With ongoing climate change and more frequent high flows and droughts, it becomes inevitable to understand potentially altered catchment processes under changing climatic conditions. Water age metrics such as median transit times and young water fractions are useful variables to understand the process dynamics of catchments and the release of solutes to the streams. This study, based on extensive high-frequency stable isotope data, unravels the changing contribution of different water ages to stream water in six heterogeneous catchments, located in the Harz mountains and the adjacent northern lowlands in Central Germany. Fractions of water up to 7 days old (Fyw7), comparable with water from recent precipitation events, and fractions of water up to 60 days old (Fyw60) were simulated by the tran-SAS model. As Fyw7 and Fyw60 were sensitive to discharge, an integrated analysis of high and low flows was conducted. This revealed an increasing contribution of young water for increasing discharge, with larger contributions of young water during wet spells compared to dry spells. Considering the seasons, young water fractions increased in summer and autumn, which indicates higher contributions of young water after prolonged dry conditions. Moreover, the relationship between catchment characteristics and the water age metrics revealed an increasing amount of young water with increasing agricultural area, while the amount of young water decreased with increasing grassland proportion. By combining transit time modelling with high-frequency isotopic signatures in contrasting sub-catchments in Central Germany, our study extends the understanding of hydrological processes under high and low flow conditions.

AUTHORS' NOTE: The paper was reviewed by three reviewers in WRR and did not pass the review. There are some technical issues with our methodology. We advise the readers to consult the authors before using any results from this work. We are sorry for this inconvenience. For readers who interested in the reviewers' comments, please contact me .Abstract: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.

A document by Danish Tantary. Click on the document to view its contents.

Excess nitrogen (N) from anthropogenic sources deteriorates freshwater resources. Actions taken to reduce N inputs to the biosphere often show no or only delayed effects in receiving surface waters hinting at large legacy N stores built up in the catchments soils and groundwater. Here, we quantify transport and retention of N in 238 Western European catchments by analyzing a unique data set of long-term N input and output time series. We find that half of the catchments exhibited peak transport times larger than five years with longer times being evident in catchments with high potential evapotranspiration and low precipitation seasonality. On average the catchments retained 72% of the N from diffuse sources with retention efficiency being specifically high in catchments with low discharge and thick, unconsolidated aquifers. The estimated transport time scales do not explain the observed N retention, suggesting a dominant role of biogeochemical legacy in the catchments’ soils rather than a legacy store in the groundwater. Future water quality management should account for the accumulated biogeochemical N legacy to avoid long-term leaching and water quality deteriorations for decades to come.

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.

The higher frequency and intensity of sustained heat events have increased the demand for cooling energy across the globe. Current estimates of summer-time energy demand are primarily based on Cooling Degree Days (CDD), representing the number of degrees a day’s average temperature exceeds a predetermined comfort zone temperature. Through a comprehensive analysis of the historical energy demand data across the USA, we show that the commonly used CDD estimates fall significantly short (±25%) of capturing regional thermal comfort levels. Moreover, given the increasingly compelling evidence that air temperature alone is not sufficient for characterizing human thermal comfort, we extend the widely-used CDD calculation to heat index, which accounts for both air temperature and humidity. Our results indicate significant mis-estimation of regional thermal comfort when humidity is ignored. Our findings have significant implications for the security, sustainability, and resilience of the grid under climate change.

Runoff events play an important role for nitrate export from catchments, but the variability of nutrient export patterns between events and catchments is high and the dominant drivers remain difficult to disentangle. Here, we rigorously asses if detailed knowledge on runoff event characteristics can help to explain this variability. To this end, we conducted a long-term (1955 - 2018) event classification using hydro-meteorological data, including soil moisture, snowmelt and the temporal organization of rainfall, in six neighboring mesoscale catchments with contrasting land use types. We related these event characteristics to nitrate export patterns from high-frequency nitrate concentration monitoring (2013 - 2017) using concentration-discharge relationships. Our results show that small rainfall-induced events with dry antecedent conditions exported lowest nitrate concentrations and loads but exhibited highly variable concentration-discharge relationships. We explain this by a low fraction of active flow paths, revealing the spatial heterogeneity of nitrate sources within the catchments and by an increased impact of biogeochemical retention processes. In contrast, large rainfall or snowmelt-induced events exported highest nitrate concentrations and loads and converged to similar chemostatic export patterns across all catchments, without exhibiting source limitation. We explain these homogenous export patterns by high catchment wetness that activated a high number of flow paths. Long-term hydro-meteorological data indicated an increase of events with dry antecedent conditions in summer and decreased snow-influenced events. These trends will likely continue and lead to an increased nitrate concentration variability during low-flow seasons and to changes in the timing of largest nitrate export peaks during high-flow seasons.

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.

Access to accurate estimates of water withdrawal is requisite for urban planners as well as operators of critical infrastructure systems to make optimal operational decisions and investment plans to ensure reliable and affordable provisioning of water. Furthermore, identifying the key predictors of water withdrawal is important to regulators for promoting sustainable development policies to reduce water use. In this paper, we developed a rigorously evaluated predictive model, using statistical learning theory, to estimate state-level, per-capita water withdrawal as a function of various geographic, climatic and socio-economic variables. We then harnessed the data-driven predictive model to identify the key factors associated with high water-usage intensity among different sectors in the U.S. We analyzed the predictive accuracy of a range of parametric models (e.g., generalized linear models) and non-parametric, flexible learning algorithms (e.g., generalized additive models, multivariate adaptive regression splines and random forest). Our results identified irrigated farming, thermo-electric energy generation and urbanization as the most water-intensive anthropogenic activities, on a per-capita basis. Among the climate factors, precipitation was also found to be a key predictor of per-capita water withdrawal, with drier conditions associated with higher water withdrawals. Results of the first-order sensitivity analysis indicated changes between +/-10% in the future water withdrawal across the U.S., in response to precipitation changes, by the end of the 21st Century under the business-as-usual scenario. Overall, our study highlights the utility of leveraging statistical learning theory in developing data-driven models that can yield valuable insights related to the water withdrawal patterns across expansive geographical areas.

The analysis of drought onset and their potential relationship to drought severity (deficit volume) are critical for providing timely information for agricultural operations, such as cultivation planning and crop productivity monitoring. A coupling between drought timing and deficit volume can be used as a proxy for drought-related damage estimation and associated risks. Despite its high importance, so far little attention was paid to determine the timing of drought and its linkage with deficit volume for hydrological droughts. This study utilizes quality-controlled streamflow observations from 1965 to 2018 to unveil regional patterns of hydrological drought onset, trends in event-specific deficit volume, and nonlinear relationships between onset timing and deficit volume across 97 rain-dominated catchments in Peninsular India (8-24o N, 72-87o E). Our results show a shift towards earlier hydrological drought onset in conjunction with a decrease in deficit volume during the Indian monsoon (June-September) season, which is contrasted by a delayed onset in the pre-monsoon (March-May) and post-monsoon (October-February) seasons. Further, approximately one-third of the catchments show a significant nonlinear dependency between drought deficit volume and onset time. We find environmental controls, such as soil organic carbon, vertical distance to channel network, and soil wetness are dominant factors in influencing droughts. Our analysis provides new insights into the causal chain and physical processes linking climatic and physiographic controls on streamflow drought mechanisms, which can support drought forecasting and climate impact assessment studies.

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.