Y. van der Velde

Nitrate response of a lowland catchment: On the relation between stream concentration and travel time distribution dynamics

Nitrate pollution of surface waters is widespread in lowland catchments with intensive agriculture. For identification of effective nitrate concentration reducing measures the nitrate fluxes within catchments need to be quantified. In this paper we applied a mass transfer function approach to simulate catchment‐scale nitrate transport. This approach was extended with time‐varying travel time distributions and removal of nitrate along flow paths by denitrification to be applicable for lowland catchments. Numerical particle tracking simulations revealed that transient travel time distributions are highly irregular and rapidly changing, reflecting the dynamics of rainfall and evapotranspiration. The solute transport model was able to describe 26 years of frequently measured chloride and nitrate concentrations in the Hupsel Brook catchment (6.6 km2 lowland catchment in the Netherlands) with an R2 value of 0.86. Most of the seasonal and daily variations in concentrations could be attributed to temporal changes of the travel time distributions. A full sensitivity analysis revealed that measurements other than just surface water nitrate and chloride concentrations are needed to constrain the uncertainty in denitrification, plant uptake, and mineralization of organic matter. Despite this large uncertainty, our results revealed that denitrification removes more nitrate from the Hupsel Brook catchment than stream discharge. This study demonstrates that a catchment‐scale lumped approach to model chloride and nitrate transport processes suffices to accurately capture the dynamics of catchment‐scale surface water concentration as long as the model includes detailed transient travel time distributions

Quantifying catchment-scale mixing and its effect on time-varying travel time distributions

Travel time distributions are often used to characterize catchment discharge behavior, catchment vulnerability to pollution and pollutant loads from catchments to downstream waters. However, these distributions vary with time because they are a function of rainfall and evapotranspiration. It is important to account for these variations when the time scale of interest is smaller than the typical time-scale over which average travel time distributions can be derived. Recent studies have suggested that subsurface mixing controls how rainfall and evapotranspiration affect the variability in travel time distributions of discharge. To quantify this relation between subsurface mixing and dynamics of travel time distributions, we propose a new transformation of travel time that yields transformed travel time distributions, which we call Storage Outflow Probability (STOP) functions. STOP functions quantify the probability for water parcels in storage to leave a catchment via discharge or evapotranspiration. We show that this is equal to quantifying mixing within a catchment. Compared to the similar Age function introduced by Botter et al. (2011), we show that STOP functions are more constant in time, have a clearer physical meaning and are easier to parameterize. Catchment-scale STOP functions can be approximated by a two-parameter beta distribution. One parameter quantifies the catchment preference for discharging young water; the other parameter quantifies the preference for discharging old water from storage. Because of this simple parameterization, the STOP function is an innovative tool to explore the effects of catchment mixing behavior, seasonality and climate change on travel time distributions and the related catchment vulnerability to pollution spreading.

Improving load estimates for NO3 and P in surface waters by characterizing the concentration response to rainfall events

For the evaluation of action programs to reduce surface water pollution, water authorities invest heavily in water quality monitoring. However, sampling frequencies are generally insufficient to capture the dynamical behavior of solute concentrations. For this study, we used on-site equipment that performed semicontinuous (15 min interval) NO(3) and P concentration measurements from June 2007 to July 2008. We recorded the concentration responses to rainfall events with a wide range in antecedent conditions and rainfall durations and intensities. Through sequential linear multiple regression analysis, we successfully related the NO(3) and P event responses to high-frequency records of precipitation, discharge, and groundwater levels. We applied the regression models to reconstruct concentration patterns between low-frequency water quality measurements. This new approach significantly improved load estimates from a 20% to a 1% bias for NO(3) and from a 63% to a 5% bias for P. These results demonstrate the value of commonly available precipitation, discharge, and groundwater level data for the interpretation of water quality measurements. Improving load estimates from low-frequency concentration data just requires a period of high-frequency concentration measurements and a conceptual, statistical, or physical model for relating the rainfall event response of solute concentrations to quantitative hydrological changes.

Direct measurements of the tile drain and groundwater flow route contributions to surface water contamination: from field-scale concentration patterns in groundwater to catchment-scale surface water quality

Enhanced knowledge of water and solute pathways in catchments would improve the understanding of dynamics in water quality and would support the selection of appropriate water pollution mitigation options. For this study, we physically separated tile drain effluent and groundwater discharge from an agricultural field before it entered a 43.5-m ditch transect. Through continuous discharge measurements and weekly water quality sampling, we directly quantified the flow route contributions to surface water discharge and solute loading. Our multi-scale experimental approach allowed us to relate these measurements to field-scale NO(3) concentration patterns in shallow groundwater and to continuous NO(3) records at the catchment outlet. Our results show that the tile drains contributed 90-92% of the annual NO(3) and heavy metal loads. Considering their crucial role in water and solute transport, enhanced monitoring and modeling of tile drainage are important for adequate water quality management.

Integrated modeling of groundwater–surface water interactions in a tile‐drained agricultural field: The importance of directly measured flow route contributions

Understanding the dynamics of groundwater-surface water interaction is needed to evaluate and simulate water and solute transport in catchments. However, direct measurements of the contributions of different flow routes from specific surfaces within a catchment toward the surface water are rarely available. For this study, we physically separated the tile drain discharge toward a 43.5 m ditch transect from the groundwater-plus-overland flow routes. Direct groundwater flow and ephemeral overland flow were jointly captured in three sheet pile in-stream reservoirs, while the effluent from three tile drain outlets was collected in vessels. Our flux measurements showed that, in response to a rainfall event, the tile drain contribution to the total ditch discharge decreased from 80% to 28%. We used these flow route measurements to calibrate a field-scale integrated water transport model. The HydroGeoSphere code was used because it simultaneously solves the flow regimes in the variably saturated domain, the tile drain domain, and the surface flow domain. This simultaneous solution is needed for a correct representation of the mutual interactions between groundwater flow, tile drain flow, and ditch water flow. Our model produced a flow distribution between the flow paths which deviated only 2% from the measured flow distribution. A sensitivity analysis showed that model parameters related to tile drain entrance resistance and to the resistance to water flow through the surface water system controlled the water flow route distribution but with little effect on groundwater levels. This indicates that a model calibration based on groundwater levels alone does not necessarily produce a correct representation of the flow route contributions.