Conceptual modelling to assess how the interplay of hydrological connectivity, catchment storage and tracer dynamics controls nonstationary water age estimates

Acknowledgements We would like to gratefully acknowledge the data provided by SEPA, Iain Malcolm. Mark Speed, Susan Waldron and many MSS staff helped with sample collection and lab analysis. We thank the European Research Council (project GA 335910 VEWA) for funding and are grateful for the constructive comments provided by three anonymous reviewers.Peer reviewedPostprin

Spatial aggregation of time-variant stream water ages in urbanizing catchments

Date of Acceptance: 25/03/2015 Acknowledgements The help of Clara F. Soulsby with the sampling is gratefully acknowledged.Peer reviewedPostprin

The role and value of distributed precipitation data in hydrological models

This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km$^{2}$). We compare four hydrological model setups and show that a distributed model setup driven by distributed rainfall only improves the model performances during certain periods. These periods are dominated by convective summer storms that are typically characterized by higher spatiotemporal variabilities compared to stratiform precipitation events that dominate rainfall generation in winter. Motivated by these findings, we develop a spatially adaptive model that is capable of dynamically adjusting its spatial structure during model execution. This spatially adaptive model allows the varying relevance of distributed rainfall to be represented within a hydrological model without losing predictive performance compared to a fully distributed model. Our results highlight that spatially adaptive modeling has the potential to reduce computational times as well as improve our understanding of the varying role and value of distributed precipitation data for hydrological models

Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models

Stable isotopes (δ18O) and tritium (3H) are frequently used as tracers in environmental sciences to estimate age distributions of water. However, it has previously been argued that seasonally variable tracers, such as δ18O, generally and systematically fail to detect the tails of water age distributions and therefore substantially underestimate water ages as compared to radioactive tracers such as 3H. In this study for the Neckar River basin in central Europe and based on a &gt;20-year record of hydrological, δ18O and 3H data, we systematically scrutinized the above postulate together with the potential role of spatial aggregation effects in exacerbating the underestimation of water ages. This was done by comparing water age distributions inferred from δ18O and 3H with a total of 21 different model implementations, including time-invariant, lumped-parameter sine-wave (SW) and convolution integral (CO) models as well as StorAge Selection (SAS)-function models (P-SAS) and integrated hydrological models in combination with SAS functions (IM-SAS). We found that, indeed, water ages inferred from δ18O with commonly used SW and CO models are with mean transit times (MTTs) of ∼ 1–2 years substantially lower than those obtained from 3H with the same models, reaching MTTs of ∼10 years. In contrast, several implementations of P-SAS and IM-SAS models not only allowed simultaneous representations of storage variations and streamflow as well as δ18O and 3H stream signals, but water ages inferred from δ18O with these models were, with MTTs of ∼ 11–17 years, also much higher and similar to those inferred from 3H, which suggested MTTs of ∼ 11–13 years. Characterized by similar parameter posterior distributions, in particular for parameters that control water age, P-SAS and IM-SAS model implementations individually constrained with δ18O or 3H observations exhibited only limited differences in the magnitudes of water ages in different parts of the models and in the temporal variability of transit time distributions (TTDs) in response to changing wetness conditions. This suggests that both tracers lead to comparable descriptions of how water is routed through the system. These findings provide evidence that allowed us to reject the hypothesis that δ18O as a tracer generally and systematically “cannot see water older than about 4 years” and that it truncates the corresponding tails in water age distributions, leading to underestimations of water ages. Instead, our results provide evidence for a broad equivalence of δ18O and 3H as age tracers for systems characterized by MTTs of at least 15–20 years. The question to which degree aggregation of spatial heterogeneity can further adversely affect estimates of water ages remains unresolved as the lumped and distributed implementations of the IM-SAS model provided inconclusive results. Overall, this study demonstrates that previously reported underestimations of water ages are most likely not a result of the use of δ18O or other seasonally variable tracers per se. Rather, these underestimations can largely be attributed to choices of model approaches and complexity not considering transient hydrological conditions next to tracer aspects. Given the additional vulnerability of time-invariant, lumped SW and CO model approaches in combination with δ18O to substantially underestimate water ages due to spatial aggregation and potentially other still unknown effects, we therefore advocate avoiding the use of this model type in combination with seasonally variable tracers if possible and instead adopting SAS-based models or time-variant formulations of CO models.</p

Hydro-meteorological trigger conditions of debris flows in Austria

Different factors influence the disposition of a watershed for initiation of debris flows, including meteorological trigger conditions as well as the hydrologic and geomorphic disposition. The latter includes slowly changing factors like relief energy or sediment availability, whereas the hydrologic state of a watershed may vary over short time scales. This contribution summarizes the outcomes of a long term project to quantify meteorological and hydrological trigger conditions leading to debris flows at different temporal and spatial scales in the Austrian Alps. The analysis employs a database of more than 4,500 debris flows over the last 100+ years, which is the period for which systematic rainfall data is available. A Bayesian analysis was carried out for determining occurrence probabilities for all Austria. For selected regions, hydrological trigger conditions were assessed using a semi-distributed, conceptual rainfall-runoff model, which was calibrated to measured runoff data. As expected we find increasing trigger probabilities with increasing rainfall amounts and intensities. However, the additional information of regional hydrological parameters as well as their temporal evolution over days prior to a debris-flow event, enables to capture different trigger conditions, including short duration rainstorms, long lasting rainfall events, and snow melt. We also find that a trigger-type resolved prediction of debris-flow susceptibility based on the hydro-meteorological catchment information is superior to simple rainfall-only approaches. The results of this analysis shall improve our understanding of long-term trigger conditions and trends of extreme mass wasting processes in the Alps and aim to become a valuable tool in engineering hazard assessment

A simple topography-driven and calibration-free runoff generation module

This study was supported by National Natural Science Foundation of China (41801036), National Key R&D Program of China (2017YFE0100700), the Key Program of National Natural Science Foundation of China (no. 41730646), and Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (KLMHESP-17-02). The authors acknowledge three anonymous reviewers for their very constructive comments and suggestions that substantially improved the quality of this paper.Peer reviewedPublisher PD

Using high resolution tracer data to constrain water storage, flux and age estimates in a spatially distributed rainfall-runoff model

Acknowledgements The authors would like to thank Jonathan Dick, Josie Geris, Jason Lessels and Claire Tunaley for data collection and Audrey Innes for lab sample preparation. We also thank Christian Birkel for discussions about the model structure and comments on an earlier draft of the paper. Climatic data were provided by Iain Malcolm and Marine Scotland Fisheries at the Freshwater Lab, Pitlochry. Additional precipitation data were provided by the UK Meteorological Office and the British Atmospheric Data Centre (BADC). We thank the European Research Council ERC (project GA 335910 VEWA) for funding the VeWa project.Peer reviewedPublisher PD

Coupling a global glacier model to a global hydrological model prevents underestimation of glacier runoff

Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (&gt;50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins