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  1. Abstract

    Baseflow is an essential water resource because it is the groundwater discharged to streams and represents long‐term storage. Understanding its future changes is a major concern for water supply and ecosystem health. This study examines the impacts of climate and agriculture on monthly baseflow in the U.S. Midwest through the end of the 21st century. We use a statistical approach to evaluate three scenarios. The first scenario is based on downscaled and bias corrected global climate model (GCM) outputs and the representative concentration pathway (RCP) 8.5, and agriculture is held constant (and equal to the mean from 2013 to 2019). In the next two scenarios, climate is held constant (2010–2019) to isolate the impact of agriculture on baseflow. In terms of agricultural changes, we consider scenarios representative of either increases or decreases with respect to the production of corn and soybeans. Changes in the climate system point to increases in baseflow that are likely a result of increased precipitation and antecedent wetness. Seasonally, warmer temperature in the winter and spring (i.e., February to July) is expected to cause increasing trends in baseflow. Changes in land use showed that agriculture would either mitigate the impact of climate change or possibly amplify it. Expanding corn and soybean areas would increase baseflow in the Corn Belt region. On the other hand, converting land back to perennial vegetation would decrease baseflow throughout the entire year. Despite its simplicity, this study can provide basic information to understand where to expect adverse effects on baseflow and thus improve land management practices in those areas.

     
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  2. Abstract

    Characterizing streamflow changes in the agricultural U.S. Midwest is critical for effective planning and management of water resources throughout the region. The objective of this study is to determine if and how baseflow has responded to land alteration and climate changes across the study area during the 50‐year study period by exploring hydrologic variations based on long‐term stream gage data. This study evaluates monthly contributions to annual baseflow along with possible trends over the 1966–2016 period for 458 U.S. Geological Survey streamflow gages within 12 different Midwestern states. It also examines the influence of climate and land use factors on the observed baseflow trends. Monthly contribution breakdowns demonstrate how the majority of baseflow is discharged into streams during the spring months (March, April, and May) and is overall more substantial throughout the spring (especially in April) and summer (June, July, and August). Baseflow has not remained constant over the study period, and the results of the trend detection from the Mann–Kendall test reveal that baseflows have increased and are the strongest from May to September. This analysis is confirmed by quantile regression, which suggests that for most of the year, the largest changes are detected in the central part of the distribution. Although increasing baseflow trends are widespread throughout the region, decreasing trends are few and limited to Kansas and Nebraska. Further analysis reveals that baseflow changes are being driven by both climate and land use change across the region. Increasing trends in baseflow are linked to increases in precipitation throughout the year and are most prominent during May and June. Changes in agricultural intensity (in terms of harvested corn and soybean acreage) are linked to increasing trends in the central and western Midwest, whereas increasing temperatures may lead to decreasing baseflow trends in spring and summer in northern Wisconsin, Kansas, and Nebraska.

     
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    Abstract Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, we propose the Water Ethics Web Engine (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision support. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community. 
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    Abstract The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources. 
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