Search

Drought estimates in terms of physically measurable variables such as precipitation deficit or streamflow deficit are key knowledge for an effective water management. How these deficits vary with the drought event severity indicated by commonly used standardized monitoring indices is often unclear. In order to study the variation of deficits with indexed severity, we compare precipitation and streamflow deficits for different index-derived drought characteristics in two case study catchments. More specifically, the analysis relies on a frequency analysis with the total probability theorem to the drought characteristics of a certain drought type. Intensity-duration-frequency (IDF) curves in terms of precipitation and water deficits for the most severe drought of each drought duration in each year are then subject to the comparison of statistical characteristics of droughts for different return periods. Precipitation and streamflow data from two catchments, the Seyhan River (Turkey) and the Kocher River (Germany) provide examples for two climatically and hydrologically different cases. They allow assessing the IDF curve based on differences in drought characteristics between the two variables and between the two catchments. We found that precipitation and streamflow deficits vary systematically reflecting seasonality and amount of precipitation and flow characteristics of the region and catchment. Deficits also change from one month to another at a given station. Higher precipitation deficits were observed in the winter months compared to the summer months in Seyhan. The seasonal variability of precipitation deficit was not that pronounced in Kocher. The maximum streamflow deficits were observed in spring in Seyhan whereas they were observed in winter in Kocher. The deficit-IDF curves converge to an asymptotic value as drought duration increases. The asymptotic value is practically the same for all return periods greater than 25-years, for the two variables and in the two catchments, limiting the meaningfulness of IDF curves for return periods longer than 25 years at long drought durations. The comparison reveals differences in the interpretability of indexed severities in different climatic and hydrological settings. Overall, the IDF curve approach might help end-users such as decision-makers to better understand the drought as an accumulated precipitation or streamflow deficit event., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Hydrological extreme events are generated by different sequences of hydrometeorological drivers, the importance of which may vary within the sample of drought events. Here, we investigate how the importance of different hydrometeorological driver sequences varies by event magnitude using a large sample of catchments in Europe. To do so, we develop an automated classification scheme for streamflow drought events. The classification scheme standardizes a previous qualitative drought typology and assigns events to one of eight drought event types—each characterized by a set of single or compounding drivers—using information about seasonality, precipitation deficits, and snow availability. The objective event classification reveals how drought drivers vary not just in space and by season, but also with event magnitude. Specifically, we show that (a) rainfall deficit droughts and cold snow season droughts are the dominant drought event type in Western Europe and Eastern and Northern Europe, respectively; (b) rainfall deficit and cold snow season droughts are important from autumn to spring while snowmelt and wet‐to‐dry season droughts are important in summer; and (c) moderate droughts are mainly driven by rainfall deficits while severe events are mainly driven by snowmelt deficits in colder climates and by streamflow deficits transitioning from the wet to the dry season in warmer climates. These differences in sequences of drought generation mechanisms for severe and moderate events suggest that future changes in hydrometeorological drivers may affect moderate and severe events differently., Key Points: We develop a standardized and objective classification scheme for streamflow droughts using hydroclimatic information. The most severe drought events are governed by other processes than moderate events. Moderate droughts are dominated by rainfall deficits and severe droughts by snowmelt deficits or prolonged rainfall deficit droughts., Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659, EC/H2020/PRIORITY 'Excellent science'/H2020 European Research Council http://dx.doi.org/10.13039/100010663, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html, https://doi.pangaea.de/10.1594/PANGAEA.887470, https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview, http://www.hydroshare.org/resource/77114d4dfdfd4dd39e0e1d99165f27b3

A limited number of gauging stations, especially for nested catchments, hampers a process understanding of the interaction between streamflow, groundwater and water usage during drought. Non‐commercial measurement devices can help overcome this lack of monitoring, but they need to be thoroughly tested. The Dreisam River in the South‐West of Germany was affected by several hydrological drought events from 2015 to 2020 during which parts of the main stream and tributaries fell dry. Therefore it provided a useful case study area for a flexible longitudinal water quality and quantity monitoring network. Among other measurements the setup employs an image‐based method with QR codes as fiducial marker. In order to assess under which conditions the QR‐code based water level loggers (WLL) deliver data according to scientific standards, we compared its performance to conventional capacitive based WLL. The results from 20 monitoring stations reveal that the riverbed was dry for >50% at several locations and even for >70% at most severely affected locations during July and August 2020, with the north western parts of the catchment being especially concerned. Highly variable longitudinal drying patterns of the stream reaches emerged from the monitoring. The image‐based method was found valuable for identification and validation of zero level occurrences. Nevertheless, a simple image processing approach (based on an automatic thresholding algorithm) did not compensate for errors due to natural conditions and technical setup. Our findings highlight that the complexity of measurement environments is a major challenge when working with image‐based methods., We monitored zero water levels in a meso‐scale catchment with temperate climate by means of image‐based and conventional water level logging techniques. A detailed analysis of the longitudinal drying patterns enables a discussion about hydrological connectivity and the processes influencing the drying., Badenova Fund For Innovation, https://doi.org/10.6094/UNIFR/228702

Data used in the manuscript different drought types and the spatial variability in their hazard, impact, and propagation characteristics. Authors: Tijdeman, E., Blauhut, V., Stoelzle, M., Menzel, L., and Stahl, K. Contact: erik.tijdeman@uni-heidelberg.de Last modified: 09-04-2022 The dataset contains monthly and annual drought hazard information (percentiles) of different variables. The data and methods used to derive the drought hazard information are described in Tijdeman et al. (2022). File description: All data is in one ".csv" file with columns separated by a ";". The structure of the file is: - Column 1,2: Location (x,y) in Gauss-Kruger zone 3 (EPSG:31467) coordinate system. * For grid cells (x,y) refers to the center of the grid cell * For catchments (x,y) refers to the center of the catchment * For groundwater (x,y) refers to the well location - Column 3: NUTS-2 region ID. * 11 = Stuttgart * 12 = Karlsruhe * 13 = Freiburg * 14 = Tubingen. - Column 4: variable. * P1 = Monthly precipitation * P3 = Monthly precipitation aggregated over a 3-month moving window * P12 = Monthly precipitation aggregated over a 12-month moving window * SM = Monthly average soil moisture * T1 = Monthly average temperature * T12 = Annual average temperature for a static window. For consitancy with other data sources, T12 is provided for each month as the same value for all months in certain year. * Q = Monthly average streamflow * GW = Monthly average Groundwater. - Column 5-364: Monthly percentile time series of all variables (column 4) at all locations (column 1,2) between 1990-2019. The column names indicate the month and year (MM-YYYY). Reference: Tijdeman, E., Blauhut, V., Stoelzle, M., Menzel, L., and Stahl, K.: Different drought types and the spatial variability in their hazard, impact, and propagation characteristics, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2021-328, in review, 2021., This work contributes to the DRIeR project supported by the Wassernetzwerk Baden-Württemberg (Water Research Network), which is funded by the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (Ministry of Science, Research, and the Arts of the State Baden-Württemberg) (grant no. AZ. 7532.21/2.1.6). We further acknowledge the DWD and LUBW for providing data. Financial support of the German Research Foundation (DFG) for SDS@hd - Scientific Data Storage is acknowledged. All analyses were carried out with the open-source software R (https://www.r-project.org/).