Evaluation of the efficiency of satellite precipitation data in simulating river flow using the IHACRES Model (Case study: Toroq Dam Watershed)

Document Type : Research Paper

Authors

1 Department of Water Sciences and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.  

2 Department of Water Sciences and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

 
Modeling and predicting the flow rate of rivers is one of the fundamental needs in water resource management. Unfortunately, many watersheds lack precipitation measurement stations. The use of processed satellite data is one of the suitable alternatives to observational data; but the accuracy of estimated precipitation values extracted from satellite data should be compared and validated with ground data in different regions. In the current study, the IHACRES rainfall-runoff model has been used to simulate river flow in the Toroq dam watershed. For this purpose, daily and monthly data from ground stations and MERRA-2 satellite data have been used on a daily and monthly basis in two periods of 9 years and 29 years. The results show that over the study period, the accuracy of estimated precipitation values based on the MERRA-2 satellite has gradually increased compared to the measured values. In the 29-year period, the model validation results are weaker than the 9-year period, which could be due to conditions resulting from land use changes in the watershed and gradual temperature increase in the watershed. With a NSE coefficient of 0.846 obtained in the validation stage of the 9-year period with daily time steps using satellite data, as well as a Pearson correlation coefficient of 0.925, there is a good correlation between the daily-simulated flow using satellite data, and the daily measured flow. Therefore, the use of satellite data produced in the past decade can be a suitable substitute for incomplete precipitation data at ground measurement stations.

Keywords

Main Subjects


Evaluationof the efficiency of satellite precipitation data in simulating river flow using the IHACRES Model (Case study: Toroq Dam Watershed)

EXTENDED ABSTRACT

Introduction

In the realm of water resource management, accurate modeling and prediction of river flow rates are essential tasks, often relying on measured or estimated data, particularly precipitation data. In watersheds lacking sufficient precipitation measurement stations, the utilization of satellite data emerges as a viable alternative due to its extensive spatial and temporal coverage. This study focuses on the application of the IHACRES rainfall-runoff model to simulate river flow in the Toroq dam watershed using ground station and MERRA-2 satellite data over 9-year and 29-year periods.  The findings of this study can be useful in predicting the flow hydrograph due to predicted rainfall, especially in areas with limited data.

Methods

In this study, the IHACRES rainfall-runoff model has been used to simulate river flow in the Toroq dam watershed (with an area of 164.76 square kilometers). daily discharge amounts at the Kertian hydrometric station were used along with ground precipitation and temperature data from the evaporation station and MERRA-2 satellite data within the vicinity of the Kertian hydrometric station located in the Toroq dam watershed over a 29-year period. After ensuring the homogeneity of these data at a 95% confidence level, the performance of the IHACRES model was investigated using ground and satellite precipitation data with simulating watershed discharge in daily and monthly time steps over two time-periods of 9 years (from 23 September 2013 to 22 September 2022) and 29 years (from 1 January 1994 to 22 September 2022). Various model performance indicators, including Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Nash-Sutcliffe Efficiency (NSE), Pearson correlation coefficient (P), Bias and Relative Bias, were employed. Additionally, a Taylor diagram was utilized to provide a comprehensive assessment of the IHACRES model's accuracy in simulating the discharge within the study area.

Results & Discussion

The results show that over the study period, the accuracy of estimated precipitation values based on the MERRA-2 satellite has gradually increased compared to measured values. In the 29-year period, the model validation results are weaker than the 9-year period, which could be due to conditions resulting from changes in land use and gradual temperature increase in the watershed. With a Nash-Sutcliffe Efficiency (NSE) coefficient of 0.8461 obtained in the validation stage of the 9-year period with daily time steps using satellite data, as well as a Pearson correlation coefficient of 0.9250, there is good correlation between the daily simulated flow with satellite data, and the daily measured flow. The IHACRES model has shown effectiveness in simulating low and moderate flows; however, it has demonstrated limited capability in simulating peak river flows. Notably, the model performed better in estimating peak flows on a daily basis over a 9-year period when utilizing MERRA-2 satellite data compared to using ground-based data. After determining the average annual rainfall over this 29-year period, years with rainfall above the period's average were considered as wet years, while years with below-average rainfall were classified as dry years. in wet years, the satellite model underestimated annual precipitation compared to actual rainfall, with the largest discrepancy between observed and satellite rainfall. Over time, the accuracy of the satellite model has improved, with the discrepancy between satellite precipitation values and observations decreasing as we approach the present. In dry years, the average simulated river flow values using MERRA-2 satellite data were closer to the observed river flow averages, whereas in wet years, ground-based data provided better results compared to satellite data. Additionally, in all scenarios, the model tended to overestimate the flow rates.

Conclusions

Although the satellite data showed almost similar results to the ground-based data over the 29-year medium-term period, overall, the model's performance in simulating flow during the 29-year medium-term period was weaker than the 9-year short-term period. In fact, the weaker performance of the IHACRES model in simulating flow over the 29-year period may be due to changes in land use, Climate changes caused by global warming, and changes in the accuracy of satellite precipitation estimates over time. Furthermore, the results showed that the IHACRES model performs better in monthly streamflow simulation compared to daily streamflow simulation, with the best performance of this model in simulating monthly streamflow for the 9-year period. The use of MERRA-2 satellite data on a daily scale for the 9-year period showed a higher Nash-Sutcliffe Efficiency coefficient compared to ground-based data for the same time period. Based on the Taylor diagram and other error evaluation parameters such as RMSE and Pearson correlation coefficient (R), which had better values compared to ground-based data for this period, it can be suggested that using estimated precipitation values based on MERRA-2 satellite data in recent years is a suitable approach for estimating river discharge in areas where station data are insufficient or statistically flawed.

Abdollahi-pour, A., Moazami-Goudarzi, S., & Zakeri-Nayeri, M. (2016). Evaluation of Three Algorithms for the Daily Hydrological Modeling of the Sarough Chai Basin Using the Satellite Precipitation Products and Applying the IHACRES Model. Water Resources Engineering, 8(27), 59-72.(in Persian)
Abushandi, E., & Merkel, B. (2013). Modelling rainfall runoff relations using HEC-HMS and IHACRES for a single rain event in an arid region of Jordan. Water resources management, 27, 2391-2409.
Ahmadi, M., Dadashi Roudbari, A., & Deyrmajai, A. (2020). Runoff Estimation Using IHACRES Model Based on CHIRPS Satellite Data and CMIP5 Models (Case Study: Gorganroud Basin- Aq Qala Area). Journal of Soil And Water Research, 51(3), 659-671.(in Persian )
Ashofteh, P., & Massah Bouani, A. R. (2010). Impact of Climate Change on Maximum Discharges: Case Study of Aidoghmoush Basin, East Azerbaijan. Journal of Water and Soil Science, 14(53), 28-38. (in Persian)
Baratto, J., de Bodas Terassi, P. M., de Beserra de Lima, N. G., & Galvani, E. (2024). Precipitation Anomalies and Trends Estimated via Satellite Rainfall Products in the Cananeia–Iguape Coastal System, Southeast Region of Brazil. Climate, 12(2), 22.
Bloom, S., Takacs, L., Da Silva, A., & Ledvina, D. (1996). Data assimilation using incremental analysis updates. Monthly Weather Review, 124(6), 1256-1271.
Choubin, M., & Bashirgonbad, M. (2023). Evaluation of IHACRES, Conceptual Rainfall Runoff Model and Artificial Neural Network Models in Simulation and Stream flow Prediction in Bakhtiary River Basin. journal of watershed management research, 14(27), 115-122.(in Persian)
Dastorani, M. T., Hajibigloo, M., & Shojaee, H. (2022). Identification of the land use changes on river flooding bed, affective on reservoir water quality (Case study: headwater of Kardeh reservoir). Geography and Development, 20(66), 255-282. https://doi.org/10.22111/j10.22111.2022.6739 (in Persian)
Dawoochund, R., Patra, K., & Swain, J. B. (2017). Adequacy of IHACRES Model on Streamflow Resulting from Landuse Changes in A Mauritius Catchment 22nd International Conference on Hydraulics,Water Resources and Coastal Engineering(HYDRO), Ahmedabad, Gujarat,India.
Dooge, J. (1973). Linear theory of hydrologic systems. Agricultural Research Service, US Department of Agriculture.
Dye, P. J., & Croke, B. F. (2003). Evaluation of streamflow predictions by the IHACRES rainfall-runoff model in two South African catchments. Environmental Modelling & Software, 18(8-9), 705-712.
Farzandi, M., Rezaee-Pazhand, H., & Mirkamandar, B. (2021). Analysis of heat Island and investigation of nonlinear trend of 130-year temperature changes in Mashhad. Journal of Meteorology and Atmospheric Science, 3(4), 375-389. https://doi.org/10.22034/jmas.2021.296910.1148 (in Persian)
Goodarzi, E., dastorani, M., Massah Bouani, A. R., & Talebi, A. (2010). Assessing the Performance of the IHACRES Rainfall-Runoff Model in Predicting Urban Floods (Case Study: Azam-Harat Yazd watershed) First National Conference on Urban Flood Management, Tehran, Iran. (in Persian)
Goodarzi, M., Motamed Vaziri, B., & Mir hoseini, M. (2017). Assessment of IHACRES Model in Surface Run-off Simulation in Climate Change Status: A case study Kan Basin [case report]. Iranian Jornal of Watershed Management Science&Engineering, 11(38), 83-94 (in Persian)
Goodarzi, M., Salahi, B., & Hoseini, A. (2018). Assessment of IHACRES Model in Simulating River Discharge in Urmia Lake Basin. Iranian Jornal of Watershed Management Science&Engineering, 12(43), 1-10.(in Persian)
Goodarzi, M. R., Pooladi, R., & Niazkar, M. (2022). Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran. Sustainability, 14(20), 13051. https://www.mdpi.com/2071-1050/14/20/13051.
Goodarzi, M. R., Sabaghzadeh, M., & Niazkar, M. (2023). Evaluation of winter snow properties effects on spring soil moisture using satellite images in the Northwest of Iran. Acta Geophysica, 1-13. https://doi.org/10.1007/s11600-023-01177-3.
Goodarzi, M. R., Zahabiyoun, B., Massah Bavani, A. R., & Kamal, A. R. (2012). Performance comparison of three hydrological models SWAT, IHACRES and SIMHYD for the runoff simulation of Gharesou basin. Water and Irrigation Management, 2(1), 25-40. https://doi.org/10.22059/jwim.2012.25090. (in Persian)
Guo, B., Xu, T., Yang, Q., Zhang, J., Dai, Z., Deng, Y., & Zou, J. (2023). Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China. Remote Sensing, 15(5), 1373.
Guo, B., Zhang, J., Xu, T., Croke, B., Jakeman, A., Song, Y., Yang, Q., Lei, X., & Liao, W. (2018). Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models. Water, 10(11), 1611.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., & Flemming, J. (2019). The CAMS reanalysis of atmospheric composition. Atmospheric Chemistry and Physics, 19(6), 3515-3556.
Jakeman, A., & Hornberger, G. (1993). How much complexity is warranted in a rainfall‐runoff model? Water resources research, 29(8), 2637-2649.
Kheirfam, H., Mostafazadeh, R., & Sadeghi, S. H. (2013). Daily Discharge Prediction Using IHACRES Model in Some Watersheds of Golestan Province. Watershed Management Research, 4(7), 114-127. (in Perisan)
Kim, T., & Kang, B. (2023). Climate stress impacts on the reservoir inflows: a decision-scaling and IHACRES modeling approach in South Korean basins. Journal of Water and Climate Change, 14(9), 3071-3085.
Lubczynski, M. W., Leblanc, M., & Batelaan, O. (2024). Remote sensing and hydrogeophysics give a new impetus to integrated hydrological models: A review. Journal of Hydrology, 633, 130901. https://doi.org/https://doi.org/10.1016/j.jhydrol.2024.130901
Masoudi, M., & Assar, Z. (2017). Hazard assessment of drought and climate change in Khorasan Razavi Province. Nivar, 41(98-99), 61-72. https://doi.org/10.30467/nivar.2017.51901. (in Persian)
Modaresi, F., Ebrahimi, K., & Araghinejad, S. (2022). Ranking Evaluation of Data-driven and Conceptual Modelling of Rainfall-Runoff Process in Monthly Time Scale. Irrigation and Water Engineering, 12(4), 258-273. https://doi.org/10.22125/iwe.2022.150737 (in Persian)
Mohammadi, B., Safari, M. J. S., & Vazifehkhah, S. (2022). IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling. Scientific Reports, 12(1), 12096. https://doi.org/10.1038/s41598-022-16215-1
Motovilov, Y. G., Gottschalk, L., Engeland, K., & Rodhe, A. (1999). Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 98, 257-277.
Oyerinde, G. T., Fademi, I. O., & Denton, O. A. (2017). Modeling runoff with satellite-based rainfall estimates in the Niger basin. Cogent Food & Agriculture, 3(1), 1363340.
Seifi, S., Khdoashenas, S., & Mosaedi, A. (2018). Evaluation of the Efficiency of the IHACRES Rainfall-Runoff Model in Simulating the Inflow Runoff to the Toroq Reservoir. 7th National Conference on Water Resources Management, Yazd University, Yazd, Iran. (in Persian)
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192.
Tramblay, Y., El Khalki, E. M., Ciabatta, L., Camici, S., Hanich, L., Saidi, M. E. M., Ezzahouani, A., Benaabidate, L., Mahé, G., & Brocca, L. (2023). River runoff estimation with satellite rainfall in Morocco. Hydrological Sciences Journal, 68(3), 474-487.
Velaayati, S. d., & Kamkar yazd nejad, M. (2008). The study of the effects of dams on the quality and quantity of underground water of detrital fan of the lowest area. Journal of Geography and Regional Development, 6(11), 167-185. https://doi.org/10.22067/geography.v6i11.4284 (in Persian)
Zandi, R., Karami, M., & Taheri, J. (2020). The role of land use changes in spatial form of heat islands in Mashhad city. Physical Social Planning, 6(4), 93-104. https://doi.org/10.30473/psp.2020.6590 (in Persian)
Zarei, M., Habibnejad roshan, M., Shahedi, K., & Ghanbarpour, M. R. (2011). Calibration and Evaluation of IHACRES Hydrological Model for Daily Flow Simulation. Journal of Water and Soil (Agricultural Sciences and Technology), 25(1), 104-114. (in Persian )
Zeydalinejad, N., Nassery, H. R., Shakiba, A., & Alijani, F. (2019). Simulation of Taraz-Harkesh River's Flow, Khouzestan Province, Under Climate Change with NEX-GDDP Data Set and IHACRES Rainfall-Runoff Model. Journal of Meteorology and Atmospheric Science, 2(2), 162-178. (in Persian)