving Datasets to force the SWAT model (CMADS),
[...] Read more.
Multisource reanalysis datasets provide an effective way to help us understand hydrological processes in inland alpine regions with sparsely distributed weather stations. The accuracy and quality of two widely used datasets, the China Meteorological Assimilation Driving Datasets to force the SWAT model (CMADS), and the Climate Forecast System Reanalysis (CFSR) in the Qinghai-Tibet Plateau (TP), were evaluated in this paper. The accuracy of daily precipitation, max/min temperature, relative humidity and wind speed from CMADS and CFSR are firstly evaluated by comparing them with results obtained from 131 meteorological stations in the TP. Statistical results show that most elements of CMADS are superior to those of CFSR. The average correlation coefficient (R) between the maximum temperature and the minimum temperature of CMADS and CFSR ranged from 0.93 to 0.97. The root mean square error (RMSE) for CMADS and CFSR ranged from 3.16 to 3.18 °C, and ranged from 5.19 °C to 8.14 °C respectively. The average R of precipitation, relative humidity, and wind speed for CMADS are 0.46; 0.88 and 0.64 respectively, while they are 0.43, 0.52, and 0.37 for CFSR. Gridded observation data is obtained using the professional interpolation software, ANUSPLIN. Meteorological elements from three gridded data have a similar overall distribution but have a different partial distribution. The Soil and Water Assessment Tool (SWAT) is used to simulate hydrological processes in the Yellow River Source Basin of the TP. The Nash Sutcliffe coefficients (NSE) of CMADS+SWAT in calibration and validation period are 0.78 and 0.68 for the monthly scale respectively, which are better than those of CFSR+SWAT and OBS+SWAT in the Yellow River Source Basin. The relationship between snowmelt and other variables is measured by GeoDetector. Air temperature, soil moisture, and soil temperature at 1.038 m has a greater influence on snowmelt than others. Full article
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