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Volume 18, Issue 64 (5-2024) |
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Satellite Precipitation Algorithms Assessment in Hydrologic Simulation by means of IHACRES Model (Case Study: Sheshpir River Watershed, Fars Province, Iran)
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Vahid Shokri Kuchak *   , Mohammadreaza Sharifi , Saeed Shokri Kuchak |
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Abstract: (924 Views) |
Introduction
Hydrological models are powerful tools for understanding and estimating the hydrological responses of a catchment. They play a crucial role in water resource management, river engineering, flood control, and flood storage. Evaluating the quantitative and qualitative aspects of hydrological models with a systematic approach is essential. The key variables affecting the hydrological cycle include precipitation, temperature, land use, soil texture, elevation, and evapotranspiration. The complex interactions among these variables make the hydrological cycle a relatively complicated process, necessitating the use of hydrological models for evaluation. The most important input parameter in hydrological simulation processes is precipitation. Availability of suitable precipitation data in different times and locations is very important. Currently, most precipitation data is collected from ground-based rain gauge stations or weather radars. However, precipitation monitoring networks, especially in developing countries like Iran, lack good spatial density. Issues like high costs, lack of monitoring equipment in remote areas, inappropriate distribution, short-term monitoring gaps, equipment malfunctions, and user errors are persistent problems for precipitation data users. Hence, the lack of reliable and complete precipitation data is a major challenge in hydrological analysis and forecasting for water resource management. Consequently, monitoring precipitation at short time scales and small spatial scales plays a crucial role in hydrological simulations and ultimately water resource management. In recent years, the use of remote sensing and satellite precipitation estimation algorithms has been developed and is being proposed as an alternative. These technologies have the potential to provide more comprehensive and accurate precipitation data, which can significantly improve the performance of hydrological models and enhance water resource management.
Materials and Methods
This study focuses on hydrological evaluation of four satellite precipitation products TMPA-3R42V7, TMPA-3B42RTV7, PERSIANN and PERSIANN-CDR in the modeling of rainfall runoff of Sheshpir river watershed (954/5 km2 ). The minimum elevation of the basin is 1527 meters and the maximum is 3666 meters above sea level. The hydrometric station is located at 51°43' east longitude and 30°01' north latitude, and has adequate daily discharge data for the river. To achieve the objectives of the research, the conceptual and continuous hydrological model of IHACRES was first extracted using measured ground-based rainfall and temperature information for the period September 22, 2004 through December 31, 2009 using calibrated observational data of Tolombe Hassani hydrometry station and model parameters. IHACRES model was then validated for the period January 1, 2010 through December 31, 2013. IHACRES is an integrated conceptual-metric model for rainfall-runoff simulation, developed through the collaborative efforts of hydrologists from the Integrated Catchment Assessment and Management (ICAM) Centre at the Australian National University and the Centre for Ecology and Hydrology (CEH) of the UK Natural Environment Research Council. IHACRES is a parsimonious, effective, and efficient model that has been applied in a wide range of climatic regions, including dry and semi-arid areas. The evaluation indices used in this study are: Coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), Root mean square error (RMSE), Logarithmic root mean square error (log RMSE).
Results and Discussion
Model calibration results for the Sheshpir and watershed showed acceptable performance, with statistical indices of Nash Sutcliffe Efficiency (NSE), and coefficient of determination (R2 ), 0.8 and 0.8, respectively, and validation results According to the above statistical indices, 0.62, and 0.66, respectively, indicate acceptable performance of the IHACRES hydrological model. Then, the satellite precipitation products that the purpose of this study was introduced as a substitute for stationary mean precipitation, according to statistical indices, results show higher abilities of PERSIANN-CRD in simulation of rainfall-runoff than other algorithms. The coefficient of determination for PERSIANN-CDR algorithm is 0.79 and for TMPA-3B42V7, TMPA-3B42RTV7, PERSIANN satellite algorithms are 0.67, 0.61, 0.1 respectively. Also, according to another statistical index under the name of NS, the evaluation of hydrological models in order to simulate the rainfall-runoff model with PERSIAN-CDR algorithm, is 0.7 in the calibration period and 0.69 in the validation period, which shows the proper performance of the IHACRES rainfall-runoff model with the PERSIANN-CDR precipitation algorithm. The PERSIANN-CDR algorithm has a higher capability than other algorithm; on the other hand, the error correction operation of the PERSIANN-CDR model is successful, but the use of the model with a time delay of TMPA-3B42V7 does not have a significant effect on the model close to the real time. In general, the IHACRES model, coupled with the PERSIANN-CDR satellite precipitation product, demonstrated promising results in simulating the rainfall-runoff processes in the Sheshpir watershed, which is located in a semi-arid climate region. The study highlights the potential of using satellite-based precipitation data as a viable alternative to ground-based observations for hydrological modeling in data-scarce regions.
Conclusion
The results of this research demonstrate the suitability and high capability of the IHACRES model in simulating watershed hydrology and estimating water flow in an integrated manner. The study also reveals the potential of satellite precipitation algorithms to replace ground observational data in cases where such data is scarce or unavailable. One of the key findings of the research is the impact of error adjustment operations on the performance of satellite precipitation products. The study shows that error adjustment in the Persiann-CDR product led to a noticeable improvement in its performance, while similar adjustments in the TMPA-3B42V7 product did not yield such significant results. The evaluation of the four satellite precipitation algorithms – PERSIANN, PERSIANN-CDR, TMPA-3B42V7, and TMPA-3B42RT – in the hydrological modeling context indicates that the PERSIANN-CDR algorithm has the highest capability . among the tested algorithms. In contrast, the performance of the PERSIANN algorithm was found to be very weak, suggesting that its use in hydrological modeling is not recommended. These findings have important implications for water resource management and decision-making. The ability to effectively utilize satellite precipitation data in hydrological modeling can be particularly valuable in regions with limited ground-based observational data. Additionally, the insights gained from the comparative analysis of the satellite precipitation algorithms can guide the selection and application of the most suitable product for specific hydrological modeling purposes. Overall, this research provides a comprehensive understanding of the suitability and performance of the IHACRES model and satellite precipitation algorithms in simulating watershed hydrology and estimating water flow. |
Article number: 3 |
| Keywords: Rainfall Runoff Modeling, Satellite Precipitation Algorithms, Assessment, Sheshpir Watershed of Fars |
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Full-Text [PDF 3100 kb]
(225 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2021/07/21 | Accepted: 2021/11/29 | Published: 2024/06/6
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Shokri Kuchak V, Sharifi M, Shokri Kuchak S. Satellite Precipitation Algorithms Assessment in Hydrologic Simulation by means of IHACRES Model (Case Study: Sheshpir River Watershed, Fars Province, Iran). jwmseir 2024; 18 (64) : 3 URL: http://jwmsei.ir/article-1-1025-en.html
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