OSA1.3 | The role of operational systems in early warning of drought and its related impacts
The role of operational systems in early warning of drought and its related impacts
Conveners: Patricia Trambauer, Micha Werner, Ilias Pechlivanidis | Co-convener: Frederiek Sperna Weiland
| Thu, 07 Sep, 09:00–10:30 (CEST)|Lecture room B1.03
| Attendance Thu, 07 Sep, 16:00–17:15 (CEST) | Display Wed, 06 Sep, 10:00–Fri, 08 Sep, 13:00|Poster area 'Day room'
Orals |
Thu, 09:00
Thu, 16:00
The session will focus on the importance that operational early warning systems have in monitoring, modelling and forecasting droughts and their related impacts. The severe socio-economic impacts of droughts both in Europe and globally have highlighted the need for reliable and usable predictions of hydro-meteorological droughts including assessment of their impacts across sectors to drive decision and policy making. The establishment of drought monitoring, modelling, forecasting and early warning is also recognised as one of the three pillars of integrated drought management. There are, however, several challenges that hamper the actional drought forecasting, including for instance the limited predictive skill and coarse spatial resolution of raw (seasonal) forecasts to address local user needs, which further set barriers in the user uptake of such early warning systems.
This session will be a cross-cutting one by addressing droughts in relation to the three proposed interconnected program streams: understanding processes, operational systems and bringing benefits to society. It aims to provide a platform for exchanging ideas on the scientific advances as well as the main challenges and opportunities to be addressed in these topics.
The conveners invite papers on various issues associated with:
• Detection of droughts and understanding of their generation mechanisms
• Innovations and challenges in (sub-)seasonal (hydrological) drought modelling and forecasting
• Lessons learnt from action-based operational drought observatory and prediction systems
• Impact-based drought forecasting to inform decision making
• Human centric approaches to the co-creation of drought early warnings
• Communication and visualisation practices of drought predictions and their uncertainties for improved action

Orals: Thu, 7 Sep | Lecture room B1.03

Chairpersons: Patricia Trambauer, Micha Werner
Onsite presentation
Vincent Humphrey, Simone Bircher-Adrot, Adel Imamovic, Christoph Spirig, and Mischa Croci-Maspoli

The intensity and frequency of dry spells in Switzerland have increased in recent years and are likely to intensify in the future. Meanwhile, increases in water use and competition between different actors also place a greater pressure on existing water resources. Because drought has been identified as one of the main risks for various economic sectors in Switzerland, a unified monitoring and forecasting system is to be established through the joint efforts of three different agencies (federal offices for environment, meteorology and climatology, and topography). The project also includes contributions from Swiss research institutions and actively involves stakeholders in its development.

In this contribution, we introduce the Swiss national drought project with a particular focus on in situ and satellite-based monitoring and its integration with long-term forecasts. Current efforts include the creation of a national in situ soil moisture monitoring network with approximately 30 stations, the  development of meteorological and agricultural drought products and indices, as well as the establishment of near real time, downscaled, sub-seasonal forecasts derived from existing systems (local area model, IFS-ENS-EXT). Integrating these highly heterogeneous data streams into seamless products ranging from historical data to sub-seasonal forecasts, all within a consistent climatological baseline, is expected to represent both a major technical challenge but also a significant step forward that will greatly benefit downstream user applications. This meteorological basis will directly feed into impact-relevant drought indices and hydrological models, with the aim of better supporting an early warning system that has to take in consideration the needs of a very diverse user community, such as hydropower production, fluvial navigation, agriculture, forestry, or artificial snow production.

How to cite: Humphrey, V., Bircher-Adrot, S., Imamovic, A., Spirig, C., and Croci-Maspoli, M.: A drought monitoring and forecasting system for Switzerland, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-485, https://doi.org/10.5194/ems2023-485, 2023.

Onsite presentation
Advances and gaps in the science and practice of impact-based forecasting of droughts
Anastasiya Shyrokaya, Florian Pappenberger, Ilias Pechlivanidis, Gabriele Messori, Sina Khatami, Maurizio Mazzoleni, and Giuliano Di Baldassarre
Online presentation
Peter Bissolli, Stefan Rösner, and Maya Körber

The German Meteorological Service (Deutscher Wetterdienst, DWD) leads the climate monitoring activities within the Regional Climate Centre Network of the WMO Regional Association (RA) VI Region (Europe). One of its mandatory functions is the issuance of Climate Watch Advisories (CWAs). These are early warning advisories on weather and climate events in the extended-range-forecast (2-4 weeks), such as heat and cold waves, heavy precipitation periods and drought, all within Europe / RA VI. The advisories are based on expert assessments of climate monitoring and extended forecast results. Users of these advisories are National Meteorological and Hydrological services (NMHSs) in the RA VI Region. It is up to the NMHSs to turn CWAs into tailored national advisories or warnings to their end users.

Drought belongs to the types of events, which are very challenging for early warning. This is due to the various categories of drought (meteorological, hydrological and agricultural drought), its slow onset, its variability in duration in the atmosphere and at various soil depths, its dependency on various parameters (precipitation, temperature, evaporation, soil moisture, heterogenous geographical climate conditions, seasonal cycle), and its variety in impacts in various economical sectors (health, agriculture, energy, forest fires etc.) and different levels of vulnerability from country to country. 

Given the complexity of the drought phenomenon, an early warning approach for Climate Watch Advisories has been chosen, which has some restrictions. The advisories refer to meteorological drought, while hydrological and agricultural aspects are considered in national advisories. For meteorological drought, precipitation totals, anomalies and percentiles seem to be the most useful variables, rather than drought indices. Since the time scale of Climate Watch Advisories is subseasonal, precipitation variability of the past 30 days is taken from monitoring products and for the next 2-4 weeks from extended forecast products.

For the future, the subjective assessment (expert decision after having looked at all available products) will be replaced by an objective approach (fixed criteria are used as input for an automatic process).

In this presentation, some examples of drought advisories will be analysed and the concept of the objective approach will be described.

How to cite: Bissolli, P., Rösner, S., and Körber, M.: Early Warning of Drought: Climate Watch Advisories for the WMO RA VI Region, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-346, https://doi.org/10.5194/ems2023-346, 2023.

Onsite presentation
Andrew Schepen, Don Gaydon, Neal Hughes, and James Bennett

Early warning of drought is critical for national water security and farm business outcomes. The Drought Early Warning Service (DEWS) project is developing indicators to measure and forecast the extent and severity of drought impacts in the Australian water and agricultural sectors. Through a multi-disciplinary approach, long-range climate forecasts are combined with agricultural, hydrological and agro-economic models to generate impact-based drought indicators, translating climate data into specific impacts such as crop yields, pasture growth, farm business outcomes and inflows.

In this work, we develop a high-resolution spatial forecasting system to generate forecasts of rainfall, temperature, solar radiation, vapour pressure and evaporation on a 5km grid across Australia, at a daily time step out to 18 months ahead. Forecasts are derived from the Bureau of Meteorology’s ACCESS-S2 climate model. Forecast calibration and downscaling are implemented using Bayesian joint probability modelling and an empirical disaggregation approach, which seamlessly extends forecasts beyond the 7-month range of the climate model.  The ensemble forecasts are targeted at multiple observational datasets to drive a suite of models: APSIM, AussieGrass, FarmPredict and FoGSS.

The outputs of the models become indicators that will be presented as percentiles for a defined historical reference period. The definition of an appropriate reference period is a challenging problem given significant changes in both temperature and rainfall patterns across Australia over the last century. Drought indicators will be published online via the prototype Climate Services for Agriculture (CSA) platform currently under development. We discuss the impacts of the forecasting system and adoption for other use cases such as hydropower, which require long-range forecasts of inflows to assess sustainability.

How to cite: Schepen, A., Gaydon, D., Hughes, N., and Bennett, J.: Long range impact-based forecasts for drought early warning in Australia, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-398, https://doi.org/10.5194/ems2023-398, 2023.

Onsite presentation
David Horsley, James Bennett, Andrew Schepen, and David Robertson

Many water management agencies rely on stochastic inflow scenarios to plan water operations. For example, Hydro Tasmania, Australia’s largest hydropower generator and water manager, relies on 20+ year inflow scenarios to assess the long-range sustainability of their power generation system. A variety of methods are available for stochastic data generation, but many assume a stationary climate. In locations where inflow has long-term trends, assuming a stationary climate in stochastic data generation is likely to underestimate future wet or dry extremes, in particular for sequences of dry or wet months or years.

To address this issue, we have developed the Trend and Uncertainty in Long Inflow Predictions (TULIP) model. TULIP is a Bayesian model that generates long-range predictions of inflows at the monthly time step. TULIP accounts for:

  • Heteroscedasticity and skew in inflow data by using data transformation with the sinh-arcsinh transformation, and zero values with censoring
  • Spatial correlation between inflow sites
  • Autocorrelation using a first-order autoregressive model
  • Linear trend in inflow
  • Seasonal variation in properties (1)-(4), using Fourier series to control the parameters

TULIP is being implemented operationally by Hydro Tasmania to replace its existing method of generating stochastic scenarios, which assumes a stationary climate. At sites with long-term trends in historical inflow, we show that TULIP produces more reliable long-range predictions than is possible if a stationary climate is assumed. This allows TULIP to produce sharper ensembles and more realistic projections of future drought, allowing Hydro Tasmania to better plan for the long-range sustainability of its system. In this presentation we describe the TULIP model and its performance. We also discuss future plans to incorporate information on inflow trends from global and regional climate models into TULIP.

How to cite: Horsley, D., Bennett, J., Schepen, A., and Robertson, D.: Better accounting of droughts in long-range inflows scenarios with TULIP, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-400, https://doi.org/10.5194/ems2023-400, 2023.

Online presentation
Clara Hauke, Uwe Ulbrich, and Henning Rust

SpreeWasser:N aims at developing strategies to gain a better understanding of the water cycle and associated hydrological extremes in the region Berlin-Brandenburg and create action plans to reduce related future risks. The goal is to develop long-term concepts regarding drought management, integrated water resources management and improved water storage systems together with water users and policy makers to pave the way for a sustainable and interdisciplinary water resource management course.


The predictability of hydrological extreme events is assessed on time scales ranging from near-term to decadal and predictors acting as potential indicators of imminent risk are inferred from statistical analyses, modeling and literature. Climate projections for different emission scenarios for Brandenburg provide a bigger picture of how climate variables will shift in the future and how this will affect the hydrological balance in the region. Ensemble methods are a helpful tool to assist in some of these tasks, including estimating uncertainties for forecasts and projections. Downscaling methods are used with convection-permitting models to provide data which can be used in hydrological models to improve the forecast of hydrological impacts. In conjunction with the project partners drought warning systems and adaptation strategies are developed. A drought forecast based on k-nearest neighbor regression is being developed using an algorithm that automatically selects those meteorological variables and regions yielding the largest forecast skill as input predictor variables during a hindcast period using reanalysis data. This machine learning approach supports the discovery of underlying physical links in atmospheric phenomena. Using weather patterns as an additional predictor variable, connections between certain states of the atmosphere and hydrological extreme weather events can be detected. Another research focus is the succession of certain weather patterns and its impact on precipitation.

How to cite: Hauke, C., Ulbrich, U., and Rust, H.: Prediction and predictability of hydrological extreme events in the region Berlin-Brandenburg for risk assessment in the project SpreeWasser:N, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-542, https://doi.org/10.5194/ems2023-542, 2023.

Posters: Thu, 7 Sep, 16:00–17:15 | Poster area 'Day room'

Display time: Wed, 6 Sep 10:00–Fri, 8 Sep 13:00
Chairpersons: Patricia Trambauer, Micha Werner
Frederiek Sperna Weiland, Patricia Trambauer, and Jan Verkade

Worldwide drought frequencies and intensities are changing, and many countries and river basins are confronted with the impacts of severe droughts leading to food insecurity and drinking water shortage.  Hydrological drought forecasts with long lead-times can be of great value to help reduce societal impacts, especially when they include local sector-specific information.

To provide high-resolution hydrological forecasts with sufficient local detail we enable the automized implementation of high-resolution basin scale hydrological models anywhere in the world instead of implementing one often lower resolution global hydrological model. The underlying distributed hydrological model – wflow_sbm – relies on so-called pedo-transfer functions that translate input base maps such as land use, digital elevation model, river network and soil type to estimate model parameter values. Herewith the model requires little calibration and can be easily implemented for additional river basins. So far, hydrological models have been included for the Paraná, Lempa,  Vardar, Rhine and Niger river basins and drought forecasts are set-up using the ECMWF seasonal forecasts (SEAS5). All has been integrated in the operational forecasting platform Delft-FEWS that facilitates the data handling, hydrological model simulations and statistical analysis.

The forecasting platform facilitates research analysis and developments on amongst others (1) spatial explicit drought forecasting skill, (2) the value of different meteorological and hydrological drought indicators such as the Standardized Precipitation Index (SPI), Standardized Precipitation and Evaporation Index (SPEI), discharge anomalies, soil moisture deficit (SMDI) and evaporation deficit indices (ETDI) and (3) the development of dedicated indicators for specific user groups defined based on their needs.

How to cite: Sperna Weiland, F., Trambauer, P., and Verkade, J.: Worldwide seasonal drought forecasts with local relevance, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-614, https://doi.org/10.5194/ems2023-614, 2023.

Monika Bláhová, Lucie Kudláčková, Milan Fischer, Markéta Poděbradská, and Miroslav Trnka

The occurrence of severe drought episodes, especially changes in their intensity and frequency, is considered one of the major impacts of changing climate. Consequences of these changes manifest on many levels ranging from ecological to societal, through all the drought types (meteorological, hydrological, agricultural, or socioeconomic). In relation to this development, the need to build complex drought early warning, monitoring, predictive, and impact assessment systems is also becoming more important. Even though many such systems are already in place, relying on complex modeling, remote sensing data, or participative data collection, we often lack the possibility of fast, near-real-time, and straightforward drought manifestation mapping with good spatiotemporal coverage. The proposed methodology presented in our study aspires to cover this gap. The presented work aims to develop a method for early warning and identifying drought impacts on vegetation based on automated interpretation of imagery from an RGB camera network. Thanks to the cooperation with Windy.com as the main partner, we are able to access the largest global web camera network with continuous time series of imagery in daily, weekly, and monthly timestep. In the first step, we prepared a training database for automated image categorization to detect vegetation in each camera image. To perform this step, we manually classified over two thousand camera images and defined the presence of vegetation and three subtypes of vegetation – forest, grassland, and agricultural vegetation. Thanks to this training dataset, we are able to categorize any other imagery in the database automatically, thanks to machine learning algorithms. After labeling images, we created time series for cameras focusing on vegetation operating during daylight hours. To lower the computational load, we focused only on vegetated parts of images. Therefore, for each camera, we created a time series and defined masks of green vegetated areas. The mask definition is based on HSV color model transformation, with statistically defined thresholds to detect green color. All image time series are subsequently masked, and the RGB image is normalized. Finally, the RGB greenness index, as (2*G)/(R+B), is computed for each masked vegetation area to assess spatiotemporal changes in greenness in the focus area of each camera. With the ability to monitor continuous camera imagery in weekly timestep, combined with the global scope of the webcam network, the proposed approach brings an operative tool to monitor current vegetation conditions. Thanks to the fully automated processing of real-time images, we are able to cover underrepresented areas and include those in drought and drought impact monitoring networks without bringing additional costs.

How to cite: Bláhová, M., Kudláčková, L., Fischer, M., Poděbradská, M., and Trnka, M.: Automated processing of webcam digital imagery for near-real-time identification of drought impacts on vegetation, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-265, https://doi.org/10.5194/ems2023-265, 2023.