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

Impact of climate change on hydro-meteorological trigger conditions for debris flows in Austria

Debris-flow activity is expected to change in a future climate. In this study we connect a susceptibility model for debris-flows on a regional scale with climate projections until 2100. We use this to assess changes of hydro-meteorological trigger conditions for debris flows in six regions in the Austrian Alps. We find limited changes on an annual basis, but distinct changes when separating between hydro-meteorological trigger types and regions. While regions in the east and in the south of Austria may experience less days susceptible to debris flows in summer, there is a general trend of increasing susceptibility earlier in the year for both, rainfall-related and snow-related trigger conditions. The outcomes of this study serve as a basis for the development of adaption strategies for future risk management from this debris-flow hazard

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

Importance of tree diameter and species for explaining the temporal and spatial variations of xylem water δ18O and δ2H in a multi-species forest

Publication history: Accepted - 17 March 2023; Published online - 23 May 2023.Identifying the vegetation and topographic variables influencing the isotopic variability of xylem water of forest vegetation remains crucial to interpret and predict ecohydrological processes in landscapes. In this study, we used temporally and spatially distributed xylem stable water isotopes measurements from two growing seasons to examine the temporal and spatial variations of xylem stable water isotopes and their relationships with vegetation and topographic variables in a Luxembourgish temperate mixed forest. Species-specific temporal variations of xylem stable water isotopes were observed during both growing seasons with a higher variability for beeches than oaks. Principal component regressions revealed that tree diameter at breast height explains up to 55% of the spatial variability of xylem stable water isotopes, while tree species explains up to 24% of the variability. Topographic variables had a marginal role in explaining the spatial variability of xylem stable water isotopes (up to 6% for elevation). During the drier growing season (2020), we detected a higher influence of vegetation variables on xylem stable water isotopes and a lower temporal variability of the xylem water isotopic signatures than during the wetter growing season (2019). Our results reveal the dominant influence of vegetation on xylem stable water isotopes across a forested area and suggest that their spatial patterns arise mainly from size- and species-specific as well as water availability-dependent water use strategies rather than from topographic heterogeneity. The identification of the key role of vegetation on xylem stable water isotopes has critical implications for the representativity of isotopes-based ecohydrological and catchments studies.This work was supported by the Luxembourg National Research Fund (FNR/CORE/C17/SR/11702136/EFFECT). The second author is supported by the Accelerator Programme (AP) 2022-24 and the Starter Scheme by the University of the West of England, Bristol

Predicting streamflow with LSTM networks using global datasets

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually feature local datasets for model development, while predictions in ungauged basins at a global scale require training on global datasets. In this study, we develop LSTM models for over 500 catchments from the CAMELS-US data base using global ERA5 meteorological forcing and global catchment characteristics retrieved with the HydroMT tool. Comparison against an LSTM trained with local datasets shows that, while the latter generally yields superior performances due to the higher spatial resolution meteorological forcing (overall median daily NSE 0.54 vs. 0.71), training with ERA5 results in higher NSE in most catchments of Western and North-Western US (median daily NSE of 0.83 vs. 0.78). No significant changes in performance occur when substituting local with global data sources for deriving the catchment characteristics. These results encourage further research to develop LSTM models for worldwide predictions of streamflow in ungauged basins using available global datasets. Promising directions include training the models with streamflow data from different regions of the world and with higher quality meteorological forcing

Debris-flow activity and sediment dynamics in the landslide-influenced Lattenbach catchment, Austria

Deep seated landslides are common phenomena in Alpine areas. In case of a direct connectivity with the channel system, the catchment’s sediment yield and the probability of other forms of mass wasting processes such as debris flows may be increased significantly. Up to now, sediment dynamics related to deep-seated landslides and debris flows have not been quantified. The Lattenbach catchment (basin area 5,3 km², relief 2134 m) in Grins (Tyrol, Austria), is an example for an active DF-site, where there is geomorphological evidence of deep-seated landslide activity. In this study we shed light (1) on the location and size of active landslides in the catchment, that may deliver sediment to the channel system. Furthermore, we want to (2) quantify the contributed sediment volumes by these landslides (3) and estimate the exported sediment by debris flow. We apply an image correlation algorithm to high resolution ALS and TLS terrain models of derived over a period of 14 years to calculate surface movement rates within the catchment and locate deep seated landslide activity. We further assess the sediment yield of these landslides to the channel system and relate that with DF-volumes measured by a monitoring station at the catchment outlet. We find that there are five deep-seated landslide bodies directly connected to the channel system in the catchment. These are the largest source of sediment and significantly increases the overall sediment yield of the catchment. Our study shall contribute to the limited knowledge about the importance of deep-seated landslides for sediment dynamics and debris-flow activity, as their presence is predicted to be more frequent in the wake of global warming