Version 1
: Received: 14 May 2024 / Approved: 15 May 2024 / Online: 15 May 2024 (08:35:53 CEST)
How to cite:
Ramahaimandimby, Z.; Randriamaherisoa, A.; Vanclooster, M.; Bielders, C. L. Raingauge vs IMERG Precipitation Data: Balancing Accuracy and Cost for a Reliable Hydrological Observatory in a Tropical River Basin. Preprints2024, 2024051004. https://doi.org/10.20944/preprints202405.1004.v1
Ramahaimandimby, Z.; Randriamaherisoa, A.; Vanclooster, M.; Bielders, C. L. Raingauge vs IMERG Precipitation Data: Balancing Accuracy and Cost for a Reliable Hydrological Observatory in a Tropical River Basin. Preprints 2024, 2024051004. https://doi.org/10.20944/preprints202405.1004.v1
Ramahaimandimby, Z.; Randriamaherisoa, A.; Vanclooster, M.; Bielders, C. L. Raingauge vs IMERG Precipitation Data: Balancing Accuracy and Cost for a Reliable Hydrological Observatory in a Tropical River Basin. Preprints2024, 2024051004. https://doi.org/10.20944/preprints202405.1004.v1
APA Style
Ramahaimandimby, Z., Randriamaherisoa, A., Vanclooster, M., & Bielders, C. L. (2024). Raingauge vs IMERG Precipitation Data: Balancing Accuracy and Cost for a Reliable Hydrological Observatory in a Tropical River Basin. Preprints. https://doi.org/10.20944/preprints202405.1004.v1
Chicago/Turabian Style
Ramahaimandimby, Z., Marnik Vanclooster and Charles L. Bielders. 2024 "Raingauge vs IMERG Precipitation Data: Balancing Accuracy and Cost for a Reliable Hydrological Observatory in a Tropical River Basin" Preprints. https://doi.org/10.20944/preprints202405.1004.v1
Abstract
Reliable hydrological monitoring is crucial for effective water resource management, but establishing observatories in remote areas with extreme weather presents significant challenges. This study aimed at identifying cost-effective methods for streamflow estimation in such environments. We evaluated the performance of the Soil and Water Assessment Tool (SWAT) model using various precipitation data sources: ground-based rain gauge networks with different densities (one-gauge (1RG), two-gauge (2RG), and five-gauge (5RG) configurations) and satellite-derived Integrated Multi-satellite Retrievals for GPM (IMERG) data. The study focused on the Sahafihitry catchment, a 200 km² area in northeastern Madagascar. The results demonstrated that denser rain gauge networks (5RG and 2RG) captured the spatial variability of rainfall more effectively than a single gauge or IMERG data. This translated into superior SWAT model performance. Denser networks achieved higher statistical metrics (R², slope of regression a, Nash-Sutcliffe efficiency NSE, root mean square error RMSE, Kling-Gupta efficiency KGE) indicating a better fit between simulated and observed streamflow. Specifically, 5RG: R² = 0.84, a = 0.92, NSE = 0.83, RMSE = 3.33, KGE = 0.82; 2RG: R² = 0.85, a = 0.95, NSE = 0.83, RMSE = 3.36, KGE = 0.88; 1RG: R² = 0.73, a = 0.80, NSE = 0.72, RMSE = 4.28, KGE = 0.88; IMERG: R² = 0.48, a = 0.63, NSE = 0.37, RMSE = 6.46, KGE = 0.66. Furthermore, rain gauge data outperformed IMERG in simulating both flood events and low-flow periods. While IMERG offers low cost, readily available data, its lower performance introduces significant uncertainty into hydrological modeling. In contrast, the two-gauge network (2RG) achieved satisfactory streamflow simulations and represents the most cost-effective option for establishing reliable observatories within the specific characteristics of this study area.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
