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Abstract Reductions in streamflow caused by groundwater pumping, known as “streamflow depletion,” link the hydrologic process of stream‐aquifer interactions to human modifications of the water cycle. Isolating the impacts of groundwater pumping on streamflow is challenging because other climate and human activities concurrently impact streamflow, making it difficult to separate individual drivers of hydrologic change. In addition, there can be lags between when pumping occurs and when streamflow is affected. However, accurate quantification of streamflow depletion is critical to integrated groundwater and surface water management decision making. Here, we highlight research priorities to help advance fundamental hydrologic science and better serve the decision‐making process. Key priorities include (a) linking streamflow depletion to decision‐relevant outcomes such as ecosystem function and water users to align with partner needs; (b) enhancing partner trust and applicability of streamflow depletion methods through benchmarking and coupled model development; and (c) improving links between streamflow depletion quantification and decision‐making processes. Catalyzing research efforts around the common goal of enhancing our streamflow depletion decision‐support capabilities will require disciplinary advances within the water science community and a commitment to transdisciplinary collaboration with diverse water‐connected disciplines, professions, governments, organizations, and communities. 

Abstract Affordable synthetic ammonia (NH₃) enables the production of nearly half of the food we eat and is emerging as a renewable energy carrier. Sodium‐promoted chemical looping NH₃synthesis at atmospheric pressure using manganese (Mn) is here demonstrated. The looping process may be advantageous when inexpensive renewable hydrogen from electrolysis is available. Avoiding the high pressure of the Haber‐Bosch process by chemical looping using earth‐abundant materials may reduce capital cost, facilitate intermittent operation, and allow operation in geographic areas where infrastructure is less sophisticated. At this early stage, the data suggest that 0.28 m³of a 50 % porosity solid Mn bed may suffice to produce 100 kg NH₃per day by chemical looping, with abundant opportunities for improvement. 

Large-scale solar promises a low-carbon energy alternative. However, solar production in North America given anticipated climate change has been studied only seasonally in terms of solar irradiance. This work integrates more of the predictive potential of climate-change models by exploring other environmental variables, such as humidity and temperature. Here, a Continental US (CONUS) model is produced by deep learning using 2593 NREL simulated solar power stations. Daily forecasts using 17 Global Climate Models (GCM’s) through 2099 are summarized monthly. Results suggest power production factors change between +4 % and 􀀀 19 % over 93 years. These results suggest more, but still modest, potential declines than previous solar irradiance-based studies. The modest impact is encouraging. For some areas, climate model variability unfortunately yielded statistically insignificant trends and practical application is less clear. For future evaluations, this work suggests the potential importance of additional variables, monthly interval summary, and accounting for model variability. 

Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed. 

In the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing cleanhybrid wind and solar systems to improve farm performance. A concern in this paradigm is the effective sizing of renewable energy systems to ensure optimal energy use within budget considerations. This research focuses on optimizing renewable energy sizing in small-scale ammonia production to meet specific farm demands and enhance local resilience, emphasizing the interplay between environmental and economic factors. These findings promise increased energy efficiency and sustainability in this innovative agricultural sector. Additionally, our approach considers small-scale ammonia plant needs and the dynamic relationships between ammonia, water, and farm demands. Simulations demonstrate substantial cost savings in farm electricity consumption. Specifically, scenarios with renewable energy integration in the farm can reduce at least 13% electricity cost compared to a grid-dependent system in the 15-year simulation. 

the central United States, PW disposal occurs through deep well injection, which can increase seismic activity. The treatment of PW for use in agriculture is an alternative to current disposal practices that can also provide supplemental water in regions where limited freshwater sources can affect agricultural production. This paper assesses the potential for developing PW as a water source for agriculture in the Anadarko basin, a major oil and gas field spanning parts of Kansas, Oklahoma, Colorado, and Texas. From 2011 to 2019, assessment of state oil and gas databases indicated that PW generation in the Anadarko Basin averaged 428 million m3/yr. A technoeconomic analysis of PW treatment was combined with geographical information on PW availability and composition to assess the costs and energy requirements to recover this PW as a non-conventional water resource for agriculture. The volume of freshwater economically extractable from PW was estimated to be between 58 million m3 per year using reverse osmosis (RO) treatment only and 82 million m3 per year using a combination of RO and mechanical vapor compression to treat higher salinity waters. These volumes could meet 1–2 % and 49–70 % of the irrigation and livestock water demands in the basin, respectively. PW recovery could also modestly contribute to mitigating the decline of the Ogallala aquifer by ~2 %. RO treatment costs and energy requirements, 0.3–1.5 $/m3 and 1.01–2.65 kWh/m3, respectively, are similar to those for deep well injection. Treatment of higher salinity waters increases costs and energy requirements substantially and is likely not economically feasible in most cases. The approach presented here provides a valuable framework for assessing PW as a supplemental water source in regions facing similar challenges. 

Occupant-centric HVAC control places a premium on factors including thermal comfort and electricity cost to guarantee occupant satisfaction. Traditional approaches, reliant on static models for occupant behaviors, fall short in capturing intra-day behavioral variations, resulting in imprecise thermal comfort evaluations and suboptimal HVAC energy management, especially in multi-zone systems with diverse occupant profiles. To address this issue, this paper proposes a novel occupant-centric multi-zone HVAC control approach that intelligently schedules cooling and heating setpoints using Multi-agent Deep Reinforcement Learning (MADRL). This approach systematically takes into account stochastic occupant behavior models, such as dynamic clothing insulation adjustments, metabolic rates, and occupancy patterns. Simulation results demonstrate the efficacy of the proposed approach. Comparative case studies show that the proposed MADRL-based, occupant-centric HVAC control reduces electricity costs by 51.09% compared to rule-based approaches and 4.34% compared to single-agent DRL while maintaining multi-zonal thermal comfort for occupants. 

Developing resilience in food, energy, and water (FEW) systems is a critical priority. The structural topology of the components of complex agricultural systems interact in ways that can only be grasped via models and simulations. We extend previous work on graph models that represent complex inter-level topology. We show some results of simulating system dynamic model as a formally tractable way of understanding resilience in these systems. 

This paper identifies common varieties of threats and perturbations in contemporary food, energy, and water (FEW) systems in order to improve system resilience. We categorize perturbations and challenges faced by subsystems and then concentrate on the structural topology of the project’s components. We provide a graph model to represent this topology as an essential tool to improve system resilience. The model is then converted to a system dynamic model for further simulation. 

There has been increasing interest in low-carbon technologies to reduce climate change impacts. However, careful assessments of their implications for the vibrancy of local economies are rare. This paper employs techno-economic analysis to assess the technical and economic feasibility of investment in one such technology: local green ammonia production and its contribution to the economic viability of the local economy. The analysis considers price projection and debt financing options, and alternative energy-to-ammonia technologies. The approach is broadly applicable and is illustrated here using a case study in which 248,188 MT of traditional ammonia are replaced with local wind energy-produced ammonia for farmers in Southwest Kansas, United States. Economic feasibility is defined as the ability to accrue enough discounted cash flow at the end of the turbines’ 25-year lifespan to enable their replacement. The alternative technologies are the traditional Haber-Bosch and the emerging solid oxide electrolysis cell (SOEC). The total plant capital cost amounted to $781.72 million while the plant operating costs were set at $100/MT with the energy supplied by the project’s energy system. The results show how economic feasibility sensitivity to technology and financing options are evaluated and communicated to scientists, policymakers, and farmers. The 6.5 MWh/MT wind energy-to-ammonia SOEC technology presented the best economic results under all price projections. The community’s investment yielded the highest return when debt was used to finance 50% of the capital investment. Returns exceeded the average annual S&P return of about 7% from 1957 to 2021. The work shows how consideration of technology efficiencies and creative financing strategies can contribute to the economic welfare of farmers and their communities even as they contributed to reducing crop production’s carbon footprint.