Groundwater management and protection has been facilitated by computational modeling of aquifer vulnerability and monitoring aquifers using groundwater sampling. The DRASTIC (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone media, and hydraulic Conductivity) model, an overlay and index GIS model, has been used for groundwater quality assessment because it relies on simple, straightforward methods. Aquifer vulnerability mapping identifies areas with high pollution potential that can be areas for priority management and monitoring. The objectives of this study are to demonstrate how aquifer vulnerability assessment can be achieved using DRASTIC with high resolution data. This includes calibrating DRASTIC weights using a binary classifier calibration method with a genetic algorithm (Bi-GA), identifying areas of high potential aquifer vulnerability, and selecting potential aquifer monitoring sites using spatial statistics. The aquifer vulnerability results from DRASTIC using Bi-GA were validated with a well database of observed nitrate concentrations for a study area in Indiana. The DRASTIC results using Bi-GA showed that approximately 42.2% of nitrate detections >2 ppm are within “High” and “Very high” vulnerability areas (representing 3.4% of study area) as simulated by DRASTIC. Moreover, 53.4% of the nitrate detections were within the “Moderate” vulnerability class (26.9% of study area), and only 4.3% of the nitrate detections were within the “Low” vulnerability class (60.1% of study area). Nitrates >2 ppm were not detected at all within the “Very low” vulnerability class (9.6% of area). “High” and “Very high” vulnerability areas should be regarded as priority areas for groundwater monitoring and efforts to prevent groundwater contamination. This case study suggests that the approach may be applicable to other areas as part of efforts to target groundwater management efforts.

Baseflow is influenced by incoming groundwater to aquifers and is closely related to watershed characteristics. Understanding baseflow characteristics is of great importance to river ecosystems and water management. Baseflow estimation typically depends on the observed streamflow in gauged watersheds, but accurate predictions of streamflow through modeling can also be useful in estimating baseflow. However, uncertainty occurs in the baseflow estimation process when modeling streamflow. Therefore, the purpose of this study is to compare the method that is proposed by Arnold and Allen (Scenario I) to an improved recession prediction method where the alpha factor (baseflow recession coefficient) is recalibrated and is applied to SWAT (Scenario II). Although the differences between the results (NSE, R², RMSE, MAE, d) of Scenarios I and II were small regarding streamflow and recession, the Scenario II method more accurately reflected the recession characteristics than the Scenario I method. Furthermore, the Scenario II method was better in baseflow prediction than for the Scenario I method proposed by Arnold and Allen. Therefore, these outputs pave the way and contribute to an efficient method for water management in watersheds.

The United States is a world leader in the production of maize and other crops and the agricultural success of the country is directly linked to the intensive use of fertilizers and irrigation. However, even in advanced agricultural systems, soils can become degraded over time due to factors such as soil organic matter (SOM) loss and erosion. Here, we use a series of scenario‐based model analyses to show that about one‐third of current annual US. N fertilizer use in maize agriculture is used to compensate for the long‐term loss of soil fertility through erosion and organic matter loss. This leads to over a half billion dollars per year in extra fertilizer supply costs to US farmers. These results highlight the potential to reduce both the input costs and environmental impacts of agriculture through the restoration of SOM in agricultural soils.