Anthropogenic Drought: Definition, Challenges, and Opportunities

Traditional, mainstream definitions of drought describe it as deficit in water-related variables or water-dependent activities (e.g., precipitation, soil moisture, surface and groundwater storage, and irrigation) due to natural variabilities that are out of the control of local decision-makers. Here, we argue that within coupled human-water systems, drought must be defined and understood as a process as opposed to a product to help better frame and describe the complex and interrelated dynamics of both natural and human-induced changes that define anthropogenic drought as a compound multidimensional and multiscale phenomenon, governed by the combination of natural water variability, climate change, human decisions and activities, and altered micro-climate conditions due to changes in land and water management. This definition considers the full spectrum of dynamic feedbacks and processes (e.g., land-atmosphere interactions and water and energy balance) within human-nature systems that drive the development of anthropogenic drought. This process magnifies the water supply demand gap and can lead to water bankruptcy, which will become more rampant around the globe in the coming decades due to continuously growing water demands under compounding effects of climate change and global environmental degradation. This challenge has de facto implications for both short-term and long-term water resources planning and management, water governance, and policymaking. Herein, after a brief overview of the anthropogenic drought concept and its examples, we discuss existing research gaps and opportunities for better understanding, modeling, and management of this phenomenon

Multihazard Scenarios for Analysis of Compound Extreme Events

Compound extremes correspond to events with multiple concurrent or consecutive drivers (e.g., ocean and fluvial flooding, drought, and heat waves) leading to substantial impacts such as infrastructure failure. In many risk assessment and design applications, however, multihazard scenarios of extremes and compound events are ignored. In this paper, we review the existing multivariate design and hazard scenario concepts and introduce a novel copula-based weighted average threshold scenario for an expected event with multiple drivers. The model can be used for obtaining multihazard design and risk assessment scenarios and their corresponding likelihoods. The proposed model offers uncertainty ranges of most likely compound hazards using Bayesian inference. We show that the uncertainty ranges of design quantiles might be large and may differ significantly from one copula model to the other. We also demonstrate that the choice of marginal and copula functions may profoundly impact the multihazard design values. A robust analysis should account for these uncertainties within and between multivariate models that translate into multihazard design quantiles

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) Optimization Framework

Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA

Coevolution of Machine Learning and Process-Based Modelling to Revolutionize Earth and Environmental Sciences: A Perspective

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co-creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high-dimensional mapping, while remaining faithful to process-based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability

Climate-Informed Environmental Inflows to Revive a Drying Lake Facing Meteorological and Anthropogenic Droughts

The rapid shrinkage of Lake Urmia, one of the world’s largest saline lakes located in northwestern Iran, is a tragic wake-up call to revisit the principles of water resources management based on the socio-economic and environmental dimensions of sustainable development. The overarching goal of this paper is to set a framework for deriving dynamic, climate-informed environmental inflows for drying lakes considering both meteorological/climatic and anthropogenic conditions. We report on the compounding effects of meteorological drought and unsustainable water resource management that contributed to Lake Urmia’s contemporary environmental catastrophe. Using rich datasets of hydrologic attributes, water demands and withdrawals, as well as water management infrastructure (i.e. reservoir capacity and operating policies), we provide a quantitative assessment of the basin’s water resources, demonstrating that Lake Urmia reached a tipping point in the early 2000s. The lake level failed to rebound to its designated ecological threshold (1274 m above sea level) during a relatively normal hydro-period immediately after the drought of record (1998–2002). The collapse was caused by a marked overshoot of the basin’s hydrologic capacity due to growing anthropogenic drought in the face of extreme climatological stressors. We offer a dynamic environmental inflow plan for different climate conditions (dry, wet and near normal), combined with three representative water withdrawal scenarios. Assuming effective implementation of the proposed 40% reduction in the current water withdrawals, the required environmental inflows range from 2900 million cubic meters per year (mcm yr−1) during dry conditions to 5400 mcm yr−1 during wet periods with the average being 4100 mcm yr−1. Finally, for different environmental inflow scenarios, we estimate the expected recovery time for re-establishing the ecological level of Lake Urmia

Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis

Groundwater is a vital source of freshwater, supporting the livelihood of over two billion people worldwide. The quantitative assessment of groundwater resources is critical for sustainable management of this strained resource, particularly as climate warming, population growth, and socioeconomic development further press the water resources. Rapid growth in the availability of a plethora of in-situ and remotely sensed data alongside advancements in data-driven methods and machine learning offer immense opportunities for an improved assessment of groundwater resources at the local to global levels. This systematic review documents the advancements in this field and evaluates the accuracy of various models, following the protocol developed by the Center for Evidence-Based Conservation. A total of 197 original peer-reviewed articles from 2010–2020 and from 28 countries that employ regression machine learning algorithms for groundwater monitoring or prediction are analyzed and their results are aggregated through a meta-analysis. Our analysis points to the capability of machine learning models to monitor/predict different characteristics of groundwater resources effectively and efficiently. Modeling the groundwater level is the most popular application of machine learning models, and the groundwater level in previous time steps is the most employed input data. The feed-forward artificial neural network is the most employed and accurate model, although the model performance does not exhibit a striking dependence on the model choice, but rather the information content of the input variables. Around 10–12 years of data are required to develop an acceptable machine learning model with a monthly temporal resolution. Finally, advances in machine and deep learning algorithms and computational advancements to merge them with physics-based models offer unprecedented opportunities to employ new information, e.g., InSAR data, for increased spatiotemporal resolution and accuracy of groundwater monitoring and prediction

Increasing Probability of Mass-Mortality During Indian Heatwaves

Rising global temperatures are causing increases in the frequency and severity of extreme climatic events such as floods, droughts, and heatwaves. Here, we analyze changes in summer temperatures, the frequency, severity and duration of heatwaves, and heat-related mortality in India between 1960 and 2009, using data from the India Meteorological Department. Mean temperatures across India have risen by more than 0.5 °C over this period, with statistically significant increases in heatwaves. Using a novel probabilistic model, we further show that the increase in summer mean temperatures in India over this period corresponds to a 146% increase in the probability of heat-related mortality events of more than 100 people. In turn, our results suggest that future climate warming will lead to substantial increases in heat-related mortality, particularly in developing, low-latitude countries such as India where heatwaves will become more frequent and populations are especially vulnerable to these extreme temperatures. Our findings indicate that even moderate increases in mean temperatures may cause great increases in heat-related mortality, and support efforts of governments and international organizations to build-up the resilience of these vulnerable regions to more and more severe heatwaves

Changes in the Exposure of California’s Levee-Protected Critical Infrastructure to Flooding Hazard in a Warming Climate

Levee systems are an important part of California\u27s water infrastructure, engineered to provide resilience against flooding and reduce flood losses. The growth in California is partly associated with costly infrastructure developments that led to population expansion in the levee protected areas. Therefore, potential changes in the flood hazard could have significant socioeconomic consequences over levee protected areas, especially in the face of a changing climate. In this study, we examine the possible impacts of a warming climate on flood hazard over levee protected land in California. We use gridded maximum daily runoff from global circulation models (GCMs) that represent a wide range of variability among the climate projections, and are recommended by the California\u27s Fourth Climate Change Assessment Report, to investigate possible climate-induced changes. We also quantify the exposure of several critical infrastructure protected by the levee systems (e.g. roads, electric power transmission lines, natural gas pipelines, petroleum pipelines, and railroads) to flooding. Our results provide a detailed picture of change in flood risk for different levees and the potential societal consequences (e.g. exposure of people and critical infrastructure). Levee systems in the northern part of the Central Valley and coastal counties of Southern California are likely to observe the highest increase in flood hazard relative to the past. The most evident change is projected for the northern region of the Central Valley, including Butte, Glenn, Yuba, Sutter, Sacramento, and San Joaquin counties. In the leveed regions of these counties, based on the model simulations of the future, the historical 100-year runoff can potentially increase up to threefold under RCP8.5. We argue that levee operation and maintenance along with emergency preparation plans should take into account the changes in frequencies and intensities of flood hazard in a changing climate to ensure safety of levee systems and their protected infrastructure

A New Normal for Streamflow in California in a Warming Climate: Wetter Wet Seasons and Drier Dry Seasons

In this study, we investigate changes in future streamflows in California using bias-corrected and routed streamflows derived from global climate model (GCM) simulations under representative concentration pathways (RCPs): RCP4.5 and RCP8.5. Unlike previous studies that have focused mainly on the mean streamflow, annual maxima or seasonality, we focus on projected changes across the distribution of streamflow and the underlying causes. We report opposing trends in the two tails of the future streamflow simulations: lower low flows and higher high flows with no change in the overall mean of future flows relative to the historical baseline (statistically significant at 0.05 level). Furthermore, results show that streamflow is projected to increase during most of the rainy season (December to March) while it is expected to decrease in the rest of the year (i.e., wetter rainy seasons, and drier dry seasons). We argue that the projected changes to streamflow in California are driven by the expected changes to snow patterns and precipitation extremes in a warming climate. Changes to future low flows and extreme high flows can have significant implications for water resource planning, drought management, and infrastructure design and risk assessment

Climate‐Induced Changes in the Risk of Hydrological Failure of Major Dams in California

Existing major reservoirs in California, with average age above 50 years, were built in the previous century with limited data records and flood hazard assessment. Changes in climate and land use are anticipated to alter statistical properties of inflow to these infrastructure systems and potentially increase their hydrological failure probability. Because of large socioeconomic repercussions of infrastructure incidents, revisiting dam failure risks associated with possible shifts in the streamflow regime is fundamental for societal resilience. Here we compute historical and projected flood return periods as a proxy for potential changes in the risk of hydrological failure of dams in a warming climate. Our results show that hydrological failure probability is likely to increase for most dams in California by 2100. Noticeably, the New Don Pedro, Shasta, Lewiston, and Trinity Dams are associated with highest potential changes in flood hazard