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Changing snow cover dynamics is one of the most apparent effects of climate change because of the phase change of snow to liquid water above zero degrees. Impacts of rising temperatures include less snow accumulation, a shorter snow cover season, and an earlier start of the snowmelt season, but little is known about the effects of warming on snowmelt rates. Faster snowmelt should intuitively be the response to a warmer climate. However, a few studies have shown slower snowmelt for snowpacks that melt earlier in the season when less energy (radiation) is available for melt, while others found varying responses across a limited number of sites. Northern Hemisphere scale studies showed a high spatial variability in melt rate change. Here we try to disentangle the complex and mixed effects of rising temperatures on snowmelt dynamics across climates. We use a new pan-Northern Hemisphere SWE dataset (NH-SWE dataset) based on historical snow depth observations and investigate trends in snowmelt rate change from 1981 to 2022. Snowmelt rate remains unchanged if a decrease in snowmelt days is proportional to a reduction in total snowmelt. The interplay between less total snowmelt, earlier snowmelt and its associated radiation change, and a change in temperature during the snow season determine whether snowmelt rates becomes faster or slower. This interplay largely depends on the temperature climate, especially the amplitude of yearly temperatures and the number of days with temperatures nearing zero degrees during the snow season., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Le changement climatique aura un impact particulièrement fort sur le bilan hydrique des régions alpines. L'augmentation de la température entraîne un recul des glaciers, la proportion des précipitations qui tombent en forme de neige diminue, et la fonte des neiges se produit plus tôt dans l'année. Ces changements peuvent entraîner une diminution des débits des cours d’eau en été et en automne et éventuellement des pénuries d'eau régionales. La façon dont le régime hydrologique des bassins versants alpins changent dépend de la quantité d'eau de fonte et de précipitation qui est temporairement stockée sous forme d'eau souterraine. Dans une certaine mesure, le stockage souterrain de l'eau pourrait compenser la diminution du stockage sous forme de neige et de glace. Cependant, on en sait relativement peu à ce jour sur le fonctionnement hydrogéologique des zones alpines. Le projet poursuivait deux objectifs. D'une part, nous avons étudié la relation entre les caractéristiques géologiques des bassins, le stockage des eaux souterraines et l’écoulement de surface dans les conditions actuelles. D'autre part, nous avons étudié comment le changement climatique pourrait affecter la dynamique des eaux souterraines et des cours d'eau, en mettant l'accent sur les périodes d’étiages estivales. En particulier, nous avons cherché à savoir si les bassins versants comportant des réservoirs d'eaux souterraines plus importants vont subir moins de changement de leur régime hydrologique. Les recherches se sont concentrées sur les petits bassins versants des hautes Alpes dont le bilan hydrique est dominé par la neige et pour lesquels on dispose principalement de données pluriannuelles pour les cours d’eau. Les recherches sur l'influence de la géologie ont montré que les débits d’étiages augmentent avec l'augmentation de la surface des sédiments non consolidés et sont également influencés dans une moindre mesure par les roches solides à plus forte perméabilité. De vastes dépôts de roche meuble favorisent probablement l'infiltration. Ils peuvent stocker des quantités importantes d'eau et compenser partiellement les déficits saisonniers. Le stockage saisonnier des eaux souterraines dans de tels formations géologiques a été confirmé par des mesures gravimétriques et isotopiques dans un bassin versant de recherche. Nous avons étudié l'effet du changement climatique sur la dynamique des eaux souterraines et les débits des cours d’eau en couplant les modèles climatiques et les modèles hydro(géo)logiques. Pour un bassin de recherche, un modèle a été utilisé qui simule de manière couplée les processus des eaux souterraines et des eaux de surface. Pour les autres bassins versants, un modèle hydrologique conceptuel a été utilisé. Les simulations ont montré qu'en raison de la fonte des neiges plus précoce, la quantité d'eau souterraine stockée et le les débits des cours d’eaux auront tendance à être plus faibles à la fin de l'été/début de l'automne. Toutefois, la diminution relative du volume des eaux souterraines est bien moindre que pour les débits des cours d’eaux. Dans un avenir lointain, le débit des cours d’eaux minimal se déplacera de l'hiver vers les mois d'été. Toutefois, le débit minimal futur en été/automne reste supérieur au minimum actuel en hiver. En outre, les bassins versants présentant des formations géologiques étendues avec une bonne capacité de stockage de l'eau présentent un débit minimal plus élevé. Cela illustre les effets d'équilibrage des processus liés aux eaux souterraines. Le projet montre que les processus hydrogéologiques jouent un rôle important dans la régulation du bilan hydrique des bassins versants alpins, en particulier dans le contexte du changement climatique. En fonction des conditions géologiques, il est possible d'estimer comment les bassins versants pourraient réagir à une fonte des neiges plus précoce et à des périodes de sécheresse estivales. Les débits d’étiages actuels en hiver fournissent également des informations importantes sur l’importance réservoirs d'eaux souterraines dans un bassin versant et leur capabilité de stocker et libérer de l’eau sur une échelle de temps de plusieurs mois. À ce jour, cependant, il n'existe que quelques sites de surveillance dans la région alpine où la dynamique des réservoirs d'eaux souterraines peut être observée directement, ce qui a entraîné des incertitudes dans le projet actuel. En outre, les données sur les précipitations dans la région alpine présentent également de grandes incertitudes. Compte tenu des grands changements attendus, la base de données pour les zones alpines devrait être améliorée.

Key Points Analytic model for the probability distribution of snow dominated streamflow Stochastic framework to link precipitation (rain snow) and streamflow dynamics 14 case studies confirm progress in statistical characterization of streamflow We propose a novel analytical description of the streamflow probability distribution functions (pdfs) in Alpine catchments characterized by pronounced snow dominated winter low flows. Knowledge about such hydrological regimes is crucial for water resources management in mountain environments and the related wide range of socio economic environmental and ecological services. We use a stochastic framework generalizing that employed by Botter et al. (2007b) to link precipitation (rain and snow) and streamflow dynamics. The effect of snow dynamics on the flow regime is specifically included by incorporating the temporary disconnection of high elevation areas that experience freezing conditions over the entire winter season and the delay produced on streamflow formation by the temporary accumulation (and later melting) of snow at lower elevations. The novel analytical model employs four parameters that can be directly estimated from observed discharge precipitation and air temperatures and one calibration parameter (the elevation threshold z delimiting catchment areas with a permanent seasonal snow cover that is nonresponsive during winter owing to snow accumulation without melt). We test the developed model for 14 catchments with contrasting hydroclimatic conditions located in the Swiss and the Italian Alps. Overall the proposed analytic model reproduces the observed streamflow pdfs remarkably well. Exceptions exist though and the possible origin of deviations between observed and modeled pdfs are discussed. We suggest that our approach marks a progress toward the general statistical characterization of catchment streamflow variability. ©2013. American Geophysical Union. All Rights Reserved.

Compared to daily rainfall data, observed subdaily rainfall times are rare and often very short. For hydrologic modeling, this problem is often addressed by generating synthetic hourly rainfall series, with rainfall generators calibrated on relevant rainfall statistics. The required subdaily rainfall statistics are traditionally derived from daily rainfall records by assuming some temporal scaling behavior of these statistics. However, as our analyzes of a large data set suggest, the mathematical form of this scaling behavior might be specific to individual gauges. This paper presents, therefore, a novel approach that bypasses the temporal scaling behavior assumption. The method uses multivariate adaptive regression splines; it is learning-based and seeks directly relationships between target subdaily statistics and available predictors (including (supra-) daily rainfall statistics and external information such as large-scale atmospheric variables). A large data set is used to investigate these relationships, including almost 340 hourly rainfall series coming from gauges spread over Switzerland, the USA and the UK. The predictive power of the new approach is assessed for several subdaily rainfall statistics and is shown to be superior to the one of temporal scaling laws. The study is completed with a detailed discussion of how such reconstructed statistics improve the accuracy of an hourly rainfall generator based on Poisson cluster models.

This paper presents a calibration framework based on the generalized likelihood uncertainty estimation (GLUE) that can be used to condition hydrological model parameter distributions in scarcely gauged river basins, where data is uncertain, intermittent or nonconcomitant. At the heart of this framework is the conditioning of the model parameters such as to reproduce key signatures of the observed data within some limits of acceptability. These signatures are either based on hard or on soft information. Hard information signatures are defined as signatures for which the limits of acceptability may be objectively derived from the distribution of long series of observed values, and which effectively constrain the model parameters. Soft signatures are less effective in parameter conditioning or their limits of acceptability cannot be objectively derived. During random parameter sampling, parameter sets are accepted as equally likely if they meet all the hard limits of acceptability. This results in an intermediate parameter distribution, which can be used to reduce the sampling limits. Then, the soft information may be introduced in a second constraining step to reach a final parameter distribution. The modeler can use the final results as a guideline for a further search for information, possibly from new observations yet to collect. In an application of the framework to the Luangwa catchment in Zambia, three information signatures are retrieved from a data set of old discharge time series and used to condition the parameters of a daily conceptual rainfall-runoff model. We performed two independent calibration experiments with two significantly different satellite rainfall estimates as model input. The results show consistent parameter distributions and considerable reduction of the prior parameter space and corresponding output realizations. These results illustrate the potential of the proposed calibration framework for predictions in scarcely gauged catchments.

This paper presents the Spatially Explicit Hydrologic Response (SEHR) model developed at the Laboratory of Ecohydrology of the Ecole Polytechnique Fédérale de Lausanne for the simulation of hydrological processes at the catchment scale. The key concept of the model is the formulation of water transport by geomorphologic travel time distributions through gravity-driven transitions among geomorphic states: the mobilization of water (and possibly dissolved solutes) is simulated at the subcatchment scale and the resulting responses are convolved with the travel paths distribution within the river network to obtain the hydrologic response at the catchment outlet. The model thus breaks down the complexity of the hydrologic response into an explicit geomorphological combination of dominant spatial patterns of precipitation input and of hydrologic process controls. Nonstationarity and nonlinearity effects are tackled through soil moisture dynamics in the active soil layer. We present here the basic model set-up for precipitation–runoff simulation and a detailed discussion of its parameter estimation and of its performance for the Dischma River (Switzerland), a snow-dominated catchment with a small glacier cover.

The new Scientific Decade 2013–2022 of IAHS, entitled “Panta Rhei—Everything Flows”, is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013–2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes.

This paper presents a calibration framework based on the generalized likelihood uncertainty estimation (GLUE) that can be used to condition hydrological model parameter distributions in scarcely gauged river basins, where data is uncertain, intermittent or nonconcomitant. At the heart of this framework is the conditioning of the model parameters such as to reproduce key signatures of the observed data within some limits of acceptability. These signatures are either based on hard or on soft information. Hard information signatures are defined as signatures for which the limits of acceptability may be objectively derived from the distribution of long series of observed values, and which effectively constrain the model parameters. Soft signatures are less effective in parameter conditioning or their limits of acceptability cannot be objectively derived. During random parameter sampling, parameter sets are accepted as equally likely if they meet all the hard limits of acceptability. This results in an intermediate parameter distribution, which can be used to reduce the sampling limits. Then, the soft information may be introduced in a second constraining step to reach a final parameter distribution. The modeler can use the final results as a guideline for a further search for information, possibly from new observations yet to collect. In an application of the framework to the Luangwa catchment in Zambia, three information signatures are retrieved from a data set of old discharge time series and used to condition the parameters of a daily conceptual rainfall-runoff model. We performed two independent calibration experiments with two significantly different satellite rainfall estimates as model input. The results show consistent parameter distributions and considerable reduction of the prior parameter space and corresponding output realizations. These results illustrate the potential of the proposed calibration framework for predictions in scarcely gauged catchments.