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To address groundwater issues, it is often necessary to develop geological and hydrogeological models. Combining geological, geophysical and hydrogeological data available on a site to build such models is often a challenge. This paper presents a methodology to integrate such data within a geologically consistent model with robust error estimation. The methodology combines the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm with a hierarchical geological modeling approach (ArchPy). Geophysical and hydrogeological field data are jointly assimilated in a stochastic ESMDA framework. To speed up the inversion process, forward responses are computed in lower‐dimensional spaces relevant to each physical problem. By doing so, the final models take into account multiple data sources and regional conceptual geological knowledge. This study illustrates the applicability of this novel approach using actual data from the upper Aare Valley, Switzerland. The results of cross‐validation show that the combination of different data types, each sensitive to different spatial dimensions, enhances the quality of the model within a reasonable computing time. The proposed methodology allows the automatic generation of groundwater models with robust uncertainty estimation and could be applied to a wide variety of hydrogeological issues. Plain Language Summary: When dealing with groundwater, it is necessary to develop underground models. However, taking into account all the different data types on a site is time‐consuming, and there is a lack of uncertainty quantification. In this study, we develop an approach that automatically combines different types of data, including boreholes, geophysical data, and hydrogeological measurements. All data are combined using a stochastic algorithm and produce an ensemble of plausible and data‐compatible models. These models can be used, for example, to forecast groundwater availability, pollutant distribution, or the effect of climate change on groundwater. Key Points: A methodology is proposed to assimilate geological, hydrogeological, and geophysical data in consistent stochastic modelsThe methodology combines a hierarchical geological modeling technique with the ensemble smoother with multiple data assimilationThe applicability of the methodology is demonstrated using actual field data from the upper Aare valley aquifer in Switzerland [ABSTRACT FROM AUTHOR]

Abstract: New measurement devices allow observing rainfall with unprecedented resolution. Such observations often reveal new features of rainfall occurring at the local scale (areas of about 1–25 km2). In particular, the joint effects of the advection of rain storms over the ground, and the deformation of spatial rain patterns along time, generate a complex rain field dependence structure characterized by strong space‐time interactions. When a high‐resolution is desired, stochastic rainfall models must therefore be upgraded to account for these new features of rain fields. In this paper, we propose to improve the meta‐Gaussian framework, which is typically used to model space‐time rain fields, to the specific case of sub‐kilometer rainfall. Particular attention is paid to the reproduction of the main features of local scale rainfall, namely: (1) a skewed distribution of rain intensities with the presence of intraevent intermittency and (2) a space‐time dependency structure with strong and complex space‐time interactions. The resulting model, able to generate high‐resolution, continuous and space‐time rain fields at the local scale, is validated and applied to a real data set collected by a network of drop‐counting rain gauges recording rainfall at a 1 min frequency. The combination of these data with the proposed model results in a complete framework that allows resolving the features of high‐resolution rainfall (1 min temporal resolution, 100 m spatial resolution) over a small alpine catchment in Switzerland. [ABSTRACT FROM AUTHOR]

In the companion to the present paper, the one-dimensional k-e lake model SIMSTRAT is coupled to a single-column atmospheric model, nicknamed FIZC, and an application of the coupled model to the deep Lake Geneva, Switzerland, is described. In this paper, the response of Lake Geneva to global warming caused by an increase in atmospheric carbon dioxide concentration (i.e., 2 x CO2) is investigated. Coupling the models allowed for feedbacks between the lake surface and the atmosphere and produced changes in atmospheric moisture and cloud cover that further modified the downward radiation fluxes. The time evolution of atmospheric variables as well as those of the lake's thermal profile could be reproduced realistically by devising a set of adjustable parameters. In a "control" 1 x CO2 climate experiment, the coupled FIZC-SIMSTRAT model demonstrated genuine skills in reproducing epilimnetic and hypolimnetic temperatures, with annual mean errors and standard deviations of 0.25°C ± 0.25°C and 0.3°C ± 0.15°C, respectively. Doubling the CO2 concentration induced an atmospheric warming that impacted the lake's thermal structure, increasing the stability of the water column and extending the stratified period by 3 weeks. Epilimnetic temperatures were seen to increase by 2.6°C to 4.2°C, while hypolimnion temperatures increased by 2.2°C. Climate change modified components of the surface energy budget through changes mainly in air temperature, moisture, and cloud cover. During summer, reduced cloud cover resulted in an increase in the annual net solar radiation budget. A larger water vapor deficit at the air-water interface induced a cooling effect in the lake. [ABSTRACT FROM AUTHOR]

A single column atmospheric model (SCM) coupled to a one-dimensional lake model devised for climate simulations is described in this paper. As a test case, this coupled model is applied to the deepest section of Lake Geneva in Switzerland. Both atmospheric and lake models require a minimal set of adjustable parameters to reproduce the local observations of temperature, moisture, and wind as well as those of the lake thermal profiles. A number of simulations have been performed to produce a sorted set of optimal model parameters that reproduces the mean and the variability of the seasonal evolution of the thermal profiles in the lake as well as those of the mean and the variability of the surface air temperature, moisture, and winds. The lake water temperature is reproduced realistically using the optimal calibration parameter values with a seasonal- and depth-averaged error of 0.41°C in summer, -0.15°C in autumn, 0.0 1°C in winter, and 0.27°C in spring when compared to the lake observations. Also, the errors of the seasonally averaged simulated anemometer-level wind speed, screen-level air temperature, and specific humidity to the station-derived values are 0.04 ms-1, 1.04°C, and 0.74 g kg-1, respectively. Results of this study contribute to the understanding of the air-lake interactions present over the deep Lake Geneva. In addition, the sensitivity experiments carried Out in this paper serve as the basis for experiments aiming at studying the thermal response of the deep Swiss Lake Geneva under future global climate change conditions reported in a companion paper. [ABSTRACT FROM AUTHOR]

This paper introduces a general framework that evaluates a numerical Bayesian multiresponse calibration approach based on a Gibbs within Metropolis searching algorithm and a statistical likelihood function. The methodology has been applied with two versions of TOPMODEL on the Haute-Mentue experimental basin in Switzerland. The approach computes the following: the parameter's uncertainty, the parametric uncertainty of the output responses stemming from parameter uncertainty, and the predictive uncertainty of the output responses stemming from an error term including, indiscriminately in a lumped way, model structure and input and output errors. Two case studies are presented: The first one applies this methodology with the classical TOPMODEL to assess the role of two-response calibration (observed discharge and soil saturation deficits) on model parameters and output uncertainty. The second one uses a three-response calibration (observed discharge, silica, and calcium stream water concentrations) with a modified version of TOPMODEL to study the uncertainty of the parameters and of the simulated responses. Despite its limitations, the present multiresponse Bayesian approach proved a valuable tool in uncertainty analyses, and it contributed to a better understanding of the role of the internal variables and the value of additional information for enhancing model structure robustness and for checking the performance of conceptual models. [ABSTRACT FROM AUTHOR]

Cosmic‐ray neutron sensors (CRNS) have been used in many studies for measuring soil moisture and snow pack over intermediate scales. Corrections for barometric pressure and atmospheric humidity are well established, however, corrections for the effect of solar activity on neutron intensity rates have been overly simplistic, typically relying on one neutron monitor station and accounting for latitude and elevation crudely or not at all. Recognizing the lack of a generalized and scientifically robust approach to neutron intensity correction, we developed a new approach for correcting CRNS count rates based on analysis of data from 110 quality‐controlled neutron monitor stations from around the world spanning more than seven decades. Count rates from each monitor were plotted against the count rates from Climax, CO, USA or Jungfraujoch, Switzerland depending on the time period covered. Relationships between relative counting rates at the site of interest versus the reference neutron monitors were found to be strongly linear. The slope and intercept of these linear relation are both dependent on a scaling factor referred to as τ $\tau $, which was shown to increase with increasing geomagnetic latitude and elevation. This dependence was represented using an empirical relationship based on a single reference neutron monitor. This generalized approach enables τ $\tau $ to be derived for any location on Earth and also lends itself to roving CRNS studies. The correction procedure also includes a location‐dependent normalization factor which enables easy substitution of an alternative reference neutron monitor into the correction procedure. Plain Language Summary: Cosmic‐ray neutron sensors (CRNS) are used for measuring soil moisture and snow pack at the scale of hectares. Corrections to account for changes in atmospheric pressure and humidity on CRNS measurements are well established and widely applied, however, corrections for changes in incoming cosmic‐ray neutron intensity because of solar activity have been overly simplistic. We developed a new approach for correcting CRNS count rates based on analysis of data from 110 neutron monitor stations from around the world with datasets spanning more than seven decades. A strong relationship was found between the relative count rate of neutron monitors from around the world, with the magnitude of the correction increasing toward the poles and with elevation. The relationship we found can be applied at any CRNS location based on its elevation, latitude, and longitude to correct for changes to incoming cosmic‐ray neutron intensity. Key Points: A new approach for correcting cosmic ray neutron sensor data for the effects of fluctuating intensities of incoming neutrons is presentedThe approach is based on analysis of data from 110 neutron monitor stations from around the world with data spanning more than seven decadesThe approach can be applied at any location on Earth and includes the ability to utilize any neutron monitor as a reference station [ABSTRACT FROM AUTHOR]

We introduce an approach for the stochastic characterization of the geometric and hydraulic parameters of a three‐dimensional (3D) discrete fracture network (DFN) and for estimating their uncertainty based on data from hydraulic tomography experiments. The inversion approach relies on a Bayesian framework and the resulting posterior distribution is characterized by generating samples by Markov chain Monte Carlo (MCMC) methods. The inversion method is evaluated for four synthetic test cases related to the Grimsel test site in Switzerland. Comparison of original and reconstructed DFN models shows that the presented approach is suitable for identifying variable fracture locations and orientations. This is especially the case for those fractures that represent the preferential flow paths in the simulated experiments. It is also revealed that the Bayesian framework is useful to discriminate fractures based on the reliability of the inversion, which is illustrated by fracture probability maps taken as sections through the studied rock mass. Moreover, it is demonstrated that the hydraulic apertures can be calibrated together with the fracture geometries. A premise for applicability in practice, however, is that the hydraulic measurements are complemented by additional information to sufficiently constrain the value ranges of the geometric and hydraulic parameters to be inverted together. The presented work expands the applicability of a previously presented promising two‐dimensional procedure based on transdimensional inversion to field‐based 3D problems. The theoretical findings here open the door for highly flexible structural characterization in practice based on hydraulic tomography, as well as alternative or complementary tomographic methods. Key Points: We infer the structural properties of a three‐dimensional discrete fracture network given data from hydraulic tomography experimentsThe method is demonstrated for synthetic test cases based on the Grimsel site in SwitzerlandThe inversion yields major geometric parameters and their uncertainties [ABSTRACT FROM AUTHOR]

The development of the adjoint state method for the system of partial differential equations (PDEs) describing radionuclide transport in heterogeneous discrete fractured porous media in steady‐state regime is presented in this study for both continuous and discrete adjoint approaches. The continuous adjoint approach has its own advantage in the sense that the adjoint system can be derived in terms of PDEs which can be solved analytically for specific performance measures. These PDEs can then be used to obtain a physical interpretation of the adjoint state functions. The discrete adjoint approach permits a simpler mathematical treatment based on the use of the same matrix (or its transpose) of the linear system of the discretized transport equations referring to from hereon as the forward problem. Analytical solutions for both the forward problem and its associated adjoint problem have been derived for radionuclide transport in layered formations including a fracture using an analytical inversion of tridiagonal matrices. A set of 14 formulas of sensitivity coefficients with respect to pertinent system parameters has been derived and presented in integral forms. Analytical results have been compared with numerical results and have shown excellent agreements. The discrete adjoint approach has been coupled with the differential sensitivity analysis method and applied to a real field case involving the performance assessment of a site currently being considered for underground nuclear storage in northern Switzerland, Zürich Nordost. The results show that when a fracture is present in the vicinity of waste repository, the state parameter controlling flow through the fracture, referred to herein as fracture transmissivity, is among the important parameters which should be considered in sensitivity analysis studies. Key Points: A full mathematical/numerical adjoint sensitivity model for steady‐state RN transport in heterogeneous discrete fractured media is presentedAnalytical solutions for concentrations and adjoint states have been derived for the case of layered formations including a fractureThe developed adjoint approach has been coupled with the differential sensitivity analysis method and applied to a real field case [ABSTRACT FROM AUTHOR]

Karst aquifers may convey significant sediment fluxes, as displayed by the intense turbidity peaks commonly observed at karst springs. The understanding of the origin of the suspended solids discharged at springs is key in assessing spring vulnerability and securing drinking water quality. The mechanisms for turbidity generation and sediment transport in karst are however difficult to investigate because of the general lack of access to the karst conduits. These processes have been examined in the Milandre Cave, which hosts a karst drain of regional importance, for more than 10 years by means of turbidity monitoring both inside and at the outlets of this karst system. Additionally, the composition of the suspended load (particle‐size distribution and Escherichia coli content) has been monitored over the course of a flood event. These data are compared against a numerical simulation of the mean boundary shear stress inside the conduit network. The following conceptual model for sediment transport through the system is derived: during minor flood events, most of the turbidity comes from underground sediment remobilization, while during medium to intense flood events, soil‐derived turbidity also reaches the spring. Hydraulics in the epiphreatic zone is tightly linked with autochthonous turbidity generation (mostly during the flooding and the flushing of conduits). In comparison, allochthonous turbidity is associated with finer particles, higher E. coli, and higher UV fluorescence. This improves the overall understanding of turbidity generation and could help the monitoring and forecast of pollution events at drinking water supplies. Key Points: Based on long‐term monitoring and numerical modeling a conceptual model for turbidity generation in the Milandre karst aquifer is proposedTurbidity peaks originating from soil erosion (allochthonous) and from karst sediment remobilization (autochthonous) have been distinguishedThe flooding and the emptying of epiphreatic conduits are pointed out as main mechanisms for autochthonous turbidity generation [ABSTRACT FROM AUTHOR]

We present an efficient mathematical/numerical adjoint sensitivity analysis model for transient radionuclide transport through heterogeneous porous media. This work extends our previous research on radionuclide transport in a steady‐state regime. Both continuous and discrete adjoint approaches have been developed in this current research. The mathematical equations associated with the adjoint system and required for the continuous approach have been derived. The methodology for deriving the discrete adjoint solution is also presented. For the homogeneous case, the adjoint system for radionuclide decay chain is solved analytically. The four‐member decay chain (238Pu → 234U → 230Th → 226Ra) is considered for validation. The validity of the analytical adjoint sensitivity model has been shown using two illustrative examples associated with two performance measures. The analytical adjoint states are compared with calculated numerical results and show excellent agreements. The sensitivity coefficients are also computed numerically using the perturbation method. The numerical sensitivity coefficients successfully reproduce the analytically derived sensitivities based on adjoint states. The adjoint model has been coupled with a derivative‐based global sensitivity method and applied to a real field case involving the performance assessment of a site currently being considered for underground nuclear storage in northern Switzerland, Zürich Nordost. An illustrative case study of spent fuel and high‐level waste (SF/HLW) repository involving 6 radionuclides and 48 parameters has been considered for identifying the most important parameters with respect to the maximum radionuclide dose at the interface geosphere/biosphere. Based on the results of sensitivity analysis, the uncertainty of the maximum radionuclide dose has been performed using Monte Carlo simulations. Key Points: An adjoint sensitivity model for transient sequentially coupled radionuclide transport in heterogeneous media is presentedAnalytical expressions including adjoint state functions and sensitivity coefficients are derived in closed forms for the homogeneous caseA derivative‐based global sensitivity method coupled with the adjoint approach is presented and applied to a real field case [ABSTRACT FROM AUTHOR]

Alpine hydroelectric power exploitation often aims to increase the volume of water stored behind impoundments, which may be achieved through flow abstraction and lateral transfer to storage. Intakes are designed to separate water from sediment which accumulates in settling basins and may be flushed sometimes at subdaily frequencies in glaciated basins. In some countries (e.g., Switzerland) intakes drain a greater basin area than impoundments yet legislation designed to improve instream ecosystems impacted by hydropower has almost entirely ignored them. Some research suggests that such streams have exceptionally low abundance and diversity of macroinvertebrates for some kilometers downstream of the intake flushing at high frequency in summer, but that populations can recover rapidly as soon as flushing frequency decreases in early autumn. However, such patterns could also result from natural flow variability, sediment transport, and morphological change in glacier‐fed streams. We combine field measurements with habitat modeling to assess the impacts of sediment flushing on macrozoobenthos as compared to what might be expected in a natural, hydromorphologically dynamic Alpine stream. We show that water abstraction in itself could improve habitat conditions because it increases the relative contribution of less turbid and groundwater/unregulated sources. However, intake flushing leads to short duration, sediment‐laden flows that can destabilize substantial areas of the stream bed and cause rates of lateral displacement of habitat much greater than the possible response by macroinvertebrates. Our results challenge current emphasis on minimum flows in such streams and argue that much more emphasis needs to be placed on sediment management. Key Points: Flow abstraction at hydropower intakes in glaciated streams improves downstream low flow habitat conditions compared to natural conditionsIntake sediment flushing degrades habitat due to rapid discharge rise, turbidity increase, euphotic depth reduction, and bed instabilitySensitivity to flushing relates to the rate of discharge increase as zones of suitable habitat move faster than possible benthos migration [ABSTRACT FROM AUTHOR]

This work presents an efficient mathematical/numerical model to compute the sensitivity coefficients of a predefined performance measure to model parameters for one‐dimensional steady‐state sequentially coupled radionuclide transport in a finite heterogeneous porous medium. The model is based on the adjoint sensitivity approach that offers an elegant and computationally efficient alternative way to compute the sensitivity coefficients. The transport parameters include the radionuclide retardation factors due to sorption, the Darcy velocity, and the effective diffusion/dispersion coefficients. Both continuous and discrete adjoint approaches are considered. The partial differential equations associated with the adjoint system are derived based on the adjoint state theory for coupled problems. Physical interpretations of the adjoint states are given in analogy to results obtained in the theory of groundwater flow. For the homogeneous case, analytical solutions for primary and adjoint systems are derived and presented in closed forms. Numerically calculated solutions are compared to the analytical results and show excellent agreements. Insights from sensitivity analysis are discussed to get a better understanding of the values of sensitivity coefficients. The sensitivity coefficients are also computed numerically by finite differences. The numerical sensitivity coefficients successfully reproduce the analytically derived sensitivities based on adjoint states. A derivative‐based global sensitivity method coupled with the adjoint state method is presented and applied to a real field case represented by a site currently being considered for underground nuclear storage in Northern Switzerland, "Zürich Nordost", to demonstrate the proposed method. The results show the advantage of the adjoint state method compared to other methods in term of computational effort. Key Points: A full mathematical/numerical adjoint sensitivity approach for steady‐state sequentially coupled radionuclide transport in heterogeneous media is presentedAnalytical expressions including adjoint state functions and sensitivity coefficients are derived in closed forms for the homogeneous caseA derivative‐based global sensitivity method coupled with the adjoint approach is presented and applied to a real field case [ABSTRACT FROM AUTHOR]

Field tracer experiments were conducted to examine tracer transport properties in a fracture‐dominated crystalline rock mass at the Grimsel Test Site, Switzerland. In the experiments reported here, both the DNA nanotracers and solute dye tracers were simultaneously injected. We compare the transport of DNA nanotracers to solute dye tracers by performing temporal moment analysis on the recorded tracer breakthrough curves and estimate the swept volumes and flow geometries. The DNA nanotracers, approximately 166 nm in diameter, are observed to travel at a higher average velocity than the solutes but with lower mass recoveries, lower swept volumes, and less dispersion. Moreover, size exclusion and potentially, particle density effects are observed during the transport of the DNA nanotracers. Compared to solute tracers, the greatest strength of DNA nanotracers is the demonstrated zero signal interference of background noise during repeat or multitracer tests. This work provides encouraging results in advancing the use of DNA nanotracers in hydrogeological applications, for example, during contaminant transport investigations or geothermal reservoir characterization. Key Points: DNA‐labeled nanotracer transport is evaluated on a first field demonstration in crystalline rockHeterogeneity of flow field and size exclusion strongly influences DNA nanotracer response curvesDNA nanotracer is highly suitable for tracer tomography and tracing particulate‐bound contaminant transport [ABSTRACT FROM AUTHOR]

A high-resolution gridded daily precipitation data set was combined with a landslide inventory containing over 2000 events in the period 1972-2012 to analyze rainfall thresholds which lead to landsliding in Switzerland. We colocated triggering rainfall to landslides, developed distributions of triggering and nontriggering rainfall event properties, and determined rainfall thresholds and intensity-duration ID curves and validated their performance. The best predictive performance was obtained by the intensity-duration ID threshold curve, followed by peak daily intensity Imax and mean event intensity Imean. Event duration by itself had very low predictive power. A single country-wide threshold of Imax = 28 mm/d was extended into space by regionalization based on surface erodibility and local climate (mean daily precipitation). It was found that wetter local climate and lower erodibility led to significantly higher rainfall thresholds required to trigger landslides. However, we showed that the improvement in model performance due to regionalization was marginal and much lower than what can be achieved by having a high-quality landslide database. Reference cases in which the landslide locations and timing were randomized and the landslide sample size was reduced showed the sensitivity of the Imax rainfall threshold model. Jack-knife and cross-validation experiments demonstrated that the model was robust. The results reported here highlight the potential of using rainfall ID threshold curves and rainfall threshold values for predicting the occurrence of landslides on a country or regional scale with possible applications in landslide warning systems, even with daily data. [ABSTRACT FROM AUTHOR]

The efficiency of regional frequency analysis (RFA) is undermined by intersite dependence, which is usually ignored in parameter estimation. We propose a spatial index flood model where marginal generalized extreme value distributions are joined by an extreme-value copula characterized by a max-stable process for the spatial dependence. The parameters are estimated with a pairwise likelihood constructed from bivariate marginal generalized extreme value distributions. The estimators of model parameters and return levels can be more efficient than those from the traditional index flood model when the max-stable process fits the intersite dependence well. Through simulation, we compared the pairwise likelihood method with an L-moment method and an independence likelihood method under various spatial dependence models and dependence levels. The pairwise likelihood method was found to be the most efficient in mean squared error if the dependence model was correctly specified. When the dependence model was misspecified within the max-stable models, the pairwise likelihood method was still competitive relative to the other two methods. When the dependence model was not a max-stable model, the pairwise likelihood method led to serious bias in estimating the shape parameter and return levels, especially when the dependence was strong. In an illustration with annual maximum precipitation data from Switzerland, the pairwise likelihood method yielded remarkable reduction in the standard errors of return level estimates in comparison to the L-moment method. [ABSTRACT FROM AUTHOR]

Projections of discharge are key for future water resources management. These projections are subject to uncertainties, which are difficult to handle in the decision process on adaptation strategies. Uncertainties arise from different sources such as the emission scenarios, the climate models and their postprocessing, the hydrological models, and the natural variability. Here we present a detailed and quantitative uncertainty assessment, based on recent climate scenarios for Switzerland (CH2011 data set) and covering catchments representative for midlatitude alpine areas. This study relies on a particularly wide range of discharge projections resulting from the factorial combination of 3 emission scenarios, 10-20 regional climate models, 2 postprocessing methods, and 3 hydrological models of different complexity. This enabled us to decompose the uncertainty in the ensemble of projections using analyses of variance (ANOVA). We applied the same modeling setup to six catchments to assess the influence of catchment characteristics on the projected streamflow, and focused on changes in the annual discharge cycle. The uncertainties captured by our setup originate mainly from the climate models and natural climate variability, but the choice of emission scenario plays a large role by the end of the 21st century. The contribution of the hydrological models to the projection uncertainty varied strongly with catchment elevation. The discharge changes were compared to the estimated natural decadal variability, which revealed that a climate change signal emerges even under the lowest emission scenario (RCP2.6) by the end of the century. Limiting emissions to RCP2.6 levels would nevertheless reduce the largest regime changes by the end of the century by approximately a factor of two, in comparison to impacts projected for the high emission scenario SRES A2. We finally show that robust regime changes emerge despite the projection uncertainty. These changes are significant and are consistent across a wide range of scenarios and catchments. We propose their identification as a way to aid decision making under uncertainty. [ABSTRACT FROM AUTHOR]

The analysis of the spatial-temporal variability of soil moisture can be carried out considering the absolute (original) soil moisture values or relative values, such as the percent of saturation or temporal anomalies. Over large areas, soil moisture data measured at different sites can be characterized by large differences in their minimum, mean, and maximum absolute values, even though in relative terms their temporal patterns are very similar. In these cases, the analysis considering absolute compared with percent of saturation or temporal anomaly soil moisture values can provide very different results with significant consequences for their use in hydrological applications and climate science. In this study, in situ observations from six soil moisture networks in Italy, Spain, France, Switzerland, Australia, and United States are collected and analyzed to investigate the spatial soil moisture variability over large areas (250-150,000 km2). Specifically, the statistical and temporal stability analyses of soil moisture have been carried out for absolute, temporal anomaly, and percent of saturation values (using two different formulations for temporal anomalies). The results highlight that the spatial variability of the soil moisture dynamic (i.e., temporal anomalies) is significantly lower than that of the absolute soil moisture values. The spatial variance of the time-invariant component (temporal mean of each site) is the predominant contribution to the total spatial variance of absolute soil moisture data. Moreover, half of the networks show a minimum in the spatial variability for intermediate conditions when the temporal anomalies are considered, in contrast with the widely recognized behavior of absolute soil moisture data. The analyses with percent saturation data show qualitatively similar results as those for the temporal anomalies because of the applied normalization which reduces spatial variability induced by differences in mean absolute soil moisture only. Overall, we find that the analysis of the spatial-temporal variability of absolute soil moisture does not apply to temporal anomalies or percent of saturation values. [ABSTRACT FROM AUTHOR]

In many rainfall-runoff models, at least some calibration of model parameters has to take place. Especially for ungauged or poorly gauged basins this can be problematic, because there is little or no data available for calibration. A possible solution to overcome the problems caused by data scarcity is to set up a measurement campaign for a limited time period. In this study, we determine the minimum amount of data required to determine robust parameter values for a simple model with two parameters. The model is constructed such that the parameters can be determined not only with automatic calibration, but also by recession analysis and a priori from Boussinesq theory. The model has been applied to a research catchment in Switzerland. For automatic calibration and recession analysis, one season (5 months) is found to be sufficient to give robust parameters for simulation of high flows over the full observation period. For automatic calibration, this should be the season with the highest precipitation, for recession analysis the season with least evapotranspiration. The Boussinesq equation is able to give good parameter estimates for modeling high flows, but detailed in situ knowledge of the catchment is required. Automatic calibration outperforms recession analysis and Boussinesq theory by far when it comes to parameter estimation with a focus on prediction of low flows. It was shown that a single set of parameters cannot simultaneously describe high and low flows with a reasonable accuracy, suggesting that more than two parameters are needed to characterize subsurface properties. [ABSTRACT FROM AUTHOR]

We present a probabilistic sediment cascade model to simulate sediment transfer in a mountain basin (Illgraben, Switzerland) where sediment is produced by hillslope landslides and rockfalls and exported out of the basin by debris flows and floods. The model conceptualizes the fluvial system as a spatially lumped cascade of connected reservoirs representing hillslope and channel storages where sediment goes through cycles of storage and remobilization by surface runoff. The model includes all relevant hydrological processes that lead to runoff formation in an Alpine basin, such as precipitation, snow accumulation, snowmelt, evapotranspiration, and soil water storage. Although the processes of sediment transfer and debris flow generation are described in a simplified manner, the model produces complex sediment discharge behavior which is driven by the availability of sediment and antecedent wetness conditions (system memory) as well as the triggering potential (climatic forcing). The observed probability distribution of debris flow volumes and their seasonality in 2000-2009 are reproduced. The stochasticity of hillslope sediment input is important for reproducing realistic sediment storage variability, although many details of the hillslope landslide triggering procedures are filtered out by the sediment transfer system. The model allows us to explicitly quantify the division into transport and supply-limited sediment discharge events. We show that debris flows may be generated for a wide range of rainfall intensities because of variable antecedent basin wetness and snowmelt contribution to runoff, which helps to understand the limitations of methods based on a single rainfall threshold for debris flow initiation in Alpine basins. [ABSTRACT FROM AUTHOR]

Daily streamflow from stations close to five Swiss glaciers is analyzed for trends with the Mann-Kendall test. We consider a common period of record (1974-2004) and longer periods based on data availability. The trend statistical significance is tested on annual and seasonal bases. We also examine changes in precipitation, temperature, and snow cover characteristics. Highly glacierized basins show statistically significant positive trends in annual streamflow caused by increasing streamflow in spring and summer. Trends are more numerous and stronger at lower and mid than at the upper quantiles. The basin characterized by lower glacier coverage, conversely, does not exhibit consistently statistically significant trends. Changes in precipitation are not sufficient to explain the observed streamflow trends. Air temperature sees an increase in mean, minimum, and maximum values at all sites. Variations in the seasonal snow accumulation and ablation process are evident. Solid precipitation is decreasing at all sites and trends may be due to a shift from snowfall into rainfall. Mean snow depth is also decreasing, and its duration is getting shorter because of a decrease in solid precipitation and enhanced melting. Trend magnitude attenuates with longer time series. Contrasting trends are detected for different subperiods in the last 70 years: statistically significant negative trends are observed in the periods 1944-1974 and 1954-1984 for Aletschgletscher, in contrast with the results for the common period. These trends are explained by different rates of ice volume changes, and the sign of trends is clearly related to phases of positive or negative glacier mass balance. [ABSTRACT FROM AUTHOR]

Modeling spatiotemporal dynamics of snow in forests is challenging, as involved processes are strongly dependent on small‐scale canopy properties. In this study, we explore how local canopy structure information can be integrated in a medium‐complexity energy balance snow model to replicate observed snow patterns at very high spatial resolutions. Snow depth distributions simulated with the Flexible Snow Model (FSM2) were tested against extensive experimental data acquired in discontinuous subalpine forest stands in Eastern Switzerland over three winters. While the default canopy implementation in FSM2 fails to capture the observed snow depth variability, performance is considerably improved when local canopy cover fraction and hemispherical sky view fraction are additionally accounted for (30% reduction in root mean square error). However, realistic snow depth distribution patterns throughout the season are only achieved if effective temperatures of near and distant canopy elements are discerned and if a mechanism to mimic preferential deposition of snow in canopy gaps is included. We demonstrate that by diversifying the canopy structure input in order to reflect respective portions of the canopy relevant to different processes, even a simple model based on widely used process parameterizations and canopy metrics can be applied for high‐resolution simulations of the sub‐canopy snow cover with just a few modifications. The presented approaches could be implemented in commonly used land surface models, allowing upscaling experiments and development of sub‐grid parameterizations without necessitating complex high‐resolution models. Key Points: Alternative strategies to represent fine‐scale forest canopy structure within a standard energy balance snow model were testedOnly canopy representations that distinguish between near and distant canopy elements simulated realistic snow distributionsThe proposed approach uses standard canopy parameters only and can thus be transferred to other model frameworks [ABSTRACT FROM AUTHOR]