UP2.5

A simple formula for estimating approximate values of return levels for sub-daily rainfall is presented. It was derived from a combination of simple mathematical principles, approximations and fitted to 10-year return levels taken from intensity-duration-frequency (IDF) curves representing 14 sites in Oslo. The formula has subsequently been evaluated against IDF curves from independent sites elsewhere in Norway. Since it only needs 24 h rain gauge data as input, it can provide approximate estimates for the IDF curves used to describe sub-daily rainfall return levels. In this respect, it can be considered as a means of downscaling regarding the timescale, given an approximate power-law dependency between temporal scales. One clear benefit of this framework is that observational data is far more abundant for 24 hr rain gauge records than for sub-daily measurements. Furthermore, it does not assume stationarity and is well-suited for projecting IDF curves for a future climate. This method also provides a framework that strengthens the connection between climatology and meteorology to hydrology, and can be applied to risk management in terms of flash flooding. The proposed formula can also serve as a 'yardstick' to study how different meteorological phenomena with different timescales influence the local precipitation, such as convection, weather fronts, cyclones, atmospheric rivers, or orographic rainfall. An interesting question is whether the slopes of the IDF curves change as a consequence of climate change and if it is possible to predict how they change. One way to address this question is to apply the framework to simulations by convective-permitting regional climate models that offer a complete representation of both sub-daily and daily precipitation over time and space. 

How to cite: Benestad, R., Lutz, J., Verpe Dyrrdal, A., Haugen, J. E., Parding, K. M., and Dobler, A.: Introduction of a simple formula for estimating approximate intensity-duration-frequency curves from daily rain gauge data, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-127, https://doi.org/10.5194/ems2021-127, 2021.

The prevailing westerly winds provide Europe with an average flow of moisture from the North Atlantic. We document the climatological transport of water vapour across Europe's western border, from the south of the Iberic Peninsula to Northern Scandinavia. Four state-of-the-art reanalyses (NCEP CFSR, JRA 55, MERRA 2 and ERA 5) are confronted to satellite observations (QUIKSCAT and ASCAT) and to radiosoundings (IGRA) from six coastal sites.  Over the datasets' common time range (2000-2011), the agreement is generally good in terms of yearly and monthly fluxes. The satellite products have generally lower values than the rest of the ensemble. Some disparities appear on the vertical. A longer time range (1980-2020) excludes satellite data but allows to detect local increases in moisture transport in the later years.

Beyond the climatological picture, day to day moisture fluxes present significant fluctuations, both in the dry and wet directions. The variability is consistent between reanalyses and radiosondes but slightly weaker in the satellite data. The fluxes on pressure levels are strongly correlated to their vertically integrated counterpart. We take advantage of the vertical coherence to stratify humidity and wind profiles according to quantiles of integrated moisture transport. The respective role of humidity and wind changes becomes apparent. Wind speed and direction determines the moisture transport more than the humidity field on a short term basis. On the scale of decades, there was no discernable change of circulation. It was moistening that drove the increase in water vapour transport. The effect was disproportionate for higher quantiles as a consequence of the non-linear Clausius-Clapeyron equation.

How to cite: Dufour, A., Gulev, S., and Zolina, O.: Upstream of hydroclimate extremes : maxima of moisture transport, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-346, https://doi.org/10.5194/ems2021-346, 2021.

Floods are the most common and disastrous natural hazards and due to climate change and socio-economic growth floods are becoming more destructive. Early warning systems are one of the best ways to decrease the effect of floods by increasing preparedness. However, uncertainties are inevitably introduced throughout streamflow forecasting systems and untreated they can limit the value of the forecasts to end-users. In recent decades several post-processing methods have been introduced and shown to improve forecast skill through bias and spread correction. The European Flood Awareness System, part of the European Commission's Copernicus Emergency Management Service, post-processes its medium-range ensemble flood forecasts at over a thousand stations across Europe where historic and near real-time observations are available. A combination of different techniques, namely the Model Conditional Processor, Ensemble Model Output Statistics, and the Kalman Filter are used to account for hydrological and meteorological uncertainties using recent observations and forecasts. Evaluation of the post-processing method is performed using two years of twice-weekly reforecasts. In general, the skill of the forecasts is improved, but the magnitude of this improvement decreases at longer lead-times as recent observations become less impactful. The improvement from post-processing is found to vary substantially between the stations and the continuous ranked probability skill score is used to investigate the impact of different station characteristics. Low-lying large catchments are shown to have the greatest increase in skill from post-processing whereas small high-elevation catchments are harder to correct. However, it was found that the flood magnitudes observed in the historic record are of greater importance than the length of the record itself for determining the effectiveness of the post-processing method.

How to cite: Matthews, G., Cloke, H., Dance, S., and Prudhomme, C.: Evaluating the post-processing of the European Flood Awareness System’s continental scale streamflow forecasts, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-178, https://doi.org/10.5194/ems2021-178, 2021.

Although Switzerland is not commonly associated with the occurrence of droughts, in recent years, Switzerland has experienced several unprecedented drought events. Considering that many sectors in Switzerland depend heavily on its water resources, hydropower production, navigation and transportation, agriculture, and tourism, it is important to establish a reliable warning system for early drought recognition. Drought forecast at subseasonal timescales, particularly the onset of a drought event, remains a challenge which is linked to the limited skill of subseasonal meteorological forecasts especially in Europe. The goal of this research is to develop a model to produce skillful subseasonal prediction of low-flows in large river basins and water levels of the major lakes in Switzerland. The envisaged methodology combines monthly hydro-meteorological forecast outputs from the hydrological model PREVAH (Precipitation-Runoff-Evapotranspiration HRU model) with machine learning algorithms. An operational setup of PREVAH has been previously implemented for Switzerland with meteorological forcing from 51 ensemble members and 32 days lead time from the operational extended-range prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). Although the PREVAH forecasts are considered semi-idealized (assuming natural flow conditions) and they do not go through an in-depth calibration process, they provide a robust representation of the hydrological processes at the catchment level. The proposed machine learning model is expected to mimic the flow routing mechanism and match PREVAH forecasts from its 300 catchments with measured streamflow and lake level in river basins. The proof-of-concept will focus on the river Aare until the station of Brügg-Aegerten, downstream of the lake of Biel. The findings of this work will highlight the potential of directly linking mesoscale hydro-meteorological forecasts with streamflow and providing subseasonal low-flow predictions in an operational set-up.

How to cite: Chang, A. Y.-Y., Jola, S., Bogner, K., Domeisen, D. I. V., and Zappa, M.: Predicting Subseasonal Hydrological Droughts for Swiss Lakes and Large Rivers – Combining Mesoscale Hydrological EPS and Machine Learning Approaches , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-375, https://doi.org/10.5194/ems2021-375, 2021.

Society is facing a challenge due to climate change. Particularly, there are several areas where droughts will impact economic activities and landscapes, and decisions must be made in order to alleviate these effects. River flow regulation plays a major role in this regard, since it reduces the existing correlation between meteorological and hydrological droughts.
The aim of this work is to investigate the response of hydrological drought to meteorological drought under the influence of reservoir. To this effect, the Guadalquivir River Basin, in the southern Iberian Peninsula, has been studied. The aridity of this basin is expected to increase in the future, with longer and more severe meteorological droughts. Moreover, the Guadalquivir presents a strong regulation along its course. Therefore, streamflow and precipitation data have been analysed. With these data, meteorological and hydrological drought indices, such as the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), and the Standardized Streamflow Index (SSI), have been calculated, focusing on how they correlate based on time scale and spatial distribution. The meteorological drought indices have been calculated in varying time scales, showing that the hydrological response is different depending on characteristics such as orography and river section. The correlation between the indices is generally strong in the study area, but the results show that its importance decreases as the streamflow becomes more regulated.
The results of this study could be added to the current tools for decision making in the economic fields that are most affected by droughts. Since droughts are a major effect of climate change in the area, this study could also act as a first step for the study of future droughts through climate and hydrological models.
Keywords: Drought indices, river regulation, hydrological response.
ACKNOWLEDGEMENTS: This work was funded by the FEDER / Junta de Andalucía - Ministry of Economy and Knowledge / Project [B-RNM-336-UGR18], and by the Spanish Ministry of Economy, Industry and Competitiveness, with additional support from the European Community Funds (FEDER) [CGL2017-89836-R].

How to cite: Romero-Jiménez, E., García-Valdecasas Ojeda, M., Yeste, P., Rosa-Cánovas, J. J., Gámiz-Fortis, S. R., Castro-Díez, Y., and Esteban-Parra, M. J.: Evaluation of the relationship between meteorological and hydrological droughts in the southern Iberian Peninsula, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-354, https://doi.org/10.5194/ems2021-354, 2021.

Current seasonal forecast systems have difficulties predicting temperature over continental regions, whereas for some regions with maritime influence their performance is better. The main driver for better skill in maritime regions is related to the ocean and its memory effect. For continental regions, the land surface can become a more important source of predictability on (sub-)seasonal time scales. Besides soil moisture, snow is a crucial component of the land surface as it stores an extensive amount of water and modulates the earth’s radiation budget each winter season. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture and river water. We compare the snow representation in seasonal forecast systems from four European weather/climate services provided by the Copernicus Climate Change Service (C3S) and their performance in predicting snow, temperature and precipitation. The goal is to identify the impact of the snow initialisation and snow modelling from the four forecasts systems. The first results show that the predicted anomalies of 2m temperature over continental regions correlate with reanalyses only for the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. While the biases among the forecast systems differ, the correlation skills are similar for the same variable, with precipitation having the lowest correlation skills. Furthermore, we will investigate the causal relationships between snow and 2m temperature with time-lagged correlation or similar methods and will consider the whole ensembles of the hindcasts.

How to cite: Risto, D., Fröhlich, K., and Ahrens, B.: Impact of Snow Representation in Seasonal Forecast Systems, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-147, https://doi.org/10.5194/ems2021-147, 2021.