ORCID Profile
0000-0002-6314-2124
Current Organisations
Swedish University of Agricultural Sciences
,
Universitat Zurich
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Publisher: Copernicus GmbH
Date: 06-11-2017
DOI: 10.5194/HESS-21-5443-2017
Abstract: Abstract. Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.
Publisher: Elsevier BV
Date: 02-2009
Publisher: Wiley
Date: 05-2021
DOI: 10.1002/HYP.14112
Publisher: Informa UK Limited
Date: 26-10-2018
Publisher: American Geophysical Union (AGU)
Date: 09-2020
DOI: 10.1029/2019JD031485
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-17393
Abstract: & & The parameterization of hydrological models in ungauged catchments remains challenging. However, the increased availability of large-s le data sets in recent years provides new opportunities for regionalization. In this study, we use the CAMELS dataset and the HBV model to simulate daily runoff in nearly 600 catchment areas in the United States. In a first step, & a lower and an upper benchmark were derived to obtain an approximation for how poor or how good runoff simulation could get in each of the catchments. For the upper benchmark the HBV model was calibrated, and the calibrated parameter values & #8203 & #8203 were related to catchment characteristics. To account for parameter uncertainty, 100 independent calibrations were performed, and then median efficiency values & #8203 & #8203 were used for further analyses. For the lower benchmark the HBV model was run for 1000 randomly selected parameter sets, and median efficiency values were again used for further analyses. In a second step, each catchment was treated as ungaued and its parameter values were estimated by multiple regionalization methods. For each regionalization method donor catchments were selected based on a certain criterion including spatial proximity, similarity of hydrological signatures or attribute similarity. Additionally, we tested the added value of single discharge observations, which could be collected during short field visits. Furthermore, to analyze the theoretical limits of regionalization in general, the best three available donors of each receiver catchment were directly used to run simulations. All regionalization approaches were evaluated based on their relative performance with regard to the upper and lower benchmark.& br& First results indicated that the use of an ensemble of parameter sets calibrated in one of the gauged catchments leads to clearly better simulations than the use of randomly selected parameter values. Using the best three donor catchments resulted in nearly as good simulations as the upper benchmark, showing that regionalization has a high potential as long as we find a way to select these most suitable donors. The regionalization approach coming closest to the upper benchmark was based on a combination of spatial proximity and the use of single discharge measurements. Yet, there was still a considerable gap to the performance of using the best three donors. Despite the potential of regionalization demonstrated in this study, there still remains the challenge to find more reliable ways to link the hydrological functioning of catchments with the similarity of model parameter values.& &
Publisher: Copernicus GmbH
Date: 22-03-2022
Abstract: Abstract. Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e., return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realisations of hourly precipitation and temperature scenarios of 10 000 years each. These realisations were then run through a bucket-type hydrological model for 79 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented, and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows to comprehensively explore possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from in idual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping.
Publisher: Copernicus GmbH
Date: 28-02-2014
Abstract: Abstract. This study investigated runoff formation processes of a pre-alpine hillslope prone to slide. The experimental pasture plot (40 m × 60 m) is located in the northern front range of the Swiss Alps on a 30° steep hillslope (1180 m a.s.l., 1500 + mm annual precipitation). A gleysol (H-Go-Gr) overlies weathered marlstone and conglomerate of subalpine molasse. We conducted sprinkling experiments on a subplot (10 m × 10 m) with variable rainfall intensities. During both experiments fluorescein line-tracer injections into the topsoil, and sodium chloride (NaCl) injections into the sprinkling water were used to monitor flow velocities in the soil. The observed flow velocities for fluorescein in the soil were 1.2 and 1.4 × 10−3 m s−1. The NaCl breakthrough occurred almost simultaneously in all monitored discharge levels (0.05, 0.25 and 1.0 m depth), indicating a high-infiltration capacity and efficient drainage of the soil. These initial observations suggested "transmissivity feedback", a form of subsurface stormflow, as the dominant runoff process. However, the results of a brilliant blue dye tracer experiment completely changed our perceptions of the hillslope's hydrological processes. Excavation of the dye-stained soils highlighted the dominance of "organic layer interflow", a form of shallow subsurface stormflow. The dye stained the entire H horizon, vertical soil fractures, and macropores (mostly worm burrows) up to 0.5 m depth. Lateral drainage in the subsoil or at the soil–bedrock interface was not observed, and thus was limited to the organic topsoil. In the context of shallow landslides, the subsoil (Go/Gr) acted as an infiltration and exfiltration barrier, which produced significant lateral saturated drainage in the topsoil (H) and possibly a confined aquifer in the bedrock.
Publisher: Copernicus GmbH
Date: 11-03-2019
DOI: 10.5194/HESS-23-1339-2019
Abstract: Abstract. Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing meteorological data are usually downscaled and bias corrected, most often by univariate approaches such as quantile mapping of in idual variables, neglecting the relationships that exist between climate variables. In this study we test the hypothesis that the explicit consideration of the relation between air temperature and precipitation will affect hydrological impact modelling in a snow-dominated mountain environment. Glacio-hydrological simulations were performed for two partly glacierized alpine catchments using a recently developed multivariate bias correction method to post-process EURO-CORDEX regional climate model outputs between 1976 and 2099. These simulations were compared to those obtained by using the common univariate quantile mapping for bias correction. As both methods correct each climate variable's distribution in the same way, the marginal distributions of the in idual variables show no differences. Yet, regarding the interdependence of precipitation and air temperature, clear differences are notable in the studied catchments. Simultaneous correction based on the multivariate approach led to more precipitation below air temperatures of 0 ∘C and therefore more simulated snowfall than with the data of the univariate approach. This difference translated to considerable consequences for the hydrological responses of the catchments. The multivariate bias-correction-forced simulations showed distinctly different results for projected snow cover characteristics, snowmelt-driven streamflow components, and expected glacier disappearance dates. In all aspects – the fraction of precipitation above and below 0 ∘C, the simulated snow water equivalents, glacier volumes, and the streamflow regime – simulations resulting from the multivariate-corrected data corresponded better with reference data than the results of univariate bias correction. Differences in simulated total streamflow due to the different bias correction approaches may be considered negligible given the generally large spread of the projections, but systematic differences in the seasonally delayed streamflow components from snowmelt in particular will matter from a planning perspective. While this study does not allow conclusive evidence that multivariate bias correction approaches are generally preferable, it clearly demonstrates that incorporating or ignoring inter-variable relationships between air temperature and precipitation data can impact the conclusions drawn in hydrological climate change impact studies in snow-dominated environments.
Publisher: Copernicus GmbH
Date: 02-09-2022
DOI: 10.5194/NHESS-22-2891-2022
Abstract: Abstract. Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from in idual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping.
Publisher: Copernicus GmbH
Date: 15-05-2019
Abstract: Abstract. Flowing stream networks dynamically extend and retract, both seasonally and in response to precipitation events. These network dynamics can dramatically alter the drainage density, and thus the length of subsurface flow pathways to flowing streams. We mapped flowing stream networks in a small Swiss headwater catchment during different wetness conditions and estimated their effects on the distribution of travel times to the catchment outlet. For each point in the catchment, we determined the subsurface transport distance to the flowing stream based on the surface topography, and the surface transport distance along the flowing stream to the outlet. We combined the distributions of these travel distances with assumed surface and subsurface flow velocities to estimate the distribution of travel times to the outlet. These calculations show that the extension and retraction of the stream network can substantially change the mean travel time and the shape of the travel time distribution. During wet conditions with a fully extended flowing stream network, the travel time distribution was strongly skewed to short travel times, but as the network retracted during dry conditions, the distribution of the travel times became more uniform. Stream network dynamics are widely ignored in catchment models, but our results show that they need to be taken into account when modeling solute transport and interpreting travel time distributions.
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/IAHS2022-528
Abstract: & & Earth and water monitoring and observation systems provide open geo data to scientists and professionals supporting distributed knowledge of major hydromet dynamics and extremes. Mobile technologies, at the same time, are empowering citizens who are nowadays informed and involved in volunteering actions designed and implemented to make our communities more safe and sustainable. Citizen science, as a consequence, is gaining momentum empowering the general public, from the & #8220 leasure of doing science& #8221 to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water-related challenges. This work illustrates a conceptual transdisciplinary assessment model that was designed with the goal of standardizing the use of citizen science for advancing hydrology. This work was promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS), and that is composed by a erse group of hydrological, computer and social science experts. A community paper (Nardi et al., in press) presented the conceptualization of this transdisciplinary framework by identifying the shared constituents, interfaces and interlinkages between hydrological sciences and other academic and non-academic disciplines. Particular emphasis was given to the integration of human sensing and behavioural mechanisms into citizen science programs addressing hydrological problems. The proposed CANDHY transdisciplinary framework is here further tested and applied to assess some selected citizen science programs to understand the knowledge gaps and opportunities arising from ongoing citizen science programs. This comparative assessment shows some interesting preliminary results demonstrating the capacity of the proposed framework in homogenizing and accumulating knowledge from the collaboration of erse participatory programs addressing similar or complementary hydrological challenges.& & & & & & & & & Nardi F. et al., in press. Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges. Hydrological Sciences Journal, 0.1080/02626667.2020.1849707& &
Publisher: Copernicus GmbH
Date: 19-10-2016
Abstract: Abstract. Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models. Estimated SFCs can be substantially uncertain when models are calibrated using traditional approaches based on minimization or maximization of statistical performance metrics (e.g. Nash–Sutcliffe efficiency). To evaluate model performance, we tested how well SFCs are simulated when the model objective function was calibrated using one or more SFCs. We calibrated a bucket-type runoff model for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 selected SFCs representing major flow regime components and different flow conditions. While the model tends to underestimate SFCs related to mean and high-flow conditions, SFCs related to low flow are overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional Nash–Sutcliffe efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration and modelled estimates need to be carefully interpreted.
Publisher: Copernicus GmbH
Date: 28-02-2011
Abstract: Abstract. Post-processing the output of different rainfall-runoff models allows one to pool strengths of each model to produce more reliable predictions. As a new approach in the frame of the "Prediction in Ungauged Basins" initiative, this study investigates the geographical transferability of different parameter sets and data-fusion methods which were applied to 5 different rainfall-runoff models for a low-land catchment in Central Sweden. After usual calibration, we adopted a proxy-basin validation approach between two similar but non-nested sub-catchments in order to simulate ungauged conditions. Many model combinations outperformed the best single model predictions with improvements of efficiencies from 0.70 for the best single model predictions to 0.77 for the best ensemble predictions. However no "best" data-fusion method could be determined as similar performances were obtained with different merging schemes. In general, poorer model performance, i.e. lower efficiency, was less likely to occur for ensembles which included more in idual models.
Publisher: American Geophysical Union (AGU)
Date: 28-09-2021
DOI: 10.1029/2021WR030437
Abstract: Model parameter values for ungauged catchments can be regionalized from hydrologically similar gauged catchments. Achieving reliable and robust predictions in ungauged catchments by regionalization, however, is still a major challenge. Here, we conduct a comparative assessment of 19 regionalization approaches based on previously published literature to contribute new insights into their performance in different geographic regions. The approaches use geographical information, physical catchment attributes, hydrological signatures, or a combination thereof to select donor catchments and to subsequently transfer their entire parameter sets to the ungauged receiver catchment. Each regionalization approach was tested in a leave‐one‐out cross‐validation with a bucket‐type catchment model (the HBV model) using 671 gauged catchments in the United States with a erse hydroclimatology. We then evaluated regionalization performance for several hydrograph aspects, compared it against calibration and regionalization benchmarks, and linked it to catchment descriptors. The results of this large‐s le regionalization study can be summarized in three major lessons: (a) Catchments can benefit from a well‐chosen regionalization approach independent of their geographic region and independent of how well they can be modeled or regionalized at best. (b) Almost perfect donors exist for most catchments and an excellent relative model performance can be reached for most catchments with current regionalization approaches. This implies that there is considerable potential for improvement in the prediction in ungauged catchments. (c) The ranking of regionalization approaches depends on how the predicted hydrographs are evaluated. These findings indicate that a multi‐criteria evaluation is essential for a robust assessment of regionalization performance.
Publisher: Copernicus GmbH
Date: 07-10-2019
Abstract: Abstract. Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, climate change impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of in idual catchments, and more importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability, and thereby provide limited insights into climate change adaptation. Here, we collaborated with Groupe E, a hydropower company operating several reservoirs in the Swiss pre-Alps and worked with them to produce hydroclimatic projections tailored to support their upcoming water concession negotiations. We started by identifying the vulnerabilities of their activities to climate change and then together chose streamflow and energy indices to characterize the associated risks. We provided Groupe E with figures showing the projected climate change impacts, which were refined over several meetings. The selected indices enabled us to simultaneously assess a variety of impacts induced by changes on i) the seasonal water volume distribution, ii) low flows, iii) high flows, and iv) energy demand. We were hence able to identify key opportunities (e.g., the future increase of reservoir inflow in winter, when electricity prices are historically high) and risks (e.g., the expected increase of consecutive days of low flows in summer and fall, which is likely to make it more difficult to meet residual flow requirements). This study highlights that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. We also illustrate the importance of identifying stakeholder needs and using them to inform the production of climate impact projections. Our user-centered approach is transferable to other impact modeling studies, in the field of water resources and beyond.
Publisher: Elsevier BV
Date: 07-2021
Publisher: Copernicus GmbH
Date: 12-2010
DOI: 10.5194/HESS-14-2383-2010
Abstract: Abstract. Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a self-developed tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden. Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and total nitrogen (Tot-N). For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME) was generated using an increasing number of members (from the 2 best to the 10 best single predictions). Finally the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME). The evolution of changes in R2 and RMSE was used as a performance descriptor of the ensemble procedure. In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R2 selected members, which resulted in the RMSE decreasing by up to 90%.
Publisher: Informa UK Limited
Date: 10-06-2021
Publisher: Copernicus GmbH
Date: 07-10-2019
Publisher: Copernicus GmbH
Date: 28-06-2018
Abstract: Abstract. Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing meteorological data are usually downscaled and bias corrected, most often by univariate approaches such as quantile mapping of in idual variables. However, univariate correction neglects the relationships that exist between climate variables. In this study glacio-hydrological simulations were performed for two partly glacierized alpine catchments using a recently developed multivariate bias correction method to post-process EURO-CORDEX regional climate model outputs between 1976 and 2100. These simulations were compared to those obtained by using the common univariate quantile mapping for bias correction. As both methods correct each climate variable’s distribution in the same way, the marginal distributions of the in idual variables show no differences. Yet, regarding the interdependence of precipitation and air temperature, clear differences are notable in the studied catchments. Simultaneous correction based on the multivariate approach lead to more precipitation below air temperatures of 0 °C and therefore more simulated snowfall than with the data of the univariate approach. This difference translated to considerable consequences for the hydrological responses of the catchments. The multivariate bias correction forced simulations showed distinctly different results for projected snow cover characteristics, snowmelt-driven streamflow components, and expected glacier disappearance dates in the future. For the historical period the fraction of precipitation above and below 0 °C, the simulated snow water equivalents, glacier volumes, and the streamflow regime resulting from the multivariate-corrected data corresponded better with reference data than the results of univariate bias correction. Differences in simulated total streamflow due to the different bias correction approaches may be considered negligible given the generally large spread of the projections, but systematic differences in the seasonally delayed streamflow components from snowmelt in particular will matter from a planning perspective. While this study does not allow concluding definitively that multivariate bias correction approaches are generally preferable, it clearly demonstrates that incorporating or ignoring inter-variable relationships between air temperature and precipitation data can impact the conclusions drawn in hydrological climate change impact studies.
Publisher: Copernicus GmbH
Date: 27-11-2019
DOI: 10.5194/HESS-23-4825-2019
Abstract: Abstract. Flowing stream networks dynamically extend and retract, both seasonally and in response to precipitation events. These network dynamics can dramatically alter the drainage density and thus the length of subsurface flow pathways to flowing streams. We mapped flowing stream networks in a small Swiss headwater catchment during different wetness conditions and estimated their effects on the distribution of travel times to the catchment outlet. For each point in the catchment, we determined the subsurface transport distance to the flowing stream based on the surface topography and determined the surface transport distance along the flowing stream to the outlet. We combined the distributions of these travel distances with assumed surface and subsurface flow velocities to estimate the distribution of travel times to the outlet. These calculations show that the extension and retraction of the stream network can substantially change the mean travel time and the shape of the travel time distribution. During wet conditions with a fully extended flowing stream network, the travel time distribution was strongly skewed to short travel times, but as the network retracted during dry conditions, the distribution of the travel times became more uniform. Stream network dynamics are widely ignored in catchment models, but our results show that they need to be taken into account when modeling solute transport and interpreting travel time distributions.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-7884
Abstract: & & All hydrological models need to be calibrated to obtain satisfactory streamflow simulations. Here we present a novel parameter regionalization approach that involves the optimization of transfer equations linking model parameters to climate and landscape characteristics. The optimization was performed in a fully spatially distributed fashion at high resolution (0.05& #176 ), instead of at lumped catchment scale, using an unprecedented database of daily observed streamflow from 4229 headwater catchments (& km& sup& & /sup& ) worldwide. The optimized equations were subsequently applied globally to produce parameter maps for the entire land surface including ungauged regions. The approach was implemented using a bounded version of the Kling-Gupta Efficiency metric (KGE& em& & sub& B& /sub& & /em& ) and a gridded version of the HBV hydrological model. Ten-fold cross-validation was used to evaluate the generalizability of the approach and to obtain an ensemble of parameter maps. For the 4229 independent validation catchments, the regionalized parameters yielded a median daily KGE& em& & sub& B& /sub& & /em& of 0.30 (equivalent to a conventional KGE of 0.46). The median KGE& em& & sub& B& /sub& & /em& improvement (relative to uncalibrated parameters) was 0.21, with improvements obtained for 88 % of the independent validation catchments. These scores compare favourably to those from previous large catchment s le studies. The degree of performance improvement due to the regionalized parameters did not depend on climate or topography. Substantial improvements were obtained even for independent validation catchments located far from the catchments used for optimization, underscoring the value of the derived parameters for poorly gauged regions. The regionalized parameters & #8212 available via bv & #8212 should be useful for numerous hydrological applications requiring accurate streamflow simulations.& &
Publisher: American Geophysical Union (AGU)
Date: 2019
DOI: 10.1029/2018WR023855
Abstract: Even in regions considered as densely monitored, most catchments are actually ungauged. Prediction of discharge in ungauged catchments commonly relies on parameter regionalization. While ungauged catchments lack continuous discharge time series, a limited number of observations could still be collected within short field c aigns. Here we analyze the value of such observations for improving parameter regionalization in otherwise ungauged catchments. More specifically, we propose an ensemble modeling approach, where discharge predictions from regionalization with multiple donor catchments are weighted based on the fit between predicted and observed discharge on the dates of the available observations. It was assumed that a total of 3 to 24 observations from a single hydrological year were available as an additional source of information for regionalization. This informed regionalization approach was tested with discharge observations from 10 different hydrological years in a leave‐one‐out cross validation scheme on 579 catchments in the United States using the HBV runoff model. Discharge observations helped to improve the regionalization in up to 94% of the study catchments in 8 out of 10 discharge s ling years. S ling years characterized by exceptionally high peak discharge, or high annual or winter precipitation were less informative for regionalization. In the least informative years, model efficiency increased with an increasing number of observations. In contrast, in the most informative s ling year, 3 discharge observations provided as much information for regionalization as 24 discharge observations. Overall, discharge observations were most effective in informing regionalization in arid catchments, snow‐dominated catchments, and winter‐precipitation‐dominated catchments.
Publisher: Copernicus GmbH
Date: 29-07-2020
DOI: 10.5194/HESS-24-3815-2020
Abstract: Abstract. Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of in idual catchments, and, importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability. Here, we collaborated with Groupe E, a hydroelectricity company operating several reservoirs in the Swiss pre-Alps, and we co-produced hydroclimatic projections tailored to support the upcoming negotiations of their water concession renewal. We started by identifying the vulnerabilities of their activities to climate change together, we then selected streamflow and electricity demand indices to characterize the associated risks and opportunities. We provided Groupe E with figures showing the projected impacts, which were refined over several meetings. The selected indices enabled us to assess a variety of impacts induced by changes in (i) the seasonal water volume distribution, (ii) low flows, (iii) high flows, and (iv) electricity demand. This enabled us to identify key opportunities (e.g., the future increase in reservoir inflow in winter, when electricity prices have historically been high) and risks (e.g., the expected increase in consecutive days of low flows in summer and fall which is likely to make it more difficult to meet residual flow requirements). We highlight that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. This stakeholder-centered approach, which relies on identifying stakeholder's needs and using them to inform the production and visualization of impact projections, is transferable to other climate impact studies, in the field of water resources and beyond.
Publisher: Copernicus GmbH
Date: 22-03-2022
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-1384
Abstract: Rare to very rare floods (associated to return periods of 1'000& #8211 '000 years) can cause extensive human and economic damage. Still, their estimation is limited by the comparatively short streamflow records available. Some of the limitations of commonly used estimation methods can be avoided by using continuous simulation (CS), which considers many simulated meteorological configurations and a conceptual representation of hydrological processes. CS also avoids assumptions about antecedent conditions and their spatial patterns.We present an implementation of CS to estimate rare and very rare floods at multiple sites in a large river basin (19 locations in the Aare River basin, Switzerland area: 17'700& #8201 km& #178 ), using exceedingly long simulations from a hydrometeorological model chain (Viviroli et al., 2022). The model chain consisted of three components: First, the multi-site stochastic weather generator GWEX provided 30 meteorological scenarios (precipitation and temperature) spanning 10'000 years each. Second, these weather generator simulations were used as input for the bucket-type hydrological model HBV, run at an hourly time step for 80 catchments covering the entire Aare River basin. Third, runoff simulations from the in idual catchments were routed for a representation of the entire Aare River system using the routing system model RS Minerve, including a simplified representation of main river channels, major lakes and relevant floodplains. The final simulation outputs spanned about 300'000 years at hourly resolution and cover the Aare River outlet, critical points further upstream as well as the outlets of the hydrological catchments. The comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations were well represented. This implied that meaningful information on floods with low probability can be inferred. Although uncertainties were still considerable, the explicit consideration of important flood generating processes (snow accumulation, snowmelt, soil moisture storage) and routing (bank overflow, lake regulation, lake and floodplain retention) was a substantial advantage compared to common extrapolation of streamflow records.The suggested approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is particularly valuable in a large river basin where the complex interaction of flows from in idual tributaries and lake regulations are typically not well represented in the streamflow records. The framework is also suitable for estimating more common, i.e., more frequently occurring floods.ReferenceViviroli D, Sikorska-Senoner AE, Evin G, Staudinger M, Kauzlaric M, Chardon J, Favre AC, Hingray B, Nicolet G, Raynaud D, Seibert J, Weingartner R, Whealton C, 2022. Comprehensive space-time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin. Natural Hazards and Earth System Sciences, 22(9), 2891& #8211 , doi:10.5194/nhess-22-2891-2022
No related grants have been discovered for Jan Seibert.