Groundwater plays a critical role in sustaining agriculture in California's Central Valley (CV). However, groundwater storage in the CV has been declining by around 3 km³/year over the last several decades, with much larger declines during the 2007–2009 and 2012–2015 droughts. Managed Aquifer Recharge (MAR) can potentially mitigate existing overdraft by recharging excess streamflows (during flood periods) to the aquifers. However, the degree to which the existing CV groundwater overdraft might be mitigated by MAR is unknown. We applied a coupled surface water‐groundwater simulation model to quantify the potential for groundwater overdraft recovery via MAR. To quantify the potential benefit of MAR, we used the coupled surface water‐groundwater model to simulate water allocation scenarios where streamflow above the 90th or 80th percentiles was reallocated to aquifers, subject to constraints on the maximum depth of applied water (0.61 and 3.05 m). Our results show that MAR could recover 9–22% of the existing groundwater overdraft CV‐wide based on a 56‐year simulation (1960–2015). However, the impact of MAR varies strongly among regions. In the southern CV where groundwater depletion is most serious, the contribution of MAR to the overdraft recovery would be small, only 2.7–3.2% in the Tulare basin and 3.2–7.8% in the San Joaquin basin. However, transferring excess winter flow from the northern to the southern CV for MAR would increase the overdraft recovery to 30% in San Joaquin and 62% in Tulare. Our results also indicate that MAR has the potential to supplement summer low flows (52–73%, CV‐wide) and reduce flood peaks.

Gravity Recovery and Climate Experiment (GRACE) data have become a widely used global dataset for evaluating the variability in groundwater storage for the different major aquifers. Moreover, the application of GRACE has been constrained to the local scale due to lower spatial resolution. The current study proposes Random Forest (RF), a recently developed unsupervised machine learning method, to downscale a GRACE-derived groundwater storage anomaly (GWSA) from 1° × 1° to 0.25° × 0.25° in the Northern High Plains aquifer. The RF algorithm integrated GRACE to other satellite-based geospatial and hydro-climatological variables, obtained from the Noah land surface model, to generate a high-resolution GWSA map for the period 2009 to 2016. This RF approach replicates local groundwater variability (the combined effect of climatic and human impacts) with acceptable Pearson correlation (0.58 ~ 0.84), percentage bias (−14.67 ~ 2.85), root mean square error (15.53 ~ 46.69 mm), and Nash-Sutcliffe efficiency (0.58 ~ 0.84). This developed RF model has significant potential to generate finer scale GWSA maps for managing groundwater at both local and regional scales, especially for areas with sparse groundwater monitoring wells.

Recent success in the many applications of information and communications technologies (ICTs) in urban settings largely defines our understanding of sustainable development in the context of smart societies and communities. The footprints of such smart transformation barely imprint in the countryside, however, where rural communities face tremendous barriers, such as the absence of ICT infrastructure, geographic isolation from technological advances, and various social inequalities. The conventional wisdom of smart societies and communities built on well-established broadband access and ICT applications in urban hubs might not be suitable for decentralized rural regions. We contend that a new form of digital divide in the context of recent ICT advancement—a smart divide—is evolving into a new challenge to smart societies and communities. This study aims to characterize the smart divide, an emerging type of social inequality. To address this aim, we conduct a literature review with a focus on the technological aspects of the smart divide in rural regions and identify potential gaps in smart infrastructure and smart applications and services between urban and rural America. The article culminates in a synthesis of potential strategies for bridging the smart divide in rural America. The study is expected to benefit scientists, planners, entrepreneurs, and policymakers interested in smart rural development.

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.