Identifying Dominant Processes in Time and Space: Time‐Varying Spatial Sensitivity Analysis for a Grid‐Based Nitrate Model

Abstract

Nitrogen (N) is vital for life (Wetzel, 2001), but its excess is a pollutant that contributes to eutrophication and dead zones in rivers, estuaries, and coastal seas worldwide, with significant economic consequences (Galloway et al., 2008). In Europe, the average N surplus reached 60 kg/ha•yr in agricultural catchments (Leip et al., 2011), which resulted in generally high N levels in rivers (Green et al., 2004), with little improvement despite implementation of EU Water Framework Directive/Nitrate Directive in recent years (Bouraoui & Grizzetti, 2011). Therefore, there remains a need to understand how excess N is transported to river systems, and how it is transformed along dominant flow pathways. However, the underlying hydrological and biogeochemical processes are generally characterized by marked spatiotemporal heterogeneity, with numerous local factors interacting (e.g., climate, topography, soils, and land management practices) (Musolff et al., 2015). As a result of the integrated effect of these numerous processes, which usually have high spatial complexity, the accurate prediction for hydrological and N fluxes is still a challenging task at the catchment scale (Grizzetti et al., 2015).Spatially distributed modeling is one way to improve predictions of catchment hydrological functioning and nutrient transport processes, as spatial details can be incorporated via regional parameterization (Rozemeijer et al., 2016). More distributed models have been developed and applied over the recent decades, due to the increase in available environmental data and computational resources (Wellen et al., 2015). However, while benefiting from better spatial representation, increased model complexity poses challenges from the increase in parameter numbers, depending on the grid resolution and number of state predictions (Tang et al., 2007). The high-dimensional, nonlinear parameter spaces make it extremely difficult to identify parameter values due to the resulting equifinality (Tonkin & Doherty, 2005), sometimes leading to an intractable and uncertain calibration

Assessing land use effects on ecohydrological partitioning in the critical zone through isotope‐aided modelling

“…The simulated ET dominated the water balance at the DMC (68–80% of precipitation) for the study year 2021. This is consistent with previous findings of ET at the DMC (83.7%; >80%) (Smith et al, 2021b; Wu et al, 2022a) and just higher than global ET averages (about 60% of precipitation) (Jung et al, 2019). Simulated transpiration (38–47%), soil evaporation (13–15%) and interception evaporation (15–20%) results are also comparable to those from Smith et al (2021b) (transpiration 49.8%, soil evaporation 9.4% and interception evaporation 24.4%) with soil evaporation being slightly higher and interception evaporation being slightly lower for our simulations.…”

“…The simulated ET dominated the water balance at the DMC (68-80% of precipitation) for the study year 2021. This is consistent with previous findings of ET at the DMC (83.7%; >80%) (Smith et al, 2021b;Wu et al, 2022a) and just higher than global ET model from Wada et al 2010). Another Germany wide study estimated a maximal possible recharge of 10-25% for the region of the DMC (Jankiewicz et al, 2005).…”

“…The DMC has been at the focus of many environmental studies as a long‐term experimental site. In the late 1990s, Gelbrecht et al (1998, 2005) focused on agricultural pollution, whereas more recent studies have concentrated on ecohydrological (Kleine et al, 2020; Kleine, Tetzlaff, Smith, Dubbert, & Soulsby, 2021; Kleine, Tetzlaff, Smith, Goldhammer, & Soulsby, 2021; Landgraf et al, 2022; Smith, Tetzlaff, Kleine, et al, 2020; Smith et al, 2021a, 2021b) and nitrate (Wu et al, 2022a, 2022b) fluxes. These investigations become especially important as the majority of agricultural land is not irrigated and forests contain a large proportion of shallow‐rooted pine trees.…”

“…Wu et al 2019 for groundwater seasonality and episodicity). Simulated recharge variesbetween $ 21% and $32% of precipitation, which is higher than the findings of 18% at the DMC fromWu et al (2022a) or 10% bySmith, Tetzlaff, Gelbrecht et al (2020). Globally, $16% of precipitation contributes to groundwater recharge, although uncertainties because of data scarcity of several regions were pointed out(Jasechko…”

Improved understanding of vegetation dynamics and wetland ecohydrology via monthly UAV‐based classification

“…Knowledge about the community distribution and its developmental progress can certainly improve the conceptualisation of interception and transpiration at finer spatial and temporal scales. The spatial distribution of different vegetation communities also provides an evidence base for the parameterisation of distributed modelling, which significantly reduces the computation burden and parametric uncertainty, while retaining a comparable representation of the spatial heterogeneity (Herman et al, 2013; Werkhoven et al, 2008; Wu, Tetzlaff, Yang, & Soulsby, 2022). For instance, the mapping acquired from this study has been used in recent modelling in this area via a physics‐based, fully distributed ecohydrological model EcH 2 O‐iso.…”