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The Upper Mississippi River Basin is the largest source of reactive nitrogen (N) to the Gulf of Mexico. Concentration‐discharge (C‐Q) relationships offer a means to understand both the terrestrial sources that generate this reactive N and the in‐stream processes that transform it. Progress has been made on identifying land use controls on C‐Q dynamics. However, the impact of basin size and river network structure on C‐Q relationships is not well characterized. Here, we show, using high‐resolution nitrate concentration data, that tile drainage is a dominant control on C‐Q dynamics, with increasing drainage density contributing to more chemostatic C‐Q behavior. We further find that concentration variability increases, relative to discharge variability, with increasing basin size across six orders of magnitude, and this pattern is attributed to different spatial correlation structures for C and Q. Our results show how land use and river network structure jointly control riverine N export. Plain Language Summary: Nitrate is a major agricultural pollutant that can harm freshwater and marine ecosystems. Understanding the relationships between the concentration of nitrate in a river and the river's flow rate (discharge) can allow us to infer drivers of nitrate release from land into waterways and to better manage nutrient pollution. Here, we found that artificial (i.e. tile) drainage increases the stability of nitrate concentrations in the rivers of the Upper Mississippi basin across a wide range of flow rates. We also found that the variability of nitrate concentrations, relative to discharge, increased as rivers grew larger and had more tributaries contributing to flow. This shows that both on‐land and in‐river processes control the concentration‐discharge relationships of nitrate. Key Points: Nitrate concentrations in UMRB show a threshold response, chemodynamic at low flows but chemostatic at high flowsTile drainage increases the degree of nitrate chemostasis in the UMRBNitrate concentrations for rivers within Upper Mississippi River Basin (UMRB) are more chemodynamic with increasing basin size [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]