HS2.1.6

Environmental data are critical for understanding and managing ecosystems, including mitigation of degraded water quality. Therefore, we provide the first large-sample water quality data set of riverine water quality combined with water quantity, meteorological and nutrient forcing data, and catchment attributes for Germany in a preprocessed and structured form. The QUADICA data set (water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany) covers 1386 German and transboundary catchments with a large range of hydroclimatic, topographic, geologic, land use and anthropogenic settings. The data set comprises time series of riverine macronutrient concentrations (species of nitrogen, phosphorus and organic carbon), discharge, meteorological and diffuse nitrogen forcing data (nitrogen surplus, atmospheric deposition and fixation). The time series are generally aggregated to an annual basis; however, for 140 stations with long-term water quality and quantity data (more than 20 years), we additionally provide monthly median discharge and nutrient concentrations, flow-normalized concentrations and corresponding mean fluxes as outputs from weighted regressions on time, discharge, and season (WRTDS). The catchment attributes include catchment nutrient inputs from point and diffuse sources and characteristics from topography, hydroclimate, land cover, lithology and soils. QUADICA is a comprehensive, freely available, ready-to-use data set that facilitates large-sample data-driven water quality assessments at catchment scale as well as mechanistic modeling studies. We hope to stimulate the hydrological and water quality communities to provide similar data sets to create novel research opportunities, increase our understanding of catchment functioning, and ultimately improve water quality management.

How to cite: Ebeling, P., Kumar, R., Lutz, S. R., Nguyen, T., Sarrazin, F., Weber, M., Büttner, O., Attinger, S., and Musolff, A.: QUADICA: A large-sample data set of water quality, discharge and catchment attributes for Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5918, https://doi.org/10.5194/egusphere-egu22-5918, 2022.

The recent publication of large-sample datasets for hydrologic modeling and analysis has led to a revival of comparative hydrology. The “CAMELS” branch of these datasets currently provide catchment attributes and meteorological time series for basins located in the United States, Chile, Brazil, Australia and Great-Britain, with a dataset for France under development. A key characteristic of these datasets is that information is provided as catchment-averaged data; i.e. each catchment is treated as a lumped entity with no spatial variability. Some progress is being made to extend large-sample hydrology to include spatially distributed data, most notably by the recent LamaH dataset which covers part of Central Europe.

Here we present progress on developing a continental domain dataset for large-sample hydrology intended for spatially distributed modeling and analysis. Our domain covers the United States and Canada, expanding both geographically and climatically on the region covered by the LamaH dataset. We focus mostly on relatively undisturbed headwater catchments, because accurate data on water management policies and infrastructure can be difficult to obtain. Our aim is to provide the necessary data for process-based modeling and analysis at a sub-daily temporal resolution. 

How to cite: Knoben, W. and Clark, M.: CAMELS-spat: catchment data for spatially distributed large-sample hydrology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6609, https://doi.org/10.5194/egusphere-egu22-6609, 2022.

Many large hydrometeorological datasets have been developed and published in recent years in support of wide applications from physical to machine learning models, and from operations forecasting to prediction in ungauged basins.  The HYSETS database (Arsenault et al. 2019) is one such large-sample dataset featuring numerous physiographic, geologic, and climate attributes associated with over fourteen thousand monitored watersheds in North America and Mexico.  The wide array of geospatial data sources used to extract the many basin attributes described by this dataset, combined with the continental scale of study regions, necessitates the assembly of geospatial data sources with non-uniform properties and the analysis of observations collected by different governing organizations.

In this study, the static basin attribute set derived for the HYSETS database was replicated.  Preliminary results suggest that incorporating updated geospatial data sources such as higher resolution DEM, and the interpretation of basin attribute derivations due to the use of different software packages, can yield distinct estimates of statistical properties of basin attributes with implications for their use as model input data.  At the very least, the preliminary results demonstrate that the greater the size and complexity of a dataset, the greater the likelihood of introducing bias and computational error.

How to cite: Kovacek, D., Eugeni, S., and Weijs, S.: Large Sample, High Dimension Hydrology Dataset Validation: Getting Bit By Bytes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10908, https://doi.org/10.5194/egusphere-egu22-10908, 2022.

Daily streamflow data are crucial for various scientific and operational water resources applications, such as climate change impact assessment, flood forecasting, and catchment classification, among others. Streamflow is typically estimated through the implementation of hydrological models, which rely on parameters to represent hypotheses about the dominant processes in a catchment. In most cases, these parameters cannot be measured at the scales relevant for model applications and are therefore estimated through model calibration. Because most streams worldwide remain ungauged, novel parameter regionalisation techniques have been developed to predict daily streamflow over ungauged catchments. These regionalisation techniques transfer calibrated model parameters from gauged to ungauged catchments. To this end, an accurate spatio-temporal representation of crucial meteorological variables such as precipitation is essential, and therefore, most regionalisation studies have been conducted over regions with a dense network of meteorological stations. However, the characterisation of precipitation over data-scarce areas is challenging and might be subject to large uncertainties when only ground-based measurements are used. Despite that few daily regionalisation studies have used gridded precipitation products, there is no precise evaluation on how the selection of a particular precipitation product can affect the performance of the existing regionalisation techniques. Therefore, this work aims to analyse how the choice of gridded daily precipitation products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. For this purpose, we calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four precipitation products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each precipitation product. Despite differences in the spatio-temporal distribution of precipitation, all products provided good performance during calibration (median KGE's > 0.77), two independent verification periods (median KGE's > 0.70 and 0.61, for near normal and dry conditions, respectively), and regionalisation (median KGE's for the best method ranging from 0.56 to 0.63). We show how model calibration can compensate, to some extent, differences between precipitation forcings by adjusting model parameters and thus the water balance components. Feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that: i) merging precipitation products and ground-based measurements does not necessarily translate into an improved hydrological model performance; ii) a precipitation product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; iii) the spatial resolution of the precipitation products does not substantially affect the regionalisation performance; and iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.

How to cite: Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Mendoza, P. A., McNamara, I., Beck, H. E., Thurner, J., Nauditt, A., Ribbe, L., and Thinh, N. X.: On the selection of precipitation products for the regionalisation of hydrological model parameters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10286, https://doi.org/10.5194/egusphere-egu22-10286, 2022.

Low flow is related to the soil and geological conditions in a basin as well as rainfall, basin scale, and topography. Nonlinearity of runoff was originally described by Horton (1936) as a storage–discharge relationship, which is now used in many hydrological models for various purposes such as water resources planning and the assessment of projections of climate change impact on runoff. The storage–discharge relationship was represented in the form of Q=K^NS^N by Ding (2011). The constant K was already considered in the recession coefficient of groundwater runoff by Ando et al. (1983). The relationship between K and N was indicated as an inversely proportional equation by Fujimura et al. (2016) who carried out a sensitivity analysis. Although the understanding of the storage–discharge equation has been developed, the uncertainties of the parameters have not been resolved. To reduce the uncertainties of the parameters and improve the accuracy of hydrological models, it is important to clarify how natural factors in a basin, such as soil and geology, affect the parameters in the hydrological models. Therefore, we aim to investigate the statistical correlations between the recession constant K and the coverage rates of specific soil types and the geology in a basin.

The nine basins selected for this study are located in mountainous regions in Japan with different topographical, geological, and climatological conditions. The basin areas range from 103 to 332km². Rainfall and runoff data were downloaded from databases of the Water Information System of the Ministry of Land, Infrastructure and Transport and the Automated Meteorological Data Acquisition System (AMeDAS) of the Meteorological Agency, respectively. The specific soil types and geological information (specific geological time and rock formations) of 1:200000 scale were obtained from databases of the Japan soil inventory of the National Agriculture and Food Research Organization (NARO) and of the Seamless Digital Geological Map of Japan of the National Institute of Advanced Industrial Science and Technology (AIST), respectively. The conceptual hydrological model for mountainous basins developed by Fujimura et al. (2011) was applied for a period of more than 15 years at hourly time steps to optimize the recession constant K for each study basin.

The results indicate that the recession coefficient K has correlations and significant differences (significance level alpha of 0.05) with the coverage rates of (a) Brown forest soils (p value of 0.00026), (b) Neogene rock formation (p value of 0.0049), and (c) Andosols / Volcanic rock formation ratio (p value of 0.012). The Andosols formation depends essentially on human activity as well as volcanic ash. The volcanic ash and volcanic rock might have been produced in the same geological time. To show the effect of human activity and other environmental factors, the area of Andosols is divided by the area of volcanic rock.

How to cite: Fujimura, K., Yanagawa, A., and Iseri, Y.: Low–flow parameters in relation to specific soil types and geology through long–term hydrological analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3408, https://doi.org/10.5194/egusphere-egu22-3408, 2022.

Catchment functions (consisting of partition, storage and discharge) are difficult to measure or model, especially considering the wide variety of landscape forms (e.g. plant canopy and soil properties) and variable climate forcing (e.g. precipitation and radiation). After formulating an analytical model to predict the seasonal water balance of the canopy, the root zone, and the saturated zone by using functions of six dimensionless parameters (Woods, 2003, Advances in Water Resources), Woods (2009, Advances in Water Resources) developed a related model for seasonal snowpack dynamics. This presentation will use enhancements of these two simple models to estimate evaporation (E) and changes in water storage (dS/dt) and then the catchment runoff (Q), driven by summary statistics of precipitation (P), temperature and potential evaporation, based on the seasonal water balance (dS/dt= P-E-Q). In this study, we (i) firstly quantify the parameters (e.g. interception capacity relative to rainfall and melt factor) used in this improved model, using an a priori approach; (ii) assess this model in many catchments around the world by using existing global data products; (iv) identify the dominant parameters controlling the water balance; (v) discuss the limitations of this model. As a result, we will find in which situations it is possible to simply and reliably estimate seasonal variation in river flow without flow measurements, and other situations where model refinement is needed. This is important both for improving our understanding of catchment hydrology, and for predicting the seasonal hydrological differences between various hydro-climatic conditions or catchments, especially in locations with sparse measurements.

How to cite: Wang, Z. and Woods, R.: Process-Based Estimates of Seasonal Catchment Hydrology: Dimensionless Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6234, https://doi.org/10.5194/egusphere-egu22-6234, 2022.

Much geomorphic research on rivers focuses on the role of frequent floods (e.g., with return periods between one and two years), which have been shown in many regions to simultaneously perform sufficient geomorphic work as well as occur often enough, so that they tend to determine the shape of the channel. As compared to the Annual Maxima (AM) method, using the Peaks-Over-Threshold (POT) method for flood frequency analysis (also known as the Partial Duration method) allows for the inclusion of a larger number of peak values from the series of past flow observations, resulting in a better estimation of the probabilistic model. As it only considers events above a threshold, POT also decreases the likelihood of incorporating smaller events, when relatively dry years occurred within the period of observations. In the AM method, such smaller events could potentially come from a different population and have an inordinate influence on the predicted floods, introducing variability. Because of all these reasons, the POT approach should result in sounder statistical analyses when predicting frequent floods, with relatively short average recurrence intervals (ARIs). However, much geomorphic research into channel-forming floods has traditionally used AM instead of POT, presumably because it is much easier to obtain annual maxima data and perform frequency analyses when there are as many data as years in the record, while there is subjectivity in choosing an adequate threshold for POT analyses.

In this work, we study the variability in peak flow estimates for frequent (return period < 3 years) events, using both AM and POT, over multiple regions in the US. The objectives are: i) to search for homogeneous hydrological regions where the relation between the two methods is similar, and ii) to study the stability of predictions obtained with the two approaches, when considering different record lengths. The former objective aims at exploring how external factors related to the geographical location and the characteristics of the basin affect discrepancies in the results achieved by the two methods, such as the climate and the size of the basin. The former affects the magnitude and average number of other flooding events, neglected by AM, that occur every year besides the annual maximum. The latter influences the extent to which different types of rainfall events, with different spatial coverages, can involve the watershed. This insight might lay some groundwork for introducing “correction coefficients” for AM-based predictions of relatively frequent floods, depending on the characteristics of the study area. The latter objective is intended to test the stability of the statistical model and check whether POT leads to less variable predictions than AM.

Special care is adopted in two crucial aspects that may introduce bias in the analysis: i) the choice of the case-study gaging stations, in order to minimize any human impact on the studied flow time series, and ii) the methodology for selecting the flood threshold in the POT method, aimed at avoiding subjective decisions.

How to cite: Dell'Aira, F., Cancelliere, A., and Meier, C. I.: Regional Variability in the Performance of Annual Maxima vs. Peaks-Over-Threshold Methods for Predicting Frequent Floods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10853, https://doi.org/10.5194/egusphere-egu22-10853, 2022.

Reservoirs play a vital role in the supply and management of water resources around the world. In Great Britain, reservoir operations are largely determined by the local water company, and there is very little national-scale information available to quantify their impact on river flows. Consequently, large-scale hydrological modelling and data analyses often focus on ‘natural’ or ‘near-natural’ catchments. To support the long-term resilience of water supply and the environment under changing climate and demand, it is essential that we understand where, when and how reservoirs are leading to deviations from a natural flow regime. This will help inform and validate advancements in the large-scale simulation of reservoir-impacted catchments, as well as deepening our understanding of how reservoir operations influence streamflow behaviour.

Due to the age and location of reservoirs across Great Britain, there is a distinct lack of upstream or pre-construction flow timeseries. As a result, we cannot use standard approaches – such as indicators of hydrological alteration- which base analysis on the pre-and-post construction flow regimes. To fill this gap, we define a suite of hydrological signatures and compare observed streamflow timeseries from 1980- 2015 across 166 reservoir catchments and 112 near-natural catchments in Great Britain. We use signatures, characterizing the water balance, flow duration curve and low flow regime, to identify differences in streamflow between these two groups of catchments, and attribute alterations to upstream reservoir operation. We find that gauges with a reservoir upstream are more likely to induce runoff deficits exceeding total PET, and that routine reservoir releases lead to plateaus in the flow duration curve. By defining two new reservoir-based catchment descriptors, our results show that the degree of flow regulation at a gauge depends on the upstream storage capacity and the contributing area of upstream reservoirs. Such descriptors begin to identify thresholds below which the influence of reservoirs is indistinguishable, and help to characterise the extent of reservoir influence across Great Britain.

This analysis highlights groups of reservoir-impacted catchments which cannot be represented by a natural regime. It is in these locations that advancements in large-scale hydrological modelling are crucial for water resource simulation, and that the influence of reservoir operations on the flow regime must be accounted for.

How to cite: Salwey, S., Coxon, G., Pianosi, F., Hutton, C., and Singer, M.: Identifying the impact of reservoirs on the flow regime across Great Britain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4442, https://doi.org/10.5194/egusphere-egu22-4442, 2022.