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The study sought to understand the relationships between meteorological and groundwater droughts on water levels and spring discharges in Edwards Aquifer, Texas. Standardized Precipitation Index (SPI)‐styled Standardized Groundwater Index (SGI) was used to quantify groundwater droughts. SGI time series signal was delayed and damped, while SPI was volatile. SGI values correlated well with SPI values that were observed five to eight months ago. Dynamic regression models with lagged SPI terms and autoregressive integrated moving average errors indicated a statistically significant yet weak relationship between Lag‐1 SPI and SGI. The utility of SPI for groundwater drought forecasting was minimal in this aquifer. Nonseasonal and seasonal autoregressive terms played an important role in forecasting SGI and highlighted the need for long‐term, high‐resolution monitoring to properly characterize groundwater droughts. Spring flows exhibited stronger and quicker responses to meteorological droughts than changes in storage. In aquifers with spring discharges, groundwater monitoring programs must make efforts to inventory and monitor them. Groundwater drought contingency measures can be initiated using SPI but this indicator is perhaps inappropriate to remove groundwater drought restrictions.

Abstract:The Ogallala Aquifer is the only reliable source of freshwater in the Southern High Plains (SHP) and is used extensively to build a strong agricultural economy with a significant impact on global food security. Groundwater models capable of simulating human-hydrologic-climate interactions are crucial to guide future water management and policy planning endeavors in this water stressed region. A well-defined conceptual model is a necessary first-step in that direction. Conceptual modeling should not be limited to compiling necessary datasets but must also focus on generating critical insights pertaining to humanclimate-aquifer interactions especially when the emphasis is on guiding future policy. Model integration and the feasibility of coupling available tools and techniques must be explored to fill-in critical data gaps and capture interactions with a high degree of fidelity. A conceptual modeling framework built on this premise was applied to guide an on-going regional-scale groundwater modeling study in the SHP. The paucity of groundwater production data was identified as a major limiting factor. A linked Decision Support System for Agro-Technology Transfer (DSSAT) model with MODFLOW is expected to be useful in obtaining groundwater production estimates through detailed crop modeling. The time to recharge is long (decades to centuries) over most of the SHP. As such, the coupling of watershed and groundwater models is perhaps not warranted. Baseflow separation indicated that surface water-groundwater interactions have diminished over the last six decades due to declining water tables. While groundwater withdrawals generally increased during droughts, the aquifer also buffered climatic influences at some locations.

The performance of four tree-based classification techniques-classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths-an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.Water 2020, 12, 1023 2 of 27 worldwide [5,6]. Intensification of agricultural activities for both food and energy will further increase the risks of nitrate contamination in aquifers across the world [7][8][9].Nitrate is mobile and fairly recalcitrant, especially in shallow groundwater systems that typically tend to be under oxidizing conditions. Nitrate exhibits the ability to spread over large areas and cannot be treated in-situ using conventional plume scale treatment technologies [10]. Therefore, individual homeowners are often required to install costly point-of-use treatment systems to mitigate nitrate risks arising from the ingestion of contaminated groundwater [11,12]. However, as nitrate is colorless and odorless, many people do not realize the risk of nitrate contamination and are unwittingly exposed to elevated levels of nitrate over long periods of time [13]. Therefore, nitrate contamination must be prevented through proper land management practices. Additionally, areas with a high susceptibility to nitrate pollution must be carefully delineated, with the goal of increasing public awareness regarding elevated health risks arising from nitrate exposures. Such an effort is also useful to prioritize monitoring activities and ensure that the limited fiscal and logistic resources are being used in a prudent manner.Mapping the susceptibility of aquifers to nitrate contamination is an essential step in mitigating and managing nitrate contamination. Multi-criteria decision making (MCDM) methods, such as DRASTIC [14], have been widely used to map aquifer vulnerabili...

Seasonal and cyclic trends in nutrient concentrations at four agricultural drainage ditches were assessed using a dataset generated from a multivariate, multiscale, multiyear water quality monitoring effort in the agriculturally dominant Lower Rio Grande Valley (LRGV) River Watershed in South Texas. An innovative bootstrap sampling-based power analysis procedure was developed to evaluate the ability of Mann-Whitney and Noether tests to discern trends and to guide future monitoring efforts. The Mann-Whitney U test was able to detect significant changes between summer and winter nutrient concentrations at sites with lower depths and unimpeded flows. Pollutant dilution, non-agricultural loadings, and in-channel flow structures (weirs) masked the effects of seasonality. The detection of cyclical trends using the Noether test was highest in the presence of vegetation mainly for total phosphorus and oxidized nitrogen (nitrite + nitrate) compared to dissolved phosphorus and reduced nitrogen (total Kjeldahl nitrogen-TKN). Prospective power analysis indicated that while increased monitoring can lead to higher statistical power, the effect size (i.e., the total number of trend sequences within a time-series) had a greater influence on the Noether test. Both Mann-Whitney and Noether tests provide complementary information on seasonal and cyclic behavior of pollutant concentrations and are affected by different processes. The results from these statistical tests when evaluated in the context of flow, vegetation, and in-channel hydraulic alterations can help guide future data collection and monitoring efforts. The study highlights the need for long-term monitoring of agricultural drainage ditches to properly discern seasonal and cyclical trends.