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The spatial distribution of soil phosphorus (P) is important to both biogeochemical processes and the management of agricultural landscapes, where it is critical for both crop production and conservation planning. Recent advances in the availability of large environmental datasets together with big data analytical tools like machine learning have created opportunities for evaluating and predicting spatial patterns in complex environmental variables like soil P. Here, we apply a random forest machine learning model to publicly available soil P datasets together with nearly 300 geospatial attributes summarizing aspects of soil type, land cover, land use, topography, nutrient inputs, and climate to predict total soil P at a 100m grid scale for the Upper Mississippi River Basin (UMRB), USA. The UMRB is one of the most intensively farmed regions in the world and is characterized by widespread water quality degradation arising from P-associated eutrophication. At the regional scale represented by our model, the variables with the greatest comparative importance for predicting soil P included a combination of soil sample depth, land use/land cover, underlying soil physical and geochemical properties, landscape features (such as slope, elevation and proximity to the stream network), nutrient inputs, and climate-related factors. An important product of this research is a fine-scale (100 m) raster data layer of predicted total soil P values for the UMRB for public use. This dataset can be used to improve conservation planning and modeling efforts to improve water quality in the region.

This study describes a collaborative modeling process deployed at the Cantareira Water Supply System (CWSS) in São Paulo City Metropolitan Area, Brazil. The CWSS faces challenges for meeting the increasing water demand, while land-use and climate change and their combined effect on its water cycle and balance have created a complex water resources management problem. Through a stakeholder engagement process—involving scientists and policymakers, the water utility company, and state administration—environmental simulation models were developed to elicit and represent multiple environmental, economic, and policy perspectives, developing a mutual language to communicate and establish common goals of water resources management. Study outputs include estimation of biophysical and economic benefits associated with prioritized native vegetation restoration activities in the source watersheds. These outputs are deployed in support of landscape planning and the decision process integrating multiple stakeholder perspectives in São Paulo state administration, the water utility company, and municipalities.

Many biophysical models exhibit epistasis (interdependence), where a conservation action impacts the effectiveness of another elsewhere. At the same time, ranking conservation actions according to the independent benefit-to-cost ratios is cost-efficient when epistasis is absent. We use benefit-to-cost rankings as starting points for an evolutionary algorithm employing an epistatic biophysical model. We model a variety of conservation actions to assess trade-offs for sediment reduction and wildlife conservation in the study watershed. We find that despite the presence of epistasis, the weighted benefit-to-cost ratio-derived solutions perform remarkably well in the decision space, but effects in objective space need the model evaluation.

The widespread availability of high-fidelity topography combined with advances in geospatial analysis offer the opportunity to reimagine approaches to the difficult problem of predicting sediment delivery from watersheds. Here we present a model that uses high-resolution topography to filter sediment sources to quantify sediment delivery to the watershed outlet. It is a reduced-complexity, top-down model that defines transfer functions-topographic filters-between spatially distributed sediment sources and spatially integrated sediment delivery. The goal of the model is to forecast changes in watershed suspended sediment delivery in response to spatially distributed changes in sediment source magnitude or delivery, whether a result of watershed drivers or intentional management actions. Such an application requires the context of a watershed model that accounts for all sediment sources, enforces sediment mass balance throughout the spatial domain, and accommodates sediment storage and delivery over time. The model is developed for a HUC-8 watershed with a flat upland dominated by corn-soybean agriculture and deeply incised valleys near the watershed outlet with large sediment contributions from near-channel sources. Topofilter computes delivery and storage of field-derived sediment according to its spatial and structural connectivity to the stream channel network; subsequently, delivery of both field- and near-channel-derived sediment along with floodplain storage are computed in the stream channel network to the watershed outlet. The model outputs provide a spatially rich representation of sediment delivery and storage on field and along the stream that is consistent with available independent information on sediment accumulations and fluxes. Rather than a single best-calibrated solution, Topofilter uses the Generalized Likelihood Uncertainty Estimate (GLUE) approach to develop many possible solutions with sediment delivery rates expressed as probability distributions across the watershed. The ensemble of simulation outputs provides a useful basis for estimating uncertainty in sediment delivery and the effectiveness of different landscape management allocation across a watershed.

In order to understand the hydrological impacts of the nature-based solutions in the Cantareira Water Supply System, this study evaluates six different land cover and land use change scenarios. The first and second consider the restoration of native vegetation in riparian areas, the third prioritizes restoration sites using biophysical characteristics (optimized restoration scenario derived from Resource Investment Optimization System—RIOS), the fourth considers best management practices and the fifth and sixth are hypothetical extreme scenarios converting all pasture to forest and vice versa. Two hydrological models were developed to represent the distributions of water and yields in the study watershed: HEC-HMS and SWAT. Simulation results indicate that when nature-based solutions are implemented, surface runoff is reduced and ambient storage increases during the rainy season (December–March); while the overall flow increases during the dry season (June–September). The combination of specific hydrologic components of RIOS-customized intervention scenario simulation outputs—namely surface flows and groundwater contribution to stream flows—indicate on average 33% increase in the overall water yield, or 206 hm3/year, across the study watershed when comparing against the baseline conditions. In the same modeling scenario, the water storage in the sub-watersheds adjacent to the reservoirs showed an increase of 58% (or 341 hm3/year). The results indicate that adopting NbS in the source watershed can mitigate the impacts of extreme drought conditions and contribute toward building long-term water security.

Despite its rich water resources, Brazil is increasingly facing extreme hydrologic events such as droughts and floods. The Sao Paulo Cantareira water supply system (CWSS) offers an opportunity to examine the potential economic benefits of nature-based solutions (NbS) to improve water security and reduce the economic cost of drought. This study explores the potential benefits under a counterfactual NbS land-use scenario compared to actual land use and assesses the economic viability of NbS investments in the CWSS. Specifically, we estimate the economic cost of the 2014–2015 drought in Sao Paulo state for the industrial and water sectors served by the CWSS. We estimate the potential avoided costs under the NbS scenario and conduct a cost–benefit analysis of the NbS scenario investments, including both water supply and carbon sequestration benefits. We estimate that the economic losses of this single drought event totaled BRL 1.6 billion. If NbS had been implemented, this cost could have been reduced by 28%. A cost–benefit analysis that includes only the water supply or both the water supply and carbon sequestration benefits indicates that the NbS scenario has a positive net present value of BRL 144 million and BRL 632 million, respectively. Thus, our results highlight the economic viability of the hypothetical NbS investment in mitigating extreme climatic events.

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the cost-effectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of near-channel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.

We propose a Topographic Filtering simulation model (Topofilter) that can be used to identify those locations that are likely to contribute most of the sediment load delivered from a watershed. The reduced complexity model links spatially distributed estimates of annual soil erosion, high-resolution topography, and observed sediment loading to determine the distribution of sediment delivery ratio across a watershed. The model uses two simple two-parameter topographic transfer functions based on the distance and change in elevation from upland sources to the nearest stream channel and then down the stream network. The approach does not attempt to find a single best-calibrated solution of sediment delivery, but uses a model conditioning approach to develop a large number of possible solutions. For each model run, locations that contribute to 90% of the sediment loading are identified and those locations that appear in this set in most of the 10,000 model runs are identified as the sources that are most likely to contribute to most of the sediment delivered to the watershed outlet. Because the underlying model is quite simple and strongly anchored by reliable information on soil erosion, topography, and sediment load, we believe that the ensemble of simulation outputs provides a useful basis for identifying the dominant sediment sources in the watershed.

Near-channel sediment loading (NCSL) is localized and episodic, making it difficult to accurately quantify its cumulative contribution to watershed sediment loading, let alone predict the effects from changes in river discharge due to climate change or land management practices. We developed a methodological framework, using commonly available stream gaging data, for estimating watershed-scale NCSL, a feature generally absent in most watershed models. The method utilizes a network of paired gages that bracket the incised river corridors of 15 tributaries to the Minnesota River, in which near-channel sources are often the dominant contributors of sediment loading. For each set of paired gages, we calculate NCSL as the difference between the upstream and downstream sediment loading minus the field contribution between the gages. NCSL generally increases with river discharge when it exceeds the observed threshold benchmark in the tributaries of Minnesota River Basin; accordingly, we developed a predictive model for quantifying NCSL using river discharge as the independent variable. This approach provides a predictive basis for evaluating the impacts on near-channel sediment supply from increases in runoff and river discharge. Application of this approach includes evaluation of watershed-scale conservation trade-offs, where benefits of landscape management practices, such as wetlands and reservoirs are measured in terms of reduction in downstream near-channel sediment loading in the incised river corridors.

Climate change, land clearing, and artificial drainage have increased the Minnesota River Basin&rsquo, s (MRB) stream flows, enhancing erosion of channel banks and bluffs. Accelerated erosion has increased sediment loads and sedimentation rates downstream. High flows could be reduced through increased water storage (e.g., wetlands or detention basins), but quantifying the effectiveness of such a strategy remains a challenge. We used the Soil and Water Assessment Tool (SWAT) to simulate changes in river discharge from various water retention site (WRS) implementation scenarios in the Le Sueur watershed, a tributary basin to the MRB. We also show how high flow attenuation can address turbidity issues by quantifying the impact on near-channel sediment loading in the watershed&rsquo, s incised reaches. WRS placement in the watershed, hydraulic conductivity (K), and design depth were varied across 135 simulations. The dominant control on site performance is K, with greater flow reductions allowed by higher seepage rates and less frequent overflowing. Deeper design depths enhance flow reductions from sites with low K values. Differences between WRS placement scenarios are slight, suggesting that site placement is not a first-order control on overall performance in this watershed. Flow reductions exhibit power-law scaling with exceedance probability, enabling us to create generalized relationships between WRS extent and flow reductions that accurately reproduce our SWAT results and allow for more rapid evaluation of future scenarios. Overall, we show that increasing water storage within the Le Sueur watershed can be an effective management option for high flow and sediment load reduction.