Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin

Zhu, Bin; Huang, Yuhan; Zhang, Zengxin; Kong, Rui; Tian, Jiaxi; Zhou, Yichen; Chen, Sheng; Duan, Zheng

doi:10.3390/w12113230

Cited by 10 publications

(6 citation statements)

References 53 publications

(81 reference statements)

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“…With the rapid development of information technology, some physical-mechanistic hydrology models such as System Hydrological European (SHE), Soil and Water Assessment Tool (SWAT), Variable Infiltration Capacity (VIC), Topography based Hydrological Model (TOPMODEL) and Xinanjiang Model (XAJ) have been developed and widely used in hydrological simulation, forecasting and prediction, etc. [5][6][7][8][9]. However, the hydrological process is a highly complex, random and nonlinear system which involves many uncertain factors [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Shao

Jing

et al. 2022

Water

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The research into rainfall-runoff plays a very important role in water resource management. However, runoff simulation is a challenging task due to its complex formation mechanism, time-varying characteristics and nonlinear hydrological dynamic process. In this study, a nonlinear autoregressive model with exogenous input (NARX) is used to simulate the runoff in the Linyi watershed located in the northeastern part of the Huaihe river basin. In order to better evaluate the performance of NARX, a distributed hydrological model, TOPX, is used to simulate the discharge as a reference, and runoff classification by cluster analysis is used to further improve the accuracy of runoff simulation. Based on the four statistics indexes of the Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean square error (RMSE) and mean relative bias (Bias), the NARX model is capable of simulating the rainfall-runoff dynamic process satisfactorily, although there is a little underestimation of the peak flow. After runoff classification, underestimation has been improved, and discharge simulation driven by NARX based on runoff classification (C-NARX) is well consistent with the observation. It is feasible to take it as a promising method, which also can be seen as a good reference and replacement for the current rainfall-runoff simulation.

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Section: Introductionmentioning

confidence: 99%

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Shao

Jing

et al. 2022

Water

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“…In the field of hydrology, construction of hydrological models and regionalization based on hydrological similarity are often used to carry out studies of uninformed watersheds [7][8][9]. Hydrological models commonly used for flow simulation are: Variable Infiltration Capacity Macroscale Hydrologic Model (VIC) [10][11][12], Soil and Water Assessment Tool model [13][14][15][16], Hydrologiska Byråns Vattenbalansavdelning model (HBV) [17,18], etc. Among them, the SWAT model is a distributed hydrological model with strong physical mechanism [19], the model can be simulated from three different time scales of year, month and day for continuous long-term simulation, and can use geographic information data and remote sensing data to establish a hydrological model in ungauged watershed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Discharge Flow in a Small Hydropower Station without Hydrological Data Based on SWAT Model

Xie

Zhu

2022

Water

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The availability of hydrological data for small hydropower plants is an important prerequisite for reservoir scheduling, reservoir flood control and integrated water resources. To address the problem of a lack of hydrological data in small hydropower plants, this paper proposes a method to predict the power generation flow of small hydropower stations without hydrological data using the Soil and Water Assessment Tool model (SWAT) when the traditional data-driven methods cannot study the problem of power generation flow prediction in small hydropower stations well. The method can use gridded meteorological data as the input of the model to solve the problem of small hydropower stations without meteorological data. The problem that small hydropower plants without hydrological data cannot calibrate the hydrological model is solved by calculating the generation flow through the output of small hydropower station and by using the similarity analysis method to migrate the generation flow of similar small hydropower stations. The model was tested in a watershed in southwest China to demonstrate the effectiveness of the proposed method. The results show that the coefficient of determination between the predicted and measured values of small hydropower stations without information is about 0.84, which achieves a better prediction.

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“…In order to solve the above shortcomings of observations for surface precipitation, countries all over the world have launched meteorological satellites with different kinds of sensors, and adopted the data of different bands sent back to the ground by the sensors in real time and combined with the data of ground rainfall stations for correction, and developed different versions of real-time and delayed remote sensing precipitation products released after correction. Representative products include TRMM, CMORPH (The Climate Prediction Center Morphing Technique), CHIRPS and PERSIANN (precision estimation from remotely sensed information using artificial neural networks), etc (Li et al 2015;Liu 2015a;Zhu et al 2020), are widely used in various hydrological studies because they can provide high-precision and grid precipitation data of global continuous time series.…”

Section: Introductionmentioning

confidence: 99%

Study on applicability of remote sensing precipitation products in hilly-plain-wetland complex area of northeast China

Wang

Wang²,

Feng

et al. 2021

Water Supply

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For the hilly-plain-wetland complex ecosystem in the cold region of Northeast China, in order to solve the problems, which include the scarcity of surface rainfall stations and the inability to provide accurate surface precipitation for hydrological process simulation, based on the observed precipitation of rainfall stations, three remote sensing precipitation products are taken as objects of evaluation. They include TRMM (Tropical Rainfall Measuring Mission) 3B42V7,3B42RT and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data,CHIP). In this paper, the observation data of rainfall stations and IDWP precipitation data interpolated by IDW (Inverse Distance Weighted) are used as true value of precision comparison, and the detection accuracy of remote sensing precipitation products from 2001 to 2010 is evaluated on the time scale (day, month and quarter) and spatial scale in Naoli River Basin.The results of the study indicated that 3B42V7 and CHIP have a high detection accuracy for precipitation, and their CC (correlation coefficient) values are 0.47 and 0.49 respectively in daily time scale. The accuracy of their observation for monthly precipitation is better than that of daily precipitation, and the CC are 0.85 and 0.87 respectively. The multi-year average precipitation at different grid positions in the basin is overestimated by 3B42RT, and its evaluation results are poor at different time scales. For the precipitation intensity range of (0,20], the observed results of 3B42V7 and rainfall station are close to each other. For the precipitation intensity ranges of (0,1) and (50,+∞), 3B42RT and CHIP have overestimated or underestimated the precipitation in different degrees. Based on the above analysis results, 3B42RT can be considered as data that can detect whether precipitation occurs on different spatial positions in the basin. 3B42V7 and CHIP can be applied to flood forecasting and non-point source pollution control in cold regions.

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Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin

Cited by 10 publications

References 53 publications

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Prediction of the Discharge Flow in a Small Hydropower Station without Hydrological Data Based on SWAT Model

Study on applicability of remote sensing precipitation products in hilly-plain-wetland complex area of northeast China

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