SignificanceWe increasingly rely on global models to project impacts of humans and climate on water resources. How reliable are these models? While past model intercomparison projects focused on water fluxes, we provide here the first comprehensive comparison of land total water storage trends from seven global models to trends from Gravity Recovery and Climate Experiment (GRACE) satellites, which have been likened to giant weighing scales in the sky. The models underestimate the large decadal (2002–2014) trends in water storage relative to GRACE satellites, both decreasing trends related to human intervention and climate and increasing trends related primarily to climate variations. The poor agreement between models and GRACE underscores the challenges remaining for global models to capture human or climate impacts on global water storage trends.

International audienceTexas experienced the most extreme one-year drought on record in 2011 with precipitation at 40% of long-term mean and agricultural losses of ~$7.6 billion. We assess the value of Gravity Recovery and Climate Experiment (GRACE) satellite-derived total water storage (TWS) change as an alternative remote sensing-based drought indicator, independent of traditional drought indicators based on in situ monitoring. GRACE shows depletion in TWS of 62.3 ± 17.7 km3 during the 2011 drought. Large uncertainties in simulated soil moisture storage depletion (14-83 km3) from six land surface models indicate that GRACE TWS is a more reliable drought indicator than disaggregated soil moisture or groundwater storage. Groundwater use and groundwater level data indicate that depletion is dominated by changes in soil moisture storage, consistent with high correlation between GRACE TWS and the Palmer Drought Severity Index. GRACE provides a valuable tool for monitoring statewide water storage depletion, linking meteorological and hydrological droughts

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Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). Big Data are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and social media have contributed to the proliferation of Big Data in many EWM fields, such as weather forecasting, disaster management, smart water and energy management systems, and remote sensing. Big Data brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics. ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn from data. ML may help unlock the power of Big Data if properly integrated with data analytics. Recent breakthroughs in AI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features from data, with better predictive performance and less human intervention. Collectively Big Data and ML techniques have shown great potential for data-driven decision making, scientific discovery, and process optimization. These technological advances may greatly benefit EWM, especially because (1) many EWM applications (e.g. early flood warning) require the capability to extract useful information from a large amount of data in autonomous manner and in real time, (2) EWM researches have become highly multidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge about many EWM processes is still incomplete, but which may now be complemented through data-driven discovery. A large number of applications on Big Data and ML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in Big Data and ML, (3) provide a systematic review of current applications, and finally (4) discuss major issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM, with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWM research.

We present a new approach for modelling macrodispersivity in spatially variable velocity fields, such as exist in geologically heterogeneous formations. Considering a spectral representation of the velocity, it is recognized that numerical models usually capture low-wavenumber effects, while the large-wavenumber effects, associated with subgrid block variability, are suppressed. While this suppression is avoidable if the heterogeneity is captured at minute detail, that goal is impossible to achieve in all but the most trivial cases. Representing the effects of the suppressed variability in the models is made possible using the proposed concept of block-effective macrodispersivity. A tensor is developed, which we refer to as the block-effective macrodispersivity tensor, whose terms are functions of the characteristic length scales of heterogeneity, as well as the length scales of the model's homogenized areas, or numerical grid blocks. Closed-form expressions are developed for small variability in the log-conductivity and unidirectional mean flow, and are tested numerically. The use of the block-effective macrodispersivities allows conditioning of the velocity field on the measurements on the one hand, while accounting for the effects of unmodelled heterogeneity on the other, in a numerically reasonable set-up. It is shown that the effects of the grid scale are similar to those of the plume scale in terms of filtering out the effects of portions of the velocity spectrum. Hence it is easy to expand the concept of the block-effective dispersivity to account for the scale of the solute body and the pore-scale dispersion.

[1] The purpose of this work is to investigate the feasibility of downscaling Gravity Recovery and Climate Experiment (GRACE) satellite data for predicting groundwater level changes and, thus, enhancing current capability for sustainable water resources management. In many parts of the world, water management decisions are traditionally informed by in situ observation networks which, unfortunately, have seen a decline in coverage in recent years. Since its launch, GRACE has provided terrestrial water storage change (DTWS) data at global and regional scales. The application of GRACE data for local-scale groundwater resources management has been limited because of uncertainties inherent in GRACE data and difficulties in disaggregating various TWS components. In this work, artificial neural network (ANN) models are developed to predict groundwater level changes directly by using a gridded GRACE product and other publicly available hydrometeorological data sets. As a feasibility study, ensemble ANN models are used to predict monthly and seasonal water level changes for several wells located in different regions across the US. Results indicate that GRACE data play a modest but significantly role in the performance of ANN ensembles, especially when the cyclic pattern of groundwater hydrograph is disrupted by extreme climate events, such as the recent Midwest droughts. The statistical downscaling approach taken here may be readily integrated into local water resources planning activities.

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Climate extremes have and will continue to cause severe damages to buildings and natural environments around the world. A full knowledge of the probability of the climate extremes is important for the management and mitigation of natural hazards. Based on Mann-Kendall trend test and copulas, this study investigated the characteristics of precipitation extremes as well as their implications in southwestern China (Yunnan, Guangxi and Guizhou Province), through analyzing the changing trends and probabilistic characteristics of six indices, including the consecutive dry days, consecutive wet days, annual total wet day precipitation, heavy precipitation days (R25), max 5 day precipitation amount (Rx5) and the rainy days (RDs). Results showed that the study area had generally become drier (regional mean annual precipitation decreased by 11.4 mm per decade) and experienced enhanced precipitation extremes in the past 60 years. Relatively higher risk of drought in Yuanan and flood in Guangxi was observed, respectively. However, the changing trends of the precipitation extremes were not spatially uniform: increasing risk of extreme wet events for Guangxi and Guizhou, and increasing probability of concurrent extreme wet and dry events for Yunnan. Meanwhile, trend analyses of the 10 year return levels of the selected indices implied that the severity of droughts decreased in Yunnan but increased significantly in Guangxi and Guizhou, and the severity of floods increased in Yunnan and Guangxi in the past decades. Hence, the policy-makers need to be aware of the different characterizations and the spatial heterogeneity of the precipitation extremes.