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Nutrient legacies in anthropogenic landscapes, accumulated over decades of fertilizer application, lead to time lags between implementation of conservation measures and improvements in water quality. Quantification of such time lags has remained difficult, however, due to an incomplete understanding of controls on nutrient depletion trajectories after changes in land-use or management practices. In this study, we have developed a parsimonious watershed model for quantifying catchment-scale time lags based on both soil nutrient accumulations (biogeochemical legacy) and groundwater travel time distributions (hydrologic legacy). The model accurately predicted the time lags observed in an Iowa watershed that had undergone a 41% conversion of area from row crop to native prairie. We explored the time scales of change for stream nutrient concentrations as a function of both natural and anthropogenic controls, from topography to spatial patterns of land-use change. Our results demonstrate that the existence of biogeochemical nutrient legacies increases time lags beyond those due to hydrologic legacy alone. In addition, we show that the maximum concentration reduction benefits vary according to the spatial pattern of intervention, with preferential conversion of land parcels having the shortest catchment-scale travel times providing proportionally greater concentration reductions as well as faster response times. In contrast, a random pattern of conversion results in a 1:1 relationship between percent land conversion and percent concentration reduction, irrespective of denitrification rates within the landscape. Our modeling framework allows for the quantification of tradeoffs between costs associated with implementation of conservation measures and the time needed to see the desired concentration reductions, making it of great value to decision makers regarding optimal implementation of watershed conservation measures.

More than a century of land-use changes and intensive agriculture across the Mississippi River Basin (MRB) has led to a degradation of soil and water resources. Nitrogen (N) leaching from the excess application of fertilizers has been implicated in algal blooms and the development of large, coastal ‘dead zones’. It is, however, increasingly recognized that water quality today is a function not only of the current-year inputs but also of legacy N within the watershed—legacy that has accumulated in soil and groundwater over decades of high-input agricultural practices. Although attempts have been made to quantify the extent to which soil organic nitrogen (SON) is being sequestered in agricultural soils with intensive fertilization, improved residue management, and the adoption of conservation tillage practices, the controls on accumulation dynamics as well as linkages between legacy N accumulation and water quality remain unclear. Here, we have used the process-based model CENTURY to quantify accumulation and depletion trajectories for soil N across a range of climate and soil types characteristic of the MRB. The model was calibrated against crop yield data and soil N accumulation data from a long-term field site. Model runs highlighted that under current management scenarios, N accumulation is greatest in regions with the highest crop yield, and this can be attributed to the higher residue rates with greater yields. We thus find that humans, through management practices, have homogenized spatial patterns of SON across the landscape by increasing SON magnitudes in warmer and drier regions. Results also suggest a regime shift in the relationship between soil organic N and N mineralization fluxes, such that N fluxes are greater now than in the 1930s, despite similar soil organic N magnitudes, mainly due to higher proportions of labile, unprotected soil organic matter. This regime shift leads to elevated N leaching to tiles and groundwater in landscapes under intensive agriculture.

Hydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement data set (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data‐driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out‐performed the traditional, regionalized power law‐based hydraulic geometry equations for both width and depth, providing R‐squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R‐squared values of 0.45 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine‐learning models, demonstrating the value of using multi‐model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM‐geo data set, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the contiguous US. Plain Language Summary Scientists and river managers use measurements of river geometry such as width and depth to forecast floods and understand river behavior. However, the methods used to estimate river geometry that have been used for decades are imprecise and thus lead to poor predictions of river discharge dynamics. Here, we've used new machine learning‐based modeling approaches to provide better predictions of river width and depth. We tested different machine‐learning models, which were developed based on the HYDRoSWOT set of measurements of rivers across the U.S. These new models all provide better estimates of river width and depth than the old methods. Our research can help us to provide better estimates of flood dynamics and improve our understanding of rivers across the U.S. Key Points Machine Learning models outperform regional (physiographic) hydraulic geometry equations for predicting stream width and depth Model performance varies by stream orders and geographical regions, demonstrating the utility of multi‐model machine‐learning approaches The STREAM‐geo data set provides predictions of river width, depth, width‐to‐depth ratio, and river area for the NHDPlus stream reaches

Increases in nitrogen (N) fertilizer application, livestock densities, and human population over the last century have led to substantial increases in nitrate contamination. While increases in riverine N loads are well-documented, the total magnitude of N accumulation in groundwater remains unknown. Here we provide a first data-driven estimate of N mass accumulation in groundwater within the Upper Mississippi River Basin (UMRB), an area of intensive row-crop agriculture and the primary contributor to Gulf of Mexico hypoxia. Using approximately 49 000 groundwater nitrate well concentration values and a suite of geospatial predictors, we developed a Random Forest model to produce gridded predictions of depth-varying nitrate concentrations. Our results suggest that approximately 15 Tg of N (328 ± 167 kg-N ha −1 ) is currently stored in UMRB groundwater recharged over the last 50 years. For context, we compare these predictions to those from a lumped statistical model, which predicts accumulation of 387 ± 133 kg-N ha −1 , as well as to a simple N mass balance model of the UMRB, which puts an upper bound on accumulation of approximately 1000 kg-N ha −1 (1967–2017). These findings highlight the importance of considering legacy N when forecasting future water quality, as N in the subsurface will continue to impair drinking water quality and elevate surface water N concentrations for decades to come.

Nitrogen (N) legacies have built up in anthropogenic landscapes over decades of agricultural intensification, and these legacies lead to time lags in water quality change measurable even beyond the moment of application of N. It is important to understand these legacies to quantify the relationship between N inputs and N concentrations in streams and implement best management practices for water quality improvement; however, little is known about the magnitude of legacies in various landscape elements like soils and groundwater. Here, we have used the ELEMeNT (Exploration of Long-tErM Nutrient Trajectories) model to explore the buildup and depletion of N legacies over a 216-year period, across the Mondego River Basin, a 6645-km 2 watershed in Portugal, where human interventions have considerably changed the characteristics of the basin to prevent floods and improve farming conditions in recent decades. The results show that the increase in the amount of inorganic fertilizer applied was the main driver for the anthropogenic N loads in the watershed from 1950 until the beginning of the 1990s. The N inputs have been decreasing since then, but N loads in the river did not document any decrease till the 1990s; after which there was a decline. This time lag between the N inputs to the watershed and the N loads in the river (about two decades) is a function of accumulation of N legacy.

More than 50% of global wetland area has been lost over the last 200 years, resulting in losses of habitat and species diversity as well as decreased hydrologic and biogeochemical functionality. Recognition of the magnitude of wetland loss as well as the wide variety of ecosystem services provided by wetlands has in recent decades led to an increased focus on wetland restoration. Restoration activities, however, often proceed in an ad hoc manner, with a focus on maximizing the total restored area rather than on other spatial attributes of the wetland network, which are less well understood. In this study, we have addressed the question of how human activities have altered the size distribution and spatial organization of wetlands over the Prairie Pothole Region of the Des Moines Lobe using high-resolution LIDAR data. Our results show that as well as the generally accepted 90% loss of depressional wetland area, there has been a preferential loss of smaller wetlands, with a marked alteration of the historical power-law relationship observed between wetland size and frequency and a resulting homogenization of the wetland size distribution. In addition, our results show significant decreases in perimeter-to-area ratios, increased mean distances between wetlands, particularly between smaller wetlands, and a reduced likelihood that current wetlands will be located in upland areas. Such patterns of loss can lead to disproportionate losses of ecosystem services, as smaller wetlands with larger perimeter-to-area ratios have been found to provide higher rates of biogeochemical processing and groundwater recharge, while increased mean distances between wetlands hinder species migration and thus negatively impact biodiversity. These results suggest the need to gear restoration efforts toward understanding and recreating the size distribution and spatial organization of historical wetlands, rather than focusing primarily on an increase in overall area.

Excess phosphorus from agricultural intensification has contributed to the eutrophication of rivers and lakes worldwide, including the transboundary Laurentian Great Lakes Basin. Algal blooms have surged in the past decade, threatening ecosystems, drinking water supplies and lake-dependent tourism economies in both large lakes (for example, Lake Erie) and smaller water bodies. Whereas previous research has focused mainly on phosphorus loads to Lake Erie, a comprehensive analysis of phosphorus species across the basin is lacking. Here we analyse changes in soluble reactive phosphorus and total phosphorus concentrations in over 370 watersheds across the Great Lakes Basin from 2003 to 2019. We find widespread increases in soluble phosphorus concentrations (83% of watersheds, with 46% showing significant increase), while total phosphorus concentrations are decreasing or non-significant. Utilizing random forest models, we identify small, forested watersheds at higher latitudes as the areas experiencing the largest relative increases in soluble phosphorus concentrations. Furthermore, we find winter temperatures to be a key driver of winter concentration trends. We propose that the increasing soluble phosphorus concentrations across the basin, along with warming temperatures, might be contributing to the increasing frequency and intensity of algal blooms, emphasizing the need for management strategies to prevent further water-quality degradation.Analyses of phosphorus concentrations in more than 370 watersheds of the Great Lakes Basin from 2003 to 2019 suggest widespread increases in soluble reactive phosphorus concentrations, despite often decreasing or non-significant trends in total phosphorus.

Increasing incidences of eutrophication and groundwater quality impairment from agricultural nitrogen pollution are threatening humans and ecosystem health. Minimal improvements in water quality have been achieved despite billions of dollars invested in conservation measures worldwide. Such apparent failures can be attributed in part to legacy nitrogen that has accumulated over decades of agricultural intensification and that can lead to time lags in water quality improvement. Here, we identify the key knowledge gaps related to landscape nitrogen legacies and propose approaches to manage and improve water quality, given the presence of these legacies. Agricultural nitrogen legacies are delaying improvements to water quality. Comprehensive management strategies that address legacy issues are needed to ensure better environmental outcomes.

Excess nitrogen (N) within the landscape can lead to environmental issues such as water pollution and downstream eutrophication. Quantification of landscape N fluxes can help identify areas of excess N and can improve our understanding of the sources, sinks, and transport of N within ecosystems. The gTREND-Nitrogen dataset provides a comprehensive, long-term (1930-2017) N mass balance for the contiguous United States at a spatial resolution of 250 meters. This dataset integrates county-scale estimates of N fluxes with gridded land use and population data to estimate grid-scale surface fluxes of N, including fertilizer, atmospheric deposition, manure inputs, biological N fixation, crop N uptake, and population-based human waste. The downscaled gTREND-Nitrogen data will allow for fine-scale insights into both historical and current N dynamics, addressing the limitations of previous datasets developed at coarser spatial scales. The data is openly available and will help to inform the development of effective policy and management strategies to mitigate the negative impacts of excess N and to promote sustainable nutrient management practices.

Rainwater harvesting (RWH), the small-scale collection and storage of runoff for irrigated agriculture, is recognized as a sustainable strategy for ensuring food security, especially in monsoonal landscapes in the developing world. In south India, these strategies have been used for millennia to mitigate problems of water scarcity. However, in the past 100 years many traditional RWH systems have fallen into disrepair due to increasing dependence on groundwater. This dependence has contributed to accelerated decline in groundwater resources, which has in turn led to increased efforts at the state and national levels to revive older RWH systems. Critical to the success of such efforts is an improved understanding of how these ancient systems function in contemporary landscapes with extensive groundwater pumping and shifted climatic regimes. Knowledge is especially lacking regarding the water-exchange dynamics of these RWH tanks at tank and catchment scales, and how these exchanges regulate tank performance and catchment water balances. Here, we use fine-scale, water-level variation to quantify daily fluxes of groundwater, evapotranspiration (ET), and sluice outflows in four tanks over the 2013 northeast monsoon season in a tank cascade that covers a catchment area of 28 km2. At the tank scale, our results indicate that groundwater recharge and irrigation outflows comprise the largest fractions of the tank water budget, with ET accounting for only 13–22 % of the outflows. At the scale of the cascade, we observe a distinct spatial pattern in groundwater-exchange dynamics, with the frequency and magnitude of groundwater inflows increasing down the cascade of tanks. The significant magnitude of return flows along the tank cascade leads to the most downgradient tank in the cascade having an outflow-to-capacity ratio greater than 2. At the catchment scale, the presence of tanks in the landscape dramatically alters the catchment water balance, with runoff decreasing by nearly 75 %, and recharge increasing by more than 40 %. Finally, while water from the tanks directly satisfies  ∼ 40 % of the crop water requirement across the northeast monsoon season via surface water irrigation, a large fraction of the tank water is \"wasted\", and more efficient management of sluice outflows could lead to tanks meeting a higher fraction of crop water requirements.