Quantitative information on regional cropland runoff is important for sustainable agricultural water quantity and quality management. This study combined the Soil Conservation Service Curve Number (SCS-CN) method and geostatistical approaches to quantify long-term (1990–2013) changes and regional spatial variations of cropland runoff in China. Estimated CN values from 17 cropland study sites across China showed reasonable agreement with default values from the National Engineering Handbook (R2 = 0.76, n = 17). Among four commonly used geostatistical interpolation methods, the inverse distance weighting (IDW) method achieved the highest accuracy (R2 = 0.67, n = 209) for prediction of cropland runoff. Using default CN values and the IDW method, estimated national annual cropland runoff volume and runoff depth in 1990–2013 were 253 ± 25 km3 yr−1 and 182 ± 15 mm yr−1, respectively. Estimated cropland runoff depth gradually increased from the drier northwest inland region to the wetter southeast coastal region (range: 2–1375 mm yr−1). Regionally, eastern, central and southern China accounted for 39% of the cultivated area and 53% of the irrigated land area and contributed to 68% of the national cropland runoff volume. In contrast, northwestern, northern, southwestern and northeastern China accounted for 61% of the cultivated area and 47% of the irrigated land area and contributed to 32% of the runoff volume. Rainfall was the main source (72%) of cropland runoff for the entire nation, while irrigation became the main source of cropland runoff in drier regions (northwestern and southwestern China). Over the 24-year study period, estimated cropland runoff depth showed no significant trends, whereas cropland runoff volume and irrigation-contributed percentages decreased by 7% and 35%, respectively, owing to implementation of water-saving irrigation technologies. To reduce excessive runoff and increase water utilization efficiencies, regionally specific water management strategies should be further promoted. As the first long-term national estimate of cropland runoff in China, this study provides a simple framework for estimating regional cropland runoff depth and volume, providing critical information for guiding developments of management practices to mitigate agricultural nonpoint source pollution, soil erosion and water scarcity.

Legacy nitrogen (N) is recognized as a primary cause for the apparent failure of watershed N management strategies to achieve desired water quality goals. The ELEMeNT-N (Exploration of Long‐Term Nutrient Trajectories for Nitrogen) model, a parsimonious and process-based model, has the potential to effectively distinguish biogeochemical and hydrological legacy effects. However, ELEMeNT-N is limited in its ability to address long-term legacy N dynamics as it ignores temporal changes in soil organic N (SON) mineralization rates. This work represents the first use and modification of ELEMeNT-N to quantify legacy effects and capture spatial heterogeneity of legacy N accumulation in China. An exponential function based on mean annual temperature was employed to estimate yearly changes in SON mineralization rate. Based on a 31-year water quality record (1980-2010), the modified model achieved higher efficiency metrics for riverine N flux in the Yongan watershed in eastern China than the original model (Nash-Sutcliff coefficient: 0.87 vs. 0.72 and R2: 0.80 vs. 0.71). The modified ELEMeNT-N results suggested that the riverine N flux mainly originated from the legacy N pool (88.2%). The mean overall N lag time was 11.9 years (95% CIs: 8.3-21.3), of which biogeochemical lag time was 9.7 years (6.3-18.4) and hydrological lag time was 2.2 years (2.0-3.0). Legacy N accumulation showed considerable spatial heterogeneity, with 219-239 kg N ha-1 accumulated in soil and 143-188 kg N ha-1 accumulated in groundwater. The ELEMeNT-N model was an effective tool for addressing legacy N dynamics, and the modified form proposed here enhanced its ability to capture SON mineralization dynamics, thereby providing managers with critical information to optimize watershed N pollution control strategies.