Assessment of TMPA 3B42V7 and PERSIANN-CDR in Driving Hydrological Modeling in a Semi-Humid Watershed in Northeastern China

Recent developments of satellite precipitation products provide an unprecedented opportunity for better precipitation estimation, and thus broaden hydrological application. However, due to the errors and uncertainties of satellite products, a thorough validation is usually required before putting into the real hydrological application. As such, this study aims to provide a comprehensive evaluation on the performances of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), as well as their adequacies in simulating hydrological processes in a semi-humid region in the northeastern China. It was found that TMPA 3B42V7 showed a superior performance at the daily and monthly time scales, and had a favorable capture of the rainfall-intensity distribution. Intra-annual comparisons indicated a better representation of TMPA 3B42V7 from January to September, whereas PERSIANN-CDR was more reliable from October to December. The Soil and Water Assessment Tool (SWAT) driven by gauge precipitation data performed excellently with NSE > 0.9, while the performances of TMPA 3B42V7- and PERSIANN-CDR-based models are satisfactory with NSE > 0.5. The performances varied under different flow levels and hydrological years. Water balance analysis indicated a better performance of TMPA 3B42V7 in simulating the hydrological processes, including evapotranspiration, groundwater recharge and total runoff. The runoff compositions (i.e., base flow, subsurface flow, and surface flow) driven by TMPA 3B42V7 were more accordant with the actual hydrological features. This study will not only help recognize the potential satellite precipitation products for local water resources management, but also be a reference for the poor-gauged regions with similar hydrologic and climatic conditions around the world, especially the northeastern China and western Russia.

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Satellite-based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia

Remote Sensing ◽

10.3390/rs13020221 ◽

2021 ◽

Vol 13 (2) ◽

pp. 221

Author(s):

Jiabin Peng ◽

Tie Liu ◽

Yue Huang ◽

Yunan Ling ◽

Zhengyang Li ◽

...

Keyword(s):

Central Asia ◽

Hydrological Modeling ◽

Assessment Tool ◽

Swat Model ◽

Tropical Rainfall Measuring Mission ◽

Northwest China ◽

Lake Basin ◽

Climate Data ◽

Bosten Lake ◽

Satellite Precipitation

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: (1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. (2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. (3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. (4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. (5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.

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Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0168.1 ◽

2017 ◽

Vol 18 (6) ◽

pp. 1617-1641 ◽

Cited By ~ 116

Author(s):

Pingping Xie ◽

Robert Joyce ◽

Shaorong Wu ◽

Soo-Hyun Yoo ◽

Yelena Yarosh ◽

...

Keyword(s):

High Resolution ◽

Global Precipitation Climatology Project ◽

Temporal Variations ◽

Cold Season ◽

Superior Performance ◽

Climate Data ◽

Integration Algorithm ◽

Satellite Precipitation ◽

Precipitation Estimates ◽

Global Precipitation

Abstract The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

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Using the nonlinear aquifer storage–discharge relationship to simulate the base flow of glacier- and snowmelt-dominated basins in northwest China

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3577-2013 ◽

2013 ◽

Vol 17 (9) ◽

pp. 3577-3586 ◽

Cited By ~ 22

Author(s):

R. Gan ◽

Y. Luo

Keyword(s):

Hydrological Modeling ◽

Assessment Tool ◽

Northwest China ◽

Base Flow ◽

Low Flow ◽

Aquifer Storage ◽

Groundwater Aquifers ◽

Recession Curves ◽

The One ◽

Linear Reservoir

Abstract. Base flow is an important component in hydrological modeling. This process is usually modeled by using the linear aquifer storage–discharge relation approach, although the outflow from groundwater aquifers is nonlinear. To identify the accuracy of base flow estimates in rivers dominated by snowmelt and/or glacier melt in arid and cold northwestern China, a nonlinear storage–discharge relationship for use in SWAT (Soil Water Assessment Tool) modeling was developed and applied to the Manas River basin in the Tian Shan Mountains. Linear reservoir models and a digital filter program were used for comparisons. Meanwhile, numerical analysis of recession curves from 78 river gauge stations revealed variation in the parameters of the nonlinear relationship. It was found that the nonlinear reservoir model can improve the streamflow simulation, especially for low-flow period. The higher Nash–Sutcliffe efficiency, logarithmic efficiency, and volumetric efficiency, and lower percent bias were obtained when compared to the one-linear reservoir approach. The parameter b of the aquifer storage–discharge function varied mostly between 0.0 and 0.1, which is much smaller than the suggested value of 0.5. The coefficient a of the function is related to catchment properties, primarily the basin and glacier areas.

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Hydrological modeling of Upper Ribb watershed, Abbay Basin, Ethiopia.

Global NEST Journal ◽

10.30955/gnj.003152 ◽

2020 ◽

Keyword(s):

Surface Runoff ◽

Hydrological Modeling ◽

Assessment Tool ◽

Swat Model ◽

Simulated Data ◽

Coefficient Of Determination ◽

Climate Data ◽

Water Balance Components ◽

Discharge Data ◽

Forested Areas

<p>Hydrological modeling of a watershed is necessary for water resources planning and management. The hydrology of upper Ribb watershed has been analyzed using spatially semi-distributed Soil and water assessment tool (SWAT) model. This study aimed to determine the water balance components and its relation with the rainfall which reaches to the surface of the earth. Different spatio-temporal (land use, soil, digital elevation model, climate data, river discharge) data were used for hydrological modelling of Upper Ribb watershed. The applicability of SWAT model in Upper Ribb watershed has been evaluated using coefficient of determination (R2) and Nash Sutcliff efficiency (NSE) parameters. The calibration results revealed the observed data showed a very good agreement with the simulated data with the R2 and NSE values of 0.90 and 0.84 respectively. Similarly, the validation results of streamflow were acceptable with the R2 and NSE values of 0.80 and 0.82 respectively. The monthly average streamflow from Upper Ribb watershed were found 13.39 m3/s. The major portion of the rainfall contributes to the surface runoff due to the major percentage of the watershed is covered with agricultural lands. The groundwater flow was high in forested areas, while evapotranspiration was found very high in water bodies (Ribb reservoir). In this study area the rainfall showed a direct relationship with the streamflow. The ratio of streamflow and evapotranspiration with rainfall was 0.61 and 0.36 respectively. Due to the presence of high amount of surface runoff and evapotranspiration the deep recharge which contributes to the ground water is not that much significant.</p>

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Hybrid climate datasets from a climate data evaluation system and their impacts on hydrologic simulations for the Athabasca River basin in Canada

10.5194/hess-2019-189 ◽

2019 ◽

Author(s):

Hyung-Il Eum ◽

Anil Gupta

Keyword(s):

River Basin ◽

Evaluation System ◽

Hydrological Modeling ◽

Water Cycle ◽

Climatic Conditions ◽

Superior Performance ◽

Climate Data ◽

Athabasca River ◽

The North ◽

Spatial Coverage

Abstract. A reliable climate dataset is a backbone for modeling the essential processes of the water cycle and predicting future conditions. Although a number of gridded climate datasets are available for the North American content, which provide reasonable estimates of climatic conditions in the region, there are inherent inconsistencies in these available climate datasets (e.g., spatial and temporal varying data accuracies, meteorological parameters, length of records, spatial coverage, temporal resolution, etc). These inconsistencies raise a valid question as to which datasets are the most suitable for the study area and how to systematically combine these datasets to produce a reliable climate dataset for climate studies and hydrological modeling. This study suggested a framework, called reference reliability evaluation system (REFRES), that systematically determines a ranking of multiple climate datasets to generate a hybrid climate dataset for a region. To demonstrate the usefulness of the proposed framework, REFRES was applied to produce a historical hybrid climate dataset for the Athabasca River basin in Alberta, Canada. A proxy validation was also conducted to prove the applicability of the generated hybrid climate datasets to hydrologic simulations. This study evaluated five climate datasets, including station-based gridded climate datasets (ANUSPLIN, Alberta Township, and PNWNAmet), a multi-source gridded dataset (Canadian Precipiation Analysis – CaPA), and a reanalysis-based dataset (NARR). The results showed that the gridded climate interpolated from station data performed better than multi-source and reanalysis based climate datasets. For the Athabasca River basin, Township and ANUSPLIN were mostly ranked first for precipitation and temperature, respectively. The proxy validation also confirmed the superior performance of hybrid climate datasets compared with the other five individual climate datasets investigated in this study. These results indicate that the hybrid climate dataset provides a better representation of historical climatic conditions and thus, enhancing the reliability of hydrologic simulations.

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Using SWAT to simulate streamflow in Huifa River basin with ground and Fengyun precipitation data

Journal of Hydroinformatics ◽

10.2166/hydro.2015.104 ◽

2015 ◽

Vol 17 (5) ◽

pp. 834-844 ◽

Cited By ~ 2

Author(s):

Honglei Zhu ◽

Ying Li ◽

Zhaoli Liu ◽

Xiaoliang Shi ◽

Bolin Fu ◽

...

Keyword(s):

River Basin ◽

Assessment Tool ◽

Hydrological Processes ◽

Validation Period ◽

Mean Values ◽

Satellite Precipitation ◽

Calibration And Validation ◽

Simulation Performance ◽

Precipitation Product ◽

Streamflow Simulation

High-resolution satellite precipitation products, which can provide a reasonable depiction of the spatial extent of rainfall, have been increasingly used to model hydrological processes. In this study, we introduced important satellite rainfall data – Fengyun (FY) precipitation product, and evaluated the data through streamflow simulation using the Soil and Water Assessment Tool model in Huifa River basin, China. Three precipitation inputs were conducted to investigate the simulation performance of the FY precipitation product: (1) available rain gauges within the watershed; (2) pixel values of FY-2 precipitation products nearest to the geographic centers of the subbasins; and (3) mean values of FY-2 precipitation pixels within the subbasins. The results showed that good model performance (deﬁned as: NSE > 0.75; Nash–Sutcliffe efficiency: NSE) was achieved for all precipitation inputs both in the calibration and validation period. Best streamflow simulation was obtained when the model was calibrated with the third precipitation input, with NSE 0.86 and 0.84, R2 0.86 and 0.86 in the calibration and validation period. This study reveals that the FY precipitation product is a significant data source in modeling hydrological processes. Moreover, it is reasonable to use the mean values of the satellite precipitation pixels within the subbasins.

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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

10.20944/preprints201612.0091.v2 ◽

2017 ◽

Cited By ~ 10

Author(s):

Xian-yong Meng ◽

Hao Wang ◽

Si-yu Cai ◽

Xue-song Zhang ◽

Guo-yong Leng ◽

...

Keyword(s):

River Basin ◽

Large Scale ◽

Hydrologic Modeling ◽

Hydrological Modeling ◽

Assessment Tool ◽

Swat Model ◽

Heihe River Basin ◽

Heihe River ◽

Climate Data ◽

Weather Stations

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

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Statistical and Hydrological Evaluations of Multiple Satellite Precipitation Products in the Yellow River Source Region of China

Water ◽

10.3390/w12113082 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3082

Author(s):

Chongxu Zhao ◽

Liliang Ren ◽

Fei Yuan ◽

Limin Zhang ◽

Shanhu Jiang ◽

...

Keyword(s):

Yellow River ◽

Source Region ◽

The Yellow River ◽

Climate Data ◽

Infiltration Capacity ◽

Satellite Precipitation ◽

Basin Scale ◽

Precipitation Estimation ◽

Yellow River Source ◽

Global Precipitation

Comprehensively evaluating satellite precipitation products (SPPs) for hydrological simulations on watershed scales is necessary given that the quality of different SPPs varies remarkably in different regions. The Yellow River source region (YRSR) of China was chosen as the study area. Four SPPs were statistically evaluated, namely, the Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Integrated Multisatellite Retrievals for Global Precipitation Measurement final run (IMERG-F), and gauge-corrected Global Satellite Mapping of Precipitation (GSMaP-Gauge) products. Subsequently, the hydrological utility of these SPPs was assessed via the variable infiltration capacity hydrological model on a daily temporal scale. Results show that the four SPPs generally demonstrate similar spatial distribution pattern of precipitation to that of the ground observations. In the period of January 1998 to December 2016, 3B42V7 outperforms PERSIANN-CDR on basin scale. In the period of April 2014 to December 2016, GSMaP-Gauge demonstrates the highest precipitation monitoring capability and hydrological utility among all SPPs on grid and basin scales. In general, 3B42V7, IMERG-F, and GSMaP-Gauge show a satisfactory hydrological performance in streamflow simulations in YRSR. IMERG-F has an improved hydrological utility than 3B42V7 in YRSR.

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Hydrological Evaluation of PERSIANN-CDR Rainfall over Upper Senegal River and Bani River Basins

Remote Sensing ◽

10.3390/rs10121884 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1884 ◽

Cited By ~ 4

Author(s):

Khalidou Bâ ◽

Luis Balcázar ◽

Vitali Diaz ◽

Febe Ortiz ◽

Miguel Gómez-Albores ◽

...

Keyword(s):

Hydrological Modeling ◽

Model Performance ◽

Water Levels ◽

Coefficient Of Determination ◽

Climate Data ◽

Senegal River ◽

Precipitation Estimation ◽

Main Input ◽

Three Rivers ◽

Climate Data Record

This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash–Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R2). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R2 > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.

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Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region

Remote Sensing ◽

10.3390/rs12213550 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3550

Author(s):

Jie Chen ◽

Ziyi Li ◽

Lu Li ◽

Jialing Wang ◽

Wenyan Qi ◽

...

Keyword(s):

Hydrological Modeling ◽

Tropical Rainfall Measuring Mission ◽

Transformation Process ◽

Hydrologic Model ◽

Hydrological Models ◽

Random Errors ◽

Climate Data ◽

Precipitation Estimation ◽

Real Performance ◽

Climate Data Record

This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets include two raw satellite datasets, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center MORPHing raw satellite dataset (CMORPH RAW); four satellite-gauge datasets, Tropical Rainfall Measuring Mission 3B42 (TRMM), PERSIANN Climate Data Record (PERSIANN CDR), CMORPH bias-corrected (CMORPH CRT), and gauge blended datasets (CMORPH BLD); and two satellite-reanalysis-gauge datasets, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS). The uncertainty related to hydrologic model physics is investigated using two different hydrological models. A set of statistical indices is utilized to comprehensively evaluate the precipitation datasets from different perspectives, including detection, systematic, random errors, and precision for simulating extreme precipitation. Results show that CMORPH BLD and MSWEP generally perform better than other datasets. In terms of hydrological simulations, all satellite-based datasets show significant dampening effects for the random error during the transformation process from precipitation to runoff; however, these effects cannot hold for the systematic error. Even though different hydrological models indeed introduce uncertainties to the simulated hydrological processes, the relative hydrological performance of the satellite-based datasets is consistent in both models. Namely, CMORPH BLD performs the best, which is followed by MSWEP, CMORPH CRT, and TRMM. PERSIANN CDR and CHIRPS perform moderately well, and two raw satellite datasets are not recommended as proxies of gauged observations for their worse performances.

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