Optimizing Drainage Design to Reduce Nitrogen Losses in Rice Field Under Extreme Rainfall: Coupling Log-Pearson Type III and DRAINMOD-N II

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R2 values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L−1, and RMSE of 0.14 and 0.91 mg L−1 for NO3−-N and NH4+-N during calibration, and R2 values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L−1, and RMSE of 0.10 and 0.34 mg L−1 during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO3−-N) and ammonium-nitrogen (NH4+-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO3−-N and NH4+-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO3−-N and NH4+-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions.