China Meteorological Assimilation Driving Datasets Research Articles

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), time-expanded climate forecast system reanalysis (CFSR+), the European Centre for Medium-Range Weather Forecast Reanalysis (ERA). and the China Meteorological Forcing Dataset (CMFD), were evaluated by using data from 28 ground-based observations (OBs) in the Source of the Yangtze and Yellow Rivers (SYYR) region and were used as input data for the SWAT model for runoff simulation and performance evaluation, respectively. And, finally, the CMADS was optimized using Integrated Calibrated Multi-Satellite Retrievals for Global Precipitation Measurement (AIMERG) data. The results show that CMFD is the most representative reanalysis data for precipitation characteristics in the SYYR region among the four reanalysis datasets evaluated in this paper, followed by ERA5 and CFSR, while CMADS performs satisfactorily for temperature simulations in this region, but underestimates precipitation. And we contend that the accuracy of runoff simulations is notably contingent upon the precision of daily precipitation within the reanalysis dataset. The runoff simulations in this region do not effectively capture the extreme runoff characteristics of the Yellow River and Yangtze River sources. The refinement of CMADS through the integration of AIMERG satellite precipitation data emerges as a potent strategy for enhancing the precision of runoff simulations. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations.

Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses

Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS). We selected the Pearson’s correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling.

Runoff Simulation under the Effects of the Modified Soil Water Assessment Tool (SWAT) Model in the Jiyun River Basin

Few studies have been conducted to simulate watersheds with insufficient meteorological and hydrological information. The Jiyun River watershed was selected as the study area. A suitable catchment area threshold was determined by combining the river network density method with the Soil and Water Assessment Tool (SWAT) models, which was driven using the CMADS dataset (China Meteorological Assimilation Driving Datasets for the SWAT model). Monthly runoff simulations were conducted for the basin from 2010 to 2014, and the calibration and validation of model parameters were completed with observed data. The results showed that the final expression for the density of the river network in the Jiyun River basin as a function of density (y) and the catchment area threshold (x) was obtained as y = 926.782x−0.47717. The “inflection point” of the exponential function was the optimal catchment area threshold. The catchment area threshold had an upper and lower limit of the applicable range and was related to the percentage of the total basin area. The simulation results would be affected if the threshold values were outside the suitable scope. When the catchment area was 1.42% of the entire watershed area, increasing the threshold value had less effect on the runoff simulation results; decreasing the threshold value would cause the simulation results to be unstable. When the catchment area reached 1.42% to 2.33% of the total watershed area, the simulation results were in good agreement with the observed values; the coefficient of determination (R2) and Nash–Sutcliffe efficiency coefficient (NSE) were more significant than 0.79 and 0.78 for the calibration periods evaluation index. Both were greater than 0.77 and 0.76 for the validation period, which met the evaluation requirements of the model. The results showed that the CMADS-driven SWAT model applied to the runoff simulation and the river network density method adoption to determine the catchment area threshold provided a theoretical basis for a reasonable sub-basin division in the Jiyun River basin.

Analysis of the Applicability of Multisource Meteorological Precipitation Data in the Yunnan-Kweichow-Plateau Region at Multiple Scales

Multisource meteorological precipitation products are an important way to make up for a lack of observation sites or a lack of precipitation data in areas with a complex topography. They have important value for local industrial, agricultural, and ecological water use calculations, as well as for water resource evaluation and management. The Yunnan-Kweichow Plateau is located in southwest China and has a relatively backward economy and few meteorological stations. At the same time, the terrain is dominated by mountain valleys, precipitation is greatly affected by the terrain, and meteorological data are lacking, making the calculation of local water resources difficult. In this study, the applicability of the 3-hourly merged high-quality/IR estimates (3B42) of the Tropical Rainfall Measuring Mission (TRMM), China Meteorological Forcing Dataset (CMFD), and China Meteorological Assimilation Driving Datasets (CMADS) in the Yunnan-Kweichow Plateau was analyzed using multiple evaluation indicators of different temporal scales and precipitation intensity levels as well as the spatial distribution of the indicators based on measured daily precipitation data from 59 national meteorological basic stations in the study area in 2008–2018. The results showed that (1) the three products had performed well and could be applied to the calculation of local water resources with CMFD performing the best; (2) the performance of precipitation products was slightly worse on the daily scale, and the overall performance of the yearly, quarterly, and monthly scales was better; (3) good results were achieved in most regions, but there were also some regions with prominent overestimation and underestimation; (4) the three precipitation products had the highest probabilities of detection and the lowest false alarm rates for no rain and light rain, and the probability of detection gradually decreased with an increase in the precipitation intensity; and (5) the mean absolute error of precipitation products in rainy months is large, so the accuracy of products in the calculation of heavy rain and flood will be limited.

Hydrological Modeling in the Chaohu Lake Basin of China—Driven by Open-Access Gridded Meteorological and Remote Sensing Precipitation Products

This study assessed the performance of two well-known gridded meteorological datasets, CFSR (Climate Forecast System Reanalysis) and CMADS (China Meteorological Assimilation Driving Datasets), and three satellite-based precipitation datasets, TRMM (Tropical Rainfall Measuring Mission), CMORPH (Climate Prediction Center morphing technique), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), in driving the SWAT (Soil and Water Assessment Tool) model for streamflow simulation in the Fengle watershed in the middle–lower Yangtze Plain, China. Eighteen model scenarios were generated by forcing the SWAT model with different combinations of three meteorological datasets and six precipitation datasets. Our results showed that (1) the three satellite-based precipitation datasets (i.e., TRMM, CMORPH, and CHIRPS) generally provided more accurate precipitation estimates than CFSR and CMADS. CFSR and CMADS agreed fairly well with the gauged measurements in maximum temperature, minimum temperature, and relative humidity, but large discrepancies existed for the solar radiation and wind speed. (2) The impact of precipitation data on simulated streamflow was much larger than that of other meteorological variables. Satisfactory simulations were achieved using the CMORPH precipitation data for daily streamflow simulation and the TRMM and CHIRPS precipitation data for monthly streamflow simulation. This suggests that different precipitation datasets can be used for optimal simulations at different temporal scales.

Assessment of an Alternative Climate Product for Hydrological Modeling: A Case Study of the Danjiang River Basin, China

Precipitation has been recognized as the most critical meteorological parameter in hydrological studies. Recent developments in space technology provide cost-effective alternative ground-based observations to simulate the hydrological process. Here, this paper aims to evaluate the performance of satellite-based datasets in the hydrological modeling of a sensitive area in terms of water quality and safety watershed. Three precipitation products, i.e., rain gauge observations (RO), the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), and Tropical Rainfall Measuring Mission Multi-satellite (TRMM) products, were used to develop the Soil and Water Assessment Tool (SWAT) model to simulate the streamflow in the Danjiang River Basin (DRB). The results show that: (1) these three precipitation products have a similar performance with regard to monthly time scale compared with the daily scale; (2) CMADS and TRMM performed better than RO in the runoff simulations. CMADS is a more accurate dataset when combined with satellite-based and ground-based data; (3) the results indicate that the CMADS dataset provides reliable results on both monthly and daily scales, and CMADS is a possible alternative climate product for developing a SWAT model for the DRB. This study is expected to serve as a reference for choosing the precipitation products for watersheds similar to DRB where the rain gauge data are limited.

Evaluation of multisource precipitation input for hydrological modeling in an Alpine basin: a case study from the Yellow River Source Region (China)

Abstract Alpine basins are typically poorly gauged and inaccessible owing to the harsh prevailing environment and complex terrain. In this study, two representative satellite precipitation products (Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RTV7 and Integrated Multi-Satellite Retrievals for GPM (IMERG) Final Run Version 06) and two reanalysis precipitation products (China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR)) in the Yellow River Source Region (YRSR) were selected for evaluation and hydrological verification against gauge-observed data (GO). Results show that the accuracy of these precipitation products in the warm season is higher than that in the cold season, and IMERG exhibits the best performance, followed by the CMADS, CFSR, and 3B42RTV7. Models that use the GO as input yielded satisfactory performance during 2008–2013, and precipitation products have poor simulation results. Although the model using the IMERG as input yielded unsatisfactory performance during 2014–2016, this did not affect the use of the IMERG as a potential data source for the YRSR. The model driven by the combination of GO and CMADS precipitation performed the best in all scenarios (R2=0.77, Nash–Sutcliffe efficiency (NSE)=0.72 at the Tangnaihai station; R2=0.53, NSE=0.48 at the Jimai station).

Application of meteorological element combination-driven SWAT model based on meteorological datasets in alpine basin

Abstract Thus far, reanalysis-based meteorological products have drawn little attention to the influence of meteorological elements of products on hydrological modeling. This study aims to evaluate the hydrological application potential of the precipitation, temperature, and solar radiation of the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and Climate Forecast System Reanalysis (CFSR) in an alpine basin. The precipitation, temperature, and solar radiation of the gauge-observed meteorological dataset (GD), CFSR, and CMADS were cross-combined, and 20 scenarios were constructed to drive the SWAT model. From the comprehensive comparisons of all scenarios, we drew the following conclusions: (1) among the three meteorological elements, precipitation has the greatest impact on the simulation results, and using GD precipitation from sparse stations yielded better performance than CMADS and CFSR; (2) although the SWAT modeling driven by CMADS and CFSR performed poorly, with CMADS underestimation and CFSR overestimation, the temperature and solar radiation of CMADS and CFSR can be an alternative data source for streamflow simulation; (3) models using GD precipitation, CFSR temperature, and CFSR solar radiation as input yielded the best performance in streamflow simulation, suggesting that these data sources can be applied to this data-scarce alpine region.

Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China

Quantifying the climatic and anthropogenic effects on hydrological processes has received considerable attention. However, diverse conclusions could be drawn when different models and forcing datasets are used. This is particularly uncertain and challenging in poorly gauged arid regions. Here we aim to tackle this issue in the poorly gauged Xiangride River Basin within the Qaidam Basin, one of the three prominent inland basins in China. We applied two distinct models (Budyko Mezentsev-Choudhurdy-Yang and process-based SWAT) to a poorly-gauged inland basin in West China. The model simulations were driven by four precipitation products including Tropical Rainfall Measuring Mission (TRMM) 3B42 V7, Global Precipitation Measurement (GPM) IMERG V6, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and China Meteorological Assimilation Driving Datasets (CMADS). Our results indicate that MSWEP performed best (NSE = 0.64 vs. 0.36–0.59 for other datasets) in the baseline period (2009–2012), whereas CMADS was more accurate during the impacted period (2013–2016); CMADS and GPM might underestimate the precipitation in the baseline and impacted period, respectively. Hydrological processes during the impacted period are presumed to be influenced by climate variation and/or human activities, compared to the relatively natural status in the baseline period. We conclude that runoff decline between the two periods was mainly affected by human activities (−66 to 94%), whereas the contribution of climate variation was more likely positive. A literature survey reveals that major anthropogenic effects in the study area includes reservoir, road construction and cropland expansion that could lead to runoff decrease. We recommend the use of process-based model (e.g., SWAT) in studies like this, as process-based models driven by high-quality remote-sensed or reanalysis climate datasets, better represents the spatiotemporal hydrological change under altered conditions, whereas the steady-state assumption of soil water for the Budyko model may not be fully satisfied during a short period.

Influence of multisite calibration on streamflow estimation based on the hydrological model with CMADS inputs

Abstract Due to the spatial heterogeneity, the hydrological model calibration results only at the total outlet of the basin may not represent the whole basin. To more accurately simulate the historical streamflow process within the Qujiang River Basin, we set up three calibration strategies (single-site, S1; multisite simultaneous, S2; and multisite sequential, S3) for four hydrological stations based on the SWAT (Soil and Water Assessment Tool) model driven by CMADS (China Meteorological Assimilation Driving Datasets for the SWAT model). In addition, the implications of these calibration issues are extended to future streamflow projections using multimodel ensemble data in CMIP6 (Coupled Model Intercomparison Project Phase 6). In the model calibration phase, the SWAT model achieved very satisfactory results in the study area. Compared with S1 and S2, S3 can effectively improve the accuracy of streamflow simulation of stations within the basin and reduce the simulation deviation. Especially at the daily scale, the average NSE values of the four stations with S3 increased by 0.26 and 0.07, and the overall deviation decreased by 0.25 and 6.43%, respectively. Parameter sensitivity analysis also shows that spatial heterogeneity can be more adequately considered when using S3 to calibrate the model. As for the results of future streamflow projection, when using the S3, the average annual streamflow of four stations in the three climate scenarios from 2021 to 2050 is about 44.21, 130.00, 321.55 and 713.24 m3/s, respectively. Correspondingly, the use of S1 and S2 would bring certain risks to future water resource management.

Predicting Tropical Monsoon Hydrology Using CFSR and CMADS Data over the Cau River Basin in Vietnam

To improve knowledge of this matter, the potential application of two gridded meteorological products (GMPs), the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR), are compared for the first time with data from ground-based meteorological stations over 6 years, from 2008 to 2013, over the Cau River basin (CRB), northern Vietnam. Statistical indicators and the Soil and Water Assessment Tool (SWAT) model are employed to investigate the hydrological performances of the GMPs against the data of 17 rain gauges distributed across the CRB. The results show that there are strong correlations between the temperature reanalysis products in both CMADS and CFSR and those obtained from the ground-based observations (the correlation coefficients range from 0.92 to 0.97). The CFSR data overestimate precipitation (percentage bias approximately 99%) at both daily and monthly scales, whereas the CMADS product performs better, with obvious differences (compared to the ground-based observations) in high-terrain areas. Regarding the simulated river flows, CFSR-SWAT produced “unsatisfactory”, while CMADS-SWAT (R2 > 0.76 and NSE > 0.78) performs better than CFSR-SWAT on the monthly scale. This assessment of the applicative potential of GMPs, especially CMADS, may further provide an additional rapid alternative for water resource research and management in basins with similar hydro-meteorological conditions.

Analysis of Non-Point Source Pollution Features of Chao River Basin Based on the CMADS-Driven SWAT Model

By the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS 1.0), this paper simulated the non-point source (NPS) pollution of Chao River Basin in the upper reach of Miyun Reservoir, and analyzed the spatial-temporal distribution pattern of nitrogen and phosphorus pollutants and the contribution rates of pollution sources. The major findings of the research are as follows. The CMADS V1.0-driven SWAT model shows good applicability to the study area. The simulation of the runoff, nitrogen and phosphorous pollution in the calibration period and the validation period has yielded a Nash-Sutcliffe efficiency (Ens) coefficient at 0.51~0.78, and a coefficient of determination at 0.73~0.88, which meets the model evaluation standards. The total nitrogen (TN) and total phosphorus (TP) pollution load in the flood season is considerably large, and the average inflow of TN and TP into the reservoir accounts for 60.62% and 75.15% the total annual inflow, respectively. The #26 sub-basin marks the biggest TN and TP loads, and it is thus worth more attention from pollution control administrations. Overall, the TN and TP load in the lower reach of the basin are larger than the upper reach. NPS pollution is the major type of pollution caused by human production and life. The livestock and poultry farming as well as fertilizers, which are the main contributors to NPS pollution, are considered the focus of NPS pollution control.

Multi-Scenario Integration Comparison of CMADS and TMPA Datasets for Hydro-Climatic Simulation over Ganjiang River Basin, China

The lack of meteorological observation data limits the hydro-climatic analysis and modeling, especially for the ungauged or data-limited regions, while satellite and reanalysis products can provide potential data sources in these regions. In this study, three daily products, including two satellite products (Tropic Rainfall Measuring Mission Multi-Satellite Precipitation Analysis, TMPA 3B42 and 3B42RT) and one reanalysis product (China Meteorological Assimilation Driving Datasets for the SWAT Model, CMADS), were used to assess the capacity of hydro-climatic simulation based on the statistical method and hydrological model in Ganjiang River Basin (GRB), a humid basin of southern China. CAMDS, TMPA 3B42 and 3B42RT precipitation were evaluated against ground-based observation based on multiple statistical metrics at different temporal scales. The similar evaluation was carried out for CMADS temperature. Then, eight scenarios were constructed into calibrating the Soil and Water Assessment Tool (SWAT) model and simulating streamflow, to assess their capacity in hydrological simulation. The results showed that CMADS data performed better in precipitation estimation than TMPA 3B42 and 3B42RT at daily and monthly scales, while worse at the annual scale. In addition, CMADS can capture the spatial distribution of precipitation well. Moreover, the CMADS daily temperature data agreed well with observations at meteorological stations. For hydrological simulations, streamflow simulation results driven by eight input scenarios obtained acceptable performance according to model evaluation criteria. Compared with the simulation results, the models driven by ground-based observation precipitation obtained the most accurate streamflow simulation results, followed by CMADS, TMPA 3B42 and 3B42RT precipitation. Besides, CMADS temperature can capture the spatial distribution characteristics well and improve the streamflow simulations. This study provides valuable insights for hydro-climatic application of satellite and reanalysis meteorological products in the ungauged or data-limited regions.

Hydrological process simulation in Manas River Basin using CMADS

Abstract 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 adj 2 {R}_{\text{adj}}^{2} ), and percentage bias ( PBIAS \text{PBIAS} ) were selected for evaluation. The results were compared with statistics obtained from Kenswat Hydrological Station, where the monthly runoff simulation efficiency was NSE = 0.64 \text{NSE}\hspace{.25em}=0.64 , R adj 2 = 0.69 {R}_{\text{adj}}^{2}\hspace{.25em}=0.69 , and PBIAS = – 0.9 \text{PBIAS}\hspace{.25em}=\mbox{--}0.9 , and the daily runoff simulation efficiency was NSE = 0.75 \text{NSE}\hspace{.25em}=0.75 , R adj 2 {R}_{\text{adj}}^{2} = 0.75, PBIAS \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.

Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China

Satellite- and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs; i.e., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (TMPA 3B42V7), Climate Prediction Center (CPC) morphing technique satellite-gauge blended product (CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang (XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient (CC) and root-mean-square error (RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias (BIAS; −22.72%); (2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation; (3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash-Sutcliffe efficiency (NSE) values (0.85 and 0.75 for calibration period and validation period, respectively); and (4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in the Global Precipitation Measurement (GPM) era.

Comparison Study of Multiple Precipitation Forcing Data on Hydrological Modeling and Projection in the Qujiang River Basin

As a key factor in the water cycle and climate change, the quality of precipitation data directly affects the hydrological processes of the river basin. Although many precipitation products with high spatial and temporal resolutions are now widely used, it is meaningful and necessary to investigate and evaluate their merits and demerits in hydrological applications. In this study, two satellite-based precipitation products (Tropical Rainfall Measurement Mission, TRMM; Integrated Multi-satellite Retrievals for GPM, IMERG) and one reanalysis precipitation product (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model, CMADS) are studied to compare their streamflow simulation performance in the Qujiang River Basin, China, using the SWAT model with gauged rainfall data as a reference. The main conclusions are as follows: (1) CMADS has stronger precipitation detection capabilities compared to gauged rainfall, while TRMM results in the most obvious overestimation in the four sub-basins. (2) In daily and monthly streamflow simulations, CMADS + SWAT mode offers the best performance. CMADS and IMERG can provide high quality precipitation data for data-scarce areas, and IMERG can effectively avoid the overestimation of streamflow caused by TRMM, especially on a daily scale. (3) The runoff projections of the three modes under RCP (Representative Concentration Pathway) 4.5 was higher than that of RCP 8.5 on the whole. IMERG + SWAT overestimates the surface water resources of the basin compared to CMADS + SWAT, while TRMM + SWAT provides the most stable uncertainty. These findings contribute to the comparison of the differences among the three precipitation products and provides a reference for the selection of precipitation data in similar regions.

Assessment of CFSR and CMADS Weather Data for Capturing Extreme Hydrologic Events in the Fuhe River Basin of the Poyang Lake

AbstractEvaluating the accuracy of gridded weather data is important because these data significantly affect the results of hydrologic simulations. In this study, we evaluated the applicability of Climate Forecast System Reanalysis (CFSR) and China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) datasets for capturing the hydrologic extreme events in the Fuhe River Basin (FRB) of the Poyang Lake, a typical humid area in eastern China. First, both the CFSR and CMADS temperature and precipitation data obtained from 2008 to 2013 were validated using ground‐based meteorological station data. Then, the SWAT model was driven by the CFSR and CMADS datasets for hydrologic predictions. The results show that both CFSR and CMADS temperature data are of high quality. The CMADS data underestimate precipitation, whereas CFSR data overestimate precipitation. Both datasets have their own advantage and disadvantage to detect extreme rainfall events. For the FRB case study, SWAT models driven by two datasets yield good streamflow simulation results. The CMADS‐driven model performs slightly better than CFSR‐driven model in predicting extreme streamflow events in the simulation time‐period (2009–2013). In general, both the CFSR and CMADS data can be used to obtain satisfactory simulation results for hydrologic predictions. They provide alternatives that enable quickly implementing a hydrologic model in data‐scarce areas.

Investigate the Applicability of CMADS and CFSR Reanalysis in Northeast China

Reanalysis datasets can provide alternative and complementary meteorological data sources for hydrological studies or other scientific studies in regions with few gauge stations. This study evaluated the accuracy of two reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and Climate Forecast System Reanalysis (CFSR), against gauge observations (OBS) by using interpolation software and statistical indicators in Northeast China (NEC), as well as their annual average spatial and monthly average distributions. The reliability and applicability of the two reanalysis datasets were assessed as inputs in a hydrological model (SWAT) for runoff simulation in the Hunhe River Basin. Statistical results reveal that CMADS performed better than CFSR for precipitation and temperature in NEC with the indicators closer to optimal values (the ratio of standard deviations of precipitation and maximum/minimum temperature from CMADS were 0.92, 1.01, and 0.995, respectively, while that from CFSR were 0.79, 1.07, and 0.897, respectively). Hydrological modelling results showed that CMADS + SWAT and OBS + SWAT performed far better than CFSR + SWAT on runoff simulations. The Nash‒Sutcliffe efficiency (NSE) of CMADS + SWAT and OBS + SWAT ranged from 0.54 to 0.95, while that of CFSR + SWAT ranged from −0.07 to 0.85, exhibiting poor performance. The CMADS reanalysis dataset is more accurate than CFSR in NEC and is a suitable input for hydrological simulations.

Temperature dependence of extreme precipitation over mainland China

The relationship between extreme precipitation and the corresponding temperature is of great importance for predicting precipitation extremes in the future, given the rapid increase of global warming. This study investigates the relationship between extreme precipitation and the corresponding daily mean temperature over mainland China based on the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (CMADS). The relationship between extreme precipitation and daily maximum temperature before rainfall as well as that between relative humidity and daily mean temperature is investigated. The results show that (1) Among the patterns of the relationship between extreme precipitation and corresponding daily mean temperature, the peak pattern is dominant over mainland China, accounting for 95.80% of all the patterns; (2) a scaling of extreme precipitation with daily mean temperature greater than that represented by the C–C scaling is observed in most parts of China; (3) the peak-point extreme precipitation decreases from the southeast to the northwest of China, whereas the peak-point temperature ranges from 15 °C to 25 °C; (4) for most regions of China, the decreases in the relative humidity and the cooling effect of heavy extreme precipitation and its synoptic system contribute to the peak or negative pattern of the relationship between extreme precipitation and the corresponding daily mean temperature; and (5) compared with the cooling effect, the decrease of relative humidity is the first-order reason for the peak or negative pattern of the relationship between extreme precipitation and the corresponding daily mean temperature.

Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China

Abstract The wide application of satellite-based and reanalysis-based precipitation data has greatly promoted hydrometeorological research in areas where precipitation observations are scarce. However, the suitability of such precipitation products needs to be carefully evaluated before applications in certain basins because their inherited errors vary with different climate zones, seasonal cycles and land surface conditions; in addition, precipitation products have not been evaluated in the Xihe River basin, China. In this paper, two representative satellite-based precipitation products (Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR)) and two reanalysis-based precipitation products (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and National Centers for Environmental Prediction - Climate Forecast System Reanalysis (CFSR)) were selected for evaluation and corrected against gauge-observed data (OBS). Furthermore, the performances of precipitation products in hydrological simulations were also assessed using the SWAT model calibrated with OBS forcing and not with individual precipitation products. The results show that satellite-based precipitation has a higher quality than reanalysis-based precipitation. The CFSR and CDR overestimate precipitation (the overestimation of CDR precipitation is mainly concentrated in the precipitation intensity range of 1 mm/d to 5 mm/d), while the TRMM and CMADS underestimate precipitation in the Xihe River basin. The TRMM precipitation performs best during the wet season, while the CDR precipitation performed best during the dry season. After bias correction, the quality of TRMM precipitation improves significantly. The Nash-Sutcliffe coefficient (NS) (the percent bias (|PBIAS|)) increases (decreases) by 0.61 (77.27%) and 0.7 (39.15%) under the two different correction scenarios during 2009–2015. Overall, the above original precipitation products cannot be used as supplements to the OBS in the Xihe River basin unless they are corrected by the OBS.