The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin

Meng, Xianyong; Wang, Hao; Cai, Siyu; Leng, Guoyong; Lei, Xiaohui; Shi, Chunxiang; Liu, Shiyin; Shang, Yizi

doi:10.20944/preprints201612.0091.v2

Cited by 28 publications

(31 citation statements)

References 33 publications

(48 reference statements)

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“…They obtained satisfactory results through parameter calibration in areas with a high glacial recharge rate. Meng et al also used three different datasets to simulate runoff in the Heihe Basin, and the results showed that the simulation accuracy of the CMADS was higher than other datasets [18]. The uncertainty analysis based on CMADS data has also been investigated [19].…”

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

Zhang

Yang

et al. 2018

Water

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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.

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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

Zhang

Yang

et al. 2018

Water

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“…Despite the fact that researchers in East Asia can now use existing published reanalysis data (e.g., CFSR, NCEP, etc.) to conduct climate analyses in the region, since the needed assimilation and revision of the above reanalysis products were not conducted by stations in most parts of East Asia [26], the reanalysis results and actual results often differ substantially. For example, the CFSR precipitation data in summer in China are severely overestimated [26].…”

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confidence: 99%

“…to conduct climate analyses in the region, since the needed assimilation and revision of the above reanalysis products were not conducted by stations in most parts of East Asia [26], the reanalysis results and actual results often differ substantially. For example, the CFSR precipitation data in summer in China are severely overestimated [26]. Recently, scientists in East Asia have started collecting small amounts of meteorological observation data by establishing field monitoring sites, and funding in atmospheric hydrological research in East Asia is being increased in an attempt to reduce the gap between atmospheric hydrology research in East Asia and worldwide.…”

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confidence: 99%

“…To address the many aforementioned difficulties, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) [26,27] was developed by Xianyong Meng using STMAS assimilation techniques [20], as well as big data projection and processing methods (including loop nesting of data, projection of resampling models, and bilinear interpolation). CMADS comprises many variables, including daily average temperature, daily maximum temperature, daily minimum temperature, daily cumulative precipitation (20-20 h), daily average relative humidity, daily average specific humidity, daily average solar radiation, daily average wind, daily average atmospheric pressure, soil temperature, and soil moisture.…”

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confidence: 99%

“…Over the past few years, the CMADS dataset has received attention from around the world, including researchers in the United States, Germany, Russia, Italy, India, and South Korea, among others. As a developer of CMADS, we have used the CMADS driven SWAT model to simulate the runoff of many watersheds, such as China's Heihe River Basin [26] and Manas River Basin [27], and obtained satisfactory results. We expect researchers around the world to take full advantage of the CMADS owing to its high spatiotemporal resolution, unified procedure (including latitude and longitude, and elevation), and reliable quality.…”

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Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia

Meng

Wang

2017

Water

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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 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.Keywords: East Asia; CMADS; meteorological input uncertainty; hydrological modelling; SWAT; non-point source pollution models China and the surrounding region in East Asia are considered to be the birthplace of human civilisation. East Asia experiences the most typical and pronounced monsoon climate in the world, and detailed analyses of the atmospheric hydrological cycle in East Asia can offer a substantial regional contribution to global climate change research.Travelling back to the 19th century, natural science research in East Asia, and even globally, still followed the paradigm of dividing the whole into smaller components (e.g., dividing systems into elements), and then studying each isolated part individually. In this context, there was no interdisciplinary approach for researching various types of scientific issues simultaneously. By the second half of the 20th century, a highly detailed and complex classification of numerous natural science disciplines had been developed, and scientists were accustomed to dividing scientific fields into a number of sub-fields. This promoted a more professional approach to research and led to the evolution of various cross-disciplines and interdisciplinary disciplines. However, in recent decades, scientists have recognised many problems associated with the single-disciplinary approach of the 19th century. More researchers reconsidered methods for systematically considering and analysing different system elements of multiple disciplines at a more comprehensive le...

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The evaluation of soil stability in loess hilly and gully region of Northern Shaanxi based on GIS

et al. 2018

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Based on the previous research in loess hilly region of Northern Shaanxi, this paper takes the soil erosion degree as the main measure of soil stability and the soil utility, annual average rainfall in flood season (from June to September), and topography, as the main measure indexes of soil stability. After that, the evaluation system of soil stability in loess hilly region of Northern Shaanxi can be constructed, which can be done by special analysis of GIS. The results illustrate that the soil stability showed a trend of high south‐east and low north‐west. Soil with good stability is mainly distributed in Ganquan County, and Yanchuan County, where vegetation coverage is high, vegetation types are mostly forest land and grassland, ecological environment is good, and precipitation erosion effect is not significant. Soil with intermediate stability is mainly distributed in Baota district and its surrounding areas, where the main vegetation types are bush fallow and grassland, and the terrain is flat and gently rolling. Soil with the worst stability is mainly distributed in Suide County, and Wuqi County. The area is mostly sandy and desert, the terrain is fragmented, soil is loose, vegetation cover is not high, making the soil the worst soil stability, and strong rainfall conditions are prone to soil erosion. The prerequisite of the implementation of soil consolidation projects is having evaluation on soil stability. The research results can be the theoretical evidence, and implement guarantee of regional soil exploitation and reorganization, and the reference to enhancing the assurance of ecological safety.

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The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin

Cited by 28 publications

References 33 publications

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

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

Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia

The evaluation of soil stability in loess hilly and gully region of Northern Shaanxi based on GIS

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