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Irrigated agriculture consumes the largest share of available fresh water, and awareness of the spatial distribution and application rates is paramount to a functional and sustainable communal consumptive water use. This remote sensing study leverages surface energy balance fluxes and vegetation indices to classify and map the spatial distribution of irrigated and non-irrigated croplands. The purpose is to introduce a classification scheme applicable across a wide variation in regional climate and inter-growing seasonal precipitation. The rationale for climate and inter-growing seasonal adaptability is founded in the derivation and calibration of the scheme based on the wettest growing season. Therefore, the scheme becomes a more efficient classifier during normal and dry growing seasons. Using empirical distribution functions, two indices are derived from evapotranspiration fluxes and vegetation indices to contrast and classify irrigated croplands from non-irrigated. The synergy of the two indices increases the classification proficiency by adding another classifying layer which re-characterizes misclassified croplands by the base index. The scheme was applied to a region with wide climate variation and to multiple years of growing seasons. The results presented, in cross validation with groundtruth, show an accurate and consistent approach to classify irrigation with overall accuracy of 92.1%, applicable from humid to semi-arid climate, and from dry to normal and wet growing seasons. [ABSTRACT FROM AUTHOR]

Determination of the nature and extent of the connection between groundwater and surface water is of paramount importance to managing water supplies. The development of analyses that detail the surface water-groundwater system may lead to more effective utilization of available water. A tool was developed to help determine the effects of groundwater and surface water interactions. The software tool includes two graphic user interfaces to allow full compatibility with numerical MODFLOW groundwater models. This case study shows the tool, in conjunction with MODFLOW groundwater models and carefully designed scenarios, can successfully calculate the rates of stream-groundwater interactions, thereby providing the basis for designating management areas with the most significant hydrologic impact. This tool can be applied in other regions with similar settings and needs for integrated water management. [ABSTRACT FROM AUTHOR]

Dependence on groundwater throughout the Central Valley of California has resulted in severe and unsustainable groundwater depletion. In this research, we used the Central Valley hydrologic model (CVHM) to model how crop types and drought scenarios impact groundwater storage throughout the Central Valley. Simulations that decreased the abundance of water-intensive crops, such as fruits and nuts, observed the largest recovery in groundwater storage. Two drought scenarios, which respectively decreased surface water availability by 25% and 75%, were also tested. Combined drought and crop type scenarios were explored to understand the relative influence of either surface condition on groundwater storage. The results showed that water-intensive crops can decrease groundwater storage by over 20 m under water deficit conditions, whereas water-saving crops can recover groundwater storage and alleviate the impact of droughts. This research suggests that under drought conditions, groundwater storage will decline substantially to compensate for the need of water-intensive crops due to loss of surface water. Policy and management implications from this research suggest decreasing water demands, such as decreasing crop water intensity, can mitigate groundwater depletion. [ABSTRACT FROM AUTHOR]