A Race Against Time: Modeling Time Lags in Watershed Response

Ilampooranan, Idhayachandhiran; Meter, K. J. Van; Basu, Nandita B.

doi:10.1029/2018wr023815

Cited by 55 publications

(59 citation statements)

References 78 publications

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“…In this scenario, all nitrogen inputs to the soil (fertilizer, manure, atmospheric deposition, and residues) are stopped after a certain time (Figure 8). The time lag between input nitrogen and instream nitrate concentration signals can be due to biogeochemical (soil) and hydrological (groundwater) time lags (Ilampooranan et al., 2019). In this study, the biogeochemical time lag corresponds to the biogeochemical reaction time scale in the soil zone while the hydrological time lag corresponds to the travel time of nitrate in the subsurface.…”

Section: Results and Validationmentioning

confidence: 99%

Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Nguyen

Kumar

Lutz

et al. 2021

Water Resources Research

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StorAge Selection (SAS) functions describe how catchments selectively remove water of different ages in storage via discharge, thus controlling the transit time distribution (TTD) and solute composition of discharge. SAS‐based models have been emerging as promising tools for quantifying catchment‐scale solute export, providing a coherent framework for describing both velocity‐driven and celerity‐driven transport. Due to their application in headwaters only, the spatial heterogeneity of catchment physiographic characteristics, land use management practices, and large‐scale validation have not been adequately addressed with SAS‐based models. Here, we integrated SAS functions into the grid‐based mHM‐Nitrate model (mesoscale Hydrological Model) at both grid scale (distributed model) and catchment scale (lumped model). The proposed model provides a spatially distributed representation of nitrogen dynamics within the soil zone and a unified approach for representing both velocity‐driven and celerity‐driven subsurface transport below the soil zone. The model was tested in a heterogeneous mesoscale catchment. Simulated results show a strong spatial heterogeneity in nitrogen dynamics within the soil zone, highlighting the necessity of a spatially explicit approach for describing near‐surface nitrogen processing. The lumped model could well capture instream nitrate concentration dynamics and the concentration–discharge relationship at the catchment outlet. In addition, the model could provide insights into the relations between subsurface storage, mixing scheme, solute export, and the TTDs of discharge. The distributed model shows results that are comparable to the lumped model. Overall, the results reveal the potential for large‐scale applications of SAS‐based transport models, contributing to the understanding of water quality‐related issues in agricultural landscapes.

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Section: Results and Validationmentioning

confidence: 99%

Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Nguyen

Kumar

Lutz

et al. 2021

Water Resources Research

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“…Adding more complexity, empirical models such as SPARROW (R. B. Alexander et al, 2001), GREEN (Grizzetti et al, 2005), and NEWS (Mayorga et al, 2010) have been used to predict riverine N loads based on a function of watershed N inputs and terrestrial and aquatic retention factors that are calibrated to nitrogen loads at the watershed outlet. Finally, more sophisticated biogeochemical models, such as DAYCENT, CENTURY, and SWAT, explicitly simulate various N fluxes (e.g., denitrification and crop uptake leaching) using detailed process‐based equations (Del Grosso et al, 2002; Ilampooranan et al, 2019; Yiridoe et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Long‐Term Shifts in U.S. Nitrogen Sources and Sinks Revealed by the New TREND‐Nitrogen Data Set (1930–2017)

Byrnes

Meter

Basu

2020

Global Biogeochemical Cycles

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Reactive nitrogen (N) fluxes have increased tenfold over the last century, driven by increases in population, shifting diets, and increased use of commercial N fertilizers. Runoff of excess N from intensively managed landscapes threatens drinking water quality and disrupts aquatic ecosystems. Excess N is also a major source of greenhouse gas emissions from agricultural soils. While N emissions from agricultural landscapes are known to originate from not only current-year N input but also legacy N accumulation in soils and groundwater, there has been limited access to fine-scale, long-term data regarding N inputs and outputs over decades of intensive agricultural land use. In the present work, we synthesize population, agricultural, and atmospheric deposition data to develop a comprehensive, 88-year (1930-2017) data set of county-scale components of the N mass balance across the contiguous United States (Trajectories Nutrient Dataset for nitrogen [TREND-nitrogen]). Using a machine-learning algorithm, we also develop spatially explicit typologies for components of the N mass balance. Our results indicate a large range of N trajectory behaviors across the United States due to differences in land use and management and particularly due to the very different drivers of N dynamics in densely populated urban areas compared with intensively managed agricultural zones. Our analysis of N trajectories also demonstrates a widespread functional homogenization of agricultural landscapes. This newly developed typology of N trajectories improves our understanding of long-term N dynamics, and the underlying data set provides a powerful tool for modeling the impacts of legacy N on past, present, and future water quality. Plain Language Summary Over the last century, people have increasingly used nitrogen fertilizer to increase crop yields. The nitrogen not taken up by crops in agricultural areas runs off of the land and pollutes rivers, lakes, and coastal areas. This excess nitrogen also forms a powerful greenhouse gas that contributes to climate change. Excess nitrogen can build up in the environment over time and pollute our water for decades. It is therefore necessary for us to know how much extra nitrogen has been applied over many decades to better understand current risks to the environment. In our study, we have used multiple data sources to calculate how much nitrogen has been added to the landscape, how much nitrogen has been removed through crop production, and how much human waste is produced, for every county in the contiguous United States from 1930 to 2017. We show that the main sources of nitrogen can be different in different areas of the country. We also show that high levels of nitrogen use can make landscapes in very different climates look very similar. This new data set will be very important for creating models that can predict how decades of high nitrogen inputs impact water quality and future changes in climate.

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“…While annual estimates of fertilizer input in a catchment or a region is sometimes available, the spatially explicit and time‐varying description of solute sources is typically insufficient for modeling applications (Costa et al, 2017; Ilampooranan et al, 2019). Consequently, recent studies investigated the spatiotemporal variabilities of solute inputs and their impact on surface water quality (Böhlke et al, 2002; Chanat & Yang, 2018; Garnier et al, 2018; Kennedy et al, 2018; McCrackin et al, 2017; McInerney et al, 2018; Zimmer et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Depth of Solute Generation Is a Dominant Control on Concentration‐Discharge Relations

Botter¹,

Hartmann

et al. 2020

Water Resources Research

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Solutes in rivers often come from multiple sources, notably precipitation (above) and generation from the subsurface (below). The question of which source is more influential in shaping the dynamics of solute concentration cannot be easily addressed due to the general lack of input data. An analysis of solute concentrations and their dependence on discharge across 585 catchments in nine countries leads us to hypothesize that both the timing and the vertical distribution of the solute generation are important drivers of solute export dynamics at the catchment scale. We test this hypothesis running synthetic experiments with a tracer‐aided distributed hydrological model. The results reveal that the depth of solute generation is the most important control of the concentration‐discharge (C‐Q) relation for a number of solutes. Such relation shows that C‐Q patterns of solute export vary from dilution (Ca2+, Mg2+, K+, Na+, and Cl−) to weakly enriching (dissolved organic carbon). The timing of the input imposes a signature on temporal dynamics, most evident for nutrients, and adds uncertainty in the exponent of the C‐Q relation.

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A Race Against Time: Modeling Time Lags in Watershed Response

Cited by 55 publications

References 78 publications

Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Long‐Term Shifts in U.S. Nitrogen Sources and Sinks Revealed by the New TREND‐Nitrogen Data Set (1930–2017)

Depth of Solute Generation Is a Dominant Control on Concentration‐Discharge Relations

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