Hydrological water quality models have gained wide acceptance from environmental scientists and water managers to address deterioration of surface water quality. Higher spatiotemporal accuracy of such models is increasingly required for better understanding the functional heterogeneity of catchments and improving management decisions at different governance levels. However, balancing spatial representation and model complexity remains challenging. We present a new flexibly designed, fully distributed nitrate transport and removal model (mHM‐Nitrate) at catchment scale. The model was developed mainly based on the mesoscale Hydrological Model (mHM) and the Hydrological Predictions for the Environment (HYPE) model. The mHM‐Nitrate model was tested in the Selke catchment (Central Germany), which is characterized by heterogeneous physiographic and land‐use conditions, using adequate observed hydrological and nitrate data at three nested gauging stations. Long term (1997–2015) daily simulations showed that the model well reproduced the seasonal dynamics of biweekly nitrate observations in forested, agricultural and urban areas. High‐frequency measurements (2010‐2015) were additionally used to validate model performance of simulating short‐term changes in stream‐water concentrations that reflect changes in runoff partitioning and event‐based dilution effects. Uncertainty analysis confirmed the model's robustness. Moreover, model calculations showed that mean terrestrial nitrate input/output (in total 105 kg ha ⁻¹ yr ⁻¹ ) and in‐stream removal (8% of mean nitrate load) were in comparable ranges with literature, respectively. The new mHM‐Nitrate model is capable of providing detailed spatial information on nitrate concentrations and fluxes, which can motivate more specific catchment investigations on nitrate transport processes and provide guidance on spatially differentiated agricultural practices and measures.

Stable isotopes of water are ideal tracers to integrate into process‐based models, advancing ecohydrological understanding. Current tracer‐aided ecohydrological modeling is mostly conducted in relatively small‐scale catchments, due to limited tracer data availability and often highly damped stream isotope signals in larger catchments (>100 km ² ). Recent model developments have prioritized better spatial representation, offering new potential for advancing upscaling in tracer‐aided modeling. Here, we adapted the fully distributed EcH ₂ O‐iso model to the Selke catchment (456 km ² , Germany), incorporating monthly sampled isotopes from seven sites between 2012 and 2017. Parameter sensitivity analysis indicated that the information content of isotope data was generally complementary to discharge and more sensitive to runoff partitioning, soil water and energy dynamics. Multi‐criteria calibrations revealed that inclusion of isotopes could significantly improve discharge performance during validations and isotope simulations, resulting in more reasonable estimates of the seasonality of stream water ages. However, capturing isotopic signals of highly non‐linear near‐surface processes remained challenging for the upscaled model, but still allowed for plausible simulation of water ages reflecting non‐stationarity in transport and mixing. The detailed modeling also helped unravel spatio‐temporally varying patterns of water storage‐flux‐age interactions and their interplay under severe drought conditions. Embracing the upscaling challenges, this study demonstrated that even coarsely sampled isotope data can be of value in aiding ecohydrological modeling and consequent process representation in larger catchments. The derived innovative insights into ecohydrological functioning at scales commensurate with management decision making, are of particular importance for guiding science‐based measures for tackling environmental changes.

Calibrating distributed hydrological models often leads to equifinality due to complex model structures, which can be further exacerbated in wetlands due to spatio‐temporal heterogeneity in ecohydrological processes. Here, step‐wise calibrations of the physically‐based distributed model EcH₂O‐iso was conducted in a data‐rich wetland by minimizing a weighted average of the errors on discharge, stream isotopes, groundwater (GW) isotopes, and soil moisture. Results showed multi‐criteria calibration outperformed single‐criterion calibration as it strongly increased the overall performance, yet only marginally degraded performance of each calibration target. Isotopes were highlighted as appropriate auxiliary data as they effectively constrained the model with relatively small weights (0.1). However, those parameter sets that minimize the errors could still lead to physically implausible simulations of uncalibrated internal states or fluxes. This was further demonstrated by an approach developed to check internal fluxes based on soft data (transpiration and lateral flow), suggesting 54% of optimized models gave “right answers for the wrong reasons.” By excluding those models against soft data, such an approach further constrained equifinality, and unraveled potential inconsistencies between observations and calibration. Modeling represented the wetland as a slow‐draining system mainly fed by GW, but also influenced by near‐surface flow during winter or summer convectional events. Further, heterogeneity in hydrological functioning was partly attributed to distinct evapotranspiration patterns between contrasting vegetation communities. Therefore, this study not only provided insights into wetland functioning, but also revealed potential equifinality even with abundant data for calibration, and potential solutions based on the integration of isotopes and soft data.

Nitrate (NO₃‐N) mobilization is generally controlled by available sources, hydrological connectivity, and biogeochemical transformations along the dominant flow paths. However, their spatial heterogeneity and complex interactions often impede integrated understanding of NO₃‐N dynamics at the catchment scale. To fully integrate spatiotemporal information for NO₃‐N simulations, a grid‐based model, mHM‐Nitrate, was applied to a 68 km² lowland, mixed land use catchment (Demnitzer Millcreek, DMC) near Berlin. The model successfully captured the spatiotemporal distribution of flow and NO₃‐N between 2001 and 2019, but was less successful in 1992–2000 due to land management changes. Re‐optimization of relative parameters was subsequently conducted for this period to understand management effects. The simulated results revealed landscape characteristics and hydroclimatic variability as the main controlling factors on respective spatial and temporal patterns. The combined effects of vegetation cover and fertilizer inputs dictated the spatial distribution of water and NO₃‐N fluxes, while wetness condition determined the temporal NO₃‐N dynamics by regulating hydrological connectivity and NO₃‐N mobilization. Denitrification was also closely coupled with hydroclimatic conditions, which accounted for ∼20% of NO₃‐N inputs. In contrast, restoration of riparian wetlands had a modest impact on NO₃‐N export (∼10% reduction during 2001–2019), suggesting further interventions (e.g., reducing fertilizer application or increased wetland areas) are needed. Our modeling application demonstrated that mHM‐Nitrate could provide robust spatially distributed simulations of hydrological and NO₃‐N fluxes over a long‐term period and could successfully differentiate the key controlling factors. This underlines the model's value in contributing to an evidence base to guide future management practices under climate change.

High spatial heterogeneity of catchment properties enhances the variability of ecohydrological responses to changing natural and anthropogenic conditions, like the European‐wide droughts in 2018–2019. Based on new adaptations of a tracer‐aided, process‐based ecohydrological model (EcH₂O‐iso), we investigated drought‐induced nonstationary ecohydrological behavior in a small agricultural headwater catchment (1.44 km²) in central Germany. Multiple environmental time‐series helped inform various aspects of catchment functioning that have been impacted by agricultural activity and changing climate conditions and helped to further constrain model calibration. Multi‐criteria calibration showed that data collected during drought years were highly informative in reproducing the changes in stream water dynamics. Further, inclusion of and data was valuable for reducing model uncertainty and increasing confidence in simulations of green‐ and blue‐water flux partitioning and storage‐flux‐age interactions. Using the best‐performing calibrations, we further analyzed the high spatiotemporal variability of internal ecohydrological processes and the varying responses of fluxes and associated water ages to prolonged drought stress. Under drought conditions, modeled stream runoff contributed from deeper, older storages increased significantly after a particularly wet season, resulting in a sharp increase in stream water age. Unlike relatively minor changes in soil evaporation, seasonally typical transpiration fluxes were initially maintained in April–June but dramatically decreased as the drought further developed in July–September. Importantly, the tracer‐based transpired water age was much older after April, providing a potential indicator of drought impacts and the need for precautionary management responses. Our findings are important for similar agricultural headwater ecosystems in other drought‐sensitive regions.