Suspended sediment routing through a small on-stream reservoir based on particle properties

Purpose A novel concept of suspended sediment (SS) routing through a small reservoir is proposed that relies on the particle properties in the reservoir inflow. Methods The SS routing through the reservoir is described following the single continuous stirred tank reactor concept with only one model parameter, the SS decay coefficient. This parameter is linked to the sediment settling velocity and water flow velocity. Hence, the model does not require a direct calibration with recorded data. This model was tested on a small reservoir in Warsaw, Poland, with seven storm events. Suspended sediment samples at the reservoir inflow and outflow were taken manually during the passage of flood flows at irregular intervals. The performance of the proposed method was verified with the approach when the model parameter is estimated directly from recorded events. Results The parameter calculated based on particle properties was about 10 times higher than the corresponding parameter optimized from recorded SS events. Hence, there was a need to introduce a correction factor to accurately predict the effluent SS. This led to a high model performance for all events (Nash-Sutcliffe = 0.672 on average). Conclusions (i) The proposed SS routing model based on particle properties has been proven to accurately simulate SS in the reservoir outlet. (ii) Thus, the parameter can be estimated from the sediment settling velocity and water flow velocity, but the correction factor must be applied. (iii) Our findings acknowledge difficulties in describing SS routing through small reservoirs and indicate a lack of knowledge on the functioning of these reservoirs

Dependence of flood peaks and volumes in modeled discharge time series: effect of different uncertainty sources

Flood estimates needed for designing efficient and cost-effective flood protection structures are usually derived using observed peak discharges. This approach neglects, firstly, that floods are characterized not only by peak discharge but also by flood volume, and, secondly, that these characteristics are subject to modifications under climate and land use changes. Bivariate flood frequency analysis based on simulated discharge time series makes it possible to consider both flood peak and flood volume in design flood estimation. Further, this approach considers changes in discharge characteristics by using discharge series generated from climate time series used as an input for a hydrological model. Such series are usually not available at an hourly resolution but at a certain aggregation level (e.g. 24 h) and might not perfectly represent observed precipitation distributions. In this study, we therefore investigate how the aggregation and distribution of precipitation series and discharge distribution affect flood peaks and volumes and their dependence. We propose a framework for assessing the uncertainty in bivariate design flood estimates that is caused by different factors in the modeling chain, which consists of precipitation-discharge modeling, flood event sampling, and bivariate flood frequency analysis. The uncertainty sources addressed are precipitation aggregation and distribution, parameter and model uncertainty, and discharge resolution. Our results show that all of these uncertainty sources are relevant for design flood estimation and that the importance of the individual uncertainty sources is catchment dependent. Our results also demonstrate that substantial uncertainty is introduced already in the first step of the model chain because commonly used calibration procedures do not take into account the reproduction of flood volumes. Researchers should be aware of such deficiencies when performing bivariate flood frequency analysis on modeled discharge time series and should aim to tailor model calibration procedures to the problem at hand

Downsizing parameter ensembles for simulations of rare floods

For extreme-flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrological model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrological model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme-flood estimation so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analysing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated thanks to a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of rare floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed

Variability of the initial abstraction ratio in an urban and an agroforested catchment

The Curve Number method is one of the most commonly applied methods to describe the relationship between the direct runoff and storm rainfall depth. Due to its popularity and simplicity, it has been studied extensively. Less attention has been given to the dimensionless initial abstraction ratio, which is crucial for an accurate direct runoff estimation with the Curve Number. This ratio is most often assumed to be equal to 0.20, which was originally proposed by the method’s developers. In this work, storm events recorded in the years 2009–2017 in two small Polish catchments of different land use types (urban and agroforested) were analyzed for variability in the initial abstraction ratio across events, seasons, and land use type. Our results showed that: (i) estimated initial abstraction ratios varied between storm events and seasons, and were most often lower than the original value of 0.20; (ii) for large events, the initial abstraction ratio in the catchment approaches a constant value after the rainfall depth exceeds a certain threshold value. Thus, when using the Soil Conservation Service-Curve Number (SCS-CN) method, the initial abstraction ratio should be locally verified, and the conditions for the application of the suggested value of 0.20 should be established

Long-term changes of hydrological variables in a small Lowland watershed in Central Poland

Climate-induced changes in small watersheds are still not well researched because long-term datasets are usually missing for these watersheds. Such studies can, however, improve our understanding of the watershed response to climatic changes at such a small scale being transparent. In this study, we investigate trends in temperature, precipitation and river-flow characteristics in a small watershed, typical for Central Poland, with 53 years of observations (1963–2015) using the Mann-Kendall test. Particularly, we examine whether any trends in hydro-meteorological variables can be identified, and if any associated changes in water resources in this region can already be observed. We found that this short period already allows for detecting some changes in hydro-meteorological variables. These changes could be characterized by a significant increase in the mean annual air temperature on a daily basis, and a significant decrease in the mean annual discharge on a daily basis and in the minimum annual discharge on a daily basis. Yet, no significant trend could be detected for the total annual precipitation, the maximum summer rainfall, and the maximum annual discharge on a daily basis. These findings indicate that water resources are decreasing in this region, which affects natural habitats, agriculture and local communities

Comprehensive space-time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

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 individual 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

Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

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 individual 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

CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland

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 (Höge et al., 2023; available at https://doi.org/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”

Delineating modelling uncertainty in river flow indicators with representative parameter sets

For mitigating negative effects of floods and droughts, estimates of flow indicators and their uncertainties are essential. The recently introduced concept of the representative parameter sets (RPSs) enables modelling uncertainty to be represented in the flow frequency space at low computational cost, using a small subset of pre-selected model parameter sets. This concept is here adapted to assess hazards of three flow indicators: annual maximal flow, annual 7-day-average low flow, and annual mean flow. An additional in-depth analysis assesses the RPS transferability to other flow indicators and to hydrological signatures. RPS-based simulations are benchmarked with a random selection of parameter sets. The results show that i) RPSs can be successfully transferred between flow indicators with only a small drop in model performance; and ii) RPSs can be used to represent modelling uncertainty in hydrological signatures. The RPS concept has thus great potential for delineating modelling uncertainty of any environmental model