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Highlights • Effect of intermittent energy supply on membrane distillation performance. • Proper protocol for operation under intermittent energy conditions. • Ultrasonic cleaning recovers the used membranes hydrophobicity. Abstract Solar-powered membrane distillation is a promising technology for water desalination, especially in remote arid areas. However, the intermittent nature of solar irradiation restricts the operation of this technology to the daylight hours. Many studies tried to extend the daily operating period but at the expense of increasing complexity and cost. Therefore, most likely the stand-alone solar-powered system will be operated intermittently. This work is focused on studying the effect of the intermittent operation on a solar-powered MD system performance. A set of experiments were carried out on a bench-scale membrane distillation unit. A programmable temperature-controlled circulating bath was used to generate a feed temperature pattern similar to the temperature output of a solar heater in a real pilot solar-powered system. Different operation shutdown procedures (protocols) were examined to identify the best operation protocol for intermittent operation of MD system. The results showed fluctuations in the daily permeate flux and quality. Moreover, the permeate flux was observed to increase with the thermal expansion of the membrane material. There was a gradual deterioration in membrane module performance in terms of salt rejection when the module left to dry overnight. On the other hand, keeping the saline feed inside the module overnight was found to preserve the performance of the module. Ultrasonic cleaning was applied to the used hollow fiber membranes and found to help in recovering their hydrophobicity. This is observed by an increase in the contact angle values of these membranes from 77° to 98°. [ABSTRACT FROM AUTHOR]

• Crop water consumption is estimated under climate and land use change effects. • Global crop GWC and BWC increase by 12% and 70% by 2090s, respectively. • Climate change leads to 8.5% increase of global GWC and dominates its trend. • Expansion in irrigated area dominates the trend of global BWC. • Global crop blue water dependence will increase, especially in arid regions. Agriculture accounts for 90% of global freshwater consumption and it is expected to intensify in the future. Climate and land use changes are two major factors affecting crop green and blue water consumption, and in this study we explicitly consider the effects of both factors in a consistent modeling framework. Two important research questions are addressed: 1) How will global crop green and blue water consumption evolve over the 21st century under climate and land use changes; and 2) what are the individual and combined effects of climate and land use changes on future crop green and blue water consumption? To tackle these two questions, a crop water use module is developed based on the Global Change Assessment Model (GCAM) and its hydrology module (i.e., Xanthos). Crop specific green and blue water consumption are then calculated at global 0.5° × 0.5° grid scale. Results show that global crop green water consumption increases by 12% in 2090s when compared with that in 1971–2000, and climate change dominates over land use change in determining the trend of global crop green water consumption. However, expansion in global irrigated area dominates the changing trend of global crop blue water consumption which increases 70% by 2090s, especially in regions with significant irrigated land expansion (e.g. northern Africa, central Asia, China, Mexico, the Middle East, Russia, southern Asia, and Argentina). Furthermore, global crop blue water dependence will increase under climate and land use changes, especially in arid regions. [ABSTRACT FROM AUTHOR]

Abstract This paper performs a multi-model comparison to assess strategies for adaptation to climate change impacts in hydropower generation in Brazil under two Representative Concentration Pathways. The approach used allows for evaluating the interactions between climate change mitigation and adaptation strategies under low and high impact scenarios through 2050. Climate change impact projections of sixteen General Circulation Models indicate that a global high emissions trajectory scenario would likely yield more severe impacts on hydropower generation than a mitigation scenario. Adaptation modeling suggests that climate change impacts can be compensated by a wide range of alternatives, whose optimality will depend on the level of mitigation effort pursued. Our results show that climate change impacts would lead to even higher emissions in the absence of climate change mitigation policies. On the other hand, mitigation strategies to pursue lower emissions are maintained under climate change impacts, meaning that mitigation strategies are robust when faced with adaptation challenges. Mitigation efforts could yield a more diverse and less carbon intensive mix of technological options for adaptation. When analyzing investment costs to adapt to climate change impacts, in some cases mitigation can lead to a lower total investment level. Highlights • Climate change impacts on hydropower are assessed for sixteen models and two scenarios. • Interactions between climate change mitigation and adaptation are analyzed. • Adaptation/mitigation strategies from six different energy models are compared. • Adaptation and mitigation investment costs are assessed and compared. [ABSTRACT FROM AUTHOR]

While global hydrological models (GHMs) are very useful in exploring water resources and interactions between the Earth and human systems, their use often requires numerous model inputs, complex model calibration, and high computation costs. To overcome these challenges, we construct an efficient open-source and ready-to-use hydrological emulator (HE) that can mimic complex GHMs at a range of spatial scales (e.g., basin, region, globe). More specifically, we construct both a lumped and a distributed scheme of the HE based on the monthly abcd model to explore the tradeoff between computational cost and model fidelity. Model predictability and computational efficiency are evaluated in simulating global runoff from 1971 to 2010 with both the lumped and distributed schemes. The results are compared against the runoff product from the widely used Variable Infiltration Capacity (VIC) model. Our evaluation indicates that the lumped and distributed schemes present comparable results regarding annual total quantity, spatial pattern, and temporal variation of the major water fluxes (e.g., total runoff, evapotranspiration) across the global 235 basins (e.g., correlation coefficient r between the annual total runoff from either of these two schemes and the VIC is > 0:96), except for several cold (e.g., Arctic, interior Tibet), dry (e.g., North Africa) and mountainous (e.g., Argentina) regions. Compared against the monthly total runoff product from the VIC (aggregated from daily runoff), the global mean Kling-Gupta efficiencies are 0.75 and 0.79 for the lumped and distributed schemes, respectively, with the distributed scheme better capturing spatial heterogeneity. Notably, the computation efficiency of the lumped scheme is 2 orders of magnitude higher than the distributed one and 7 orders more efficient than the VIC model. A case study of uncertainty analysis for the world's 16 basins with top annual streamflow is conducted using 100 000 model simulations, and it demonstrates the lumped scheme's extraordinary advantage in computational efficiency. Our results suggest that the revised lumped abcd model can serve as an efficient and reasonable HE for complex GHMs and is suitable for broad practical use, and the distributed scheme is also an efficient alternative if spatial heterogeneity is of more interest. [ABSTRACT FROM AUTHOR]

Climate change is projected to increase hydropower generation in some parts of the world and decrease it in others. Here we explore the possible consequences of these impacts for the electricity supply sector at the global scale. Regional hydropower projections are developed by forcing a coupled global hydrological and dam model with downscaled, bias-corrected climate realizations. Consequent impacts on power sector composition and associated emissions and investment costs are explored using the Global Change Assessment Model (GCAM). We find that climate-driven changes in hydropower generation may shift power demands onto and away from carbon intensive technologies. This causes significantly altered power sector CO 2 emissions in several hydro-dependent regions, although the net global impact is modest. For drying regions, we estimate a global, cumulative investment need of approximately one trillion dollars (±$500 billion) this century to make up for deteriorated hydropower generation caused by climate change. Total investments avoided are of a similar magnitude across regions projected to experience increased precipitation. Investment risks and opportunities are concentrated in hydro-dependent countries for which significant climate change is expected. Various countries throughout the Balkans, Latin America and Southern Africa are most vulnerable, whilst Norway, Canada, and Bhutan emerge as clear beneficiaries. [ABSTRACT FROM AUTHOR]

Despite significant effort to quantify the interdependence of the water and energy sectors, global requirements of energy for water (E4W) are still poorly understood, which may result in biases in projections and consequently in water and energy management and policy. This study estimates water-related energy consumption by water source, sector, and process for 14 global regions from 1973 to 2012. Globally, E4W amounted to 10.2 EJ of primary energy consumption in 2010, accounting for 1.7%--2.7% of total global primary energy consumption, of which 58% pertains to fresh surface water, 30% to fresh groundwater, and 12% to nonfresh water, assuming median energy intensity levels. The sectoral E4W allocation includes municipal (45%), industrial (30%), and agricultural (25%), and main process-level contributions are from source/conveyance (39%), water purification (27%), water distribution (12%), and wastewater treatment (18%). While the United States was the largest E4W consumer from the 1970s until the 2000s, the largest consumers at present are the Middle East, India, and China, driven by rapid growth in desalination, groundwater-based irrigation, and industrial and municipal water use, respectively. The improved understanding of global E4W will enable enhanced consistency of both water and energy representations in integrated assessment models. [ABSTRACT FROM AUTHOR]

Water is essential for the world's food supply, for energy production, including bioenergy and hydroelectric power, and for power system cooling. Water is already scarce in many regions of the world and could present a critical constraint as society attempts simultaneously to mitigate climate forcing and adapt to climate change, and to provide for a larger and more prosperous human population. Numerous studies have pointed to growing pressures on the world's scarce fresh water resources from population and economic growth, and climate change. This study goes further. We use the Global Change Assessment Model to analyze interactions between population, economic growth, energy, land, and water resources simultaneously in a dynamically evolving system where competing claims on water resources from all claimants-energy, land, and economy-are reconciled with water resource availability-from renewable water, non-renewable groundwater and desalinated water sources -across 14 geopolitical regions, 151 agriculture-ecological zones, and 235 major river basins. We find that previous estimates of global water withdrawal projections are overestimated. Model simulations show that it is more economical in some basins to alter agricultural and energy activities rather than utilize non-renewable groundwater or desalinated water. This study highlights the importance of accounting for water as a binding factor in agriculture, energy and land use decisions in integrated assessment models and implications for global responses to water scarcity, particularly in the trade of agricultural commodities and land-use decisions. [ABSTRACT FROM AUTHOR]

There is evidence that warming leads to greater evapotranspiration and surface drying, thus contributing to increasing intensity and duration of drought and implying that mitigation would reduce water stresses. However, understanding the overall impact of climate change mitigation on water resources requires accounting for the second part of the equation, i.e., the impact of mitigation-induced changes in water demands from human activities. By using integrated, high-resolution models of human and natural system processes to understand potential synergies and/or constraints within the climate–energy–water nexus, we show that in the United States, over the course of the 21st century and under one set of consistent socioeconomics, the reductions in water stress from slower rates of climate change resulting from emission mitigation are overwhelmed by the increased water stress from the emissions mitigation itself. The finding that the human dimension outpaces the benefits from mitigating climate change is contradictory to the general perception that climate change mitigation improves water conditions. This research shows the potential for unintended and negative consequences of climate change mitigation. [ABSTRACT FROM AUTHOR]

A coupled simulation-optimization with reanalysis-based short-term weather forecasts from a regional climate model (RCM) is proposed to optimize an irrigation scheduling problem. Using different physical configurations of the climate extension of a weather research and forecasting model (CWRF) that is driven by national atmospheric model project reanalysis data, five ensemble outlooks of 15 consecutive daily forecasts have been generated during five different crop-growing seasons. Six daily climatic variables are forecasted, namely, rainfall, minimum temperature, maximum temperature, humidity, wind speed, and solar radiation. To correct the forecasts for any inherent bias, the quantile mapping method is applied to all six daily climatic variables. After bias correction, a skill assessment of the reanalysis-based RCM forecasts indicate that only the first three climatic variables are predicted with reliable accuracy; thus, average climatic means are used to replace the remaining three variables (humidity, wind speed, and solar radiation). The framework is applied to the Havana Lowlands region, Illinois, as a case study, and the value of forecasts is assessed against two baseline scenarios: no-rain forecast (a pessimistic case) and average climatology (a normal case). Using reanalysis-based RCM forecasts to guide farmers' irrigation decisions could yield about 1-3% in expected profit gain and 4-6% in water reduction when compared to the no-rain forecast scenario, and 1-6% in expected profit gain when compared to the average climatology scenario. This study is a first preliminary attempt to use an ensemble of weather simulations in the optimization of irrigation scheduling, and the developed framework can be used to incorporate operational forecasting once the reanalysis boundary is replaced by global weather forecasts. [ABSTRACT FROM AUTHOR]

Three future projections of global municipal water use are established: business-as usual (BAU), low technological improvement (Low Tech), and high technological improvement (High Tech). A global municipal water demand model is constructed using global water-use statistics at the country scale, calibrated to the base year of 2005, and simulated to the end of the 21st century. Since the constructed water demand model hinges on socio-economic variables (population, income), water price, and end-use technology and efficiency improvement rates, projections of those input variables are adopted to characterize the uncertainty in future water demand estimates. The water demand model is linked to the Global Change Assessment Model (GCAM), a global change integrated assessment model. Under the reference (BAU) scenario, the global total water withdrawal increases from 466 km3year−1in 2005 to 1098 km3year−1in 2100, while withdrawals in the High and Low Tech scenarios are 437 and 2000 km3year−1, respectively. EditorZ.W. Kundzewicz;Associate editorD. Gerten CitationHejazi, M., Edmonds, J., Chaturvedi, V., Davies, E., and Eom, J.Y., 2013. Scenarios of global municipal water use demand projections over the 21st century.Hydrological Sciences Journal, 58 (3), 519–538 [ABSTRACT FROM PUBLISHER]

This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers' practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role of forecasts for irrigation scheduling in a more effective way. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we promote a novel approach to develop reservoir operation routines by learning from historical hydrologic information and reservoir operations. The proposed framework involves a knowledge discovery step to learn the real drivers of reservoir decision making and to subsequently build a more realistic (enhanced) model formulation using stochastic dynamic programming (SDP). The enhanced SDP model is compared to two classic SDP formulations using Lake Shelbyville, a reservoir on the Kaskaskia River in Illinois, as a case study. From a data mining procedure with monthly data, the past month’s inflow (Q t−1), current month’s inflow (Q t ), past month’s release (R t−1), and past month’s Palmer drought severity index (PDSI t−1) are identified as important state variables in the enhanced SDP model for Shelbyville Reservoir. When compared to a weekly enhanced SDP model of the same case study, a different set of state variables and constraints are extracted. Thus different time scales for the model require different information. We demonstrate that adding additional state variables improves the solution by shifting the Pareto front as expected while using new constraints and the correct objective function can significantly reduce the difference between derived policies and historical practices. The study indicates that the monthly enhanced SDP model resembles historical records more closely and yet provides lower expected average annual costs than either of the two classic formulations (25.4% and 4.5% reductions, respectively). The weekly enhanced SDP model is compared to the monthly enhanced SDP, and it shows that acquiring the correct temporal scale is crucial to model reservoir operation for particular objectives. [Copyright &y& Elsevier]

Abstract: Input variable selection (IVS) is a necessary step in modeling water resources systems. Neglecting this step may lead to unnecessary model complexity and reduced model accuracy. In this paper, we apply the minimum redundancy maximum relevance (MRMR) algorithm to identifying the most relevant set of inputs in modeling a water resources system. We further introduce two modified versions of the MRMR algorithm (α-MRMR and β-MRMR), where α and β are correction factors that are found to increase and decrease as a power-law function, respectively, with the progress of the input selection algorithms and the increase of the number of selected input variables. We apply the proposed algorithms to 22 reservoirs in California to predict daily releases based on a set from a 121 potential input variables. Results indicate that the two proposed algorithms are good measures of model inputs as reflected in enhanced model performance. The α-MRMR and β-MRMR values exhibit strong negative correlation to model performance as depicted in lower root-mean-square-error (RMSE) values. [Copyright &y& Elsevier]

Abstract: Closing the gap between theoretical reservoir operation and the real-world implementation remains a challenge in contemporary reservoir operations. Past research has focused on optimization algorithms and establishing optimal policies for reservoir operations. In this study, we attempt to understand operators’ release decisions by investigating historical release data from 79 reservoirs in California and the Great Plains, using a data-mining approach. The 79 reservoirs are classified by hydrological regions, intra-annual seasons, average annual precipitation (climate), ratio of maximum reservoir capacity to average annual inflow (size ratio), hydrologic uncertainty associated with inflows, and reservoirs’ main usage. We use information theory – specifically, mutual information – to measure the quality of inference between a set of classic indicators and observed releases at the monthly and weekly timescales. Several general trends are found to explain which sources of hydrologic information dictate reservoir release decisions under different conditions. Current inflow is the most important indicator during wet seasons, while previous releases are more relevant during dry seasons and in weekly data (as compared with monthly data). Inflow forecasting is the least important indicator in release decision making, but its importance increases linearly with hydrologic uncertainty and decreases logarithmically with reservoir size. No single hydrologic indicator is dominant across all reservoirs in either of the two regions. [Copyright &y& Elsevier]

Governance measures such as restrictions on groundwater pumping and adjustments to sectoral water pricing have been suggested as response strategies to curtail recent increases in groundwater pumping and enhance sustainable water use. However, little is known about the impacts of such sustainability strategies. We investigate the implications of such measures, with the United States (U.S.) as an example. Using the Global Change Analysis Model (GCAM) with state-level details in the U.S., we find that the combination of these two governance measures can drastically alter agricultural production in the U.S. The Southwest stands to lose upwards of 25% of their total agricultural production, much of which is compensated for by production increases in river basins on the east coast of the U.S. The implementation of future sustainable water governance measures will require additional investments that allow farmers to maximize production while minimizing water withdrawals to avoid potentially detrimental revenue losses. [ABSTRACT FROM AUTHOR]

This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5 ∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective. [ABSTRACT FROM AUTHOR]

In nail split, the nail plate is divided into two parts. Trauma involving the nail matrix, the generator of the nail plate, can result in nail plate splitting which may take some months to become apparent. Photographic documentation of this condition in the literature had hitherto been lacking. [ABSTRACT FROM AUTHOR]

This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the GranD database to Xanthos' 0.5-degree spatial resolution so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling-Gupta Efficient (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only seven. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use normalizedroot- mean-square-error (NRMSE) and the coefficient of determination (R²). Out of the 296 hydropower reservoirs, NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44% of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow for the analysis of future global reservoir development and management from a coupled human-earth system perspective. [ABSTRACT FROM AUTHOR]

One of the most important interactions between humans and climate is in the demand and supply of water. Humans withdraw, use, and consume water and return waste water to the environment for a variety of socioeconomic purposes, including domestic, commercial, and industrial use, production of energy resources and cooling thermal-electric power plants, and growing food, fiber, and chemical feed stocks for human consumption. Uncertainties in the future human demand for water interact with future impacts of climatic change on water supplies to impinge on water management decisions at the international, national, regional, and local level, but until recently tools were not available to assess the uncertainties surrounding these decisions. This paper demonstrates the use of a multi-model framework in a structured sensitivity analysis to project and quantify the sensitivity of future deficits in surface water in the context of climate and socioeconomic change for all U.S. states and sub-basins. The framework treats all sources of water demand and supply consistently from the world to local level. The paper illustrates the capabilities of the framework with sample results for a river sub-basin in the U.S. state of Georgia. [ABSTRACT FROM AUTHOR]

This paper describes GCAM-USA v5.3_water_dispatch, an open-source model that represents key interactions across economic, energy, water, and land systems in a consistent global framework with subnational detail in the United States. GCAM-USA divides the world into 31 geopolitical regions outside the United States (US) and represents the US economy and energy systems in 51 state-level regions (50 states plus the District of Columbia). The model also includes 235 water basins and 384 land use regions, and 23 of each fall at least partially within the United States. GCAM-USA offers a level of process and temporal resolution rare for models of its class and scope, including detailed subnational representation of US water demands and supplies and sub-annual operations (day and night for each month) in the US electric power sector. GCAM-USA can be used to explore how changes in socioeconomic drivers, technological progress, or policy impact demands for (and production of) energy, water, and crops at a subnational level in the United States while maintaining consistency with broader national and international conditions. This paper describes GCAM-USA's structure, inputs, and outputs, with emphasis on new model features. Four illustrative scenarios encompassing varying socioeconomic and energy system futures are used to explore subnational changes in energy, water, and land use outcomes. We conclude with information about how public users can access the model. [ABSTRACT FROM AUTHOR]

This paper presents a method to separate harvested area and yield for irrigated crops from rainfed crops in a region, given gross harvested area and yield, and climatic, agronomic and economic data for crops. The method is based on the principle of general maximum entropy, which combines incomplete data, empirical knowledge and a priori information to derive desired information. The model is applied to three large basins with aggregated climatic and agricultural conditions, and to five counties in Texas and California. The modeled results and assessed values in these study areas are compared. While the dependability of model outputs relies on empirical knowledge and judicious parameter estimation, the model remains reliable even for the significant level of uncertainty produced by subjectively predetermined major parameters. The model can be applied to retriving historical data for irrigated and rainfed crops; it can also be used for irrigated and rainfed agriculture planning based on climatic and technological projections. Moreover, the model provides other useful information, including water allocation by crop, water use efficiency and the impact of other agricultural inputs. [ABSTRACT FROM AUTHOR]

This paper describes GCAM-USA v5.3_water_dispatch, an open source model that represents key interactions across economic, energy, water, and land systems in a consistent global framework, with subnational detail in the United States. GCAM-USA divides the world into 31 geopolitical regions outside the United States (U.S.) and represents the U.S. economic and energy systems in 51 state-level regions (50 states plus the District of Columbia). The model also includes 235 water basins and 384 land-use regions; 23 of each fall at least partially within the United States. GCAM-USA offers a level of process and temporal resolution rare for models of its class and scope, including detailed subnational representation of U.S. water demands and supplies and sub-annual operations (day/night for each month) in the U.S. electric power sector. GCAM-USA can be used to explore how changes in socioeconomic drivers, technological progress, or policy impact demands for, and production of, energy, water, and crops at a subnational level in the United States, while maintaining consistency with broader national and international conditions. This paper describes GCAM-USA's structure, inputs, and outputs, with emphasis on new model features. Four illustrative scenarios encompassing varying socioeconomic and energy system futures are used to explore subnational changes in energy, water, and land-use outcomes. We conclude with information about how public users can access the model. [ABSTRACT FROM AUTHOR]

Projections of building electricity consumption are used in multiple fields and for a variety of purposes, from energy utility investment decisions to global climate assessments. Existing approaches to modeling building electricity consumption span a range of structural methodologies, spatial resolutions, and temporal scales, potentially leading to divergent projections. This paper compares how two models with different structures and resolutions respond to a common set of population and climate drivers in the western U.S. The BEND model simulates hourly residential and commercial building electricity consumption at the county scale by weighting the results of simulations of thousands of representative buildings. In contrast, the projected electricity demand in GCAM-USA is determined for each state, year, sector (residential/commercial), and service (e.g., heating, cooling, and others), based on population, income, technology, energy prices, and average annual climate. This paper aggregates the two models' results to a common resolution: annual residential and commercial building electricity consumption by state. Both models show similar responses to future projected population change and climate change, with population change having the larger impact between the two. Differences are primarily due to how the models capture changes in the aggregate energy efficiency of the building stock as it evolves over time. • Models of building electricity consumption are methodologically diverse. • Two models of building electricity using very different approaches are compared. • The models mostly agree in their projections of building electricity consumption. • How the efficiency of buildings evolves drives disagreement between the models. [ABSTRACT FROM AUTHOR]

Thermal stratification in reservoirs is a critical process that regulates downstream riverine energy and biogeochemical cycling. Current stratification models either simplify vertical energy process, reservoir geometry or neglecting the effects of reservoir operation. Here we present a new multilayer reservoir stratification model that can be applied for reservoir and stream temperature simulation at regional or global scale. With a multilayer vertical discretization, we introduce a newly developed storage-area-depth dataset to improve parameterization of advection processes in and out of the reservoir. The new model better represents vertical temperature gradient and subsequently temperature of water released to downstream. The stratification model is applied to 1,400 reservoirs over the contiguous United States and validated against observed surface, profile, and outflow temperature data over 130 reservoirs subjected to various levels of regulation. The Nash-Sutcliffe values are higher than 0.5 for about 77% of the validated reservoirs using surface temperature while the average values of root mean square error and bias are 3.6 °C and -1.1 °C, respectively. Using the new reservoir storage-area-depth dataset improves the simulation of surface temperature at over 69% of the validated reservoirs compared to using simplified reservoir geometry. The reservoir stratification model contributes to improving predictive understanding of anthropogenic impact on terrestrial hydrological, ecological, and biogeochemical cycles. [ABSTRACT FROM AUTHOR]

There is a lack of understanding of the complex spatiotemporal patterns of heat exposure during heat waves, and the impact of urbanization intensity and urban morphology on heat exposure in urban thermal environments. To address these issues, this study used mean radiant temperature (Tmrt) as an index to indicate human exposure to extreme heat, and generated hourly heat exposure maps at a 1-m spatial resolution in Summer 2020 for heat wave and non-heat wave days across three diversely urbanized and climatically different U.S. cities (Riverside, CA; Des Moines, IA; and Boston, MA) using the SOlar LongWave Environmental Irradiance Geometry (SOLWEIG) model and multi-source remote sensing and GIS data. Based on these high-frequency and microscale maps, we found that heat exposure in urban canyons of downtown areas was high due to relatively low building's height to street's width (H/W) ratio, which resulted in a limited shading effect in the studied cities. Heat exposure during heat waves was enhanced by 6 °C to 10 °C compared to non-heat wave conditions, with the main differences occurring in the early afternoon between 12 pm and 2 pm. We found that hot cities (Riverside, 63 °C) had higher heat exposure than warm cities (Des Moines and Boston, 53 °C and 51 °C) during heat waves. Heat exposure in urban core areas was approximately 5C higher than that in rural areas during heat waves. Additionally, we found that sky view factor was the most important urban morphology factor influencing heat exposure, with a relative importance of over 67% in these cities, but the role of impervious surface and trees varied among these cities. Impervious surface area (ISA) contributed more to heat exposure than trees in dry and hot regions (Riverside), but not in humid and warm cities (Des Moines and Boston). This study is the first to generate hourly heat exposure maps at a 1-m resolution for heat wave and non-heat wave days, and to investigate spatiotemporal patterns and the impacts of urbanization intensity and urban morphology on heat exposure in multiple cities. The findings of this study can be useful in developing urban policies to improve urban thermal environments in diverse urban settings, and our transferable framework can potentially be applied to other cities for heat exposure studies. • We investigated human exposure to extreme heat based on mean radiant temperature. • We generated hourly heat exposure at 1-m during heat waves in three U.S. cities. • Heat exposure during heat waves was found to be 6 °C to 10 °C higher. • Sky view factor was identified as the most influential morphology factor. • The proposed framework has the potential to be applied to other cities. [ABSTRACT FROM AUTHOR]

In the 2015 Paris Agreement, nations worldwide pledged emissions reductions (Nationally Determined Contributions—NDCs) to avert the threat of climate change, and agreed to periodically review these pledges to strengthen their level of ambition. Previous studies have analyzed NDCs largely in terms of their implied contribution to limit global warming, their implications on the energy sector or on mitigation costs. Nevertheless, a gap in the literature exists regarding the understanding of implications of the NDCs on countries’ Energy-Water-Land nexus resource systems. The present paper explores this angle within the regional context of Latin America by employing the Global Change Assessment Model, a state-of-the-art integrated assessment model capable of representing key system-wide interactions among nexus sectors and mitigation policies. By focusing on Brazil, Mexico, Argentina and Colombia, we stress potential implications on national-level water demands depending on countries’ strategies to enforce energy-related emissions reductions and their interplays with the land sector. Despite the differential implications of the Paris pledges on each country, increased water demands for crop and biomass irrigation and for electricity generation stand out as potential trade-offs that may emerge under the NDC policy. Hence, this study underscores the need of considering a nexus resource planning framework (known as “Nexus Approach”) in the forthcoming NDCs updating cycles as a mean to contribute toward sustainable development. [ABSTRACT FROM AUTHOR]

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in 5-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region's demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use 11 scenarios, varying the socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios demonstrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model. [ABSTRACT FROM AUTHOR]

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in five-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region's demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use eleven scenarios, varying socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios illustrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model. [ABSTRACT FROM AUTHOR]

Hydropower plays an important role as the global energy system moves towards a less carbon-intensive and sustainable future as promoted under the Sustainable Development Goals (SDGs). This article provides a systematic review of the impacts from policy, climate change and Water-Energy-Food (W-E-F) nexus on hydropower development at global scale. Asia, Africa and Latin America are hotspots promoting hydropower development with capacity expansion, while Europe and North America focus on performance improvement and environment impacts mitigation. Climate change is projected to improve gross hydropower potential (GHP) at high latitude of North Hemisphere and tropical Africa and decrease that in the US, South Africa and south and central Europe. Analysis from W-E-F nexus highlights the importance of integrated approaches as well as cross-sectoral coordination so as to improve resources use efficiency and achieve sustainable hydropower development. These three factors together shape the future of hydropower and need to be considered for planning and operation purpose. [ABSTRACT FROM AUTHOR]

A new large-scale stream temperature model has been developed within the Community Earth System Model (CESM) framework. The model is coupled with the Model for Scale Adaptive River Transport (MOSART) that represents river routing and a water management model (WM) that represents the effects of reservoir operations and water withdrawals on flow regulation. The coupled models allow the impacts of reservoir operations and withdrawals on stream temperature to be explicitly represented in a physically based and consistent way. The models have been applied to the Contiguous United States driven by observed meteorological forcing. Including water management in the models improves the agreement between the simulated and observed streamflow at a large number of stream gauge stations. It is then shown that the model is capable of reproducing stream temperature spatiotemporal variation satisfactorily by comparing against the observed data from over 320 USGS stations. Both climate and water management are found to have important influence on the spatiotemporal patterns of stream temperature. Furthermore, it is quantitatively estimated that reservoir operation could cool down stream temperature in the summer low-flow season (August-October) by as much as 1∼2°C due to enhanced low-flow conditions, which have important implications to aquatic ecosystems. Sensitivity of the simulated stream temperature to input data and reservoir operation rules used in the WM model motivates future directions to address some limitations in the current modeling framework. [ABSTRACT FROM AUTHOR]

Climate sensitivity and climate-carbon cycle feedbacks interact to determine how global carbon and energy cycles will change in the future. While the science of these connections is well documented, their economic implications are not well understood. Here we examine the effect of climate change on the carbon cycle, the uncertainty in climate outcomes inherent in any given policy target, and the economic implications. We examine three policy scenarios-a no policy 'Reference' (REF) scenario, and two policies that limit total radiative forcing-with four climate sensitivities using a coupled integrated assessment model. Like previous work, we find that, within a given scenario, there is a wide range of temperature change and sea level rise depending on the realized climate sensitivity. We expand on this previous work to show that temperature-related feedbacks on the carbon cycle result in more mitigation required as climate sensitivity increases. Thus, achieving a particular radiative forcing target becomes increasingly expensive as climate sensitivity increases. [ABSTRACT FROM AUTHOR]

The Platform for Regional Integrated Modeling and Analysis (PRIMA) is an innovative modeling system developed at Pacific Northwest National Laboratory (PNNL) to simulate interactions among natural and human systems at scales relevant to regional decision making. PRIMA brings together state-of-the-art models of regional climate, hydrology, agriculture and land use, socioeconomics, and energy systems using a flexible coupling approach. Stakeholder decision support needs underpin the application of the platform to regional issues, and an uncertainty characterization process is used to identify robust decisions. The platform can be customized to inform a variety of complex questions, such as how a policy in one sector might affect the ability to meet climate mitigation targets or adaptation goals in another sector. Current numerical experiments focus on the eastern United States, but the framework is designed to be regionally flexible. This paper provides a high-level overview of PRIMA's functional capabilities and describes some key challenges and opportunities associated with integrated regional modeling. [ABSTRACT FROM AUTHOR]

Since the 1950's, global fertilizer usage has increased by more than 800% resulting in detrimental impacts to the environment. The projected increase in crop production due to increasing demands for food, feed, biofuel, and other uses, may further increase fertilizer usage. Studies have examined achieving agricultural intensification in environmentally sustainable ways, however, they have not focused on the whole-system economic aspects of changes in fertilizer usage over the long term. We utilize the Global Change Analysis Model (GCAM) to explore the impact of reducing global fertilizer usage on land use change, agricultural commodity price and production, energy production, and greenhouse gas emissions. We find that constrained fertilizer availability results in reduced global cropland area, particularly land used for bioenergy production, and expanded forested area. These results are driven by price impacts which lead to shifts in agricultural production between commodity types, regions, and technologies, and which lead to decreased agricultural commodity demands. • Imposing fertilizer restrictions results in reduction in cropland area and increase in forested area. • Cost of food production increases and emissions increase marginally. • These impacts are driven by shift in agricultural production. [ABSTRACT FROM AUTHOR]

Summary: Recent research points to changing frequency and intensity of heavy rainfall events and consequently, flood flows in Northeastern Illinois. Flood frequency modeling is a key component of managing floodwaters in this highly populated metropolitan area, which includes Chicago and surrounding urban areas, and has enormous economic and environmental significance. In this research, 100-year 24-h precipitation totals in Northeastern Illinois were quantified using the L-moments method with 1948–2004 hourly precipitation data at six stations in the region for comparison with published results from the US Weather Bureau Technical Paper No. 40 (TP-40), Illinois State Water Survey Bulletin 70 (Bulletin 70) and National Oceanic and Atmospheric Administration Atlas-14 (NOAA-14). Sensitivity analyses were conducted to examine the effects of various factors on 100-year, 24-h precipitation at Aurora College station, in particular the effects of selecting different periods of the precipitation record, different regions, and different underlying distributions. The sensitivity analyses used 1900–2004 daily precipitation data at 12 stations in the region. Finally, the HEC-HMS rainfall-runoff model was used to illustrate relative impacts of changing estimates of design precipitation on flood peaks at 12 small watersheds in the region. It was demonstrated that the oldest source, TP-40, produced significantly smaller 100-year, 24-h rainfall totals, than Bulletin 70, NOAA-14, and the current study. It was also shown that the variability in design rainfall calculated based on different 50-year records (nearly 200%) was much larger than those based on the choice of underlying statistical distribution (50%), or the selection of region (25%). The average relative increase in hydrologic peaks exceeded that of rainfall peaks. This could partly be explained by the non-linear nature of the hydrologic system. [Copyright &y& Elsevier]

Background CRE infections cause significant mortality, in large part because rapid identification of these infections is challenging. We tested the hypothesis that CRE and their isogenic carbapenem-susceptible counterparts have differential metabolic responses to carbapenem therapy. Methods We generated isogenic pairs of E. coli , E. cloacae , and K. pneumoniae by inserting a bla NDM-1-containing plasmid into carbapenem-susceptible E. coli , E. cloacae , and K. pneumoniae. We confirmed phenotypic meropenem (MPM) resistance per CLSI breakpoints for Enterobacteriaceae (MIC ≥4) in the NDM-1+ member and susceptibility (MIC≤1) in the NDM-1- member of each pair. We administered 2 × 108 CFU of each isolate intranasally to 23–28 g male C57BL/6J mice, infecting 6 mice with the NDM-1+ member and 6 with the NDM-1− member of each species pair (12 mice per bacterial species). 24 hours after infection, we treated 3 mice in each NDM-1+ and NDM-1− bacterial species cohort with MPM over 4 hours, and the other 3 mice in each cohort with saline over 4 hours as controls, confirming adequate infection (a target of 106 CFU/g of lung tissue) in quantitative lung homogenate cultures. We then collected breath samples from each mouse via tracheostomy using a murine ventilator, identifying all volatile metabolites in each sample using thermal desorption-gas chromatography/tandem mass spectrometry. We used Wilcoxon tests to examine differences in metabolite abundance between MPM and saline-treated control mice in the NDM-1+ and NDM-1− a member of each species pair, with a two-sided P -value threshold of < 0.1. Results Several breath volatile metabolites changed differentially within each NDM-1+/NDM-1- pair, outlined in Table 1 (E. coli), Table 2 (E. cloacae), and Table 3 (K. pneumoniae). Each listed metabolite that changed with MPM did not change with MPM in mice infected with each isogenic counterpart Conclusion There are differential in vivo metabolic responses with effective vs. ineffective treatment of mice with pneumonia caused by E. coli, E. cloacae , and K. pneumoniae pairs that are genetically identical other than bla NDM-1; this differential treatment response can potentially be used to identify these infections. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

The world has witnessed a paradigm shift in the last decade in the way countries engage with each other. A cooperation based international paradigm is being challenged by countries engaging in rivalry with each other, motivated by their national interests. The developmental pathway the world charts in the future is going to be path defining for global energy and emissions use. India, expected to play a critical role in the global energy and emissions debate, would continue to impact and be impacted by these pathways. In India specific literature, however, little attention has been paid to development pathways related uncertainties and their impact on India's energy and resource futures. We seek to address this gap by modelling India's electricity futures and associated water demands under different development pathways. Our analysis shows that alternative development pathways would have significantly different implications for the energy access and equity, electricity generation mix, associated water withdrawals, and carbon dioxide emissions in India. We conclude by arguing that energy, emissions and water policies should be conceived with an explicit understanding of uncertainties related to potential development pathways that the world and India can chart. • Development pathways would have critical implications for India's resource policy. • These imply vastly different states of electricity, water and emissions futures. • Resource policy should explicitly consider implications of development pathways. • India needs to start considering dry cooling seriously as an adaptation option. • We also present an India specific dataset of water coefficients for power generation. [ABSTRACT FROM AUTHOR]

Background CDI is a frequent cause of morbidity and mortality in hospitalized patients. Despite advances in rapid CDI testing, there are often delays between the onset of symptoms and receipt of test results. We sought to test the hypothesis that the altered CDI intestinal microbiome has a unique volatile metabolite profile, distinct from the profile of patients with other causes of antibiotic-associated diarrhea, which potentially can be used to identify patients with CDI. Methods We prospectively collected fresh stool samples from inpatients with suspected CDI at an academic tertiary care hospital from July 2015 to November 2017, adsorbed volatile metabolites from each sample onto sorbent tubes within an hour of sample collection, and used thermal desorption-gas chromatography/tandem mass spectrometry to identify each metabolite. All patients were exposed to at least one antibiotic agent in the prior 90 days, and only patients receiving empiric CDI treatment or with formed stool samples were excluded. We used logistic regression models, adjusting for prior anti-anaerobic antibiotic therapy and CDI severity (serum albumin <3 g/dL and WBC ≥ 15,000/mm3 or abdominal tenderness) and adjusting for multiple testing using Storey's q-value procedure (with a threshold of q ≤ 0.05), to examine the relationship between CDI, as determined by the reference standard of the cell culture cytotoxicity neutralization assay, and each metabolite. Results In our 565-patient cohort, median age was 61 years (IQR 50, 70) and 277 (49%) were male; 173 (31%) had abdominal pain in the 24 hours before testing, 59 (10%) had fevers in the prior 24 hours, 22 (4%) had an ileus, 74 (13%) had mental status changes in the prior 24 hours, 89 (16%) were hospitalized in the ICU at the time of testing, 45 (7%) were receiving pressors, 82 (15%) had a WBC ≥ 15,000/mm3, and 137 (24%) had a serum lactate > 1.5 mmol/L. Ultimately, 155 patients were diagnosed with CDI. Ten metabolites (Table 1, Figure 1) were differentially distributed in patients with and without CDI. Conclusion We identified a suite of volatile metabolites that differentiates stool from patients with and without CDI; this profile may ultimately be used to identify patients with CDI. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

Electricity demand is expected to grow over the coming decades creating a need for long-term power sector planning. Planning will require two key considerations. First, maintaining reliability requires enough capacity to supply electricity during hours of peak demand. This requires an understanding of the character of load profiles and the availability of dispatchable electricity. Second, understanding the resilience of the power sector to future stressors will require an assessment of the interactions of the power sector with co-evolving natural and human systems within a multi-sectoral context. For example, future temperature increases could lead to an increase in the demand for air conditioning resulting in increased peak demands and consequently the need for additional capacity.Many analytical tools are used for long-term power sector planning. Power sector focused models capture the complexities of electricity supply and demand but are limited in capturing the interactions of the sector with the broader system. In contrast, multi-sector human-Earth system models capture complex relationships across sectors and systems but represent power sector dynamics at fairly aggregate levels. We study the evolution of the electricity system by simultaneously accounting for power sector focused process dynamics and the interactions of the power sector with the broader system under a consistent integrated framework. To do so, we make improvements to GCAM-USA, a long-term multi-sector human-Earth system model. We incorporate i.) Information about sub-annual demand profiles combined with the ability to separate investment and operation decisions to track capacity requirements in response to changes in peak demands and ii.) Empirically derived relationships between temperature change and peak electricity demands in the building sector. Using the improved model, we study investments and operations in the US power sector under plausible scenarios of future temperature variability. [ABSTRACT FROM AUTHOR]

In the next few decades increasing and shifting populations, urbanization, technological advances, pollution and climate change will all contribute to increased stress on limited water, energy, and land resources in several regions across the world. It is becoming increasingly clear that the feedback between these different sectors are critical and that planners and decision makers need to account for the inter-sectoral dependencies in order to capitalize on key opportunities as well as avoid unforeseen challenges which may be ignored when using single-sector approaches. In addition to cross-sectoral linkages, equally important is the spatial scale at which the resources are analyzed and managed, both because of the inherent characteristics of each resource as well as the administrative or legislative boundaries over which decisions are made and implemented. As sectoral planners adopt integrated planning approaches, a key challenge is collecting, harmonizing and synchronizing data across sectors and at decision-relevant spatial scales from large multinational regions to river sub-basins.This study seeks to contribute to this integrated panning challenge by introducing a new framework which offers planners a tool to analyze cross-sector dynamics at decision-relevant spatial scales. The framework is designed to utilize data from various models operating at different resolutions so as to capture both broader global dynamics across a range of earth and human systems (as usually done in global integrated assessment models), as well as finer-resolution sector-specific models which may capture details outside the scope of coarse-scale integrated assessment models. All data is harmonized to the spatial boundaries of interest to the stakeholder and then inter-sectoral and inter-regional links are captured via technological and trade input-output matrices. Impacts of policies and changes in one sector can then be tracked across other sectors and regions.In partnership with various government ministries and universities in Latin America, several case studies are designed to demonstrate the capabilities of the framework to analyze how various policies of interest and socio-economic changes influence the evolution of national and sub-national energy, water, and land systems in the context of a changing climate. The resulting sectoral synergies, conflicts and investment needs carry important implications for national resource management and infrastructure planning. [ABSTRACT FROM AUTHOR]