Sustainability is meeting the needs of the present without jeopardizing quality of life for future generations. Adaptation is adjustment of resource utilization and planning by current generations to ensure sustainability. Mitigation, for this study, narrowly refers to damage repair and restoration costs incurred after natural hazard occurrence. Climate is dynamic and ever changing. Recent observed changes in weather patterns identify that drought and intense precipitation, leading to flooding, are more likely to occur in the near future. An example dynamic probabilistic risk assessment (PRA) for flood inundation is created and applied to understand benefits to, and limitations on, PRA for sustainable water resource management. This example addresses the issue of sustainable decision making related to outdated, but historically regulatory compliant, infrastructure. The observed increase in likelihood for large floods means that many assets were designed for inapplicable conditions and are more likely to be damaged in the future. Results from this example PRA demonstrate that it provides for optimizing the degree of sustainability included in resource management and decision making. Sustainability optimization is obtained by balancing likelihood for future mitigation costs against potential cost savings garnered from present-day adaptation.

Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), learns by training and is restricted by the amount and quality of training data. The primary AI-ML risk in water resources is that uncertain data sets will hinder statistical learning to the point where the trained AI will provide spurious predictions and thus limited decision support. Overfitting is a significantly smaller prediction error during training relative to trained model generalization error for an independent validation set (that was not part of training). Training, or statistical learning, involves a tradeoff (the bias–variance tradeoff) between prediction error (or bias) and prediction variability (or variance) which is controlled by model complexity. Increased model complexity decreases prediction bias, increases variance, and increases overfitting possibilities. In contrast, decreased complexity increases prediction error, decreases prediction variability, and reduces tendencies toward overfitting. Better data are the way to make better AI–ML models. With uncertain water resource data sets, there is no quick way to generate improved data. Fortunately, data assimilation (DA) can provide mitigation for data uncertainty risks. The mitigation of uncertain data risks using DA involves a modified bias–variance tradeoff that focuses on increasing solution variability at the expense of increased model bias. Conceptually, increased variability should represent the amount of data and model uncertainty. Uncertainty propagation then produces an ensemble of models and a range of predictions with the target amount of extra variability.

Artificial intelligence (AI) is the effort to automate intellectual tasks normally performed by humans, and it includes machine learning (ML) and deep learning (DL) approaches. AI-ML is based on statistical learning, which tries to learn statistics-based rules for data analyses from known examples of inputs and corresponding outcomes. Data sets that are noisy, include significant uncertainty, and have extreme values hinder statistical learning. ML and DL aquifer recharge predictors are developed to: (1) examine prediction skill when trained using noisy and uncertain data and (2) identify advantages of AI-ML relative to traditional physics- and process-based calculations. Recharge was selected as the learning outcome because it is not observed and is inherently uncertain. A common-sense baseline is developed and implemented to account for uncertainty and noise in AI-ML predictions. The baseline provides a lower goodness-of-fit threshold that identifies when trained AI-ML generates prediction skill and an upper goodness-of-fit threshold above which the AI-ML is learning to reproduce noise and bias in the training data set (and is likely overfitting). Identified advantages for AI-ML (relative to physics- or process-based calculations) are the ability to use dimensionless trends for features and to represent a complex scenario with the same level of effort as for a simple case.

Data assimilation (DA) is used to integrate artificial intelligence including machine learning (AI-ML) and process-based models to produce a dynamic operational water balance tool for groundwater management. The management tool is a three-step calculation. In the first step, a traditional process-based water budget model provides forward model predictions of aquifer storage from meteorological observations, estimates of pumping and diversion discharge, and estimates of recharge. A Kalman filter-based DA approach is the second step and generates updated storage volumes by combining a trained AI-ML model, providing replacement 'measurements' for missing observations, with forward model predictions. The third 'correction' step uses modified recharge and pumping, adjusted to account for the difference between Kalman update storage and forward model predicted storage, in forward model re-simulation to approximate updated storage volume. Use of modified inputs in the correction provides a mass conservative water budget framework based on AI-ML predictions. Pumping and recharge values are uncertain and unobserved in the study region and can be adjusted without contradicting measurements.

Placement of a saltwater disposal well (SDW) within the footprint of a mature oil and gas (O&G) exploration and production region is a bad idea. A long history of O&G exploration means many deep wells (> 5,000 ft below ground surface, bgs) have been installed, and many of these wells were installed prior to modern construction standards, permitting requirements, and data tracking capabilities. Consequently, there are likely to be many poorly constructed and unplugged deep wells whose locations have been forgotten. The purpose of deep well disposal is to segregate the disposal fluids, i.e., harmful waste, from the environment which includes underground sources of drinking water (USDW). Sequestration and containment of harmful wastes is eliminated when there are unknown deep and improperly abandoned wells that pierce containment. This issue of lack of adequate confinement for deep waste disposal is common in Texas because of the prevalence of legacy O&G fields and relatively relaxed permitting requirements for SDWs. This paper demonstrates that locating Texas Class II disposal wells (SDWs) and O&G activities within the same area increases waste containment failure likelihood by 2 times relative to generic SDWs in other states and 100 times relative to Class I hazardous waste (Class IH) injection well systems.

A methodology is presented for predicting impacts and risks to water resources, at the watershed scale, from somewhat unknown future climate. It is then applied to estimate impacts to a semi-arid watershed in Texas. Because all models of water movement and storage in watersheds provide estimates (and best guesses), rather than absolute answers, and because the specifics of future weather are unknown, this approach uses likelihoods (or probabilities) for relative change in magnitude, ∆, between future and historical precipitation, evapotranspiration, storm runoff, and aquifer recharge to evaluate future risk to water availability. Projected (future) climate trends for the study site from climate models are a 3 ˚C increase in average temperature, which means that the potential for evapotranspiration will increase, no significant change in average annual precipitation, which means that there generally will not be more water available for evaporation, and a semi-arid classification from 2011–2100. Future precipitation is projected as unchanged for typical conditions. Consequently, no significant change is estimated for evapotranspiration, runoff, or recharge for average conditions. With expectations for significant temperature increase, an increase in the amount of rainfall is needed to increase evapotranspiration, runoff, and recharge. Increases in rainfall during infrequent large storms are included in the analysis for future conditions, which produces increased water availability during infrequent extreme events but does not change expectations for average conditions.

Climate change and changes to land use and land cover (LULC) both impact water resources, and they have interacting influences on the amount of water available for management and consumption. The framework for the assessment of relative risk to watershed-scale water resources from systemic changes presented in 'Projecting Climate Change Impacts to Watershed Water Resources' is used again to predict combined climate and LULC change impacts from 2011–2100 for the same semi-arid watershed in Texas. In the application, an increase in impervious area from economic development is the LULC change. It generates a 1.1 times increase in average water availability, relative to future climate trends, from increased runoff and decreased evapotranspiration.