Groundwater Arsenic (As) data are often sparse and location-specific, making them insufficient to represent the heterogeneity in groundwater quality status at unsampled locations. Interpolation techniques have been used to map groundwater As data at unsampled locations. However, the results obtained from these techniques are affected by various inherent and external factors, which lead to uncertainties in the interpolated data. This study was designed to determine the best technique to interpolate groundwater As data. We selected ten interpolation techniques to predict the As concentration in the groundwater resources of Punjab, Pakistan. Two external factors, the spatial extent of the study area and data density, were considered to assess their impact on the performance of interpolation techniques. Our results show that the Inverse Distance Weighting (IDW) and Spline interpolation techniques demonstrate the highest accuracy with the lowest RMSE (13.5 ppb and 16.7 ppb) and MAE (87.8 ppb and 89.5 ppb), respectively, while the Natural Neighbor technique shows the lowest accuracy with the highest RMSE (2508.7 ppb) and MAE (712.1 ppb) to interpolate groundwater As data. When the study area’s extent was modified, IDW showed the best performance, with errors within ±1.5 ppb for 95% of the wells across the study area. While data density has a positive correlation with interpolation accuracy among all techniques, the IDW remained the best method for interpolation. It is therefore concluded that IDW should be used to interpolate groundwater quality data when observed data are sparse and randomly distributed. The utilization of IDW can be useful for As monitoring and management in groundwater resources.

Assessment of soil water balance is essential to understand water dynamics for optimal use of water and fertilizers. The study intended to simulate soil water dynamics in sweet corn production under tropical rainfed conditions. Surface runoff, subsurface leaching, and evapotranspiration are the main components of water balance, especially in tropical environments. Therefore, intensive field experiments and HYDRUS-1D numerical modeling were applied to investigate the water balance components and analyzing water dynamics. The study was carried out in a sweet corn field for two growing seasons under the rainfed conditions at the Malaysian Agricultural Research and Development Institute (MARDI), Serdang, Malaysia. The total water inputs during the first and second seasons were 75.8 cm and 79.7 cm, respectively. Simulated results of evapotranspiration (ET) accounted for 40.7% and 33.1% of total water input during the first and second seasons. Surface runoff accounted for 41% and 28.6% in the first and second season, respectively. Water leaching accounted for 10.6%–26.8% of total water input during both seasons respectively. As rainfall fulfilled the crop water requirement throughout the growing seasons no additional irrigation was required. The overall simulation results validate the HYDRUS-1D as an effective tool to simulate soil water dynamics under rainfed conditions.

Nitrogen loss from agricultural fields results in contamination of ground and surface water resources due to leaching and runoff, respectively. Nitrogen transport dynamics vary significantly among agricultural fields of different climates, especially in the tropical climate. This study intended to evaluate the rainfall impact on nitrogen distribution and losses under tropical rain-fed conditions. The study was carried out in a sweet corn field for two growing seasons at the Malaysian Agricultural Research and Development Institute (MARDI) research field. The HYDRUS-1D numerical model was used to simulate nitrogen transport dynamics in this study. The observed nitrogen concentrations were used for calibration and validation of the model. Total nitrogen input to sweet corn was 120 kg/ha for both seasons. Nitrogen losses through surface runoff and leaching were dominating pathways. Surface runoff accounted for 35.3% and 22.2% of total nitrogen input during the first and second seasons, respectively. The leaching loss at 60 cm depth accounted for 4.0% (first season) and 18.5% (second season). The crop N uptake was 37.5% and 24.9% during the first and second seasons, respectively. Nitrate was the dominant form of N uptake by the crop that accounted for 83.6% (first season) and 78.5% (second season). The HYDRUS-1D simulation results of nitrogen concentrations and fluxes were found in good agreement with observed data. The overall results of simulation justified the HYDRUS-1D for improved fertilizer use in the tropical climate.