2,3- 1Laboratory of Hydraulics and Environmental Modeling (LMHE), National Engineering School of Tunis (ENIT), Tunis, Tunisia (abir.naceur@etudiant-enit.utm.tn)
- 2Department of Agricultural Sciences, University of Naples “Federico II”, 80055 Portici, NA, Italy(gchirico@unina.it)
- 3Department of Civil Engineering, University of Salerno, 84084 Fisciano, SA, Italy(apelosi@unisa.it)
Using a two-stage evaluation framework, this study evaluates five near-real-time (NRT) satellite precipitation products (GPM-IMERG V07, GSMAP V06, GSMAP V07, GSMAP V08, PERSSIAN PDIR NOW) over northern Tunisia. The evaluation is conducted at hourly temporal resolution using complementary point-to-pixel statistical analyses and hydrological modelling experiments.
The first stage consists of a comprehensive statistical assessment based on continuous, categorical, and event-based verification metrics. While continuous and categorical approaches have been widely used in previous studies, event-based evaluation methods have been applied far less frequently; their joint use in this study therefore provides a more comprehensive and complementary assessment of NRT precipitation products.
The second stage involves a rainfall–runoff model to investigate how errors in satellite-derived precipitation propagate through the hydrological system and affect simulated streamflow.
Continuous metrics highlight considerable differences in performance among the five products. GSMaP-V8 and GPM-IMERG demonstrate the most consistent with gauge observations, followed by GSMaP-V6, with Pearson correlation coefficients (PCC) ranging from 0.32 to 0.35 and RMSE values below 0.20 mm. By contrast, GSMaP-V7 shows lower performance. PERSIANN-PDIR-NOW systematically exhibits the weakest accuracy, characterized by low correlation and large error magnitudes.
Categorical verification validates that GPM-IMERG presents the highest rainfall detection capability, achieving probability of detection (POD) values exceeding 0.45 and critical success index (CSI) values above 0.23 for light and moderate rainfall thresholds. Conversely, PERSIANN-PDIR-NOW suffers from frequent false alarms, contributing to decreased categorical skill.
Event-based analyses reveal a general tendency of satellite products to overestimate rainfall event frequency and peak characteristics. GSMaP-V8 exhibits the most balanced and consistent overall performance. GPM-IMERG and GSMaP-V6 better reproduce mean event intensity. GSMaP-V7, however, systematically overestimates event depth, intensity, and peak timing. Moreover, PERSIANN-PDIR-NOW underestimates the mean event precipitation rate, accompanied by a peak rainfall timing shifted earlier relative to observations.
The hydrological evaluation shows that rainfall–runoff modeling propagates precipitation uncertainties non-linearly into simulated streamflow. GPM-IMERG, GSMAP-V7 and GSMAP-V6 yield the most realistic flow simulations (KGE up to 0.68), Other products with comparable rainfall-level statistics nonetheless generate biased streamflow responses
Overall, the findings provide relevant information for improving NRT satellite precipitation algorithms and offer practical guidance for Community stakeholders and practitioners in selecting suitable alternative precipitation datasets in hydrological applications across specific basins, regions, or climatic zones.
Keywords: Hourly rainfall, Near-real-time satellite precipitation products, GPM-IMERG V07, GSMAP V06, GSMAP V07, GSMAP V08, PERSSIAN PDIR NOW, Northern Tunisia
How to cite: Naceur, A., Dakhlaoui, H., Chirico, G. B., and Pelosi, A.: Benchmarking High-Resolution Quasi–Real-Time Satellite Precipitation Products over Northern Tunisia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21546, https://doi.org/10.5194/egusphere-egu26-21546, 2026.
