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Abstract The nitrogen regulatory policy (NRP) solution is introduced as a mitigation measure against environmental nitrogen losses and keeps food production in the Safe Operating Space of the Nitrogen Planetary Boundary. Meanwhile, scientific research shows that steps taken to reduce environmental harm can increase the unpredictability of calorie production from crops. This study sought to investigate the impact of NRP solutions on the level of risk of accessibility to calorie sources from domestic production, the variations in calorie sources by livestock and non‐livestock diet components, and the responses of different dietary preferences, namely, poor, medium, and rich livestock protein diets, against NRP solutions in the Zayandeh‐Rud River basin, Iran. We developed the aggregate household food security index (AHFSI) and combined it with outputs of crop simulation model to examine how changes in dietary energy supplies under three NRP scenarios—low, moderate, and high nitrogen fertilizer application—affect the stability of three regional dietary preferences. The comparison of NRP scenarios movements realized that increases (or decreases) in nitrogen fertilizer rates contradicted the stability in AHFSI. Additionally, a one‐unit change in the average calories from non‐livestock sources, such as wheat and potatoes, results in greater fluctuations in the standard deviations of produced calories compared to changes in meat and dairy production. We proposed that in order to prevent adverse effects of NRP solutions on food security, mitigation strategies addressing the NRP solution should be structured based on (i) regional heterogeneities, (ii) type of crops, that is, food and feed crops, (iii) the range of nitrogen rates movement; (iv) and the socioeconomic background related to dietary preferences or economic deciles of food expenditure.

Crop yield is usually affected by impending weather, climate conditions, and human interventions like irrigation. Hence, the prompt detection of regions experiencing water scarcity can aid in implementing effective mitigation strategies. Our study utilized a data-driven approach to compute a Water Demand Index (WDI), which incorporates crucial first-order geophysical variables like ambient temperature, vegetation status, and soil moisture, to identify water-stressed fields in Senegal’s agricultural regions during the millet planting, growing, and harvesting periods. We have also explored various scenarios for enhancing the accuracy of millet yield prediction by incorporating other drought indices, soil characteristics, and a bias correction factor. The novel aspects of this research include the development of regional crop yield predictive models based on sub-seasonal and unaggregated crop information to reflect local variances. To optimize the hyperparameters of machine learning (ML) models, various techniques were utilized. Meanwhile, the performance of these ML models was evaluated using a nested cross-validation approach. The outcomes of the analysis demonstrate that the Random Forest Regressor model exhibits superior predictive performance. The results imply that a holistic approach, encompassing diverse environmental factors and crop growth stages, could result in more precise and dependable millet yield predictions. These results mark a leap forward in crop yield prediction for data-poor regions, paving the way for more accurate and efficient agricultural management. By emphasizing the critical role of detailed, spatio-temporal data, this work substantially enhances model precision, informing targeted strategies to boost yields. This advancement stands to serve producers and consumers alike, fostering sustainable, high-yield agriculture.

Abstract In Senegal, agriculture is an important sector underpinning the socioeconomic fabric of the populace. Notably, the agricultural production in this region exhibits heightened sensitivity to climatic perturbations, particularly droughts and heat waves. This study aims to determine the resilience of different agronomic interventions for farmers practicing mixed farming that produce both crops (i.e., groundnut (Arachis hypogaea L.) and pearl millet (Pennisetum glaucum (L.) R. Br.)) and raise animals in the Groundnut Basin in Senegal, which holds historical and socioeconomic significance. To understand the current situation regarding demographics, economics, consumption behavior, and farm operations for smallholder farmers, data were comprehensively collected from government and nongovernment organizations (NGO) reports, scientific papers, organization databases, and surveys. Additionally, the Agricultural Production Systems sIMulator (APSIM) was used to understand how combinations of three planting dates, three plant densities, and six urea nitrogen (N) fertilizer rates affected the yield of pearl millet, which were used as the alternative scenarios to the baseline in the farm modeling and analyses. All the collected and generated data were used as inputs into the Farm Simulation Model (FARMSIM) to generate economic, nutritional, and risk data associated with mixed farming systems. The generated data were then used to determine the resilience of the alternative scenarios against the baseline. Initially, a multi‐objective optimization was employed to meet nutritional needs while maintaining a healthy diet at the lowest cost. Then, the scenarios that met the population's nutritional requirements were evaluated based on four economic indicators: net cash farm income (NCFI), ending cash reserves (EC), net present value (NPV), and internal rate of return (IRR). Lastly, those that passed the economic feasibility test were ranked based on risk criteria certainty equivalent (CE) and risk premium (RP). The analyses found N fertilizer rates of 0, 20, and 100 kg N ha−1 were generally economically not feasible. Additionally, medium (early‐July to late‐August) and late (late‐July to mid‐September) planting dates generally performed better than early (early‐June to late‐July) planting dates, while plant densities of 3.3 and 6.6 pL m−2 performed better than 1.1. The robust resilience approach introduced in this study is easily transferable to other regions.

AbstractWe conducted a comprehensive review of livestock farming in Bangladesh to unveil current challenges and potential opportunities in this agriculture sector. Six challenges were selected as the major constraints to livestock farming in Bangladesh: climate change and natural hazards, poor veterinary care, breeding and management resources, marketing and international trade, and the SARS-CoV-2 pandemic. Solutions to these challenges (such as hazardous weather shelters, intensified surveillance of biosecurity for farms and markets, and non-government, government, and private organizations working together to educate and assist farmers) must target specific regions where the solutions would have the greatest effect.

Subsurface drainage in humid areas prevents field waterlogging but also transports nutrients to freshwater systems. Controlled drainage (CD) reduces drainage discharge and nutrient transport from fields. Some regions are expected to experience increased precipitation in the future, requiring CD to be evaluated under a changing climate. The objective of this study was to compare the performance of CD under two weir managements for a future period (2030–2059) and historical period (1992-2021) in southeast Michigan, USA. Climate projections were obtained for the shared socioeconomic pathway 245 emission scenario. Aggressive management involved maintaining the weir height at 40 cm during the growing season and 15 cm during the non-growing season, with a longer period of managed flow compared to common management, which maintained the weir height at 50 cm during the growing season and 30 cm during the non-growing season. It was predicted that the 30-year average annual precipitation would not change significantly in the future. The 30-year average mean monthly temperature would increase by 3.0°C in the future compared to the historical period. We performed simulations using the calibrated Root Zone Water Quality Model 2 (RZWQM2). The average drainage discharge in the future indicated a 20% increase in the 30-year average drainage discharge for a field with free drainage. The CD with common and aggressive managements reduced drainage discharge by 59% and 67% for the historical period, whereas the performance of CD was even better for the future period (63% and 72%, respectively). The improved future performance of CD can be attributed to a shift in precipitation patterns, with reduced precipitation during the growing season and increased precipitation during the non-growing season. As a result, the more aggressive weir management during this period created additional opportunities for reducing drainage discharge. In conclusion, aggressive management resulted in a slightly better flow-reducing performance than common management while indicating that both methods would effectively reduce drainage discharge in the likely future scenario.

IntroductionThe lack of a holistic view of agriculture’s social and economic aspects emerges as a limiting factor in policy implementations of sustainable intensification. The aim of this study was to (i) synthesize data from the five domains of sustainable agriculture intensification framework focusing on Senegal as a case study, (ii) harmonize and scale data used to minimize spatial–temporal differences, and (iii) build a digital tool aiming researchers and policymakers to facilitate the data visualization and connection.MethodsWe propose and prototype an interactive digital decision-support tool as a potential solution to integrate environmental, social, economic, human and production domains in agriculture projects. To demonstrate the use of the tool, a case study using data from Senegal was developed to show the benefits of open science and connectivity among domains.Results and discussionThe digital tool presented here allows users to explore an open repository from Senegal serving as a foundational approach to integrate diverse agricultural domains when developing sustainable intensification projects.

Abstract Malawi's food and nutrition security heavily relies on rainfed agricultural production, which is prone to the effects of climate change, such as floods and dry spells. To improve the situation, policies have been established as a guide to assist in the implementation of agricultural technologies in the country. This study aims to evaluate the effect of two national policies on addressing nutrition, economics, and food security by introducing modern/existing technologies in the Phalombe district in Malawi. To gain insight into the representative farm operations, financial, demographic, and consumption behaviors of smallholder farmers, the data were obtained through a combination of focus group discussions, expert opinions, and various published reports, including both government and nongovernment sources. The farm simulation model (FARMSIM) was utilized to analyze district‐level crop and livestock farm operations. In this study, four irrigation technologies (treadle pump system, motorized pump system, solar‐powered system, and river diversion irrigation system) were assessed among the agricultural interventions implemented in Malawi. Also, seven land uses (i.e., corn, onion, bean, rice, tomato, leafy vegetable, and cabbage) were developed and simulated under irrigation scenarios. The irrigation systems and land use scenarios were evaluated based on economics, productivity, nutrition, and risk. The treadle pump and leafy vegetable scenario were generally identified as the most preferred combination. In addition, the productivity and riskiness of the other scenarios can be improved by selecting the best combination of crop and irrigation systems. The study concludes that government and private investment in subsidies for large‐scale irrigation systems, as well as improvements to market prices and infrastructure, are necessary to boost food security and enhance the livelihood of smallholder farmers in rural Malawi.

Understanding the end-use of water is essential to a plethora of critical research in premise plumbing. However, direct access to end-use data through physical sensors is prohibitively expensive for most researchers, building owners, operators, and practitioners. Therefore, machine learning models can alleviate these costs by predicting downstream end-use events (e.g., sink, shower, dishwasher, and washing machine) via an affordable subset of upstream sensors. Choosing which upstream sensors, as well as data preprocessing methods, are best for machine learning has historically been a manual process. This paper proposes a novel approach to systematically configure the machine platform automatically. The optima were determined through a Pareto analysis of the exhaustive combinations of upstream predictors and preprocessing methods. The model was trained and validated with real-world data obtained from a house that has been extensively monitored for over a year. Results from the analysis suggested that downstream events can be effectively predicted with minimum overfitting error for most categories, using as few as two to four upstream sensors. This study automatically implemented highly accurate machine learning models to predict downstream features within premise plumbing systems, significantly lowering the costs of researching residential plumbing best practices such as water conservation. HIGHLIGHTS Physical end-use monitoring is prohibitively expensive for most practitioners.; Machine learning is a viable method of lowering the cost of end-use categorization.; Here we proposed a novel approach to systematically configure the machine platform automatically.; Effective end-use prediction is possible with a small subset of upstream features.;

Study Region: The Lenguazaque River Basin is a 290 km2 headwater catchment of the Fuquene Lake Watershed in the Colombian Andes. Regional stakeholders are optimizing water allocations for all users, given numerous quantity related issues such as its simultaneous use for conservation, agriculture, and coal mining. Study Focus: Developing hydrological models in headwater catchments is challenging, especially in developing countries where technical resources and data are limited. This study addresses these challenges by proposing strategies to develop reliable yet mathematically simple models, requiring fewer inputs than data-intensive alternatives. Three stages are proposed, focused on 1) assessing hydrological data, 2) preparing datasets for modeling, and 3) developing models in daily and sub-daily resolutions. The last stage is grounded on a data-based mechanistic modeling approach and includes a novel combination of baseflow separation with digital filters and multi-objective optimization principles. Having a hydrophysical meaning, obtained models are suitable for simulating alternative scenarios. New hydrological insights for the region: Resulting daily models achieved an acceptable performance, significantly better than a semi-distributed model. The performance of sub-daily models was sub-optimal, yet it can be improved by enhancing the quality of sub-daily datasets and the efficiency of computational algorithms. Due to their mathematical nature, the proposed strategies are expected to be applicable to other headwater catchments where improving water management is an imperative need.

Crop models are widely used to predict plant growth, water input requirements, and yield. However, existing models are very complex and require hundreds of variables to perform accurately. Due to these shortcomings, large-scale applications of crop models are limited. In order to address these limitations, reliable crop models were developed using a deep neural network (DNN) – a new approach for predicting crop yields. In addition, the number of required input variables was reduced using three common variable selection techniques: namely Bayesian variable selection, Spearman's rank correlation, and Principal Component Analysis Feature Extraction. The reduced-variable DNN models were capable of estimating future crop yields for 10,000,000 different weather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables. To establish clear superiority of the methodology, the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance (mRMR). The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals. Specifically, the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly (78.6% accuracy) to the original DNN crop model with 400 neurons in 10 layers, even though the size of the neural network was reduced by 80-fold. This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.

River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifaceted field yet being a dynamic task in nature. It is characterized by high stochasticity, non-linearity, non-stationarity, and feature redundancy. Various artificial intelligence (AI) modeling frameworks have been introduced to solve river sediment problems. The present survey is designed to provide an updated account of the latest and most relevant AI-based applications for modeling the sediment transport in river basin systems. The review is established to capture the subsequent developments in the advanced AI models applied for river sediment transport prediction. Also, several hydrological and environmental aspects are identified and analyzed according to the results produced in those studies. The merits and constraints of the well-established AI models are further discussed in much detail, particularly considering state-of-the art, modeling frameworks and their application-specific appraisal, and some of the key proposed future research directions. Together with the synthesis of such information to drive a new understanding of models and methodologies related to suspended river sediment prediction, this review provides a future research vision for hydrologists, water scientists, water resource engineers, oceanography and environmental planners.

Following potential reforestation in the Amazon Basin, changes in the biophysical characteristics of the land surface may affect the fluxes of heat and moisture behavior. This research examines the impacts of potential tropical reforestation on surface energy and moisture budgets, including precipitation and temperature. The study is novel in that while most studies look at the opposite driver (deforestation), this one examines the impact of potential forest rehabilitation on atmospheric behavior using WRF.V3.9 (weather research and forecast model). We found that forest rehabilitation across the Amazon Basin can make the atmosphere cooler with more moisture and latent heat (LH), especially during May-November. For instance, the mean seasonal temperature decreased significantly by about 1.2 °C, indicating the cooling effects of reforestation. Also, the seasonal precipitation increased by 5 mm/day in reforested areas. By reforestation, the mean monthly LH also increased as much as 50 W m−2 in August in certain areas, while available moisture to the atmosphere increased by 27%, indicating possible causal mechanisms between increased LH and precipitation and emphasizing the mechanisms that were identified between the onset of the wet season and forest cover. Therefore, it is likely that forest regrowth across the basin leads to, if not reverses regional climate change, at least slowing down the rate of changes in the climate.

Livestock farming is a livelihood activity and is critically important for the food and nutritional security of the majority of the population in West African countries, including Senegal. Nevertheless, livestock farming operates far below the optimum production potential, mainly due to demographical, biophysical, economic, environmental, and sociopolitical challenges. To address these issues, we conducted this review with an overall objective of characterizing different livestock farming systems and to identify challenges and opportunities to improve livestock production in West Africa through the broader perspectives from the case of Senegal. Pastoral, agropastoral, and off-land systems are the three major livestock production systems in this region, which are unique in terms of agroclimatology and degree of intensification and integration. The major challenges identified in livestock farming systems are lack of pasture and quality feed, scarcity of water resources, climate change, undeveloped breeding and management of livestock, poor marketing and trade, and socioeconomic constraints. Moreover, we contribute to the literature on crop-livestock farming in Senegal and West Africa by proposing plausible interventions to improve the productivity of the farming system to improve food and nutritional security. Concentrated efforts must be taken in co-designing effective management interventions for sustainable intensification of livestock sector in the region, considering site-specific approaches.

Increasing climate variability and extreme weather events impose significant challenges to the crop production systems throughout the world. Alternative agricultural treatment systems have been proposed to manage these challenges. However, these treatments have not been sufficiently studied for their ability to improve climate resilience, especially in terms of profitability and risk management, which are important metrics of resilience that determine farm-level adaptation. Hence, we evaluated the climate resilience of three alternative agricultural treatments for a long-term (27-years) rotation of corn-soybean-wheat, cast in the temperate humid climate of Southwest Michigan, United States. The three alternative treatments include no-till, reduced input, and US Department of Agriculture (USDA) certified organic. These are compared to the conventional treatment along with the same crop rotation. Means and volatility of expected gross margins and risk preferences were used as the evaluation metrics. Results demonstrate that the net revenues from the USDA certified organic are largely expected to exceed the net revenues of conventional treatment. Also, for all commodities, organic treatment may exert greater annual stability in revenues. The no-till treatment dominates conventional and reduced input practices in expected annual net revenues with a relatively lower risk to those revenues in light of climate extremes. Furthermore, the organic and no-till treatments showed appropriateness to cater to a range of farmers with different risk preferences. Therefore, the organic and the no-till treatments were deemed climate-resilient. The conventional and reduced input treatments did not show resilience thus will be vulnerable to the changing climate. Despite the economic support for adopting resilient practices, growers have been slow to adopt new approaches. We suggest future research needs for understanding grower motivations for adopting climate-resilient practices and consider policy implications.

Aquaculture in Bangladesh has grown dramatically in an unplanned manner in the past few decades, becoming a major contributor to the rural economy in many parts of the country. National systems for the collection of statistics have been unable to keep pace with these rapid changes, and more accurate, up to date information is needed to inform policymakers. Using Sentinel-2 top of atmosphere reflectance data within Google Earth Engine, we proposed six different strategies for improving fishpond detection as the existing techniques seem unreliable. These techniques include: (1) identification of the best time period for image collection, (2) testing the buffer size for threshold optimization, (3) determining the best combination of image reducer and water-identifying indices, (4) introduction of a convolution filter to enhance edge-detection, (5) evaluating the impact of ground truthing data on machine learning algorithm training, and (6) identifying the best machine learning classifier. Each enhancement builds on the previous one to develop a comprehensive improvement strategy called the enhanced method for fishpond detection. We compared the results of each improvement strategy to known ground truthing fishponds as the metric of success. For machine learning classifiers, we compared the precision, recall, and F1 score to determine the quality of results. Among four machine learning methods studied here, the classification and regression trees performed the best with a precision of 0.738, recall of 0.827, and F1 score of 0.780. Overall, the proposed strategies enhanced fishpond area detection in all districts within the study area.

Global demand for livestock products is expected to double by 2050, mainly due to improvement in the worldwide standard of living. Meanwhile, climate change is a threat to livestock production because of the impact on quality of feed crop and forage, water availability, animal and milk production, livestock diseases, animal reproduction, and biodiversity. This study reviews the global impacts of climate change on livestock production, the contribution of livestock production to climate change, and specific climate change adaptation and mitigation strategies in the livestock sector. Livestock production will be limited by climate variability as animal water consumption is expected to increase by a factor of three, demand for agricultural lands increase due to need for 70% growth in production, and food security concern since about one-third of the global cereal harvest is used for livestock feed. Meanwhile, the livestock sector contributes 14.5% of global greenhouse gas (GHG) emissions, driving further climate change. Consequently, the livestock sector will be a key player in the mitigation of GHG emissions and improving global food security. Therefore, in the transition to sustainable livestock production, there is a need for: a) assessments related to the use of adaptation and mitigation measures tailored to the location and livestock production system in use, and b) policies that support and facilitate the implementation of climate change adaptation and mitigation measures.

In recent years, complexities related to a variety of sustainable development criteria and several preferences of stakeholders have caused a serious challenge for selecting the more desirable urban water strategy within watershed. In addition, stakeholders might have several risk attitudes depending on the number of criteria satisfied by water strategies. Accordingly, a risk-based consensus-based group decision-support system model is proposed for choosing the more desirable water strategy, using the external modified ordered weighted averaging (EMOWA) and internal modified ordered weighted averaging (IMOWA) operators. The operators calculate the scores of strategies in several risk-taking attitudes of group decision-making, considering the sustainable development criteria. Additionally, the consensus-seeking phase is considered using a risk-based weighted Minkowski’s method. This model is successfully implemented for the Kashafroud urban watershed in Iran, for selecting the more desirable urban water strategy in 2040. Accordingly, in the completely risk-averse viewpoint, the stakeholders select the combined supply-demand management strategy satisfying all of the criteria. In contrast, in the completely risk-prone standpoint, the stakeholders choose the demand management strategy satisfying at least one criterion. Developing the risk-based consensus-based group decision-support system model is suggested for integrated urban watershed management for selecting the more desirable strategy, satisfying the sustainable development criteria.

Study region: The Kalamazoo River Watershed, southwest Michigan, USA. Study focus: Climate change is projected to have significant impacts on agricultural production. Therefore, understanding the regional impacts of climate change on irrigation demand for crop production is important for watershed managers and agricultural producers to understand for effective water resources management. In this study, the Soil and Water Assessment Tool was used to assess the impact of climate change on corn and soybean irrigation demand in the Kalamazoo River Watershed. Bias-corrected statistically downscaled climate change data from ten global climate models and four emissions scenarios were used in SWAT to develop projections of irrigation demand and yields for 2020–2039 and 2060–2079. Six adaptation scenarios were developed to shift the planting dates (planting earlier and later in the growing season) to take advantage of periods with greater rainfall or lower temperature increases. New hydrological insights for the region: Uncertainty in irrigation demand was found to increase moving from 2020–2039 to 2060–2079, with demand generally decreasing moving further into the future for corn and soybean. A shift in timing of peak irrigation demand and increases in temperature lead to corn yield reductions. However, soybean yield increased under these conditions. Finally, the adaptation strategy of planting earlier increased irrigation demand and water available for transpiration, while delaying planting resulted in demand decreases for both crops. Keywords: Irrigation demand, Climate change, SWAT, Crop yield, Adaptation, Uncertainty

Understanding water flow and its relative quantities through different pathways is vital for watershed management. Like many problems in hydrology, numbers of methods have been proposed for streamflow partitioning. Five methods were identified as being the most relevant and least input intensive. This study tested performance of these methods against separately measured surface and subsurface flow data from the coastal plain physiographic region of the southeastern United States. Separately measured surface and subsurface flow were collected for 12 years (1970-1981) in a field scale watershed by the Southeast Watershed Research Laboratory of the USDA-Agricultural Research Service. Results of comparative analysis indicated that Method IV performed the best. Results also indicated that accuracy of this method is highly dependent upon the proper estimation of the 'fraction coefficient' that is based on many physical and hydrologic characteristics of the watershed. This study concluded that deterministic/empirical methods such as Boughton's Method IV, require proper parameter value for increased accuracy. DOI: 10.1061/(ASCE)IR.1943-4774.0000093 CE Database subject headings: Subsurface flow; Overland flow; Base flow; Streamflow; Case studies.

Accurate and efficient estimation of streamflow in a watershed’s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost-effective approach to water resources planning and management. Two data-driven methods, Bayesian regression and adaptive neuro-fuzzy inference system (ANFIS), were tested separately as a faster alternative to a calibrated and validated Soil and Water Assessment Tool (SWAT) model to predict streamflow in the Saginaw River Watershed of Michigan. For the data-driven modeling process, four structures were assumed and tested: general, temporal, spatial, and spatiotemporal. Results showed that both Bayesian regression and ANFIS can replicate global (watershed) and local (subbasin) results similar to a calibrated SWAT model. At the global level, Bayesian regression and ANFIS model performance were satisfactory based on Nash-Sutcliffe efficiencies of 0.99 and 0.97, respectively. At the subbasin level, Bayesian regression and ANFIS models were satisfactory for 155 and 151 subbasins out of 155 subbasins, respectively. Overall, the most accurate method was a spatiotemporal Bayesian regression model that outperformed other models at global and local scales. However, all ANFIS models performed satisfactory at both scales.

Evaluating the potential effects of changes in climate on conservation practices can help inform strategies to protect freshwater biodiversity that are robust, even as conditions change. Here we apply a climate change “test” to a framework for estimating the amount of agricultural conservation practices needed to achieve desired fish conservation outcomes for four watersheds in the Saginaw Bay region of Michigan, USA. We developed three climate scenarios from global climate model outputs (high emissions scenario, “2080s” timeframe) to provide insight on potential impacts of a climate driver that represents a key uncertainty for this management system, the amount and timing of spring and summer precipitation. These scenarios were used as inputs to agricultural watershed models, which produced water quality outputs that we compared to thresholds in fish biodiversity metrics at the subwatershed scale. Our results suggest that impacts of climate change on evaporation rates and other aspects of hydrology will shift the relative importance of key stressors for fish (i.e., sediment loadings vs. nutrient concentrations) across these different watersheds, highlighting the need to design resilient implementation plans and policies. Overall, we found that changes in climate are likely to increase the need for agricultural conservation practices, but that increasing the implementation rate above current levels will likely remain a good investment under current and future climate conditions. [ABSTRACT FROM AUTHOR]

In this study, ten bioenergy crop rotations (corn, corn-stover, sorghum, soybean, corn-soybean, corn-soybean-canola, corn-stover-soybean, miscanthus, switchgrass, and sorghum-soybean) were selected based on local stakeholder (economically motivated) and regulator (environmentally motivated) preferences. These crops were implemented on diverse landscapes (agricultural, marginal, and agricultural plus marginal lands) one at time for 17 years using a SWAT model of the Saginaw River Watershed in Michigan. The bioenergy crops were evaluated based on 100 percent, 50 percent, and zero percent weight assigned to both stakeholders’ and regulators’ preferences using analytic hierarchy process (AHP), an optimization and decision-making technique that aims to satisfy multiple conflicting objectives. The corn-soybean-canola rotation was selected in all landscapes based on economic benefits (stakeholders’ preferences). Meanwhile, perennial grasses (miscanthus and switchgrass) were selected based on environmental benefits (regulators’ preferences), because they maintain permanent cover, require fewer inputs than traditional row crops, and are less management intensive. When implementing bioenergy crops on marginal lands, pollution generation greatly increased at the field level, indicating that these lands are likely not viable for bioenergy crop production to meet potential future renewable energy demand. [ABSTRACT FROM AUTHOR]

“How much conservation is enough?” is one of the most important and difficult questions to answer. In this work, we demonstrate an approach to specifically answer this question for conservation strategies designed to address nonpoint source pollution in agriculturally-dominated watersheds. We developed empirical models relating conservation investments and actions to measures of stream water quality and fish community health. Our results are consistent with other studies that demonstrate a need for extensive implementation of conservation practices in agricultural landscapes to see measurable improvements in ecological conditions. Our results also demonstrate the influence spatial grain can have on answering “how much conservation is enough?” Our coarse-grained analyses suggest that water quality in at the outlets of four watersheds could be improved to the point that water quality was no longer limiting the fish community with only about 18% of the agricultural lands treated with conservation practices and incentive payments totaling $7.7M. Yet, finer-grained subbasin analyses predict fish communities would still be limited in many tributaries of these watersheds even with ~ 50% of lands treated and incentive payments totaling ~$44M. Consequently, coarsegrained analyses could significantly underestimate scope of the solution needed to address these impacts to stream ecosystems. Finding balanced solutions to address agricultural nonpoint source pollution throughout the Great Lakes will require unprecedented collaboration from local to regional scales. Herein, we provide examples of how this work is supporting collaborative efforts to establish realistic ecological goals and associated performance measures and strategic implementation of practices throughout the Saginaw Bay drainage. [ABSTRACT FROM AUTHOR]

Direct measurement of biotic indices used in monitoring stream health is time consuming, costly, and usually limited to few sites and locations. This severely limits the spatial extent and the temporal interval of assessment; hence, continuous long-term monitoring of all reaches becomes impossible. Therefore, modeling approaches are commonly used as an alternative. However, modeling complex natural systems are not without challenges and the error in modeling these systems is usually high. This study focuses on modeling four biotic indices, including one fish and three macroinvertebrate indices, using 171 water quantity and 78 water quality variables. This study introduces a new two-phase approach in modeling biotic indices. In the first phase, an initial estimate of the biotic index along with an estimate of the error associated with those initial predictions is obtained. In the second phase, these initial estimates are combined to develop a new predictive model. Although different modeling methods can be used in each phase, to demonstrate the concept, in this study we tested Partial Least Square Regression (PLSR) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed approach was evaluated based on monitoring data form the Flint River watershed, located in Michigan, USA. The results demonstrate that the two-phase approach that uses PLSR (first phase) and ANFIS (second phase) is superior to common-single-phase approach ( R 2 for the stream health predictive models increased on average from 0.5 in the first phase to over 0.9 in the second phase). Additionally, the two-phase approach eliminates the need for variable selection, a common pre-processing step, and provides satisfactory results despite the limited number of samples, which makes the approach more reliable, robust, and applicable. Although in this study the proposed two-phase approach is applied to biotic indices, the process can be extended to other natural and physical systems. [ABSTRACT FROM AUTHOR]

Ecologically relevant hydrologic indices are being used for developing environmental flow baselines. EflowStats is an R-package developed by the US Geological Survey that calculates 171 ecologically relevant hydrologic indices. Although EflowStats provides a set of easy to use functions, once it is applied to a large area with thousands of streams, it requires a long processing time. This particularly limits its application in problems that are dealing with optimizations, requiring evaluation of thousands of scenarios. As a result, a new package called MATLAB Hydrological Index Tool (MHIT) was developed, incorporating different technologies, such as vectorization, memoization, and parallelism to considerably reduce the processing time. Both EflowStats and MHIT were tested against a long-term (20 years) daily streamflow data set from the Saginaw River Watershed in Michigan. In summary, MHIT reduces the processing time by 227 times for the study area. Therefore, MHIT is much more suitable for dealing with Big Data. [ABSTRACT FROM AUTHOR]

With one-third of the population living in drought-prone areas, Africa is considered the most vulnerable continent to climate change. This article reviews the impacts of population growth and climate change on water resources across eight East African countries, namely, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Uganda, Tanzania, and Zambia. Total population in the region is projected to exceed 800 million by 2060 and 1,150 million by 2090, compared to 286 million in 2012. Based on per capita water availability, six of the eight countries are either already water stressed or close to it. By the end of this century, per capita water availability will drop to water scarcity level in all the countries reviewed except Mozambique. ENSO is currently a dominant force that drives the climate in the region; however, future changes in this phenomenon are not well understood. Temperature projections show a 0.9°C to 3.4°C increase by the 2060s and 1.3°C to 5.5°C increase by the 2090s. However, precipitation projections are less certain in both the direction and magnitude of change, although the projections are more inclined toward positive change. For example, findings from an ensemble of models under the A2, A1B, and B1 scenarios projected a 15% reduction to a 48% increase in precipitation by 2090 in the countries studied. Projected runoff trends are largely dependent on precipitation variability and short-term rainfall distribution, which are highly uncertain in future climate scenarios. Although climate models are uncertain, runoff is generally predicted to increase in the eastern part of the region and decrease in the southern part. Glaciers and ice covers in the region are projected to disappear in next few decades; however, the impact of this disappearance on water resources in the region is predicted to be minimal. Evaporative demand is projected to increase with the increase in temperature and may reach as much as 53%, increasing water demand and moisture stress. Groundwater resources are more resilient than surface water resources; however, there is an overall lack of knowledge on how groundwater responds to climate change at the local scale. In order to mitigate the impact of climate change on water resources, water conservation and development of irrigation facilities have been recommended. [ABSTRACT FROM AUTHOR]

• Sparse population sampling is a simple population initialization routine with a linear run time and one to two parameters for large-scale sparse multi-objective optimization problems. • For large-scale sparse multi-objective optimization problems, sparse population sampling improves hyper-volume across all studied population based algorithms with minimal side effects. • NSGA-II combined with sparse population sampling outperforms SparseEA in high-dimension problems (i.e., greater than 2,500 decision variables). Sparse optimization problems involving sparse non-zero variables are common among large-scale single or multi-objective real-world problems. However, generic optimization algorithms are inefficient at solving most of these large scale problems. Moreover, algorithms to solve these problems are currently under-researched. This paper proposes a novel approach to improve large-scale sparse multi-objective algorithms using a sparse population sampling (SPS) method. SPS is an optimization subroutine that replaces uniform sampling for population initialization, and can be applied to any population-based algorithm, including evolutionary or particle swarm algorithms. Through rigorous testing, we have found that SPS leads to near-universal improvements in hyper-volume for common population-based algorithms in large-scale (i.e. over 8000 decision variable) and sparse multi-objective optimization problems. Additionally, the elitist non-dominated sorting genetic algorithm (NSGA-II) with SPS is found to outperform a selection of other existing multi-objective optimization algorithms with SPS for handling sparse high-dimensional decision spaces. Considering these benefits and its simplicity, the proposed SPS method is an evidently promising method for boosting performance in large-scale sparse multi-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

Hydrologic variables in the Great Lakes region have been altered relative to pre-settlement conditions in response to major land use changes during the past 150 years. One of the goals of the present work is to develop a baseline scenario relative to which the impacts of land use changes on hydrological and environmental processes can be evaluated. In addition, this study can help in quantifying the potential impacts of future projected changes in land use in order to mitigate the negative impacts of these changes, especially in regard to a shift toward urbanization and second-generation bio-energy crop production derived from lignocellulosic crops. The present study explores the relationship between land use changes and hydrologic indicators within the agricultural regions of Michigan and Wisconsin. Two sets of land use data, the circa 1800 county base and the 2001 National Land Cover Dataset, were used to set up the Soil and Water Assessment Tool (SWAT) model. First, sensitivity analyses were performed based on both pre-settlement and current land use scenarios. Results showed that parameter sensitivity analysis may not always explain how the variation in model output can be attributed to different sources of variation in the model input. Therefore, effort should be made to determine the true importance of sensitive parameters by considering their placement in model algorithms. The model was then calibrated against measured daily streamflow data obtained from eight U.S. Geological Survey gauging stations. The impacts of land use changes were studied at three scales: subwatershed, watershed, and basin. At the subwatershed scale, most of the hydrologic behavior can be described by percent change in land cover. However, the trend was more apparent for land use conversion from mixed forest to urban and agriculture lands than for other land use conversions. At the watershed scale, significant differences were observed based on the long-term average hydrologic variables under the current and presettlement scenarios. In addition, an increase in evapotranspiration (up to 16.5%) and surface runoff (up to 93.9%) contribution to streamflow, a decrease in recharge to aquifers (up to -51.5%) and baseflow (up to -50.1%), and mixed impacts on water yield (-21.5% to 24.6%) were detected. However, at the basin scale, more than 70% of the study area experienced decreased lateral subsurface flow and recharge to aquifers, while 65% of the area experienced increased overland flow and minor changes in evapotranspiration and water yield. [ABSTRACT FROM AUTHOR]

This study compared the utility and results of four watershed models that include water quality elements: Spreadsheet Tool for Estimating Pollutant Load (STEPL), a GIS-based model to calculate pollutant loads for watersheds (PLOAD), Long-term Hydrologic Impact Assessment (L-THIA), and ArcGIS Soil and Water Assessment Tool (ArcSWAT). These models are different in their levels of complexity, data requirements, time step, and simulation techniques. Therefore, different scenarios were conducted to test some of the standard applications of each model based on data quality and model capabilities. Climatological, land use, and soil datasets as well as flow rates, sediment, nitrate, and total P concentrations were collected for the Pomona Lake watershed in Kansas over 30 years. Stakeholders' information was integrated into the models to the extent possible. Using results obtained from STEPL, L-THIA, PLOAD, and uncalibrated ArcSWAT for estimating long-term (30 years) average pollution loads in a watershed can result in incorrect load estimation. In addition, the STEPL, L-THIA, and PLOAD models may not be the most reliable in identifying pollutant source areas in a watershed. Therefore, in the context of effective resource allocation, use of simple models (such as STEPL, L-THIA, and PLOAD) is not suggested for critical source area targeting for watershed planning if the nature of the problem is complex. However, the simple models can still be used to evaluate relative contribution of different land uses to overall pollution loads. [ABSTRACT FROM AUTHOR]

Hydrologic fluxes in the Great Lakes region have been altered relative to pre-settlement conditions in response to major land use changes during the past 150 yr. Land surface characteristics and processes including leaf area index, roughness, albedo, soil moisture, and rates of momentum, energy and water vapor exchange are strongly influenced by land use. Changes in land use including urbanization and de(/re)forestation continue to affect the nature and magnitude of groundwater -- surface water interactions and water availability influencing ecosystems and their services. One of the goals of the present work is to develop a baseline scenario relative to which the impacts of land use changes on hydrological and environmental processes can be evaluated. In addition, the study can help in quantifying the potential impacts of future projected changes in land use in order to mitigate the negative impacts of these changes on goods and services of value to society. The present study explores the relationship between land use changes and hydrologic indicators within the agricultural regions of Michigan and Wisconsin. Two sets of land use data, the circa 1800 County Base and the 2001 National Land Cover Dataset, were used to setup the Soil and Water Assessment Tool (SWAT) model. First, sensitivity analyses were performed both based on pre-settlement and current land use scenarios and the most sensitive parameters were identified. Then, the model was calibrated against measured daily stream flow data obtained from eight United States Geological Survey gauging stations. The impacts of land use changes were studied at three scales: subbasin-level, watershed-level, and basin-level. At the subbasin level, most of the hydrologic behavior can be described by percent change in land cover. At the watershed scale, significant differences were observed based on the long-term average hydrologic fluxes under the current and pre-settlement scenarios. In addition, an overall increase in the amount of evapotranspiration and overland flow and overall decrease in the amount of baseflow and water yield were observed. However, at the basin-level, the majority of the area experienced increased overland flow, decreased baseflow, lateral flow, and recharge to aquifers, and minor changes in evapotranspiration and water yield. [ABSTRACT FROM AUTHOR]