The transit time (TT) of streamflow encapsulates information about how catchments store and release water and solutes of different ages. The young water fraction (Fyw), the fraction of streamflow that is younger than a certain age (normally 2–3 months), has been increasingly used as an alternative metric to the commonly used mean TT (mTT). In the commonly used (‘traditional’) procedure presented by Kirchner (2016), the age threshold (τyw) of Fyw separating young from old water is not pre-defined and differs from catchment to catchment depending on the shape of the (gamma) transit time distribution. However, it can be argued that it is important to use the same pre-defined τyw for inter-catchment comparison of Fyw. In this study, we propose an alternative (‘proposed’) procedure for the estimation of Fyw with any pre-defined τyw. This allows us to also compare the effects of data sampling frequencies on the results of Fyw estimation using the same τyw. We applied the traditional and proposed procedures using daily oxygen isotope (δ18O) data in the Alp and Erlenbach catchments, Switzerland. We found that our proposed and the traditional procedure can give very different Fyw values. With the proposed procedure, the estimated Fyw significantly increases when the sampling frequency changes from sub-monthly to monthly time steps. Overall, our study highlights the importance of the selection of τyw and the sampling frequency in Fyw estimation, which should be given more attention.
Accurate flow prediction is a primary goal of hydrological modeling studies, which can be affected by the use of varying rainfall datasets, autocalibration methods, and performance indices. The combined effect of three rainfall datasets — Fifth generation of European ReAnalysis (ERA-5), Gridded meteorological data (gridMET), Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG) — and three autocalibration techniques — Dynamically Dimensioned Search (DDS), Generalized Likelihood Uncertainty Estimation (GLUE), Latin Hypercube Sampling (LHS) — on SWAT+ river flow prediction was measured using three evaluation metrics — Nash Sutcliffe Efficiency (NSE), Kling Gupta Efficiency (KGE) and coefficient of determination (R 2) — for two watersheds in North Carolina (Cape Fear, Tar Pamlico) using the Soil Water Assessment Tool Plus (SWAT+) model. Five parameters in the SWAT+ model, cn2, revap_co, flo_min, revap_min, and awc, were found to be significantly sensitive under all combinations for both watersheds. Simulated flow varied more with the change in rainfall than the calibration technique used. We discovered that GPM IMERG gave the best results of the rainfall datasets, followed by ERA-5 and gridMET. We observed that the NSE score is more sensitive to different combinations of rainfall datasets and calibration techniques than the KGE scores. SWAT+ underperformed in the prediction of base flow for the groundwater-driven watershed. Overall, we recommend using the GPM IMERG rainfall dataset with the GLUE optimization technique and KGE performance index for optimal flow simulations. The results from this study will help hydrological modelers choose an optimal combination of rainfall dataset, autocalibration technique, and performance index depending on watershed characteristics.
Remotely sensed evapotranspiration (ETRS) is increasingly used for streamflow estimation. Earlier reports are conflicting as to whether ETRS is useful in improving streamflow estimation skills. We believe that it is because earlier works used calibrated models and explored only small subspaces of the complex relationship between model skills for streamflow (Q) and ET. To shed some light on this complex relationship, we design a novel randomized, large sample experiment to explore the full ET-Q skill space, using seven catchments in Vietnam and four global ETRS products. For each catchment and each ETRS product, we employ 10,000 SWAT (Soil and Water Assessment Tool) model runs whose parameters are randomly generated via Latin Hypercube sampling. We then assess the full joint distribution of streamflow and ET skills using all model simulations. Results show that the relationship between ET and streamflow skills varies with regions, ETRS products, and the selected performance indices. This relationship even changes with different ranges of ET skills. Parameter sensitivity analysis indicates that the most sensitive parameters could have opposite contributions to ET and streamflow skills. Conditional probability assessment reveals that with certain ETRS products, the probabilities of having good streamflow skills are high and increase with better ET skills, but for other ETRS products, good model skills for streamflow are only achievable with certain intermediate ranges of ET skills, not the best ones. Overall, our study provides a useful approach for evaluating the value of ETRS for streamflow estimation.
In 2018–2019, Central Europe experienced an unprecedented multi-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997-2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modelling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration-discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 70% higher compared to the long-term load-discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times (<2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream (>20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe multi-year droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.
Understanding catchment controls on catchment solute export is a prerequisite for water quality management. StorAge Selection (SAS) functions encapsulate essential information about catchment functioning in terms of discharge selection preference and solute export dynamics. However, they lack information on the spatial origin of solutes when applied at the catchment scale, thereby limiting our understanding of the internal (subcatchment) functioning. Here, we parameterized SAS functions in a spatially explicit way to understand the internal catchment responses and transport dynamics of reactive dissolved nitrate (N-NO3). The model was applied in a nested mesoscale catchment (457 km²), consisting of a mountainous partly forested, partly agricultural subcatchment, a middle-reach forested subcatchment, and a lowland agricultural subcatchment. The model captured flow and nitrate concentration dynamics not only at the catchment outlet but also at internal gauging stations. Results reveal disparate subsurface mixing dynamics and nitrate export among headwater and lowland subcatchments. The headwater subcatchment has high seasonal variation in subsurface mixing schemes and younger water in discharge, while the lowland subcatchment has less pronounced seasonality in subsurface mixing and much older water in discharge. Consequently, nitrate concentration in discharge from the headwater subcatchment shows strong seasonality, whereas that from the lowland subcatchment is stable in time. The temporally varying responses of headwater and lowland subcatchments alternates the dominant contribution to nitrate export in high and low-flow periods between subcatchments. Overall, our results demonstrate that the spatially explicit SAS modeling provides useful information about internal catchment functioning, helping to develop or evaluate spatial management practices.
Elevated nitrate concentrations in German water bodies are a widespread problem, potentially resulting from a long history of excess nitrogen (N) inputs. Here, we investigated long-term (1950-2014) N dynamics across 89 German catchments using a process-based model. Results showed that the mean fractions of N surplus (excess) exported to the river, removed by denitrification, accumulated in the soil zone, and accumulated in groundwater across all catchments are 27%, 58%, 14%, and 1%, respectively. Dissolved inorganic N in groundwater could affect the stream N levels over decades as indicated by long groundwater transit times. A cluster identified four catchment groups with distinct archetypal long-term N transport and retention dynamics, which can be partly linked to the catchments’ topographic and geological conditions. This hints at underlying mechanisms that explain spatial differences in the fate of diffuse N inputs to catchments and opens the possibility for better-targeted management
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 and celerity driven transport. However, 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. In this study, 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 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 satisfactorily represent the relations between subsurface storage, mixing scheme, solute export, and the TTDs of discharge. The distributed model shows comparable results with 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.
Evaluating the spatial and temporal model performance of distributed hydrological models is necessary to ensure that the simulated spatial and temporal patterns are meaningful. In recent years, spatial and temporal remote sensing data have been increasingly used for model performance evaluation. Previous studies, however, have focused on either the temporal or spatial model performance evaluation. In addition, temporal (or spatial) model performance evaluation is often done in a spatially (or temporally) lumped approach. Here, we evaluated (1) the temporal model performance evaluation in a spatially distributed approach (spatiotemporal) and (2) the spatial model performance in a temporally distributed approach (temporospatial) model performance evaluation. This study demonstrated that both spatiotemporal and temporospatial model performance evaluations are necessary since they provide different aspects of the model performance. For example, spatiotemporal model performance evaluation helps in detecting the areas with an issue in the simulated temporal patterns. However, temporospatial model performance evaluation helps in detecting the time with an issue in the simulated spatial patterns. The results also show that an increase in the spatiotemporal model performance will not necessarily lead to an increase in the temporospatial model performance and vice versa, depending on the evaluation statistics. Overall, this study has highlighted the necessity of a joint spatiotemporal and temporospatial model performance evaluation to understand/improve spatial and temporal model behavior/performance.