Uncertainty in critical source area predictions from watershed-scale hydrologic models

Equivalent biodiversity area: A novel metric for No Net Loss success in Brazil's changing biomes

This study presents a new method to incorporate the No Net Loss (NNL) principle within corporate Environmental, Social, and Governance (ESG) frameworks. This principle aims to ensure that biodiversity losses from human activities are fully offset. In this context, we tackle two main challenges: managing epistemic uncertainties in environmental modeling and accurately assessing compensatory areas needed to replace lost habitats. Focusing on Brazil's diverse biomes, which are undergoing rapid changes, we highlight the role of expert opinion surveys in addressing the uncertainties of the InVEST Habitat Quality, a model that simulates changes in landscape integrity under different land use scenarios. Our analysis across three of Brazil's regions - Caatinga Semi-arid, Cerrado Savanna, and Atlantic Forest - leverages open-source data to reveal substantial habitat losses due to activities like wind farm development, mining, and intensive agriculture, leading to a widespread decline in habitat quality. We introduce the Equivalent Biodiversity Area (EBA) metric to support NNL and Net Gain of Biodiversity efforts, measured in hectares. Findings show a reduction in EBA across all studied areas, highlighting the need for effective compensation strategies. Such strategies should merge Legal Reserves and ecological restoration into ESG policies, encourage landholder collaboration, and align with larger environmental efforts, such as watershed revitalization and Biodiversity Credits markets.

Unraveling spatial patterns and source attribution of nutrient transport: Towards optimal best management practices in complex river basin

A comprehensive understanding of nutrient transport patterns and clarification of pollutant sources' load contributions are critical prerequisites for developing scientific pollution control strategies in complex river basins. Here, we focused on the Minjiang River Basin (MRB) and employed the Soil and Water Assessment Tool (SWAT) model to systematically investigate the nitrogen (N) and phosphorus (P) loads from both point and non-point sources. Results revealed that the key source areas of N and P pollution in the MRB were predominantly located along the riverbanks, influenced by a combination of sediment, precipitation, agricultural activities such as fertilization. Our analysis indicated that soil nutrient loss, fertilization, and livestock farming were the major contributors to N and P inputs, accounting for over 70 % of the total input, followed by rural residential and urban point sources. Based on the identification of non-point source pollution as the primary load source, a multi-objective optimization was conducted using response surface methodology (RSM) coupled with the non-dominated sorting genetic algorithm-II (NSGA-II), resulting in the identification of optimal best management practices (BMPs) that achieve a reduction of 40.04 % in N load, 39.22 % in P load, and a net economic benefit of -1.13 billion yuan per year. Compared to the RSM and automated optimization results, the proposed management measures exhibited significant improvements in N and P load reduction and net benefits. Overall, the findings provide important insights for formulating agricultural management policies in the MRB and offering valuable implications for pollution management in other complex river basins.

Assessing the effectiveness of potential best management practices for science-informed decision support at the watershed scale: The case of the Mar Menor coastal lagoon, Spain

Coastal lagoons are ecosystems of high environmental importance but are quite vulnerable to human activities. The continuous inflow of pollutant loads can trigger negative impacts on the ecological status of these water bodies, which is contrary to the European Green Deal. One example is the Mar Menor coastal lagoon in Spain, which has experienced significant environmental degradation in recent years due to excessive external nutrient input, especially from non-point source (NPS) pollution. Mar Menor is one of the largest coastal lagoons of the Mediterranean region and a site of great ecological and socio-economic value. In this study, the highly anthropogenic and complex watershed of Mar Menor, known as Campo de Cartagena (1244 km²), was modelled with the Soil and Water Assessment Tool (SWAT) to analyse potential options for recovery of this unique system. The model was used to simulate several best management practices (BMP) proposed by recent Mar Menor regulations, such as vegetative filter strips, shoreline buffers, contour farming, removal of illegal agriculture, crop rotation management, waterway vegetation restoration, fertiliser management and greenhouse rainwater harvesting. Sixteen scenarios of individual and combined BMPs were analysed in this study. We found that, as individual measures, vegetative filter strips and contour farming were most effective in nutrient reduction: approximately 30 % for total nitrogen (TN) and 40 % for total phosphorus (TP). Moreover, waterway vegetation restoration showed the highest sediment (S) reduction at approximately 20 %. However, the combination of BMPs demonstrated clear synergistic effects, reducing S export by 38 %, TN by 67 %, and TP by 75 %. Selecting the most appropriate BMPs to be implemented at a watershed scale requires a holistic approach considering effectiveness in reducing NPS pollution loads and BMP implementation costs. Thus, we have demonstrated a way forward for enabling science-informed decision-making when choosing strategies to control NPS contamination at the watershed scale.

Comparing critical source areas for the sediment and nutrients of calibrated and uncalibrated models in a plateau watershed in southwest China

Controlling non-point source pollution is often difficult and costly. Therefore, focusing on areas that contribute the most, so-called critical source areas (CSAs), can have economic and ecological benefits. CSAs are often determined using a modelling approach, yet it has proved difficult to calibrate the models in regions with limited data availability. Since identifying CSAs is based on the relative contributions of sub-basins to the total load, it has been suggested that uncalibrated models could be used to identify CSAs to overcome data scarcity issues. Here, we use the SWAT model to study the extent to which an uncalibrated model can be applied to determine CSAs. We classify and rank sub-basins to identify CSAs for sediment, total nitrogen (TN), and total phosphorus (TP) in the Fengyu River Watershed (China) with and without model calibration. The results show high similarity (81%-93%) between the identified sediment and TP CSA number and locations before and after calibration both on the yearly and seasonal scale. For TN alone, the results show moderate similarity on the yearly scale (73%). This may be because, in our study area, TN is determined more by groundwater flow after calibration than by surface water flow. We conclude that CSA identification with the uncalibrated model for TP is always good because its CSA number and locations changed least, and for sediment, it is generally satisfactory. The use of the uncalibrated model for TN is acceptable, as its CSA locations did not change after calibration; however, the TN CSA number changed by over 60% compared to the figures before calibration on both yearly and seasonal scales. Therefore, we advise using an uncalibrated model to identify CSAs for TN only if water yield composition changes are expected to be limited. This study shows that CSAs can be identified based on relative loading estimates with uncalibrated models in data-deficient regions.

Land Use and Cover Changes versus climate shift: Who is the main player in river discharge? A case study in the Upper Paraná River Basin

Assessing the relative contribution of Land Use and Cover Changes (LUCC) and climate changes on runoff still represents a great challenge for water resources management. This issue is particularly critical for the Upper Paraná River Basin (UPRB), one of the most important basins in South America and responsible for most of the production of food, ethanol, and electricity generation in Brazil. In this paper, we used the Soil and Water Assessment Tool (SWAT) to quantitatively assess the relative contribution of both forcings. The simulation period included a time of great importance for climate studies, known as the 1970s global climate shift, and of great impact on river discharge within the UPRB. Three land use and cover scenarios were assigned to the 1961-1990 period of simulations, representing land use and cover during a pristine period (around the Year 1500), 1960, and 1985. Thirteen years of precipitation before and after the climate shift (considered to be the period 1974-1977) were analyzed and compared. Results showed a precipitation increase for the basin in general after the climate shift. The increase in rainfall reached up to 15% in many northern areas and more than 20% in the southern parts of the basin. By comparing all simulations, results indicate that both LUCC and precipitation increase due to the climate shift had a significant effect on the changes in annual discharge of the largest rivers of the UPRB. However, the results suggest that the impact of the precipitation increase on the discharge exceeded that of the LUCC. Between 1960 and 1985 the LUCC accounts for about 16% of the increase of the median annual discharge, whereas climate shift accounts for an increase of about 32%. These findings, suggesting a more relevant role for the climate, are consistent with two recent water crisis experienced by the country in the last decades, caused by prolonged below-normal rainfall throughout 2001/2002 and again in 2014/2015.

Particulate PhozzyLogic Index for policy makers-an index for a more accurate and transparent identification of critical source areas

The identification of critical source areas (CSAs) is a key element in a cost-effective mitigation of diffuse emissions of phosphorus from agricultural soils into surface waters. One of the challenges related to CSAs is how to couple complex, data-intensive fate and transport models with easy-to-use information on field level for management purposes at the scale of large watersheds. To fill such a gap and create a bridge between the two tasks, this study puts forward the new Particulate PhozzyLogic Index (PPLI) based on the innovative combination of the results of a complex watershed model (in this case the PhosFate model) with fuzzy logic. Its main feature is the ability to transform the results of diverse scenarios or even models into a final map showing a catchment-wide ranking of the possibility of high PP emissions reaching surface waters for all agricultural fields. Further, this study enhances the PhosFate model with a new algorithm for the allocation of particulate phosphorus (PP) loads entering surface waters to their sources of origin. This is a basic requirement for the identification of critical PP source areas and in consequence for a cost-effective implementation of mitigation measures. By means of a sensitivity analysis, this study investigates the impacts of storm drains, discharge frequencies and flow directions on the designation of CSAs with the help of present-day scenarios for a case study catchment with an area of several hundred square kilometres. The upfront model calibration exhibits a Nash-Sutcliffe efficiency (NSE) of about 0.95 and a modified Nash-Sutcliffe efficiency (mNSE) of around 0.83. A core result of the sensitivity analysis is that the scenarios at least partially disagree on the identified CSAs and suggest that especially open furrows at field borders have the potential to lead to deviating outcomes. All scenario results nevertheless support the 80:20 rule, which states that about 80% of the phosphorus inputs into the surface waters of a catchment originate from only about 20% of its area.

Evaluating the efficacy of targeting options for conservation practice adoption on watershed-scale phosphorus reductions

Conservation identities of farmers in the Maumee River watershed, derived from farmer surveys, were embedded into a SWAT watershed model. This was done to improve the representation of the heterogeneity among farmers in the decision-making process related to the adoption of conservation practices. Modeled farm operations, created with near field-level Hydrologic Response Units (HRUs) within the SWAT model, were assigned a modeled primary operator. Modeled primary operators held unique conservation identities driven by their spatial location within the watershed. Five pathways of targeting the adoption of subsurface placement of phosphorus and buffer strips to HRUs within the watershed were assessed. Targeting pathways included targeting by HRU-level phosphorus losses, conservation identity of model operators, a hybrid approach combining HRU-level phosphorus losses and conservation identity of the model primary operator managing the HRU, and a proxy measure for random placement throughout the watershed. Targeting the placement of subsurface phosphorus application to all agricultural HRUs resulted in the greatest reduction in total phosphorus losses (32%) versus buffer strips (23%). For both conservation practices, targeting by HRU-level total phosphorus losses resulted in the most efficient rate of phosphorus reduction as measured by the ratio of phosphorus reduction to conservation practice adoption rates. The hybrid targeting approach closely resembled targeting by phosphorus losses, indicating near optimal results can be obtained even when constraining adoption by farmer characteristics. These results indicate that by developing management strategies based on a combination of field-level information and human-operator characteristics, a more efficient use of limited resources can be used while achieving near-maximal environmental benefits as compared to managing environmental outcomes solely based on field-level information.

Estimate of nutrient sources and transport into Bohai Bay in China from a lower plain urban watershed using a SPARROW model

In the development of a land-sea coordination management strategy, it is necessary to analyze pollution sources and loads of pollutants entering the sea. This study estimated the sources and transport of total nitrogen (TN) and total phosphorus (TP) entering Bohai Bay in Tianjin, a lower plain urban watershed, using a SPAtially Referenced Regression On Watershed attributes (SPARROW) model. We calibrated the model using TN and TP data from 26 and 27 sites, respectively. The results demonstrated that the R² values of TN and TP were both above 0.99. In 2013, the TN load delivered to Bohai Bay was 21,320 ton, which could be traced to various sources: upstream (39%), industrial discharge (10%), sewage discharge (34%), fertilizer application (3%), livestock breeding (7%), aquaculture (5%), and rural communities (2%). The TP load delivered to Bohai Bay was 1504 ton, which originated from upstream (33%), industrial discharge (5%), sewage discharge (21%), fertilizer application (5%), livestock breeding (12%), aquaculture (10%), and rural communities (14%). Rational management of the water resources in streams, enhancement of water circulation between rivers and wetlands, and making full use of the effect of both land and water on pollutant retention are the suitable strategies in watershed management, reducing marine pollution.

Effect of Projected Land Use and Climate Change on Water Quality of Old Woman Creek Watershed, Ohio

The separate and synergistic effects of land use and climate change on water quality variables in Old Woman Creek (OWC) watershed were evaluated using a hydrological model set up in Soil and Water Assessment Tool (SWAT) for the OWC watershed. Model calibration was done using a multi-objective evolutionary algorithm and pareto optimization. The Parameter-Elevation Regressions on Independent Slopes Model (PRISM) climate data and the 20 different Global Circulation Models (GCMs) developed by the Coupled Model Intercomparison Project Phase five (CMIP5) were used. Validation was done using the streamflow data from USGS gaging station and water quality data from the water quality lab, Heidelberg University. The simulation was divided into two land use scenarios: Scenario 1 for constant land use and Scenario 2 where land use was varied. Both land use simulations were run in four time periods to account for climate change: historical (1985–2014), current to near future (2018–2045), mid-century (2046–2075), and late-century (2076–2100) climate windows. For the historical period, the average of all the simulations made from the 20 different CMIP5 GCMs shows good agreement with the PRISM results for flow and the water quality variables of interest with smaller inter-model variability compared to PRISM results. For the other three climate windows, the results of Scenario 1 show an increase in flow and eight water quality variables (sediment (total suspended sediment), organic nitrogen, organic phosphorus (particulate p), mineral phosphorus (soluble reactive p), chlorophyll a, carbonaceous biochemical oxygen demand (CBOD), dissolved oxygen, total nitrogen) across the climate windows but a slight decrease in one water quality variable, mineral phosphorus in the mid-century. The results of Scenario 2 show a greater increase in flow, and the eight water quality variables across the climate windows show a relatively larger decrease in one water quality variable (mineral phosphorus). The projected land use change has little impact compared to the projected climate change on OWC watershed in the 21st century.

Wetland restoration yields dynamic nitrate responses across the Upper Mississippi river basin

Wetland restoration is a primary management option for removing surplus nitrogen draining from agricultural landscapes. However, wetland capacity to mitigate nitrogen losses at large river-basin scales remains uncertain. This is largely due to a limited number of studies that address the cumulative and dynamic effects of restored wetlands across the landscape on downstream nutrient conditions. We analyzed wetland restoration impacts on modeled nitrate dynamics across 279 subbasins comprising the ∼0.5 million km2 Upper Mississippi River Basin (UMRB), USA, which covers eight states and houses ∼30 million people. Restoring ∼8,000 km2 of wetlands will reduce mean annual nitrate loads to the UMRB outlet by 12%, a substantial improvement over existing conditions but markedly less than widely cited estimates. Our lower wetland efficacy estimates are partly attributed to improved representation of processes not considered by preceding empirical studies - namely the potential for nitrate to bypass wetlands (i.e., via subsurface tile drainage) and be stored or transformed within the river network itself. Our novel findings reveal that wetlands mitigate surplus nitrogen basin-wide, yet they may not be as universally effective in tiled landscapes and because of river network processing.