Calibration using R-programming and parallel processing at the HUC12 subbasin scale in the Mid-Atlantic region: Development of national SWAT hydrologic calibration.

The first phase of a national scale Soil and Water Assessment Tool (SWAT) model calibration effort at the HUC12 (Hydrologic Unit Code 12) watershed scale was demonstrated over the Mid-Atlantic Region (R02), consisting of 3036 HUC12 subbasins. An R-programming based tool was developed for streamflow calibration including parallel processing for SWAT-CUP (SWAT- Calibration and Uncertainty Programs) to streamline the computational burden of calibration. Successful calibration of streamflow for 415 gages (KGE ≥0.5, Kling-Gupta efficiency; PBIAS ≤15%, Percent Bias) out of 553 selected monitoring gages was achieved in this study, yielding calibration parameter values for 2106 HUC12 subbasins. Additionally, 67 more gages were calibrated with relaxed PBIAS criteria of 25%, yielding calibration parameter values for an additional 150 HUC12 subbasins. This first phase of calibration across R02 increases the reliability, uniformity, and replicability of SWAT-related hydrological studies. Moreover, the study presents a comprehensive approach for efficiently optimizing large-scale multi-site calibration.

Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs.

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 µg L-1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.

Holistic Sustainability Assessment of Riparian Buffer Designs: Evaluation of Alternative Buffer Policy Scenarios Integrating Stream Water Quality and Costs.

Riparian buffer zones (RBZs) have been shown to be effective best management practices (BMPs) in controlling non-point source pollutants in waterbodies. However, the holistic sustainability assessment of individual RBZ designs is lacking. We present a methodology for evaluating the holistic sustainability of RBZ policy scenarios by integrating environmental and economic indicators simulated in three watersheds in the southeastern USA. We developed three unique sets of 40, 32, and 48 RBZ policy scenarios as decision management objectives (DMOs), respectively, in Back Creek, Sycamore Creek, and Greens Mill Run watersheds (Virginia and North Carolina) by combining the RBZ-widths with vegetation types (grass, urban, naturalized, wildlife, three-zone forest, and two-zone forest). We adapted the RBZ-hydrologic and water quality system assessment data of instream water quality parameters (dissolved oxygen, total phosphorus, total nitrogen, total suspended solids-sediment and biochemical oxygen demand) as environmental indicators, recently published by U.S. EPA. We calculated 20-year net present value costs as economic indicators using the RBZ's establishment, maintenance, and opportunity costs data published by the Natural Resources Conservation Service. The mean normalized net present value costs varied by DMOs ranging from 4% (grass RBZ-1.9 m) to 500% (wildlife RBZ-91.4 m) across all watersheds, due primarily to the width and the opportunity costs. The mean normalized environmental indicators varied by watersheds, with the largest change in total nitrogen due to urban RBZs in Back Creek (60-95%), Sycamore Creek (37-91%), and Greens Mill (52-93%). The holistic sustainability assessments revealed the least to most sustainable DMOs for each watershed, from least sustainable wildlife RBZ (score of 0.54), three-zone forest RBZ (0.32), and three-zone forest RBZ (0.62), respectively, for Back Creek, Sycamore Creek, and Greens Mill, to most sustainable urban RBZ (1.00) for all watersheds.

Catchment scale runoff time-series generation and validation using statistical models for the Continental United States.

We developed statistical models to generate runoff time-series at National Hydrography Dataset Plus Version 2 (NHDPlusV2) catchment scale for the Continental United States (CONUS). The models use Normalized Difference Vegetation Index (NDVI) based Curve Number (CN) to generate initial runoff time-series which then is corrected using statistical models to improve accuracy. We used the North American Land Data Assimilation System 2 (NLDAS-2) catchment scale runoff time-series as the reference data for model training and validation. We used 17 years of 16-day, 250-m resolution NDVI data as a proxy for hydrologic conditions during a representative year to calculate 23 NDVI based-CN (NDVI-CN) values for each of 2.65 million NHDPlusV2 catchments for the Contiguous U.S. To maximize predictive accuracy while avoiding optimistically biased model validation results, we developed a spatio-temporal cross-validation framework for estimating, selecting, and validating the statistical correction models. We found that in many of the physiographic sections comprising CONUS, even simple linear regression models were highly effective at correcting NDVI-CN runoff to achieve Nash-Sutcliffe Efficiency values above 0.5. However, all models showed poor performance in physiographic sections that experience significant snow accumulation.

Sensitivity of Riparian Buffer Designs to Climate Change-Nutrient and Sediment Loading to Streams: A Case Study in the Albemarle-Pamlico River Basins (USA) Using HAWQS.

Riparian buffer zones (RBZs) provide multiple benefits to watershed ecosystems. We aimed to conduct an extensive sensitivity analysis of the RBZ designs to climate change nutrient and sediment loadings to streams. We designed 135 simulation scenarios starting with the six baselines RBZs (grass, urban, two-zone forest, three-zone forest, wildlife, and naturalized) in three 12-digit Hydrologic Unit Code watersheds within the Albemarle-Pamlico river basin (USA). Using the hydrologic and water quality system (HAWQS), we assessed the sensitivity of the designs to five water quality indicator (WQI) parameters: dissolved oxygen (DO), total phosphorous (TP), total nitrogen (TN), sediment (SD), and biochemical oxygen demand (BD). To understand the climate mitigation potential of RBZs, we identified a subset of future climate change projection models of air temperature and precipitation using EPA's Locating and Selecting Scenarios Online tool. Analyses revealed optimal RBZ designs for the three watersheds. In terms of watershed ecosystem services sustainability, the optimal Urban RBZ in contemporary climate (1983-2018) reduced SD from 61-96%, TN from 34-55%, TP from 9-48%, and BD from 53-99%, and raised DO from 4-10% with respect to No-RBZ in the three watersheds. The late century's (2070-2099) extreme mean annual climate changes significantly increased the projected SD and BD; however, the addition of urban RBZs was projected to offset the climate change reducing SD from 28-94% and BD from 69-93% in the watersheds. All other types of RBZs are also projected to fully mitigate the climate change impacts on WQI parameters except three-zone RBZ.

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge.

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Spatio-Temporal Modeling for Forecasting High-Risk Freshwater Cyanobacterial Harmful Algal Blooms in Florida.

Due to the occurrence of more frequent and widespread toxic cyanobacteria events, the ability to predict freshwater cyanobacteria harmful algal blooms (cyanoHAB) is of critical importance for the management of drinking and recreational waters. Lake system specific geographic variation of cyanoHABs has been reported, but regional and state level variation is infrequently examined. A spatio-temporal modeling approach can be applied, via the computationally efficient Integrated Nested Laplace Approximation (INLA), to high-risk cyanoHAB exceedance rates to explore spatio-temporal variations across statewide geographic scales. We explore the potential for using satellite-derived data and environmental determinants to develop a short-term forecasting tool for cyanobacteria presence at varying space-time domains for the state of Florida. Weekly cyanobacteria abundance data were obtained using Sentinel-3 Ocean Land Color Imagery (OLCI), for a period of May 2016-June 2019. Time and space varying covariates include surface water temperature, ambient temperature, precipitation, and lake geomorphology. The hierarchical Bayesian spatio-temporal modeling approach in R-INLA represents a potential forecasting tool useful for water managers and associated public health applications for predicting near future high-risk cyanoHAB occurrence given the spatio-temporal characteristics of these events in the recent past. This method is robust to missing data and unbalanced sampling between waterbodies, both common issues in water quality datasets.

Quantification and valuation of ecosystem services in life cycle assessment: Application of the cascade framework to rice farming systems.

The integration of ecosystem service (ES) assessment with life cycle assessment (LCA) is important for developing decision support tools for environmental sustainability. A prequel study has proposed a 4-step methodology that integrates the ES cascade framework within the cause-effect chain of life cycle impact assessment (LCIA) to characterize the physical and monetary impacts on ES provisioning due to human interventions. We here follow the suggested steps in the abovementioned study, to demonstrate the first application of the integrated ES-LCIA methodology and the added value for LCA studies, using a case study of rice farming in the United States, China, and India. Four ES are considered, namely carbon sequestration, water provisioning, air quality regulation, and water quality regulation. The analysis found a net negative impact for rice farming systems in all three rice producing countries, meaning the detrimental impacts of rice farming on ES being greater than the induced benefits on ES. Compared to the price of rice sold in the market, the negative impacts represent around 2%, 6%, and 4% of the cost of 1 kg of rice from China, India, and the United States, respectively. From this case study, research gaps were identified in order to develop a fully operationalized ES-LCIA integration. With such a framework and guidance in place, practitioners can more comprehensively assess the impacts of life cycle activities on relevant ES provisioning, in both physical and monetary terms. This may in turn affect stakeholders' availability to receive such benefits from ecosystems in the long run.

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT.

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter-elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network-Daily (GHCN-D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN-D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN-D based SWAT-simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge-based measurements can improve hydrologic model performance, especially for extreme events.

PiSCES: Pi(scine) stream community estimation system.

The Piscine Stream Community Estimation System (PiSCES) provides users with a hypothesized fish community for any stream reach in the conterminous United States using information obtained from Nature Serve, the US Geological Survey (USGS), StreamCat, and the Peterson Field Guide to Freshwater Fishes of North America for over 1000 native and non-native freshwater fish species. PiSCES can filter HUC8-based fish assemblages based on species-specific occurrence models; create a community abundance/biomass distribution by relating relative abundance to mean body weight of each species; and allow users to query its database to see ancillary characteristics of each species (e.g., habitat preferences and maximum size). Future efforts will aim to improve the accuracy of the species distribution database and refine/augment increase the occurrence models. The PiSCES tool is accessible at the EPA's Quantitative Environmental Domain (QED) website at https://qed.epacdx.net/pisces/.

Mapping Aedes aegypti (Diptera: Culicidae) and Aedes albopictus Vector Mosquito Distribution in Brownsville, TX.

Aedes mosquitoes are vectors of several emerging diseases and are spreading worldwide. We investigated the spatiotemporal dynamics of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) mosquito trap captures in Brownsville, TX, using high-resolution land cover, socioeconomic, and meteorological data. We modeled mosquito trap counts using a Bayesian hierarchical mixed-effects model with spatially correlated residuals. The models indicated an inverse relationship between temperature and mosquito trap counts for both species, which may be due to the hot and arid climate of southern Texas. The temporal trend in mosquito populations indicated Ae. aegypti populations peaking in the late spring and Ae. albopictus reaching a maximum in winter. Our results indicated that seasonal weather variation, vegetation height, human population, and land cover determine which of the two Aedes species will predominate.

Towards integrating the ecosystem services cascade framework within the Life Cycle Assessment (LCA) cause-effect methodology.

The assessment of ecosystem services (ES) is covered in a fragmented manner by environmental decision support tools that provide information about the potential environmental impacts of supply chains and their products, such as the well-known Life Cycle Assessment (LCA) methodology. Within the flagship project of the Life Cycle Initiative (hosted by UN Environment), aiming at global guidance for life cycle impact assessment (LCIA) indicators, a dedicated subtask force was constituted to consolidate the evaluation of ES in LCA. As one of the outcomes of this subtask force, this paper describes the progress towards consensus building in the LCA domain concerning the assessment of anthropogenic impacts on ecosystems and their associated services for human well-being. To this end, the traditional LCIA structure, which represents the cause-effect chain from stressor to impacts and damages, is re-casted and expanded using the lens of the ES 'cascade model'. This links changes in ecosystem structure and function to changes in human well-being, while LCIA links the effect of changes on ecosystems due to human impacts (e.g. land use change, eutrophication, freshwater depletion) to the increase or decrease in the quality and/or quantity of supplied ES. The proposed cascade modelling framework complements traditional LCIA with information about the externalities associated with the supply and demand of ES, for which the overall cost-benefit result might be either negative (i.e. detrimental impact on the ES provision) or positive (i.e. increase of ES provision). In so doing, the framework introduces into traditional LCIA the notion of "benefit" (in the form of ES supply flows and ecosystems' capacity to generate services) which balances the quantified environmental intervention flows and related impacts (in the form of ES demands) that are typically considered in LCA. Recommendations are eventually provided to further address current gaps in the analysis of ES within the LCA methodology.

Life cycle assessment of a rainwater harvesting system compared with an AC condensate harvesting system.

This study presents a life cycle assessment (LCA) of a rainwater harvesting (RWH) system and an air-conditioning condensate harvesting (ACH) system for non-potable water reuse. U.S. commercial buildings were reviewed to design rooftop RWH and ACH systems for one to multi-story buildings' non-potable water demand. A life cycle inventory was compiled from the U.S. EPA's database. Nine scenarios were analyzed, including baseline RWH system, ACH system, and combinations of the two systems adapted to 4-story and 19-story commercial buildings in San Francisco and a 4-story building in Washington, DC. Normalization of 11 life cycle impact assessment categories showed that RWH systems in 4-story buildings at both locations outperformed ACH systems (45-80% of ACH impacts) except equivalent in Evaporative Water Consumption. However, San Francisco's ACH system in 19-story building outperformed the RWH system (51-83% of RWH impacts) due to the larger volume of ACH collection, except equivalent in Evaporative Water Consumption. For all three buildings, the combined system preformed equivalently to the better-performing option (≤4-8% impact difference compared to the maximum system). Sensitivity analysis of the volume of water supply and building occupancy showed impact-specific results. Local climatic conditions, rainfall, humidity, water collections and demands are important when designing building-scale RWH and ACH systems. LCA models are transferrable to other locations with variable climatic conditions for decision-making when developing and implementing on-site non-potable water systems.

Sustainability assessment of agricultural rainwater harvesting: Evaluation of alternative crop types and irrigation practices.

Rainwater harvesting (RWH) has been used globally to address water scarcity for various ecosystem uses, including crop irrigation requirements, and to meet the water resource needs of a growing world population. However, the costs, benefits and impacts of alternative crop types and irrigation practices is challenging to evaluate comprehensively. We present an assessment methodology to evaluate the sustainability of agricultural systems as applied to a southeastern U.S. river basin. We utilized detailed, crop-level cultivation information to calculate sustainability indicators (relative to well-water irrigation) at the basin scale (6-digit Hydrologic Unit Codes). 40 design configurations comprising crop types and irrigation practices were evaluated to demonstrate the methodology's robustness. Four RWH designs and four major crops (pasture-grass, soybeans, corn, and cotton) resembling current practices were evaluated, as well as six combined systems (combined RWH and well-water systems) with four globally representative crops (corn, soybeans, wheat, and quinoa). Sustainability scores were calculated by integrating seven life cycle impact indicators (cumulative energy demand, CO2 emission, blue water use, ecotoxicity, eutrophication, human health-cancer, and life cycle costs). At a basin-wide RWH adoption rate of 25%, the benefits, relative to 100% well-water, of the RWH systems irrigating soybeans and supported with well-water (0.4 well-water: 0.6 RWH) provided cumulative energy savings of 39 Peta Joule and reductions in CO2 emission, blue water use, ecotoxicity, eutrophication, and human health-cancer at 1.9 Mt CO2 eq., 6.9 Gm3, 5.7 MCTU, 6.6 kt N eq., and 0.07 CTU, respectively. These benefits increased linearly with RWH scaling variables including the adoption rates, system service life, crop area, and water needs. Our methodology integrates the three pillars of agricultural sustainability specific to rainwater harvesting into a single score. It is applicable to other locations worldwide facing water scarcity by modifying the RWH system design, selecting other crop types, and obtaining appropriate data.

Phenology-adjusted dynamic curve number for improved hydrologic modeling.

The Soil Conservation Service Curve Number (SCS-CN, or CN) is a widely used method to estimate runoff from rainfall events. It has been adapted to many parts of the world with different land uses, land cover types, and climatic conditions and successfully applied to situations ranging from simple runoff calculations and land use change assessment to comprehensive hydrologic/water quality simulations. However, the CN method lacks the ability to incorporate seasonal variations in vegetated surface conditions, and unnoticed landuse/landcover (LULC) change that shape infiltration and storm runoff. Plant phenology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and evapotranspiration. This study used regression analysis to develop a dynamic CN (CNNDVI) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phenological development. A time series of 16-day MODIS NDVI (MOD13Q1 Collection 5) images were used to monitor vegetation development and provide NDVI data necessary for CNNDVI model calibration and validation. Twelve years of rainfall and runoff data (2001-2012) from four small watersheds located in the Konza Prairie Biological Station, Kansas were used to develop, calibrate, and validate the method. Results showed CNNDVI performed significantly better in predicting runoff with calibrated CNNDVI runoff increasing by approximately 0.74 for every unit increase in observed runoff compared to 0.46 for SCS-CN runoff and was more highly correlated to observed runoff (r = 0.78 vs. r = 0.38). In addition, CNNDVI runoff had better NSE (0.53) and PBIAS (4.22) compared to the SCS-CN runoff (-0.87 and -94.86 respectively). In the validated model, CNNDVI runoff increased by approximately 0.96 for every unit of observed runoff, while SCS-CN runoff increased by 0.49. Validated runoff was also better correlated to observed runoff than SCS-CN runoff (r = 0.52 vs. r = 0.33). These findings suggest that the CNNDVI can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions.

Land-Use/Land-Cover Change from Socio-Economic Drivers and Their Impact on Biodiversity in Nan Province, Thailand.

The rate of deforestation declined steadily in Thailand since the year 2000 due to economic transformation away from forestry. However, these changes did not occur in Nan Province located in northern Thailand. Deforestation is expected to continue due to high demand for forest products and increased agribusiness. The objectives of this paper are (1) to predict land-use change in the province based on trends, market-based and conservation scenarios, (2) to quantify biodiversity, and (3) to identify biodiversity hotspots at greatest risk for future deforestation. This study used a dynamic land-use change model (Dyna-CLUE) to allocate aggregated land demand for three scenarios and employed FRAGSTATS to determine the spatial pattern of land-use change. In addition, the InVEST Global Biodiversity Assessment Model framework was used to estimate biodiversity expressed as the remaining mean species abundance (MSA) relative to their abundance in the pristine reference condition. Risk of deforestation and the MSA values were combined to determine biodiversity hotspots across the landscape at greatest risk. The results revealed that most of the forest cover in 2030 would remain in the west and east of the province, which are rugged and not easily accessible, as well as in protected areas. MSA values are predicted to decrease from 0.41 in 2009 to 0.29, 0.35, and 0.40, respectively, under the trends, market-based and conservation scenarios in 2030. In addition, the low, medium, and high biodiversity zones cover 46, 49 and 6% of Nan Province. Protected areas substantially contribute to maintaining forest cover and greater biodiversity. Important measures to protect remaining cover and maintain biodiversity include patrolling at-risk deforestation areas, reduction of road expansion in pristine forest areas, and promotion of incentive schemes for farmers to rehabilitate degraded ecosystems.

Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors.

Monitoring and control of West Nile virus (WNV) presents a challenge to state and local vector control managers. Models of mosquito presence and viral incidence have revealed that variations in mosquito autecology and land use patterns introduce unique dynamics of disease at the scale of a county or city, and that effective prediction requires locally parameterized models. We applied Bayesian spatiotemporal modeling to West Nile surveillance data from 49 mosquito trap sites in Nassau County, New York, from 2001 to 2015 and evaluated environmental and sociological predictors of West Nile virus incidence in Culex pipiens-restuans. A Bayesian spike-and-slab variable selection algorithm was used to help select influential independent variables. This method can be used to identify locally-important predictors. The best model predicted West Nile positives well, with an Area Under Curve (AUC) of 0.83 on holdout data. The temporal trend was nonlinear and increased throughout the year. The spatial component identified increased West Nile incidence odds in the northwestern portion of the county, with lower odds in wetlands on the south shore of Long Island. High Normalized Difference Vegetation Index (NDVI) areas, wetlands, and areas of high urban development had negative associations with WNV incidence. In this study we demonstrate a method for improving spatiotemporal models of West Nile virus incidence for decision making at the county and community scale, which empowers disease and vector control organizations to prioritize and evaluate prevention efforts.

Basin-wide Impacts of Climate Change on Ecosystem Services in the Lower Mekong Basin.

Water resources support more than 60 million people in the Lower Mekong Basin (LMB) and are important for food security-especially rice production-and economic security. This study aims to quantify water yield under near- and long-term climate scenarios and assess the potential impacts on rice cultivation. The InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs) forecasted water yield, and land evaluation was used to delineate suitability classes. Pattern-downscaled climate data were specially generated for the LMB. Predicted annual water yields for 2030 and 2060, derived from a drier overall scenario in combination with medium and high greenhouse gas emissions, indicated a reduction of 9-24% from baseline (average 1986-2005) runoff. In contrast, increased seasonality and wetter rainfall scenarios increased annual runoff by 6-26%. Extreme drought decreased suitability of transplanted rice cultivation by 3%, and rice production would be reduced by 4.2 and 4%, with and without irrigation projects, relative to baseline. Greatest rice reduction was predicted for Thailand, followed by Lao PDR and Cambodia, and was stable for Vietnam. Rice production in the LMB appears sufficient to feed the LMB population in 2030, while rice production in Lao PDR and Cambodia are not expected to be sufficient for domestic consumption, largely due to steep topography and sandy soils as well as drought. Four adaptation measures to minimize climate impacts (i.e., irrigation, changing the planting calendar, new rice varieties, and alternative crops) are discussed.

Mobile device application for monitoring cyanobacteria harmful algal blooms using Sentinel-3 satellite Ocean and Land Colour Instruments.

Cyanobacterial harmful algal blooms (cyanoHAB) cause human and ecological health problems in lakes worldwide. The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management and for targeted deployment of water quality monitoring resources. Software platforms that permit timely, useful, and cost-effective delivery of information from satellites are required to help managers respond to cyanoHABs. The Cyanobacteria Assessment Network (CyAN) mobile device application (app) uses data from the European Space Agency Copernicus Sentinel-3 satellite Ocean and Land Colour Instrument (OLCI) in near realtime to make initial water quality assessments and quickly alert managers to potential problems and emerging threats related to cyanobacteria. App functionality and satellite data were validated with 25 state health advisories issued in 2017. The CyAN app provides water quality managers with a user-friendly platform that reduces the complexities associated with accessing satellite data to allow fast, efficient, initial assessments across lakes.

Ecosystem quality in LCIA: status quo, harmonization, and suggestions for the way forward.

PURPOSE: Life cycle impact assessment (LCIA) results are used to assess potential environmental impacts of different products and services. As part of the UNEP-SETAC life cycle initiative flagship project that aims to harmonize indicators of potential environmental impacts, we provide a consensus viewpoint and recommendations for future developments in LCIA related to the ecosystem quality area of protection (AoP). Through our recommendations, we aim to encourage LCIA developments that improve the usefulness and global acceptability of LCIA results. METHODS: We analyze current ecosystem quality metrics and provide recommendations to the LCIA research community for achieving further developments towards comparable and more ecologically relevant metrics addressing ecosystem quality. RESULTS AND DISCUSSION: We recommend that LCIA development for ecosystem quality should tend towards species-richnessrelated metrics, with efforts made towards improved inclusion of ecosystem complexity. Impact indicators-which result from a range of modeling approaches that differ, for example, according to spatial and temporal scale, taxonomic coverage, and whether the indicator produces a relative or absolute measure of loss-should be framed to facilitate their final expression in a single, aggregated metric. This would also improve comparability with other LCIA damage-level indicators. Furthermore, to allow for a broader inclusion of ecosystem quality perspectives, the development of an additional indicator related to ecosystem function is recommended. Having two complementary metrics would give a broader coverage of ecosystem attributes while remaining simple enough to enable an intuitive interpretation of the results. CONCLUSIONS: We call for the LCIA research community to make progress towards enabling harmonization of damage-level indicators within the ecosystem quality AoP and, further, to improve the ecological relevance of impact indicators.