Vulnerability of recently recharged groundwater in principal [corrected] aquifers of the United States to nitrate contamination.

Recently recharged water (defined here as <60 years old) is generally the most vulnerable part of a groundwater resource to nonpoint-source nitrate contamination. Understanding at the appropriate scale the interactions of natural and anthropogenic controlling factors that influence nitrate occurrence in recently recharged groundwater is critical to support best management and policy decisions that are often made at the aquifer to subaquifer scale. New logistic regression models were developed using data from the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) program and National Water Information System for 17 principal aquifers of the U.S. to identify important source, transport, and attenuation factors that control nonpoint source nitrate concentrations greater than relative background levels in recently recharged groundwater and were used to predict the probability of detecting elevated nitrate in areas beyond the sampling network. Results indicate that dissolved oxygen, crops and irrigated cropland, fertilizer application, seasonally high water table, and soil properties that affect infiltration and denitrification are among the most important factors in predicting elevated nitrate concentrations. Important differences in controlling factors and spatial predictions were identified in the principal aquifer and national-scale models and support the conclusion that similar spatial scales are needed between informed groundwater management and model development.

Large-scale climate variability controls on climate, vegetation coverage, lake and groundwater storage in the Lake Urmia watershed using SSA and wavelet analysis.

Lake Urmia has shrunk by 88% since 1995 and is an outstanding example of an environmental tragedy in the Middle East, and the lake plays a critical role in the environment, economics, and society in the north-western part of Iran. It has been hypothesized that the drying of Lake Urmia has caused by climate variation and a climate-derived increase in droughts. Therefore, it is necessary to understand the teleconnections between the interannual to multidecadal climate variability and Lake Urmia because of the tangible implications for water resource management and policy decisions in the region. In this study, we use singular spectrum analysis (SSA), wavelet coherence analysis, and lag correlation calculations to analyze and quantify the impacts of the El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) on hydro-climate variables of precipitation, temperature, lake level, groundwater fluctuations, soil moisture, vegetation coverage, and insolation clearness index in the Lake Urmia watershed. Overall, the results indicate that climate oscillations attributed to the Pacific Ocean (i.e., ENSO and PDO) have a more powerful influence than Atlantic Ocean oscillations (NAO and AMO) on the variability in the water level of Lake Urmia as well as on other hydro-climate variables, except for temperature that appears influenced by the Atlantic Ocean oscillations, particularly AMO. PDO is the first dominant mode of variability in all the hydro-climate variables (63.46% on average), except for the temperature. Overall, the wavelet coherence analysis findings indicate relatively greater PDO influence than ENSO on variability in the precipitation, soil moisture, vegetation coverage, and insolation clearness index. Furthermore, hydro-climate variables in the area have a relatively highest statistical correlation with PDO (0.69 on average, ranging from 0.54 to 0.78) compared to ENSO, NAO, and AMO. Moreover, a moderate coherence between PDO and the groundwater levels in most adjacent aquifers has occurred at the >8-year period from ~1980 to 2015. In general, the hydro-climate variables statistically have a weak lag correlation with NAO (0.19 on average, ranging from 0.13 to 0.24). AMO comprises the first mode variability in temperature (71.77%), and its coherence with temperature is moderate (~0.5) at >16-year period for the time earlier than 2000. The lag correlation between AMO and temperature (0.66) is relatively near strong. These findings have important implications for decision-makers and scientists to improve water resources planning and operations in Lake Urmia under future climate uncertainty.

A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships.

Groundwater recharge indicates the existence of renewable groundwater resources and is therefore an important component in sustainability studies. However, recharge is also one of the least understood, largely because it varies in space and time and is difficult to measure directly. For most studies, only a relatively small number of measurements is available, which hampers a comprehensive understanding of processes driving recharge and the validation of hydrogeological model formulations for small- and large-scale applications. We present a new global recharge dataset encompassing >5000 locations. In order to gain insights into recharge processes, we provide a systematic analysis between the dataset and other global-scale datasets, such as climatic or soil-related parameters. Precipitation rates and seasonality in temperature and precipitation were identified as the most important variables in predicting recharge. The high dependency of recharge on climate indicates its sensitivity to climate change. We also show that vegetation and soil structure have an explanatory power for recharge. Since these conditions can be highly variable, recharge estimates based only on climatic parameters may be misleading. The freely available dataset offers diverse possibilities to study recharge processes from a variety of perspectives. By noting the existing gaps in understanding, we hope to encourage the community to initiate new research into recharge processes and subsequently make recharge data available to improve recharge predictions.

Latin hypercube approach to estimate uncertainty in ground water vulnerability.

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability.