Major challenge in rice production is to achieve the goal of enhancing both food production and fertilizer use efficiency. Rice growth simulation model, ORYZA (v3) was used in the present study to evaluate the model under continuous flooded (CF) and alternate wetting and drying (AWD) regimes with different fertilizer nitrogen (N) rates with different N splits using a historical data of past 45 years. The model satisfactorily simulated crop biomass and nitrogen uptake at both irrigation regimes and fertilizers N rates and splits. The yield differences among the years were large due to climate change, but enhanced by N rates. The response of N curves was different at both water regimes. At 0 N rate, the slope for agronomic efficiency (AE) was high which tends to decrease with increase in N rates. With the one split basal application of N, lowest yield was found with high physiological efficiencies (PE), lowest fertilizer recoveries (RE) and lowest agronomic efficiency (AE). For both water applications and fertilizer levels, high yield with high nitrogen uptake, AE, RE and partial factor productivity (PFP) were witnessed high at four split (3:3:3:1), while having low physiological efficiency. The water productivity (irrigation + rainfall) WP_I+R at basal in one N split for AWD at 150 kg N ha⁻¹ was 1.19 kg m⁻³ and for CF was 0.82 kg m⁻³, whereas for 225 kg N ha⁻¹ WP_I+R of AWD was 1.50 kg m⁻³ and 1.14 kg m⁻³ for CF. In general, AWD exhibited high WP_I+R with no rice yield penalty compared to CF. Splitting with the proper amount of fertilizer N resulted in good water productivity and nitrogen efficiencies, could lead to high rice yield.

To reveal the impact of soil moisture distributions on nitrous oxide (N₂O) emissions from wet soils irrigated by sub-surface drip irrigation (SDI) with different surface soil wetting proportions, pot experiments were conducted, with surface irrigation (SI) as a control. Results indicated that irrigation triggered N₂O pulsing effect in all SDI treatments, yet N₂O values reduced with the decrease of surface soil wetting proportions of SDI irrigated soils, and the occurrence times were lagged. The peak N₂O fluxes and the corresponding soil water filled pore space (WFPS), as well as the coefficients of determination (R²) of the exponential function between N₂O fluxes and soil WFPS, decreased with the reduction of surface soil wetting proportions with SDI treatment, and from the central sub-region to the periphery sub-region. The pulse period contributed most to the reduction of N₂O emissions in SDI compared to SI treatments and should be a key period for N₂O emission mitigation. Over the whole experimental period, the area-weighted average cumulative N₂O fluxes from SDI treatments were 82.3–157.3 mg N₂O m⁻² lower than those from SI treatment, with periphery sub-regions of R3 and R4 (radius of 19–27 cm and 28–36 cm from the emitter horizontally) contributing to more than 75.8% of the total N₂O emission mitigation. These results suggest that reducing surface soil wetting proportions or the increments of topsoil WFPS for SDI irrigated soils is a promising strategy for N₂O emission reduction.

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.

Vegetation change in cryosphere-affected mountain basins reflects interacting climate and human pressures but their relative influence remains uncertain in the Upper Indus Basin. The novelty of this study is the integration of satellite vegetation, climate variables, human pressure indicators, residual attribution and diagnostic validation in a data-scarce high-mountain basin. We evaluated growing-season Normalized Difference Vegetation Index dynamics and associated drivers from 2001 to 2023 using trend analysis, correlation, Random Forest diagnostics, Sentinel 2 validation, and residual trend analysis. The results showed widespread greening across 96.59% of the basin, with stronger improvement in the lower and central areas. Significant greening covered 69.94% of the basin, while only 1.55% showed significant browning. Precipitation and temperature were predominantly positive drivers of vegetation change, whereas potential evapotranspiration and solar radiation were mostly negative. Soil moisture played a strong regulatory role along elevation gradients. Residual trend analysis provided approximate and method-dependent estimates of the possible anthropogenic influence on vegetation change at 73.09% and climatic drivers at 26.91% rather than direct causal decomposition. These values are approximate and method-dependent estimates, not direct causal decomposition. The findings highlight human-related greening in lower valleys and climate-controlled vegetation responses in high-mountain areas.