Accurate rainfall information is essential for rainfall–runoff modeling in monsoon-influenced basins, where pronounced spatial variability and limited gauge coverage introduce significant uncertainty. Satellite precipitation products provide spatially continuous estimates but are affected by systematic biases, and improvements in statistical rainfall accuracy do not necessarily translate into hydrologically consistent model forcing. This study interpreted satellite rainfall bias correction through a rainfall–runoff framework in the Phetchaburi River Basin, Thailand, using the DWCM-AgWU hydrological model. Simulations were driven by gauge observations and multiple satellite-based rainfall products (GSMaP, CMORPH, CHIRPS, and PERSIANN-CCS), with bias correction applied using Linear Scaling and Quantile Mapping under rainfall-specific calibration. Results showed that bias correction significantly modified rainfall characteristics in distinct ways. Linear Scaling primarily preserved temporal and spatial structure while adjusting rainfall magnitude, whereas Quantile Mapping improved the distributional representation of rainfall intensities. These differences propagated through hydrological processes, leading to systematic variations in runoff responses across multiple metrics, including water balance consistency, peak magnitude, and timing errors. This suggests that each method performs differently depending on the aspect of system response. Rather than identifying a universally optimal method, the findings highlight trade-offs in how rainfall correction strategies influence hydrological system response. Runoff behavior is interpreted as a process-level indicator of rainfall representation, emphasizing that hydrological consistency depends not only on rainfall accuracy but also on its interaction with model structure. These results suggest a process-oriented perspective for interpreting the role of satellite rainfall products in regulated and monsoon-affected basins.

As climate change worsens, irrigation modernization has become critical for better water distribution and maintaining rice production in the face of increasing water constraints. However, there remains a gap in quantification regarding the environmental trade-offs between pump-managed and gravity-based irrigation systems, especially in integrated assessments that relate economic performance, carbon emissions, and water use. This study used an integrated framework of water productivity (WP), consumptive water footprint (WF), carbon footprint, and eco-efficiency to compare gravity-based and pump-managed systems in the Don Chedi Operation and Maintenance Project, Thailand, from 2021 to 2023. The results showed no significant differences in WP and WF between systems. WP averaged 0.39 kg m⁻³ during the wet seasons and 0.54 kg m⁻³ during the dry seasons, while the WF averaged 2517 m³ t⁻¹ and 1854 m³ t⁻¹, respectively. These findings indicate that pump-managed irrigation enhanced operational flexibility and yield stability but did not substantially improve water use efficiency. However, compared with the gravity-based system, the pump-managed system produced much greater carbon emissions, with total carbon footprints ranging from 1.252 to 1.333 tCO₂eq t⁻¹, or five times higher in the irrigation process. Eco-efficiency metrics rose by up to 8.11% despite this environmental burden, indicating enhanced economic resilience amid fluctuating water conditions. These results show a recurring trade-off between low-carbon agricultural development and irrigation modernization. The study therefore emphasizes the importance of integrating renewable energy and low-carbon technologies into pump-based irrigation systems to support climate-resilient and sustainable agricultural transitions.