Understanding the propagation of drought from one form to another has become a prime topic of research during recent decades. The majority of research has used a correlation-based approach to study drought propagation; however, such techniques are ineffective in areas with considerable seasonality in precipitation, such as India. Only a few studies have employed an event-based approach to study drought propagation. Moreover, none of the previous studies considered the sequential propagation of drought, starting from meteorological to hydrological drought through agricultural drought. This work aims to analyse drought propagation from meteorological to hydrological drought through agricultural drought using an event-based approach in the Krishna River Basin of India. The Standardised Precipitation Evapotranspiration Index (SPEI) represents meteorological drought, the Standardised Soil Moisture Index (SSMI) represents agricultural drought, and the Standardized Streamflow Index (SSI) represents hydrological drought is estimated at a 1-month timescale at sub-basin scale. The precipitation and temperature data are procured from the India Meteorological Department (IMD) Pune, the soil moisture data is obtained from the European Space Agency (ESA) Climate Change Initiative (CCI) v03.3, and the streamflow data is downloaded from India-WRIS. Two different cases of drought propagation are analysed: meteorological to agricultural drought (SPEI-SSMI) and agricultural to hydrological drought (SSMI-SSI). Propagation of drought is quantified through the estimation of three-time matrices: (1) the time difference between the initiation of droughts, (2) the time difference between the peak of droughts, and (3) the time difference between the termination of droughts. The results from the study revealed that the SSMI drought was initiated after 6.4 months of the SPEI drought, while the SSI drought was initiated after 8.4 months of the SSMI drought. The peak of SSMI drought is found to be after 6.3 months of the peak of SPEI drought, while the peak of SSI drought is found to be after 34.7 months of the peak of SSMI drought. Once the SPEI drought terminates, it lasts for 8.3 months for the SSMI drought to terminate, while after the SSMI drought terminates, it lasts for 30.7 months for the SSI drought to terminate. Thus, it was found that the propagation of drought from SPEI-SSMI is faster than the propagation of drought from SSMI-SSI. The present work will provide essential information on drought propagation, which will be helpful in the management and mitigation of droughts in India. 

Keywords: Drought Propagation, Propagation Time, SPEI, SSMI, SSI.

How to cite: Gupta, A., Jain, M. K., and Pandey, R. P.: Drought propagation from meteorological to hydrological drought in the Krishna River Basin of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2685, https://doi.org/10.5194/egusphere-egu24-2685, 2024.

As droughts propagate both in time and space, their impacts increase because of changes in drought properties. Even though drought propagation has two dimensions – a temporal and spatial one – these are mostly studied separately, which neglects that the propagation of droughts through the hydrological cycle may extend from local to spatial characteristics. Therefore, it is yet unknown how the spatial extent and connectedness of droughts change as droughts propagate from the atmosphere to and through the hydrosphere.
In this study, we assess not only how local meteorological droughts propagate through the hydrological cycle to streamflow and groundwater but also how drought spatial extent and connectedness change with drought propagation. To do so, we use a large-sample dataset of 70 catchments in the Central Alps for which both observed streamflow and groundwater data are available.
We show that drought propagation from the atmosphere to the hydrosphere affects both local and spatial drought characteristics and leads to longer, delayed, and fewer droughts with larger spatial extents. 75% of the precipitation droughts propagate to P-ET or further, 20% to streamflow, and only 10% to groundwater. Of the streamflow droughts, 40% propagate to groundwater but 60% do not propagate.  Drought extent and connectedness increase during drought propagation from precipitation to streamflow thanks to synchronizing effects of the land-surface such as widespread soil moisture deficits but decrease again for groundwater because of sub-surface heterogeneity. These findings have implications for drought prediction and management. They suggest a partial predictability of streamflow and groundwater droughts by atmospheric and hydrological deficits and that large scale streamflow deficits may be partly compensated by groundwater, which shows less frequent and spatially extensive droughts than streamflow.

How to cite: Brunner, M. I. and Chartier-Rescan, C.: Drought duration and spatial dependence increase during propagation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2075, https://doi.org/10.5194/egusphere-egu24-2075, 2024.

The GRaCCE project (Groundwater Recharge and Climate Change Effects - Quantification of resilience of water resources in carbonate aquifers to drought conditions) aims to develop process-based integrated and data-driven surrogate methods for determining groundwater recharge and predicting droughts in order to support water management in semi-arid regions such as Israel, Palestine and Jordan. Previous studies have shown that the thick vadose zones (several hundred meters) prevalent in the region can be relevant for water management as long-term reservoirs and, if considered as a dynamic water resource, can contribute to mitigating supply shortages during long-term droughts. In order to evaluate this water resource, it is necessary to characterize and monitor the moisture distribution in the vadose zone. In principle, borehole- and surface-based geophysical methods as well as remote sensing data can be used for this purpose. In order to assess the possibilities of the various methods for the specific site conditions of the Western Mountain Aquifer, the water balance of the area was investigated for the period from 1950 to 2020 using a double permeability variably saturated HydroGeoSphere model. Moreover, the distribution of soil moisture content at four intervals up to a cumulative depth of two-meter was inspected utilizing FLDAS2 NASA daily dataset. The temporal development of vertical moisture profiles was extracted from the HydroGeoSphere and FLDAS2 models for some selected locations. The profiles show a strong “intra-annual variation” at soil level which is strongly dampened by a depth two meter. This variability is generally not observed in higher depth profiles, as generated by HydroGeoSphere, where the shift from wet to dry periods made some “inter-annual variation” of moisture content. This result further supports the former studies claiming the importance of vadose zone on regulation of drought periods at aquifer level. Moreover, based on this assessment, a site-specific initial assessment of the suitability of the measurement methods such as cosmic ray neutron sensing, ground penetrating radar, resistivity measurements, nuclear magnetic resonance and remote sensing was carried out.

How to cite: Dietrich, P., Maier, U., Kavousi, A., Rieß, A., Engelhardt, I., and Sauter, M.: Evaluation of opportunities to characterize and monitor moisture in the unsaturated zone above the Western Mountain Aquifer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17245, https://doi.org/10.5194/egusphere-egu24-17245, 2024.

Drought is a complex phenomenon that cannot be easily detected in its early stages and advances slowly, but cumulatively. Its consequences can be short-term, such as water deficiency in rivers and dams, and long-term like saltwater intrusion and ecosystem degradation. These impacts make agricultural productivity vulnerable, exacerbate waterborne diseases and increase the risk or wildfires, posing a threat to food security, safety and sovereignty. Cyprus, characterized by a semi-arid climate, experienced in recent years prolonged and frequent droughts that had multiple impacts on agricultural production and consequently the ecosystem and the economy. In the face of a changing climate and increased frequency of droughts, monitoring and understanding such phenomena is crucial in mitigating their impacts. Our overall goal is to investigate the relationships between climatic trends, reservoir fluctuations and vegetation dynamics over the study period. Therefore, we provide a comprehensive analysis of the previously mentioned relationships for the hydrological region of Paphos, (Cyprus) for the period between 2013 and 2023. Vegetated areas are extracted using the European Space Agency WorldCover tree cover, shrubland, grassland, and cropland land cover classes. The study integrates measurements at meteorological stations and satellite-derived time series to assess the relationship between climatic variables and vegetation processes. In particular, we compare the Standardized Precipitation Index (SPI) calculated using CHIRPS data (Climate Hazards Group InfraRed Precipitation with Station Data), with hydrological drought indices provided by the Water Development Department. The latter are estimated on the basis of a drought indicator system that utilizes monthly dam Inflows and mean daily flows of hydrometric stations. Additionally, we analyze spatial climatic variables (such as the MODIS Land Surface Temperature and Evapotranspiration) and vegetation indices (such as MODIS and Sentinel-2 Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Green Chlorophyll Index). Preliminary results show that vegetation dynamics and drought patterns vary based on seasons and the studied land cover classes. The findings of our study are anticipated to contribute to sustainable land and water resources management in the Paphos region.

Acknowledgements

The authors acknowledge the ‘GreenCarbonCY’: Transitioning to Green agriculture by assessing and mitigating Carbon emissions from agricultural soils in Cyprus. The ‘GreenCarbonCy project has received funding from the European Union - Next Generation, the Recovery and Resilience Plan “Cyprus_tomorrow”, and the Research & Innovation Foundation of Cyprus under the Restart 2016-2020 Program with contract number CODEVELOP-GT/0322/0023.

How to cite: Loulli, E., Varvaris, I., Eliades, M., Papoutsa, C., and Tzouvaras, M.: Exploring the Interplay of Climatic Trends, Reservoir Fluctuations, and Vegetation Dynamics in Paphos, Cyprus: A Decade-Long Study Towards Sustainable Resource Management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15433, https://doi.org/10.5194/egusphere-egu24-15433, 2024.

Can CMIP6 decadal projections be effective for multi-decadal water resources planning? This is the underlying question that motivates the present research, investigating what are the key deficiencies that limit their direct use for applications, and whether cleverly formulated mathematical alternatives can be used as effective postprocessors. This study focuses on the development of a robust framework for predicting droughts over interannual to decadal scales to enhance water resource management. The proposed framework utilizes the Wavelet System Prediction (WASP) methodology, which refines the spectral attributes inherent to climate indices to improve the skill of drought forecasts. Further improvement in forecasting capability is achieved through the Hierarchical Linear Combination (HLC) logic, which incorporates forecasts from ten climate indices. These indices, including ENSO-related sea surface temperature anomalies and other climate drivers closely linked to Australian rainfall, are derived from decadal predictions of the Decadal Climate Prediction Project (DCPP). The results of projected drought indices across various scales in Australia demonstrate the substantial potential of the integrated HLC-WASP framework to significantly improve the forecast skills of medium to long-term drought scenarios. This advancement enables the water industry to adapt their strategic plans and optimize reservoir operations effectively. By providing more reliable near-term projections of water availability, this research contributes to effective water resource management, facilitating informed decision-making for water allocation and conservation initiatives.

How to cite: Jiang, Z., Kibria, G., and Sharma, A.: Navigating Water Resource Management: A Forecasting Framework for Interannual Drought Projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13279, https://doi.org/10.5194/egusphere-egu24-13279, 2024.

Droughts and heatwaves pose serious challenges for water management and severely increase water scarcity in many regions of the world. It is increasingly recognized that water scarcity represents more than just a physical lack of water, referring to the imbalance between the supply and the demand of water of suitable quality for different uses. Changes in both climate and socioeconomic systems influence the availability, use and quality of water resources. Water scarcity thus amplifies when either one or more of the following three driving mechanisms intensify: 1) decreasing water availability; 2) increasing sectoral water use, and 3) deterioration of water quality resulting in unsuitability for use. Droughts and heatwaves are particularly critical as they adversely affect all three driving mechanisms, which are also highly interrelated¹. However, limited understanding exists regarding the complex interplay, particularly between water quality and sectoral water use. Here we show responses in sectoral water use and water quality under droughts and heatwaves based on reported data for 1980-2019 globally and discuss a global assessment framework to unravel water scarcity and its drivers under these hydroclimatic extremes.

Our results show that heatwaves and compound drought-heatwave events increase water use mainly for domestic and irrigation water use sectors². River water quality tends to deteriorate during droughts and heatwaves in most cases as demonstrated based on a global literature survey³ and analyses of river water quality records of 314,046 water quality monitoring stations globally⁴. This showed for instance on average a 17% decrease in dissolved oxygen and 24% increase in river salinity under droughts and heatwaves over 1980-2019 globally⁴. Increasing sectoral water use, deterioration of water quality and decreasing water availability each amplify water scarcity in their own right, but more so together due to important interactions. For instance, a decline in water availability during a drought increases water scarcity directly, but also indirectly as less water is available to dilute pollutants, thereby leading to a deterioration of water quality^3,4. This may result in higher water scarcity, when water quality thresholds for certain uses are temporary exceeded (e.g., increased salinity for irrigation). Increases in sectoral water use, such as for domestic use and irrigation², result in higher water scarcity directly, but also indirectly due to water quality impacts. We propose a new integrated modelling framework building on the PCR-GLOBWB2 hydrological model coupled to the DynQual global surface water quality model⁵ to quantify water scarcity under droughts and heatwaves. Here we consider the two-way interactions between sectoral water use, water quality and water availability to improve understanding of the complex interplay between these water scarcity drivers, and test solutions options towards sustainable water management.

¹ van Vliet, M.T.H. (2023) Nature Water 1, 902–904

² Cárdenas Belleza, G.A., M.F.P. Bierkens, M.T.H. van Vliet (2023) Environ. Res. Lett. 18 104008

³ van Vliet, M.T.H. et al (2023) Nature Reviews Earth Environ. 4, 687–702

⁴ Graham D.J., M.F.P. Bierkens, M.T.H. van Vliet (2024), J. of Hydrology 629, 130590

⁵ Jones, E.R., M.F.P. Bierkens, N. Wanders, E.H. Sutanudjaja, L.P.H. van Beek, M.T.H. van Vliet (2023) Geosci. Model Dev. 16, 4481–4500

How to cite: van Vliet, M., Cardenas Belleza, G., Graham, D., and Jones, E.: Water scarcity under droughts and heatwaves: understanding the complex interplay of water quality and sectoral water use, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10512, https://doi.org/10.5194/egusphere-egu24-10512, 2024.

Agricultural drought threatens global water security, food security, and natural ecosystems. Accurate identification of agricultural drought is a crucial task to mitigate its consequences. However, it is challenging to achieve reliable and accurate regional agricultural drought assessment in both wet and dry climates at the same time. Therefore, the objective of this study is to identify a reliable and accurate agricultural drought index that performs well in both dry and wet climates. Drought indices such as the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), the Soil Moisture Anomaly index (SMA), and the Drought Severity Index (DSI) were calculated and compared against in situ drought information devised by official sources in China. The results showed that: (1) DSI based on the Global Land Data Assimilation System (GLDAS) products performed the best in identifying agricultural drought in both dry and wet climate regions of China. (2) Agricultural regions such as Northern arid and semiarid regions, Northeast China Plain, Huang-Huai-Hai Plain, and Loess Plateau, experienced moderate and severe agricultural droughts with a frequency of 20%. (3) The frequency of agricultural droughts observed in Northern arid and semi-arid regions and Northeast China Plain has slowed significantly over the last two decades with a significance level of 0.01. On the other hand, the number of agricultural droughts has increased in Yunnan-Guizhou Plateau since 2002.

How to cite: Pan, Y. and Xu, H.: Accuracy of agricultural drought indices and analysis of agricultural drought characteristics in China between 2000 and 2019, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12858, https://doi.org/10.5194/egusphere-egu24-12858, 2024.

Drought is a complex natural hazard involving multiple variables that, depending on the measured parameters, can be categorized into meteorological, hydrological or agricultural drought. Among them, agricultural drought, which refers to soil moisture deficits that fail to meet crop growth, has been attracting more attention for severely threatening food security worldwide. In the context of climate change and the increased occurrence of drought events, it is crucial to monitor drought drivers and progression to plan the subsequent efforts in drought prevention, adaptation, and migration. However, the comprehensive knowledge of agriculture drought still needs to be clarified. Previous works often focused on precipitation or evapotranspiration and failed to capture other potential drivers of drought. This study proposes a novel framework to comprehensively monitor agricultural drought with ensemble machine learning by constructing an integrated agriculture drought index with high temporal-spatial resolution. In addition, the Shapley Additive Explanation (SHAP) explainable model was applied to reveal the driving mechanism behind the drought event that occurred in northern Italy in the summer of 2022. Results indicate that the proposed explainable ensemble machine learning model could effectively reflect the evolution of agricultural drought with spatially continuous maps on a weekly scale. The SHAP analysis demonstrated that the severe agricultural drought in the summer of 2022 was closely related to meteorological indicators, namely precipitation and land surface temperature, crucial in controlling soil moisture. Moreover, the new findings also revealed that soil textures could significantly affect agricultural drought. By combining explainable ensemble machine learning and various earth-observation data involving meteorology, soil, geomorphology, and vegetation conditions, the study constructed an integrated index to monitor and assess agricultural drought in northern Italy. The proposed research framework could effectively contribute to improving the methodology in agricultural drought research, potentially bringing more instructive insights for drought prevention and mitigation.

How to cite: Xue, C., Ghirardelli, A., Chen, J., and Tarolli, P.: Explainable machine learning revealing the mechanism behind drought events in northern Italy: the case of the 2022 drought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10886, https://doi.org/10.5194/egusphere-egu24-10886, 2024.

An improved forecast of low flow events in catchment basins could be a valuable tool for the operation and decision making of dependent infrastructure (e.g. wastewater discharge, water abstraction) along corresponding rivers. Therefore, the classification of 6642 independent low-flow-events (being the Q347 as the discharge less than the 95%- exceedance quantile of the FDC) from 55 catchment basins within the Kanton Solothurn (Switzerland) was performed by five different machine learning algorithms (i.e. knn, decision tree, random forest, support vector machine, logistic regression). Herein, each low flow event was characterized by 47 static and dynamic parameters (i.e. description of catchment and event history), being supplemented by differently defined (near) non-low-flow events, leading up to a total population of approx. 18000 discharge events.

The validation and verification showed different qualities of the classification accuracy for the forecast of low-flow events, being dependent on the selection of the defined event populations, the selected machine learning algorithm and the definition of classes. In general, the support vector machine and random forest may lead, with the presumption of carefully selected classes, to forecast accuracies of >90%.

How to cite: Lebrenz, H., Pavia, D., and Staufer, P.: Identification of low flow events by machine learning algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4094, https://doi.org/10.5194/egusphere-egu24-4094, 2024.

Many developing countries strongly depend on agriculture, but the sector is challenged by the increasing occurrence of droughts.  Unfortunately, advanced agricultural drought monitoring that can trigger early warning and early action is still not widely available for many countries even though it is crucial to stakeholders including local and regional governments, NGOs, farmers, and vulnerable households. Classic drought monitoring tools often rely on precipitation data, which are influenced by the density of station data. Recently, satellite soil moisture data has gained interest, because of its direct link to plant available water content and the increased availability and quality of satellite soil moisture products over remote regions.  Furthermore, when using radar observations, such as those from Sentinel-1 and Metop ASCAT spatial resolutions up to kilometers can be achieved and information on spatial variability of drought within districts can be provided. Despite advancements in the development of satellite soil moisture products, there remains a significant gap in their adoption and utilization by stakeholders in drought monitoring tools and operational systems. Although a large number of drought indicators are available (Vreugdenhil. et al. 2022), they lack rigorous quality-control with impact data and are not analysis-ready. In addition, users are not familiar with the data or its benefits and have difficulties interpreting the indicators in the context of operational decision-support. 

This study will demonstrate the potential of satellite soil moisture for drought monitoring and yield prediction over Eastern Africa, highlighting strengths and weaknesses of satellite soil moisture. Particularly during the growing season, high correlations are found between different soil moisture products from H SAF Metop ASCAT, ESA CCI and ERA5-Land. During the dry season deviations occur due to subsurface scattering effects on the soil moisture signal.  When analyzing droughts, the onset, intensity and duration of droughts differ strongly with the different indicators. For example, for the Gaza region in Mozambique, severe to extreme drought conditions occurred for 1, 4 or 47 months within a 15 year period depending on the chosen drought indicator.  The impact of using different drought indicators and thresholds on drought severity classification creates challenges for integrating satellite soil moisture drought indicators in operational systems and parametric drought insurance. 

This research is funded by the Austrian Space Application Programme ROSSIHNI project : Remote Sensing and Social Interest for Humanitarian Insights.

Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Camici, S., Dorigo, W., Enenkel, M., van der Schalie, R., Steele-Dunne, S., Wagner, W., 2022. Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges. Frontiers in Water 4.

How to cite: Vreugdenhil, M., Massart, S., Muguda Sanjeevamurthy, P., Villegas-Lituma, C., Enenkel, M., and Wagner, W.: Drought monitoring and early warning with satellite soil moisture data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19536, https://doi.org/10.5194/egusphere-egu24-19536, 2024.

The pace of Earth’s climate warming obviously sped up, especially after 2000s. Droughts are increasingly becoming  frequent, longer and more severe, with lasting impacts on ecosystems, communities and people. Thus, addressing the problem of monitoring global (or regional) climate trends and  water storage changes is crucial. We propose a novel Multivariate Drought Severity Index (MDSI) estimated through the concept of Frank copulas that is based on DSIs determined from satellite-based geodetic data. The new multivariate approach is based on data provided by the Global Positioning System (GPS) and the Gravity Recovery and Climate Experiment (GRACE).

In this study, we analyze short-term (<9 months) signals of monthly-resampled vertical displacements for 25 GPS stations that are classified as benchmarks for hydrogeodesy within Amazon river basin. We show that despite GPS and/or GRACE limitations arising in data products or their quality, the GPS- and GRACE-based DSIs are characterized with a general coherent spatial pattern to the traditional climate indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). Moreover, GPS- and GRACE-based DSIs are capable of capturing extreme hydrometeorological events reported for the Amazon basin. However, DSI variations from GPS and GRACE do not always reflect real hydrological changes as they could sometimes under- or overestimate them. Our analyses show that the newly proposed MDSI is a step towards strengthening  the credibility of combined GPS and GRACE data in drought assessment to improve  understanding of climate change impact on freshwater. We demonstrate that the MDSI recognizes the exact number of events, or one event less than index chosen as the most reliable for over 90% of selected stations. We notice that MDSI series are temporally consistent with extreme precipitation values. The wet and dry periods captured by MDSI are related with precipitation anomalies over 400 mm/month and below 100 mm/month, respectively.

How to cite: Lenczuk, A., Ndehedehe, C., Klos, A., and Bogusz, J.: A novel multivariate drought severity index: study of short-term hydrological signals within Amazon river basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2560, https://doi.org/10.5194/egusphere-egu24-2560, 2024.

The challenges of climate change and water scarcity in Morocco highlight the need for Remote Sensing (RS) and Machine Learning (ML) for drought monitoring. Droughts pose socio-economic and environmental challenges and have significant impacts on the country's agriculture-based economy and water management strategies. This study provides a comprehensive review of advanced RS technologies and ML algorithms, with a focus on their effectiveness in monitoring and forecasting drought conditions. RS provides extensive spatial coverage and captures important data on factors such as vegetation health, soil moisture, and precipitation trends, which are crucial for early detection and response to droughts. Incorporating ML algorithms significantly improves the precision and efficiency of drought prediction models, aiding in the development of comprehensive drought indices and forecasting models for agricultural planning and effective water resource management.

The study evaluates various RS methods utilized in Morocco, including the analysis of satellite imagery and vegetation indices such as NDVI, and assesses ML techniques like support vector machines (SVM) and artificial neural networks (ANN) for predicting drought-induced agricultural impacts. The combined use of these technologies provides a holistic approach to drought monitoring, enabling timely interventions to assist communities affected by drought. However, the study also highlights challenges in areas such as data availability, model validation, and associated costs. To effectively manage drought risks, the paper recommends that Moroccan policymakers and stakeholders leverage these technological advancements while emphasizing the importance of continuing research, interdisciplinary collaboration, and capacity building in these areas.

Key words: Drought,remote sensing, machine learning, climate change, Morocco

How to cite: El Goumi, S., Namous, M., Elaloui, A., Krimissa, S., and El-alaouy, N.: Exploring Drought Monitoring in Morocco: A Review of Remote Sensing and Machine Learning Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-731, https://doi.org/10.5194/egusphere-egu24-731, 2024.

In recent decades, the phenomenon of drought has become a hazard with increasing frequency, with multiple societal and environmental impacts. One of these impacts concerns water resources and their availability for various uses. Numerous indices have been used over time to quantify the severity of drought and to assess its effects on socio-economic activities and environmental components. Among the spatial indices, the most numerous belong to remote sensing, being easy to use to analyse drought consequences especially on landcover and vegetation. However, regarding the hydrological drought, the indices used are mainly calculated based on hydroclimatic data, without taking into account spatial variables, such as the topographical, geological or pedological characteristics. The aim for this study is to compute a hydrological drought index which integrates several drought control variables, using both GIS and remote sensing techniques in order to map the susceptibility to hydrological drought within the Teleorman watershed.

Located in the central-southern part of Romania, the Teleorman River has a length of 169 km and a catchment area of 1.427 km². The most part of  the catchment overlaps the central sector of the Romanian Plain, an important agricultural area, highly sensitive to water deficit. According to Köppen-Geiger classification, the analyzed catchment has a humid continental climate with hot summers (Dfa), meaning that the drought could occur in the basin.

A series of free data and information sources has been accessed in order to compute the hydrological drought index, such as: Worldclim, Landsat Archive, Geological Map of Romania, Pedological Map of Romania, Shuttle Radar Topography Mission (SRTM), Topographical Map of Romania. The following parameters were derived from these sources: Topograhic Wetness Index (TWI); Drainage Density (resulted from hydrographic network); Normalized Difference Drough Index (NDDI), resulted from ratio between Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI); Temperature Condition Index (TCI), extracted from Land Surface Temperature (LST); Aridity Index (AI) computed as a ratio between Precipitation and Potential Evapotranspiration (PET); De Martonne Index (based on ratio between Precipitation and Air temperature); Lithology and Soil Texture. Because some of the parameters had a different spatial resolution, the regridding method was used to bring the database to a resolution of 30 meters. Analytic hierarchy process method (AHP) was used to determine the influence of each factor and for the bonitation process. Based on the total obtained score, 5 classes from to lowest to highest hydrological drought susceptibility resulted. Finally, the Weighted Overlay and Raster Calculator tools from ArcGisPro software were used to map the index.

The resulted map allows the identification in the studied watershed of areas the most susceptible to hydroclimatic drought allowing the focus in these areas of appropriate actions to improve drought risk management. GIS and Remote sensing proved to be useful tools in spatial analysis of drought based on a composite index integrating several drought control factors. In the future, we intend to improve the method by considering other variables controlling the hydrological drought, such as the streamflow and the groundwater depth.

How to cite: Costache, M.-Ș. and Zaharia, L.: Mapping the susceptibility to hydrological drought using GIS and remote sensing techniques in the Teleorman watershed (Romania), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3408, https://doi.org/10.5194/egusphere-egu24-3408, 2024.

Droughts are characterized by periods of below-average precipitation leading to an imbalance in the hydrological cycle and reduced water availability.
In the last decades, higher average temperatures and shifts in annual rainfall patterns have increased the frequency, intensity, and length of droughts across the globe.

With the majority of its population living in rural areas and a high economic dependency on rain-fed agriculture, Mozambique is particularly vulnerable to droughts, as water shortages have devastating environmental, agricultural, and economic impacts. Therefore, monitoring droughts in Mozambique is key to developing early warning systems and adequate planning for drought impact mitigation.

In this study, we propose a novel approach to retrieve a drought index at a kilometer-scale resolution based on surface soil moisture (SSM) products derived from Sentinel-1 (S1) and ASCAT. First, both SSM products are processed over the Mozambican region using a change detection method (Sentinel-1 sampled at 1km and ASCAT at 6.25km) and compared to SSM from ERA5-Land. Then, by combining the long-term ASCAT data record with the high spatial resolution of Sentinel-1, we generate a monthly kilometer-scale drought index for the period 2016 to 2023 over six study areas located in South-central Mozambique (Chokwé, Mabote, Massinga, Buzi, Muanza and Govuro). The S1-ASCAT indicator is then evaluated against state-of-the-art drought indices based on precipitation data (Standardised precipitation index from CHIRPS (Rainfall Estimates from Rain Gauge and Satellite Observations)) and vegetation data (Normalized difference vegetation index from the Copernicus Global Land Service.

This study explores the potential of high-resolution SSM based on active microwave remote sensing to monitor agricultural droughts. Our results show that a drought indicator based on Sentinel-1 and ASCAT can temporally and spatially capture sub-regional drought patterns over Mozambique.

How to cite: Massart, S., Vreugdenhil, M., Hahn, S., Muguda Sanjeevamurthy, P., Villegas-Lituma, C., and Wagner, W.: High-resolution drought monitoring with Sentinel-1: A case-study over Mozambique, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5291, https://doi.org/10.5194/egusphere-egu24-5291, 2024.

Droughts, with their far-reaching and detrimental effects across multiple domains, remain critical climate events that demand better monitoring and early warning capabilities. Regions that are highly dependent on water supply for agriculture, such as the Mediterranean, are vitally dependent on timely monitoring of drought conditions. The crop losses in the region as a consequence of drought events continue to rise. Simultaneously, climate models agree regarding the exacerbation of drought following global warming in the region. Therefore, better understanding and monitoring of drought occurrence is imperative to mitigate drought adverse effects and improve water resource management in the region and beyond.
Operational drought monitoring, whether based on models or observed data, commonly employs a set of drought indices designed to assess anomalies in land or atmosphere dryness. However, these indices are typically available at relatively coarse spatio-temporal scales, rendering them unsuitable for evaluating the local drought impacts that are relevant to agriculture and ecosystems. This limitation does not facilitate decision-making by local authorities and farmers and impedes the straightforward development of on-site adaptation strategies.
In this study, we undertake the assessment and validation of an evaporation-based drought index, the Standardized Evaporation Deficit Index (SEDI: Kim&Rhee 2016, GRL), at an unprecedentedly high resolution (1 km, daily) over the Mediterranean domain. The index is constructed using data of potential and actual evaporation derived using GLEAM (Miralles et al. 2011, HESS), as part of the ESA 4DMED-Hydrology project. Unlike most other drought indices, SEDI is directly related to plant water stress, given the significance of the evaporation deficit for plant hydraulic and physiological processes. Such approach offers the potential to provide early-warning information on ecological and agricultural plant water stress at local scales. Our study of the relationship between SEDI and vegetation stress over seven years (2015–2021) and across 28 Mediterranean river basins, sheds light on critical factors that cause differential stress in crops and natural ecosystems under drought conditions. We also explore the role of irrigation in the SEDI–vegetation stress relationship using 1 km irrigation volumes obtained during the 4DMED-Hydrology project. In the future, the framework will be extended globally, with the subsequent aim to provide valuable information for optimizing irrigation timing in major irrigated breadbasket regions. 

How to cite: Yu. Petrova, I., G. Miralles, D., M. Vicente-Serrano, S., and Massari, C.: Monitoring agricultural drought in the Mediterranean region using a high-resolution (1-km) standardized evaporation deficit index, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1946, https://doi.org/10.5194/egusphere-egu24-1946, 2024.

Lakes play a crucial role in the global hydrological cycle and biogeochemical cycle. In China, lakes are an important part of water resources, providing 40.6% of drinking water. In recent years, droughts in the middle and lower reaches of the Yangtze River in China have led to a significant shrinkage of important freshwater lakes, such as Dongting Lake and Poyang Lake, posing a threat to local water security. However, there has been limited research on the extent to which thousands of lakes across China are affected by droughts. This study used remote sensing product of lake area to comprehensively investigate the impact of drought on the area of 4,702 lakes (natural lakes and reservoirs) in China from 1985 to 2018, covering the three stages of response, shrinkage, and recovery. The results indicate that lakes in China are highly vulnerable to drought. The average response probability of lakes is 72.8%, which typically occurs within six months to two years after the onset of drought. The shrinking area of the lake is 12.7% of the original area, and the shrinking process takes an average of 14 months. Lakes also show a strong resilience to drought, with 95.7% of lakes more likely to experience an increase in area following drought-induced shrinkage. However, only 49.4% of lakes are more likely to grow beyond their pre-shrinkage levels. Compared to natural lakes, artificial reservoirs exhibit a higher response probability by 4.6%, a larger shrinkage area percentage by 1.2%, and a higher recovery probability by 2.9%. Consequently, artificial reservoirs exhibit greater vulnerability and resilience to drought, reflecting the impact of human activities. Furthermore, the spatial distribution of vulnerability and resilience is inconsistent. In Northeast China, including the Songhua and Liaohe river basins, and the Mongolian endorheic basin, lakes exhibit higher vulnerability but lower resilience. Therefore, this region is considered a hotspot where the impact of drought on lake area is particularly severe. This study is expected to provide a basis for the implementation of sustainable water resource management and effective drought mitigation measures in China.

How to cite: Ma, S., Garcia-Garcia, A., Li, X., and Peng, J.: High vulnerability yet strong resilience of China's lakes to drought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5692, https://doi.org/10.5194/egusphere-egu24-5692, 2024.

Droughts pose significant challenges to water resources, agriculture, and socioeconomic stability, particularly in regions susceptible to climatic extremes such as Central Asia (CA) with its complex topography and diverse ecosystem. In the past several years there has been a substantial decrease in water storage in the region which further could lead to socioeconomic instability. Water is mainly used for irrigation and hydropower production in the region.

In CA, the availability of ground observations is restricted, with most of the measurement stations being outdated since the Soviet era with little or no data sharing between the countries. Consequently, the utilization of widely available remotely sensed data proves advantageous in overcoming these limitations and improving the accuracy of water availability assessment in the region.

CA relies predominantly on water resources derived from the melting of snow and glaciers in the Pamir, Tian Shan, and Hindukush mountains. In the study, we consider the two largest upstream river basins, Amu Darya and Naryn, the eastern headstream of Syr Darya. These two largest rivers in CA are crucial sources of water in the region, supporting agriculture and the ecosystem in the whole of CA.

The study specifically focuses on evaluating snow cover and Snow Water Equivalent (SWE) during the winter months, especially preceding the onset of drought periods, and the Total Water Storage (TWS) in the drought months. The objective is to comprehend and quantify the correlation between these climatic elements and historical droughts, utilizing the Drought Severity Index (DSI) and the widely used Standardized Precipitation Index (SPI).  DSI is based on the TWS value that is derived from the GRACE and GRACE-FO satellite missions. It shows a significant decrease in water storage in both basins since the start of the GRACE mission in 2002, with more intense arid conditions in the last 6 years. SPI-6 and SPI-9 based on precipitation and SWE data, show a slight increase in the trend in the Amu Darya basin, while in Naryn all indices show an increase in drought periods. This indicates that the arid conditions in the summer months in the Amu Darya basins are driven by human-induced water depletion. Finally, all indices can depict severe droughts in 2008, 2011 and 2018 in both basins. The study shows the potential of using globally available TWS data for drought assessment on a regional scale such as in CA.

How to cite: Latinovic, M., Selyuzhenok, V., and Gafurov, A.: Analysis of droughts and arid conditions in Central Asia using climate indices, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9170, https://doi.org/10.5194/egusphere-egu24-9170, 2024.

Drought is one of the natural hazard risks that badly affect both agricultural and socio-economic sectors. Hungary, which is located in Eastern Europe, has already been suffering from different drought periods, and the driest year since 1901 was 2011 when the annual precipitation in Hungary was only 72 percent of the normal value. To better understand droughts and to provide information for adaptation strategies and risk-management systems, there is a strong need for a methodological framework to simulate drought events. However, it is uncertain whether climate models can simulate extreme droughts given the well-known model bias of simulating too light rainfall too frequently. So, the aim of the current study is to investigate the effects of the different model settings on the reproduction of drought characteristics.

In order to quantify the impact of the use of different parameterization schemes on regional climate model outputs, hindcast experiments were completed applying RegCM4.7 to the Carpathian region and its surroundings at 10-km horizontal resolution using ERA-Interim reanalysis data as initial and boundary conditions. In this study, we are testing various combinations of the physics schemes (land surface, microphysics, cumulus and boundary layer schemes) for the year 2011. Each parameterization combination leads to different simulated climates, so their spread is an estimate of the model uncertainty arising from the representation of the unresolved phenomena. The analysis of the RegCM-output ensemble indicates systematic precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons.

Based on the results, RegCM is sensitive to the applied convection scheme, but the interactions with the other schemes (e.g., land surface or microphysics) affect the precipitation. Due to the different treatment of moisture in the schemes, there are differences not only between the representation of the precipitation cycle, but also in other climatological variables such as soil moisture, latent and sensible heat fluxes and cloud cover, which affect the drought characteristics.

The research was funded by the NKFIH-471-3/2021 project (the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014).

How to cite: Kalmár, T. and Pongrácz, R.: Parameterization-based uncertainties in RegCM simulations over Hungary in a dry year – a case study , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12456, https://doi.org/10.5194/egusphere-egu24-12456, 2024.

Hydrological drought occurs frequently all over the world and has a great impact on human beings. Hydrological drought attribution contributes to a better understanding of the mechanisms of drought occurrence, improves the accuracy of predictions of drought events, and can provide a basis for drought risk reduction. At present, hydrological models which possess physical mechanisms are widely used in attribution analysis. However, this kind of models is complex in calculation, and has very limited time scale. In this study, we developed a hydrological drought attribution method via AdaBoost algorithm. The method divided the study period into natural period unaffected by non-climatic factors and impacted period. Taken the natural period as training period, the impacted period as test, the runoff was obtained to calculate the three-months standardized runoff index (SRI-3). Based on the run-length theory, we calculated average drought characteristics in the impacted period. Finally, the proportion of the average drought characteristics obtained by simulated SRI-3 series to those obtained by observed SRI-3 series is considered as the contribution of the climatic factors to the drought events.

We applied this method in the Yangtze River Basin and the results showed that climatic factors are the dominate factors affecting hydrological droughts in this region, with the contributions at all the gauge stations are over 50%. Among all the drought characteristics, average drought severity is the most affected by the climatic factors, the corresponding contributions are all greater than 100%, shown as “excess contributions” (with non-climatic factors shown as negative contributions). Through the applications in various sub-basins of the Yangtze River Basin, the method was shown to provide new ideas for hydrological drought attribution, and the method can also be extended for applications such as meteorological hazards attribution, stock market volatility attribution and so on.

How to cite: Wang, W., She, D., and Xia, J.: Separate the impact of climate change and non-climatic factors on hydrological drought based on AdaBoost algorithm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4870, https://doi.org/10.5194/egusphere-egu24-4870, 2024.

Drought projection is critical for water resource planning and management, as well as disaster prevention and mitigation. As a strategic national water source for China, the Yangtze River Basin (YRB) plays a vital role in the connectivity of rivers and economic development, flowing through 11 provincial administrative regions and is injected into the East China Sea, with a total length of 6,397 km. The watershed covers an area of 1.8 million square kilometers, accounting for about 1/5 of China's total land area. However, frequent droughts have caused water shortages in the YRB in recent years. Based on observed meteorological and hydrological data, the CMIP6 model and SPEI (standardized precipitation evapotranspiration index) drought models were used to elucidate the risk of future simultaneous droughts in the upper and mid-lower reaches of the YRB from 2015 to 2100. SRI has been used based on SWAT model to study the transfer process of meteorological drought to hydrological drought. The results indicated that, (1) The average of 10 CMIP6 models showed a good verification of historical precipitation and temperature for drought predictions. The MMK and Sen’s slope demonstrated consistency for historical and future droughts in the YRB. From a historical perspective (1961–2019), the middle reaches of the YRB experienced intensifying drought frequency with the highest total drought (Moderate and above drought events) frequency (> 17%); (2) In the future (2020–2100), the higher emission signifies higher moderate and total drought frequency, intensity, and scope of the YRB in FF, lower in NF. The ratio of autumn severe and extreme droughts would increase in mid-twenty-first century; (3) Severe drought risk encounters were projected in the upper and meanwhile in the middle-lower reaches in YRB, especially in the 2030–2040 period. Under all three scenarios, severe droughts occurred more frequently with SPEI close to − 2. The middle-lower reaches of the YRB are forecast to witness the largest scope and highest intensity of drought under the SSP1-2.6 scenario.; (4) The future runoff in the YRB during the dry period varied less, but in May and June during the main flood season the runoff under SSP1-2.6 would be the largest. Maximum decrease in runoff in the mid-lower reaches under the SSP2-4.5 scenario would be 2045, reaching 13.9%. Extreme flooding events and extreme meteorological droughts would happen accompanying with hydrological droughts would occur more frequently and severely under different scenarios. More attention and improved strategies should be brought to bear to address future simultaneous droughts in the upper and mid-lower YRB.

How to cite: Zhang, Y. and Zhang, Z.: The increasing risk of future simultaneous droughts over the Yangtze River basin based on CMIP6 models , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3557, https://doi.org/10.5194/egusphere-egu24-3557, 2024.

Australia is frequently susceptible to droughts. Major droughts within the last 50 years such as that of 1982-1983 and the Millennial drought of 1997-2010 severely impacted crop growth across the country. Soil moisture can be in deficit during droughts due to a lack of recharge and high evapotranspiration from the soil. Dryland agriculture is particularly sensitive to droughts as there is no irrigation input into the soil. Soil water availability is a critical constraint to agricultural productivity, so the ability to predict its current and future state accurately is key in informing decisions relating to irrigation, fertiliser use, and yield targets. While soil moisture forecasting has been conducted in literature previously, there is limited understanding of the spatial, seasonal, and meteorological patterns that underlie the forecastability of soil moisture in a particular field. Hence this research aims to understand the spatial, seasonal, and meteorological factors that influence the forecast accuracy of soil moisture in dryland fields in Australia. 

Across Australia an increasing number of growers have soil moisture probes, which report current and historic soil moisture. The domain of this work is in the CosmOz probe network, consisting of 26 cosmic ray soil moisture probes across Australia, accounting for various geophysical and climatic regions. The probes measure average soil moisture to depths in the soil between 10 to 50 cm. Forecasting soil moisture requires the addition of various modelled/remotely sensed data such as meteorological, vegetation type, and soil property data. Using this data and lagged soil moisture as predictors, soil moisture has been forecasted at the locations of each CosmOz probe. With up to 13 years of training data, machine learning models have been fitted to forecast soil moisture with high accuracy forecasts of up to 30 days. To improve predictions a neural network autoencoder has been employed to engineer features that account for anomalous periods in the predictors.

A key outcome of this study is identifying patterns in forecast accuracy and predictor importance with respect to region, soil type, meteorological conditions, and time of year. These patterns create a nationwide perspective of soil moisture forecastability and the potential for forecasting in areas with no soil moisture probe data available. 

How to cite: Amir, Q. M. and Bishop, T.: Soil moisture forecasting for dryland fields in Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2072, https://doi.org/10.5194/egusphere-egu24-2072, 2024.

As per USAID’s Famine Early Warning System Network Team (FEWS NET) 110-120 million people are projected to need emergency food assistance across all FEWS NET-monitored countries. Climate shocks such as droughts contribute to acute food insecurity. Better identification and earlier warning of anomalous conditions leading to food insecurity are critical to support decision-making to mitigate the impacts of food insecurity on lives and livelihoods. Agricultural production outlooks are one of the critical components of the famine early warning scenario generation process. Thus far these outlooks have mainly been based on estimates of seasonal rainfall or remotely sensed indicators of vegetation greenness whereas soil moisture estimates (remotely sensed or modeled) have been used as drought indicators but not directly used for crop yield forecasting to assess production shocks, particularly in operational settings. Our past research, which focused on crop yield forecasting in southern Africa, revealed a promising level of skill when soil moisture monitoring products or forecasts were used as predictors of crop yield, relative to traditional predictors such as December to February ENSO. Additionally, a separate study focused on East Africa revealed when and where soil moisture can be the best predictor of crop yield relative to other earth observations. Building upon this initial research, here we investigate the applicability of soil moisture monitoring and forecasting products in crop yield forecasting in up to 20 FEWS NET monitored countries for which processed crop yield data are available at sub-national scale. We first use soil moisture monitoring products, both remotely sensed (such as ESA-CCI) and modeled (such as FEWS NET Land Data Assimilation System) to implement and validate machine learning based within-season crop yield forecasting. We then use seasonal-scale soil moisture forecasts (up to 6 months in future) to enhance the lead-time of crop yield forecasting and implement and validate pre-season (before the start of a crop growing season) long-lead crop yield forecasting, as earlier estimates of food insecurity can provide additional critical time needed for launching famine prevention responses by governments and donor agencies.

How to cite: Shukla, S., Davenport, F., Yoon, E., Das, B., Anderson, W., Hazara, A., Slinski, K., and McNally, A. L.: Enabling long-lead forecasting of agriculture production shocks with soil moisture monitoring and forecasting products to support food insecurity early warning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21136, https://doi.org/10.5194/egusphere-egu24-21136, 2024.

Food insecurity is a global concern resulting from various complex processes and a diverse range of drivers. Due to its complexity, it is one of the most challenging drought impacts to predict. In this study, we introduce a novel machine learning model designed to forecast food crises in the Horn of Africa up to 12 months in advance. We trained an “XGBoost” model using more than 20 different input datasets to capture key food security drivers such as drought, economic shocks, conflicts and livelihood vulnerability. The model shows a promising ability to predict food security dynamics several months in advance (R²>0.6, three months in advance). Notably, it accurately predicted 20% of crisis onsets in pastoral regions (n = 84) and 40% of crisis onsets in agro-pastoral regions (n = 23) with a 3-month lead time. We compared these results to the established FEWS NET early warning system, and found a similar performance over these regions. However, our model is clearly less skilled in predicting food security for crop-farming regions than FEWS NET. This study underscores the importance of integrating machine learning into operational early-warning systems like FEWS NET and expanding these techniques to the continental or global-scale.   

How to cite: Busker, T., van den Hurk, B., de Moel, H., van den Homberg, M., van Straaten, C., A. Odongo, R., and C.J.H. Aerts, J.: Predicting Food-Security Crises in the Horn of Africa Using Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4330, https://doi.org/10.5194/egusphere-egu24-4330, 2024.

Drought, as a natural calamity, has serious economic and environmental implications, especially as the impacts of climate change continue to escalate globally. In many regions, monitoring and comprehending changes in drought patterns have become imperative. As climate change increasingly influences hydrological cycles, there is a need to grasp and interpret drought behaviour in diverse geographical areas. This study is particularly focused on a landlocked state in the north-eastern region of India, which is characterised by a predominantly monsoon climate with high humidity and an annual rainfall of 1800–2500 mm. The study focuses on the state of Nagaland, India, and is aimed at evaluating the efficacy of artificial intelligence (AI) models such as Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Genetic-Algorithm Adaptive Neuro-Fuzzy Inference System in predicting drought. For analysing the drought conditions, the Effective Drought Index (EDI) is used. By utilising rainfall data from 1987–2021, the EDI drought index has been computed, recognising the pivotal role of rainfall in comprehending prevailing drought conditions. The drought conditions are categorised from extremely dry to near normal, excluding the wet conditions in the study region. The investigation into the effectiveness of AI in predicting and detecting drought yielded insightful results, highlighting the informative and promising capabilities of AI models. The results of the study facilitate a comparative analysis of the three models, MLP, LSTM, and GA-ANFIS, using the evaluation metrics. The study findings indicate that LSTM exhibits superior prediction accuracy in the study region in terms of its ability to predict drought conditions in the given geographical area. This outcome is crucial for understanding and addressing the impacts of drought. This study contributes to the broader understanding of drought prediction and emphasises how AI models can improve their ability to predict drought conditions, which will ultimately contribute to enhanced water resource management and climate adaptability.

How to cite: Kikon, A.: Exploring the effectiveness of Artificial Intelligence-powered insights in drought study in Nagaland, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-853, https://doi.org/10.5194/egusphere-egu24-853, 2024.

Given their profound socio-economic impact and increasing occurrence, compound heat and drought extremes (CHDEs) have become a focal point of widespread concern. Numerous studies have attempted to reproduce and predict these extremes using general circulation models (GCMs); however, the performance of these models in capturing extreme events remains controversial. This study presents an improved historical simulation of CHDEs over China by using the regional Climate-Weather Research and Forecasting model (CWRF) to downscale the projections of two GCMs that participated in the Coupled Model Intercomparison Project Phase 6. The CWRF downscaling improved GCMs in capturing the thresholds of extreme hot and extreme dry conditions and demonstrates a better agreement with observations in the temporal trends and spatial patterns of extreme heat and extreme drought events. The performance of CWRF downscaling to reproduce CHDEs also surpasses that of GCMs, with an even greater enhancement compared to univariate extreme events. The improvement is particularly pronounced in sub-humid areas, which is primarily attributed to the enhanced simulation of temperature-precipitation coupling relationships by CWRF downscaling. This superiority is found to be associated with the finer land surface processes and land-atmosphere interaction processes of CWRF. This study highlights the important role of land-atmosphere interactions in shaping CHDEs and the efficacy of using regional climate models to reduce uncertainty in extreme event simulations.

How to cite: Zhang, S. and Zhang, H.: Towards improved prediction of compound heat and drought extremes by CWRF downscaling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8013, https://doi.org/10.5194/egusphere-egu24-8013, 2024.

Long-lasting droughts have become more common worldwide in recent decades, such as in Australia (2001-2009), California (2012-2014), Chile (2010-2023), and Europe (2018-2022). The combination of droughts and heatwaves has led to intense flash droughts, worsening soil moisture deficits. This has resulted in global shortages of essential food, serious public health issues, and prolonged forest fires that harm air quality in populated areas. Extended droughts also contribute to food insecurity, reduced energy production, increased health crises, and the destruction of natural landscapes, causing significant economic setbacks in various regions. International agencies, such as the WMO, and water authorities are actively promoting the advancement of seasonal soil moisture monitoring and forecasting systems. In this presentation, we'll give you an update on ULYSSES [2], the global multi-model hydrological seasonal predictions system supported by the Copernicus Climate Change Service. This fully operational system runs directly at the ECMWF's HPC and aims to be the first seamless multi-model hydrological seasonal prediction system with global coverage at a spatial resolution of 0.1 degrees.

The ULYSSES modeling chain builds on the successful EDgE proof of concept [3], employing four advanced hydrological models (Jules, HTESSEL, mHM, PCR-GLOBWB). Notably, this production chain features a distinctive aspect: the utilization of a standard set of physiographical datasets (e.g., DEM, soil properties) with consistent spatio-temporal resolutions and similar forecast inputs for all hydrological models, as well as the same multi-scale routing model (mRM). The seasonal forecasts are initialized using the ERA5-land product from ECMWF. The Equitable Thread Score (ETS) skill is employed to assess the ensemble forecasting abilities for drought events, specifically when soil moisture exceeds 80% of the time, across lead times ranging from one to three months.

In a recent assessment, the global ensemble Equitable Thread Score (ETS) for the system stands at 63%, 43%, and 34% for lead times ranging from 1 to 3 months. Notably, over Europe, the ensemble ETS is significantly higher, reaching 91%, 71%, and 61% for the corresponding lead times. Contrasting these findings with a prior study that employed the mHM initialized with E-OBS forcing and the NMME ensemble over Europe [4], our analysis suggests potential reasons for the diminished performance of the current system. These factors may include: 1) the meteorological forcings utilized for initializing the hydrological models, and/or 2) the skill level of the NWF model ensemble. In this study, we will present the sensitivity of ETS when one of the models (mHM) is initialized with different available forcings procucts available such as EM-EARTH, MSWEP, WE5E, and E-OBS. Finding of this study is key for the further improvement of the system.

References

• [1] https://doi.org/10.1029/2021EF002394
• [2] https://www.ufz.de/ulysses
• [3] https://doi.org/10.1175/BAMS-D-17-0274.1
• [4] https://doi.org/10.1175/JHM-D-12-075.1
• [5] https://doi.org/10.1175/JHM-D-19-0095.1

How to cite: Samaniego, L., Modiri, E., Sutanudjaja, E. H. (., Shrestha, P., Martinez-de la Torre, A., Rakovec, O., Schweppe, R., Kelbling, M., Facer-Childs, K., Chevuturi, A., Tanguy, M., Wanders, N., Kumar, R., and Thober, S.: On the predictability of the seasonal droughts at global scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11049, https://doi.org/10.5194/egusphere-egu24-11049, 2024.

In the period between 2021 and 2023, the Euro-Mediterranean region experienced a series of significant thermo-pluviometric anomalies. In particular, in the central Mediterranean, the Copernicus Climate Change Service identified exceptional temperature anomalies and a complex and intense drought, also highlighted in the "European State of the Climate (ESOTC)" reports. Prolonged periods characterised by extreme weather events pose a serious threat to both society and human activities, even in advanced countries. 

Water scarcity and water resources management play a prominent role among the climatic threats, as their impacts represent the main pressure mechanisms for human beings, ecosystems, and many human activities. Therefore, it becomes imperative to develop advanced systems for forecasting and anticipating climate variability to provide crucial information to decision-makers and users, facilitating preparation for mitigation actions. Addressing this challenge requires the implementation of operational predictive systems on a seasonal scale that are reliable, salient, and easily adaptable, aiming to enhance economic and societal resilience. To this end, the Drought Observatory (DO) of CNR IBE, a web-based climate service open to the public, has developed and maintained a prediction system based on various components: a seamless prediction system based on the European model SEAS5, coupled with a bias adjustment algorithm; a Non-Homogeneous Poisson process trend analysis of individual drought severity classes; and an evaluation of vegetation stress trough indices calculated from both atmospheric variables and remotely sensed quantities. The DO has been conceived to share both the outcomes of ever-evolving scientific research and a structured set of scientific information. Tailored to different levels of complexity, this information aims to address the informational needs of both technical experts and decision-makers, as well as a wider audience and media representatives.

The DO develops these components in close collaboration with stakeholders and users engaged in institutional activities and national and international research projects. This interaction strengthens decision-making processes for adapting to meteorological and climatic risks and adversities.

An integrated approach, that relies on “converging evidence”, has been adopted to achieve an even more pertinent level of information. The 2021-2023 period, characterized by extreme climatic conditions, has been studied as a rare multiyear event to assess the effectiveness of seasonal-scale anticipation systems for climate anomalies. Moreover, this timeframe proves particularly valuable for understanding and addressing challenges associated with climate change.

Verification analysis shows that seasonal forecast skills vary over time and geographical areas. It is thus possible to identify windows of opportunity for specific tasks in cooperation with users. Within this framework, bias-corrected seasonal forecasts provide valuable supporting information for water resources management and decision-making processes. Throughout the drought period from 2021 to 2023, the Drought Observatory played a pivotal role, extensively utilized by national and international media to disseminate precise information regarding the drought trend in Italy. This underscores the crucial requirement for timely and science-based data to enlighten the broader public.

How to cite: Pasqui, M., Magno, R., Di Paola, A., Quaresima, S., Rapisardi, E., Rocchi, L., and Di Giuseppe, E.: Empowering communities through seasonal forecasts use: a lesson learned from the Euro Mediterranean 2021-2023 drought event, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17370, https://doi.org/10.5194/egusphere-egu24-17370, 2024.

The intensity and frequency of dry spells in Switzerland have increased in recent years and are likely to increase 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 national monitoring and forecasting system is to be established through the joint efforts of three different governmental agencies (federal offices for the environment, meteorology and climatology, and topography). The project also actively involves stakeholders in its development.

In this contribution, we introduce the Swiss national drought project with a particular focus on user-centered design, in situ and satellite-based monitoring, and the integration of sub-seasonal forecasts. Results from a user-survey revealed that even though drought is multi-dimensional and affects stakeholders in different ways, one of their primary needs is still a holistic “combined” drought index that can serve as a common ground for discussion and decision-making. Simple, local-scale-focused designs were assessed as the most efficient and useful, whereas designs showcasing nationwide maps or scientific quantities (SPI, etc.) were the least meaningful to educated but not expert users.

Further 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 (ECMWF IFS Extended). Integrating these highly heterogeneous data streams into seamless products ranging from historical observations 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 novel 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 into consideration the needs of a very diverse user community, such as hydropower production, navigation, agriculture, forestry, artificial snow production, or ecology.

How to cite: Humphrey, V., Hüsler, F., Bircher-Adrot, S., and Imamovic, A.: A drought monitoring and forecasting system for Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16037, https://doi.org/10.5194/egusphere-egu24-16037, 2024.