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Physical agricultural drought indices generally use soil moisture to represent root‐zone water availability (RZWA). The uncertainty in root‐zone properties, especially in deep‐rooting regions, may lead to significant uncertainty in the results. This study adopted a conceptual model that requires no specific root‐zone properties to model the RZWA. A RZWA‐based drought index (AgDI) was then developed by standardizing the root‐zone water deficit. A comparison over 20 catchments with different terrain and vegetation demonstrated the effectiveness of AgDI in characterizing agricultural droughts, with the correlations between vegetation indices (normalized difference vegetation index [NDVI] and gross primary productivity [GPP]) and AgDI (mean ~0.54) being significantly higher than those between the same vegetation indices and the Palmer Drought Severity Index (PDSI, ~0.22), Standardized Precipitation‐Evapotranspiration Index (SPEI, ~0.33), and Standardized Soil moisture Index (SSI, ~0.37). Compared to the SSI, the AgDI was not equally advantageous everywhere, but it generally showed better performance in deep‐rooting regions. Plain Language Summary: Agricultural drought can adversely affect agroecosystems; therefore its detection and quantification are essential for hazard mitigation. Limited root‐zone water availability (RZWA) is the direct cause of agricultural drought. Generally, physical agricultural drought indices are based on the soil moisture of several soil layers (e.g., 0–100 cm). However, these methods may result in uncertainties because the soil moisture in such soil profiles may not fully reflect the RZWA, especially in deep‐rooting ecosystems. In addition, estimation of RZWA is challenging because of the uncertainty in root‐zone properties (e.g., roots distribution and hydraulic properties). To overcome these known limitations, we developed a conceptual model that does not require root zone properties to model the RZWA, using which a new agricultural drought index (AgDI) was then developed by standardizing the root zone water deficit. A comparison study over 20 catchments with different terrain and vegetation properties showed that the AgDI is more effective in characterizing agricultural drought than the PDSI, SPEI and SSI. Compared to the soil moisture based SSI, the AgDI is not advantageous everywhere but generally exhibits better performance in deep‐rooting regions. We also found that the meteorological droughts are the main triggers of, but do not always lead to, agricultural droughts. Key Points: RZWA was inferred by using a conceptual model needing no specified root‐zone propertiesOur RZWA‐based drought index (AgDI) is effective in characterizing agricultural droughtsThe AgDI generally performs better in deep‐rooting regions than soil moisture based indices [ABSTRACT FROM AUTHOR]

Quantification of the predictive uncertainty of subsurface models has long been investigated. The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance. Not only are these procedures computationally expensive, but they also have issues in maintaining geological model constraints during the calibration step. Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step. In general, DSI approaches seek to establish a statistical relationship between the observed and forecast variables, as well as to quantify the predictive uncertainty of the forecast variables, by using an ensemble of uncalibrated prior models. Existing DSI approaches all require a number of complex transformation and mapping operations, which may deter their widespread use. In this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results. Our LDFA framework is demonstrated using two supervised learning algorithms, artificial neural network (ANN) and support vector regression (SVR), on two representative examples from reservoir engineering and geological carbon storage. Results suggest that our approach provides accurate forecast results (e.g., future oil production rate or cumulative injected CO 2 ) and reasonable predictive uncertainty intervals. Our framework is generic and may be applied to other surface and subsurface problems. [ABSTRACT FROM AUTHOR]

Global assessment of the spatiotemporal variability in terrestrial total water storage anomalies (TWSA) in response to hydrologic extremes is critical for water resources management. Using TWSA derived from the gravity recovery and climate experiment (GRACE) satellites, this study systematically assessed the skill of the TWSA-climatology (TC) approach and breakpoint (BP) detection method for identifying large-scale hydrologic extremes. The TC approach calculates standardized anomalies by using the mean and standard deviation of the GRACE TWSA corresponding to each month. In the BP detection method, the empirical mode decomposition (EMD) is first applied to identify the mean return period of TWSA extremes, and then a statistical procedure is used to identify the actual occurrence times of abrupt changes (i.e., BPs) in TWSA. Both detection methods were demonstrated on basin-averaged TWSA time series for the world's 35 largest river basins. A nonlinear event coincidence analysis measure was applied to cross-examine abrupt changes detected by these methods with those detected by the Standardized Precipitation Index (SPI). Results show that our EMD-assisted BP procedure is a promising tool for identifying hydrologic extremes using GRACE TWSA data. Abrupt changes detected by the BP method coincide well with those of the SPI anomalies and with documented hydrologic extreme events. Event timings obtained by the TC method were ambiguous for a number of river basins studied, probably because the GRACE data length is too short to derive long-term climatology at this time. The BP approach demonstrates a robust wet-dry anomaly detection capability, which will be important for applications with the upcoming GRACE Follow-On mission. [ABSTRACT FROM AUTHOR]

Summary: Regional groundwater models are increasingly used for short- and long-term water resources planning, in anticipation of greater climate variability and population growth. However, many of these models are subject to structural and parametric uncertainties because of the lack of field measurements. In recent years, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has shown great potential for tracking total water storage changes over large regions. The pattern of groundwater storage changes inferred from GRACE may be incorporated as an additional regularization mechanism for calibrating regional groundwater models. Motivated by the demonstrated success of GRACE for monitoring groundwater storage changes, this study explores the combined use of in situ water level measurements and GRACE-derived groundwater storage changes for calibrating regional groundwater models. The resulting optimization problem is solved using an evolutionary optimization algorithm. We demonstrate the proposed calibration strategy for the hydraulically connected Edwards–Trinity Plateau and Pecos Valley aquifers (total area 115,000km2) in west Texas. Monthly GRACE data from 2002 to 2007 were used to recalibrate a regional groundwater model developed for the area. Our results indicate that (i) calibration using in situ data alone may yield multiple plausible solutions, a phenomenon well known to hydrologists; and (ii) GRACE data helped further constrain model parameters over the study period and, thus, may be continuously assimilated, among other sources of data, for enhancing existing regional groundwater models. [ABSTRACT FROM AUTHOR]

Abstract: Groundwater models are critical decision support tools for water resources management and environmental remediation. However, limitations in site characterization data and conceptual models can adversely affect the reliability of groundwater models. Therefore, there is a strong need for continuous model uncertainty reduction. Ensemble filters have recently emerged as promising high-dimensional data assimilation techniques. Two general categories of ensemble filters exist in the literature: perturbation-based and deterministic. Deterministic ensemble filters have been extensively studied for their better performance and robustness in assimilating oceanographic and atmospheric data. In hydrogeology, while a number of previous studies demonstrated the usefulness of the perturbation-based ensemble Kalman filter (EnKF) for joint parameter and state estimation, there have been few systematic studies investigating the performance of deterministic ensemble filters. This paper presents a comparative study of four commonly used deterministic ensemble filters for sequentially estimating the hydraulic conductivity parameter in low- and moderately high-dimensional groundwater models. The performance of the filters is assessed on the basis of twin experiments in which the true hydraulic conductivity field is assumed known. The test results indicate that the deterministic ensemble Kalman filter (DEnKF) is the most robust filter and achieves the best performance at relatively small ensemble sizes. Deterministic ensemble filters often make use of covariance inflation and localization to stabilize filter performance. Sensitivity studies demonstrate the effects of covariance inflation, localization, observation density, and conditioning on filter performance. [Copyright &y& Elsevier]

Summary Karst landform represents about 10% of the continental area and plays key roles in water supplies for almost a quarter of the global population. Knowledge of ecohydrological responses of karst landform to climate change is critical for both water resources management and ecological protection in these regions. This study investigated the effects of karst landform on the elasticity of actual evapotranspiration (derived by the Budyko equation), estimated the contribution of climate change and evaluated the implications, on the basis of 13 typical catchments that have different karst landform coverages in southwest China. Catchment properties, including the vegetation coverage, portion of karst landform (POK), drainage area, surface roughness, mean topographic wetness index, mean slope, and mean aspect, were selected to test the influencing factors for the elasticity of actual evapotranspiration. Results indicate that POK is the most influencing factor for the elasticity of actual evapotranspiration in this region. Moreover, the actual evapotranspiration in karst catchments is more sensitive to precipitation change and less sensitive to the potential evapotranspiration change than that in the non-karst catchments. On the other hand, the contribution of climate change to actual evapotranspiration was generally negative in this region. Furthermore, relatively large negative contributions mainly occurred in the karst-dominated catchments, suggesting that the karst catchments were exposed to higher degradation stress brought by the climate change than that in non-karst catchments. [ABSTRACT FROM AUTHOR]