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We present CAMELS-CH (Catchment Attributes and MEteorology for Large-sample Studies – Switzerland), a large-sample hydro-meteorological data set for hydrologic Switzerland in central Europe. This domain covers 331 basins within Switzerland and neighboring countries. About one-third of the catchments are located in Austria, France, Germany and Italy. As an Alpine country, Switzerland covers a vast diversity of landscapes, including mountainous environments, karstic regions, and several strongly cultivated regions, along with a wide range of hydrological regimes, i.e., catchments that are glacier-, snow- or rain dominated. Similar to existing data sets, CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes. CAMELS-CH (; available at 10.5281/zenodo.7784632) encompasses 40 years of data between 1 January 1981 and 31 December 2020, including daily time series of stream flow and water levels, and of meteorological data such as precipitation and air temperature. It also includes daily snow water equivalent data for each catchment starting from 2 September 1998. Additionally, we provide annual time series of land cover change and glacier evolution per catchment. The static catchment attributes cover location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers. This Swiss data set complements comparable publicly accessible data sets, providing data from the "water tower of Europe". [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Obtaining discharge predictions in ungauged catchments remains to be challenging. This study addresses this challenge by assuming that it would be possible to take a limited number of discharge measurements in an ungauged catchment. More specifically, it is assumed that a water level sensor is installed during a first field visit and that discharge measurements are occasionally performed during follow-up visits. Here we present a novel approach to decide at which points in time these discharge measurements would be most informative for model calibration. First, a hydrological model is calibrated for the (almost) ungauged catchment using only the continuous water level information. Iteratively, the timing of the next discharge measurements is then chosen using the concept of active learning. Thereby, simulation uncertainty resulting from multiple model parameterizations indicates when additional discharge information is needed most to improve model predictions. We tested this approach for 100 catchments in the United States covering a wide range of hydroclimatic conditions. Results indicate that the prediction of discharge using solely water level information leads to a varying model performance. Including discharge information, which could be collected during a few strategically selected field visits, to the model calibration, can considerably improve the prediction accuracy. Overall, this study demonstrates the combined value of water level information and a few discharge measurements at strategically chosen points in time for discharge predictions in (almost) ungauged basins. [ABSTRACT FROM AUTHOR]

The parameterization of hydrological models in ungauged catchments remains challenging. However, the increased availability of large data sets in recent years provides new opportunities for regionalization. In this study, we use the CAMELS dataset and the HBV model to simulate runoff in nearly 600 catchment areas in the United States. The HBV model was calibrated for all areas, and the calibrated parameter values were related to catchment characteristics. To account for parameter uncertainty, 100 independent calibrations were performed, and then median values and percentile ranges were used for the further analyses. For the simulation of runoff in ungauged catchments, we evaluated the use of regression equations compared to the transfer of complete parameter sets based on similarities between catchments leads to better model simulations. Additionally, we tested the added value of single discharge observations, which we pretended to be available. The regionalization approaches were furthermore benchmarked by runoff predictions using random parameter sets.First results indicated that the use of an ensemble with parameter sets calibrated in one of the other catchments leads to clearly better simulations than the use of randomly selected parameter values. Surprisingly, however, a hydrologically based selection of catchments – and the transfer of the corresponding parameter values – did not lead to better simulations than using the ensemble with parameter sets from all 600 catchments. Overall, this study indicates possibilities and limitations of regionalization with the help of large data sets. [ABSTRACT FROM AUTHOR]