HS4.2

Flash droughts cause a rapid depletion of soil moisture, which severely affect vegetation growth and agricultural production. Notwithstanding the growing importance of flash droughts under the warming climate, drivers of flash droughts across the Europe are not well understood. Here we estimate the changes in flash droughts characteristics across Europe using the latest release of ERA5 reanalysis for 1950-2019 period. We find a substantial increase in the frequency and spatial extent of flash droughts across Europe (with 76\% of the total area) during the growing season in the recent decades. Increased occurrence of flash drought is largely attributed to frequent occurrence of warmer and drier compound extremes, with a sharp gradient of changes being noticed in Mediterranean and Central European regions. Compound extremes causing the flash drought events across Europe are pre-dominantly driven by the recent climate warming. With unabated greenhouse gas emissions and current pace of climate warming, Europe is likely to face an increased occurrence of flash droughts, requiring prompt response for effective drought adaptation and management strategies.

How to cite: Shah, J., Hari, V., Rakovec, O., Markonis, Y., Samaniego, L., Mishra, V., Hanel, M., Hinz, C., and Kumar, R.: Increasing footprint of climate warming on flash droughts occurrence in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4944, https://doi.org/10.5194/egusphere-egu22-4944, 2022.

In the Indian subcontinent, the uncertainty associated with potential evapotranspiration (PET) over drought characterization is inadequately studied. This study was conducted to understand the sensitivity of PET estimation methods towards drought characterization using multiple PET-based drought indices under future climate change. We used eleven PET estimation methods (Blaney-Criddle (BC), Hamon (HM), Hargreaves (HG), Kharrufa (KF), Thornwaithe (TW), Dalton (DN), Meyer (MR), Irmak-Rn (IRN), Irmak-Rs (IRS), Priestley-Taylor (PT), and Penman-Monteith (PM)) for the future period (from the Coupled Model Intercomparison Project 5). Further, for drought characterization six PET-based drought indices are utilized in this study: the Standardized Precipitation Evaporation Index (SPEI), the Supply-Demand Drought Index (SDDI), the Reconnaissance Drought Index (RDI), the self-calibrated Palmer Drought Severity Index (sc-PDSI), the Standardized Moisture Anomaly Index (SZI), and the Standardized Palmer Drought Index (SPDI). We also employed a variance-based global sensitivity analysis to determine the relative sensitivity of projected drought indices to the GCM and PET estimation methodologies under climate change scenarios. Results indicate that different PET-based drought indices show vastly different drought projections for the future, which is highly influenced by the PET methods. Overall, SPEI and SDDI produce comparable results, indicating an increase in future drought estimates compared to the rest (RDI, SPDI, SZI, and sc-PDSI). The TW method reported higher drought projections compared to other PET methods irrespective of the drought indices.  This is due to the fact that the TW method also showed the highest increase in PET compared to the rest of the methods. Results from the sensitivity analysis indicate that all the drought indices are more sensitive to the choice of PET methods compared to the GCM. However, analysis was done after excluding the TW approach significantly altered the sensitivity, and GCMs were found to be more sensitive compared to PET methods. The results from this study reveal that drought projections derived from multiple PET estimation methodologies indicate drier conditions in the future, albeit at variable levels. Thus, the selection of the PET estimation method and drought index will be crucial in the Indian subcontinent for future drought investigations.

How to cite: Varghese, F. C. and Mitra, S.: Sensitivity of PET estimation methods towards drought characterization under climate change in the Indian subcontinent, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-235, https://doi.org/10.5194/egusphere-egu22-235, 2022.

Drought indices are used to identify and monitor drought events. Standardized precipitation evapotranspiration index (SPEI) is a widely used index based on accumulated water balance. There is, however, no broad consensus on which probability distribution is most appropriate for water balances. We investigate this issue for Ethiopia using 125 meteorological stations spread over the country. Based on long-term series, a selection was made among the generalized extreme value, Pearson type 3, and generalized logistics (Genlog) distributions. Additionally, the effect of using actual instead of potential evapotranspiration and a limited amount of data (10, 15, 20, and 25 years) is explored.

Genlog is found to be the best distribution for all accumulation periods. Furthermore, there is a considerable difference amongst the SPEI values estimated from the three distributions on the identification of extreme wet or extreme dry periods. Next, there are significant differences between standardized precipitation actual evapotranspiration index (SPAEI) and SPEI, signifying the importance of drought index selection and input data for proper drought monitoring. Finally, time series of 20 or 25 years of data lead to almost similar SPEI values as those estimated using more than 30 years of data so could potentially be used to assess drought in Ethiopia.

Key words: Drought; SPEI; Candidate distribution; Global datasets; SPAEI; short time series

How to cite: Yimer, E. A., Van Schaeybroeck, B., Van De Vyver, H., and Van Griensven, A.: Evaluating probability distribution functions for the Standardized Precipitation Evapotranspiration Index over Ethiopia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-947, https://doi.org/10.5194/egusphere-egu22-947, 2022.

Concurrent high temperature and low soil moisture during flash drought (FD) can become significantly hazardous, posing a devastating impact on human health, agriculture, and the ecosystem. Strong land-atmospheric coupling influences the intensity of flash drought events. Despite flash drought having detrimental impacts, the soil moisture (SM)-temperature (T) relationship and their characteristics are poorly understood in a coupled land-atmospheric scenario. Using variables from ERA5 reanalysis, we identify the major flash drought events and evaluate the SM-T coupling in India for the 1980-2019 period. We find that the summer monsoon season experiences most flash drought events during the monsoon breaks. Temperature anomalies and FD intensities remained strongly correlated (r= 0.78 and r= 0.67, respectively) with the SM-T coupling. Central India and Indo-Gangetic Plain experienced higher FD intensity and SM-T coupling compared to other parts of the country. Moreover, the SM-T coupling during flash drought increased by three-fold against the normal condition with an increasing trend over India. The strengthening of SM-T coupling is attributed to the increasing temperature and potential of declining soil moisture to influence the partitioning of the heat budget in the warming climate. Overall, we find that SM-T coupling is a key factor in deciding the intensity of flash drought, which may further increase under the future warming climate. Exacerbated flash drought intensity can severely affect crop production, irrigation demand, and ecological health.

How to cite: Mahto, S. S. and Mishra, V.: Land-atmospheric coupling amplify the flash drought intensity in India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3365, https://doi.org/10.5194/egusphere-egu22-3365, 2022.

Drought is a natural disaster that has serious economic, social and environmental impacts. Drought monitoring is one of the components of drought risk management. The main requirement of drought monitoring is to have a reliable and accurate long-term rainfall dataset. SM2RAIN datasets are among the available rainfall products that estimate rainfall from satellite soil moisture observations. The high performance of SM2RAIN products has been shown in several studies over different regions of the globe. The aims here are as follow: 1) to develop the long-term climatological SM2RAIN datasets that cover the period of 1998-2020 at 0.25° spatial and monthly temporal resolution on the global scale. This dataset is designed by merging two rainfall SM2RAIN products including SM2RAIN-CCI (1998-2015) and SM2RAIN-ASCAT (2007-2020). For this purpose, the quantile mapping method is applied to remove the bias between these two products and match the monthly values. In the QM method, a correction factor is calculated during the overlap period (2007-2015) as a reference period and then applied to for the entire study period, 2) to analyses of drought based on standardized precipitation index on the global scale. In addition, the analysis is compared with drought analysis of other ground observations and reanalysis rainfall products such as the Global Precipitation Climatology Project (GPCP) and ERA5. The results show that the developed SM2RAIN-based rainfall product has the potential to improve global drought monitoring by capturing the drought events accurately.

How to cite: Mosaffa, H., Filippucci, P., Massari, C., Ciabatta, L., and Brocca, L.: Long-term climatological SM2RAIN dataset for drought monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4547, https://doi.org/10.5194/egusphere-egu22-4547, 2022.

The increasing risk of dry extremes and droughts and their further projected exacerbation due to climate change urges the development of reliable risk assessments and mitigation pathways on a regional and global scale. This foremost requires accurate and unambiguous model predictions of dry extremes, as this underpins the effectiveness of the proposed strategies. At present, however, the confidence in regional drought projections is defined as ‘medium to low' by the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6), and reducing this uncertainty remains one of the main goals in coming years.
In this study, the bias in future projected changes in annual meteorological drought duration (hereafter, longest annual drought, LAD) is assessed in the ensemble of CMIP5 and CMIP6 models. The analyses show that it is the present-day inter-model spread in LAD climatology that largely determines the inter-model uncertainty in future predicted LAD changes. Hereby, both CMIP5 and CMIP6 model ensembles indicate a robust “dry-model-gets-drier” relationship in future LAD projections on a global and regional scale. Correcting for this bias using emerging constraint principles and past observational LAD information, we find that nearly half of the world's land area with projected increases in drought duration is underestimating the predicted model ensemble mean change, imposing higher-than-expected risks to the societies and ecosystems. Analysis of physical mechanisms that could underlie this emergent “present-future relationship” points to differences in the responses of “dry models” and “wet models” to CO2 forcing. Dry and wet models show differences in climate states, which support the role of land–atmosphere feedbacks and convective scheme sensitivity to atmospheric moisture in the spread of future LAD change projections.
In conclusion, the study reveals world regions where climate change may cause stronger drought duration aggravation than expected, and emphasizes the importance of reducing systematic model errors, which are presently largely owed to rainfall biases. Correcting these biases will increase the confidence of future dry extremes predictions, a prerequisite for the effective drought risk reduction in the near future with direct benefits for human and natural systems.

How to cite: Yu. Petrova, I., G. Miralles, D., Brient, F., Donat, M., Kim, Y.-H., and Min, S.-K.: Underestimated increase in duration of annual meteorological drought in future climate projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9418, https://doi.org/10.5194/egusphere-egu22-9418, 2022.

Flash drought is a new type of drought with rapid onset, which occurred frequently in recent years over the world. Compared with the traditional drought, the rapid onset makes it difficult to predict in time, and it poses a serious threat to agriculture and ecosystem. However, causes of the rapid onset and underlying mechanisms are still unclear. Considering that the land-atmosphere coupling can regulate the evolution of extreme drought, here we investigate the coupling characteristics during flash droughts over South China, and carry out the attribution by using the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. Through the synthetic analysis of flash drought onset, it is found that extreme precipitation deficit and strong evapotranspiration provide favorable conditions for flash drought onset, and the dry coupling between land and atmosphere further aggravates the decline in soil moisture, and increases the onset speed. In addition, with the increase of onset speed, the contribution of evapotranspiration increases accordingly, and the dry coupling between land and atmosphere further dominates the evolution. This suggests that the land-atmosphere coupling plays a key role in increasing the onset speed of flash drought. Furthermore, the impact of climate change on the onset speed of flash drought also can’t be ignored. The results of detect and attribution show that anthropogenic climate change (caused by the emissions of greenhouse gases and aerosols, etc) has increased the likelihood of flash drought onset speed over South China in 2019 by 24±16%, which is closely related to anthropogenically increased evapotranspiration.

How to cite: Wang, Y. and Yuan, X.: Land-atmosphere coupling speeds up flash drought over South China in a changing climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6639, https://doi.org/10.5194/egusphere-egu22-6639, 2022.

In the context of the Alpine Drought Observatory (ADO) project, a database of discharge measurements with more than 1400 gauging stations on alpine rivers with, on average, 35 years of records was assembled. This wealth of information constitutes an ideal source for data-driven discharge modelling with Machine Learning (ML). Discharge forecasting is relevant for many sectors related to the water cycle, such as agriculture and energy production. Moreover, appropriate river low streamflow prediction can improve preparedness for drought-related risks.

This paper proposes comparing two ML algorithms for discharge prediction using meteorological reanalysis and modelled snow variables over the gauging stations' catchment area as predictors. The selected meteorological variables are total precipitation, temperature, and potential evapotranspiration. ERA5 reanalysis [1] bias-corrected with quantile mapping and down-scaled to a 5.5 km grid is the source. The last predictor is the snow water equivalent (SWE), obtained with an adaptation of the SNOWGRID model [2]. All the predictors have a daily temporal resolution.

First, we build on existing work [3] with Support Vector Regression (SVR). The experiments aim at predicting the monthly discharge mean in the present and up to several months of advance. We evaluate the performances of the different approaches, investigate each input variable's importance for several test catchments with different hydrological regimes, and carry out trials with different temporal and spatial aggregations to find the best configuration.

We evaluate the prediction with the r² metric. Depending on the size and water management in the studied basin, results range from 0,7 to 0,85 for the present. We also perform the analysis based on discharge anomalies (computed as the deviation from the average discharge for the specific day) to erase the climatology effect. In this case, the r² metric ranges from 0,5 up to 0,7. For predictions of the future discharge, the model's performance decreases in about one month to the level of climatology. The SWE is a relevant predictor since the performance decrease is slower for larger basins with a nivo-glacial regime.

The results show the suitability of ML for discharge prediction on different kinds of alpine basins with up to one month of advance. The subsequent development will be to conduct a similar analysis with convolutional neural networks (CNN). This class of deep networks should allow the model to learn the spatial pattern in the input data.

[1] Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), date of access. https://cds.climate.copernicus.eu/cdsapp#!/home

[2]: Olefs, M.; Koch, R.; Schöner, W.; Marke, T. Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach. Atmosphere 2020, 11, 1330. https://doi.org/10.3390/atmos11121330

[3]: De Gregorio, L., Callegari, M., Mazzoli, P. et al. Operational River Discharge Forecasting with Support Vector Regression Technique Applied to Alpine Catchments: Results, Advantages, Limits and Lesson Learned. Water Resour Manage 32, 229–242 (2018). https://doi.org/10.1007/s11269-017-1806-3

How to cite: Mazzolini, M., Greifeneder, F., Bertoldi, G., Quintero, D., Callegari, M., Haslinger, K., and Seyerl, G.: Machine learning for discharge prediction in the Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6492, https://doi.org/10.5194/egusphere-egu22-6492, 2022.

Droughts that exceed the magnitudes of historical variation ranges could occur increasingly frequently under future climate conditions. However, the time of the emergence of unprecedented drought conditions under climate change has rarely been examined. Here, using multimodel hydrological simulations, we investigate the changes in the frequency of hydrological drought (defined as abnormally low river discharge) under high and low greenhouse gas concentration scenarios and existing water resource management measures and estimate the timing of the first emergence of unprecedented regional drought conditions. When investigating 59 subcontinental-scale regions, the times are detected for 11 and 18 regions under low and high greenhouse gas concentration scenarios, respectively. Three regions (Southwestern South America, Mediterranean Europe, and Northern Africa) exhibit particularly robust and early timings under the high-emission scenario. These three regions are likely to confront unprecedented conditions within the next 30 years with a high likelihood regardless of the emission scenarios. Additionally, the results obtained herein demonstrate the benefits of the lower-emission pathway in reducing the likelihood of emergence. The Paris Agreement goals are shown to be effective in reducing the likelihood to the unlikely level in most regions. However, appropriate and prior adaptation measures are considered indispensable when facing unprecedented drought conditions. The results of this study underscore the importance of improving drought preparedness within the considered time horizons.

How to cite: Satoh, Y., Yoshimura, K., Pokhrel, Y., Kim, H., Shiogama, H., Yokohata, T., Hanasaki, N., Wada, Y., Burek, P., Byers, E., Müller Schmied, H., Garten, D., Ostberg, S., Gosling, S., Boulange, J., and Oki, T.: The timing of unprecedented hydrological drought under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6687, https://doi.org/10.5194/egusphere-egu22-6687, 2022.

Flash droughts have raised a wide concern in recent years. Besides many regional analyses, global distributions of flash droughts have been discussed in a few studies. With certain differences due to different drought indices or datasets, a few hotspots consistently show increasing flash droughts among studies. However, to date, there is no global picture on whether flash droughts have been intensified, or whether the intensification will continue into the future. Here we propose a method to quantify the intensification of global flash droughts, and investigate the historical trends (trends in the past 60 years) by using global reanalysis data and CMIP6 climate models with or without human-induced climate change. The human fingerprint can be identified for the global trends, which suggests the important role of anthropogenic intensification of global flash droughts in the past. Moreover, future projection of flash drought is also carried out over IPCC SREX regions by using CMIP6 future scenarios. The results show that intensification of flash droughts is projected to continue across most regions, with larger increase under higher emission scenarios. This raises an urgent need to adapt to the intensifying flash droughts in the future.

How to cite: Yuan, X. and Wang, Y.: Global intensification of flash droughts in the past and future, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7613, https://doi.org/10.5194/egusphere-egu22-7613, 2022.

Agriculture and livestock represent 21% of the economic sector of the Department of Chocó (Colombia), being drought and flood events one of the main difficulties. Although this Department has the largest records of annual precipitation, in some seasons with scarce precipitation, it shows great drought problems and crop deterioration.

Consequently, several institutes use short-term decadal climate simulations using general circulation models (GCM), which consider climate warming as well as the predictable climate signal associated with the initial climate conditions to inform water resource managers.

This work analyzes the potential use of the decadal predictions of precipitation from the Japanese model BCC-CSM2-MR to predict of drought events in the Department of Chocó through the analysis of the hindcasts in the period 1960-2018. The choice of this model is based on its suitability to reproduce the main patterns of climate variability that affect the study area. Drought events will be characterized by the Standardized Index of Precipitation (SPI) on different time scales.

Since the resolution of this GCM is very vast and does not allow to solve regionalized characteristics, such as topographic factors, land-sea distribution, or vegetation types, etc., a statistical downscaling of the decadal hindcasts for precipitation will be carried out from which the SPI will be calculated. These results will be compared with those obtained from the observational database "Global Precipitation Climatology Centre (GPCC)”.

Keywords: drought, Colombia, SPI, decadal predictions, GCM, statistical downscaling.

Acknowledgments: Y.M. Toro-Ortiz acknowledges the Colombian Ministry of Science, Technology, and Innovation for the predoctoral fellowship (grant code: 860). This research was funded by the Spanish Ministry of Economy and Competitiveness project CGL2017-89836-390R, with additional support from FEDER Funds, by FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento, project B-RNM-336-UGR18, and by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades (project P20_00035).

How to cite: Toro Ortiz, Y. M., Gámiz Fortis, S. R., Castro Díez, Y., Palomino Lemus, R., Esteban Parra, M. J., and Córdoba Machado, S.: Analysis of high-resolution decadal prediction of drought events in the Department of Chocó-Colombia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9914, https://doi.org/10.5194/egusphere-egu22-9914, 2022.

This study investigated the link between meteorological and hydrological droughts in two rivers with and without glaciers in Сhirchik river basin of Western Tian Shan. Observed monthly hydrometeorological data was used to estimate Standardized Precipitation Indexes (SPI) and Standardized Streamflow Indexes (SSI) to analyze the hydrological response of snow-glacier fed Pskem and snow-rain fed Ugam rivers in the region. The Pearson correlation coefficient has been used to estimate statistical relations between SPI and SSI indices for the selected rivers. The SPI-SSI correlation coefficient has shown a positive trend with an increase in timescales, and it was more evident in indexes between the 6-month to 12-month  timescales in both rivers. The statistical relationships between the meteorological and hydrological drought indexes showed that the SPI-SSI relationship varies with river flow generation and its dynamics, and it was more in the Ugam River than the Pskem River. That indicates snow-dominated Ugam River is more prone to meteorological droughts, whereas the glaciers in the Pskem River basin were buffering hydrological drought and its frequency and severity. Obtained results allow better-informed forecasting of hydrological droughts in the river basin and, consequently, enable efficient water management in agricultural and hydropower sectors.

How to cite: Umirzakov, G., Remesan, R., Rakhmonov, K., Mujumdar, S., and Omonov, N.: Hydrological response to meteorological drought in Chirchik river basin of Western Tian-Shan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10927, https://doi.org/10.5194/egusphere-egu22-10927, 2022.

Flash droughts are regional phenomena that can manifest in region areas with a rapid intensification, and that often last for short periods of time. Flash droughts have received considerable scientific attention in recent years. However, their prediction is still a challenge, largely due to their abrupt onset and often unknown regional drivers. Here, we establish a forecast system to predict flash droughts at a medium-range weather scale. The system uses forcing data from the Multi-Source Weather (MSWX), an operational, high-resolution (3‑hourly, 0.1°), bias-corrected meteorological product with global coverage from 1979 to several months into the future (Beck et al. 2021). MSWX data are used as input to the Global Land Evaporation Amsterdam Model (GLEAM), more specifically its recent hybrid version (Koppa et al., 2021). This allows us to compute forecasts of actual and potential evaporation;  the ratio of both (also know as 'evaporative stress') is used here as flash drought diagnostic. This forecast system is evaluated on its ability to predict flash droughts globally and 2, 4, 7 and 10 days advance. The new tool shows potential to improve our understanding of flash droughts, and it serves as an early prediction system to enable more efficient agricultural and water management.

References:

Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. M., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F., MSWEP V2 global 3‑hourly 0.1° precipitation: methodology and quantitative assessment. Bulletin of the American Meteorological Society. 100(3), 473–500, 2019

Koppa, A., Rains, D., Hulsman, P., and Miralles, D. M., A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation. Preprint. 2021. 10.21203/rs.3.rs-827869

How to cite: Gou, Q., koppa, A., E. Beck, H., Hulsman, P., and G. Miralles, D.: Flash droughts early warning based on evaporative stress forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12797, https://doi.org/10.5194/egusphere-egu22-12797, 2022.

How to cite: Panis, M., Phùng, P., Ottow, B. P., and Teklesadik, A.: Forecasting a proxy of humanitarian drought impact with machine learning using meteorological predictors; a case study for Zimbabwe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11590, https://doi.org/10.5194/egusphere-egu22-11590, 2022.

The world is confronted with the increasing threat of food insecurity which is driven by several shocks including droughts, floods, and conflict. The United Nations World Food Programme (WFP) is currently feeding over 95million people around the globe in urgent need of food including those in high emergency countries like Southern Madagascar, Haiti, Afghanistan, Northern Nigeria, South Sudan, Syria, and Yemen. The situation has been worsened by the impacts of COVID - 19 interrelated factors of movement restrictions and reduced economic activity, which together have caused income losses at the household level. Discussions with institutions like the Southern Africa Development Community (SADC), United Nations (UN) partners and respective governments in Southern Africa have clearly shown that the impacts of these shocks are more devastating in countries where early warning systems are weak. Over the years, USAID's Famine Early Warning Systems Network (FEWS NET) has invested in building the capacity of partners and governments to timely identify key shocks that are likely to cause food insecurity in different countries. Using a methodology called scenario development, FEWS NET has been able to develop understanding of the current situation, create informed assumptions about the future, compare their possible effects to food security and the likely responses of various actors. The ability to develop early warning systems helps to estimate future food security outcomes many months in advance, so that decision makers have adequate time to plan for and respond to potential humanitarian crises. This presentation seeks to (i) explore the different methods used to project the likely impacts of shocks on food security in different environments, (ii) highlight the strengths of collaborative partnerships in enhancing early warning systems to promote early action in food security response, and (iii) discuss the use of science products to improve forecasting of future food insecurity outcomes. The use of agrometeorological and remote sensing products including Water Requirement Satisfaction Index (WRSI), Normalized Difference Vegetation Index (NDVI) and CHIRPS Rainfall Estimates has proved useful in identifying hotspots of drought and have helped to facilitate projections in areas where physical access is impossible due to factors like conflict. Practical examples including those from southern Africa will be used to enrich discussions under this topic.

How to cite: Kafera, G.: Understanding and mitigating challenges to food security through observations, projections, and early warning and action capabilities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6356, https://doi.org/10.5194/egusphere-egu22-6356, 2022.

Hydrological extremes, such as floods or droughts, cause significant social and economic damages, posing risks to lives worldwide. Quantifying the spatially variability of water availability across the entire river basin is, thus, deemed important for preparedness to the intensification of such hydro-extremes.

In 2021, the Po River District Authority (AdbPo) undertook the implementation of this modelling system on the whole territory of the district in accordance with the GCU-M (Gruppo di Coordinamento Unificato-Magre) to update the existing numerical modelling for water resource management. This development is part of the research project “Data and models integrated system for the water resources management and the Po River basin district planning” which supports the use of innovative modelling tools and strengthen studies, research, monitoring and simulations of the main hydrological variables characterizing the territory of the Po River District. To reach this goal the GEOframe system has been adopted. This is an open source, hydrological modelling system developed by a technical and scientific international community, leaded by the University of Trento and already used at operational level, including the Civil Protection Agency of the Basilicata Region.

In particular, in this framework the action plan of the Po River District Authority aims to:

• deploy the GEOframe system over all the catchments in its territory (covering Valle d’Aosta, Piemonte, Lombardia, Emilia-Romagna, Veneto and Marche regions), capable to account for the major lakes and reservoirs;
• calibrate and verify the results obtained by the hydrological and hydraulic models against measured discharges and water levels across the whole area;
• interface the GEOframe system with the Deltares-DEWS system;
• analyse the water resources management impacts resulting from climate change or land use changes scenarios.

The implementation has begun on the Valle d’Aosta Region since it is in the most upstream part of the district, which makes this region a good starting point for the initial calibration of the model and the assessment of all the single components (e.g. energy balance, evapotranspiration, snow melting and river discharge components).

The activity is being carried out according to different phases:

• data collection, validation, and preliminary elaboration;
• geomorphological analysis;
• spatial interpolation of the meteorological data (mainly temperature and precipitation) through the krigings components;
• multi-site calibration of the snow melting and rainfall-runoff model parameters;
• validation of the model results against measured data.

Additionally, the calibration phase is essential to test the effectiveness of the model in simulating the components of the hydrological cycle, such us river discharge, evapotranspiration and snow water equivalent and, therefore, to determine the possible identification of drought periods in real-time forecast and long-term prediction, including climate change impacts.

In this work, the initial results achieved in the Valle d’Aosta region will be presented and a detailed analysis on the GEOframe elaboration of information is provided, with a focus on the high flexibility and modularity of the system. The results from a first comparison against river discharge and snow evolution measured in multiple points of the Valle D’Aosta are promising and encouraging.

How to cite: Roati, G., Formetta, G., Pecora, S., Brian, M., Rigon, R., and Stevenin, H.: Hydrological modeling and water budget quantification of the Po river basin through the GEOframe system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12562, https://doi.org/10.5194/egusphere-egu22-12562, 2022.

The onset of drought is very crucial from an agricultural as well as water management point of view in a catchment. A meteorological drought results from a lack of rainfall beyond a certain threshold and is translated to a hydrological drought when the water bodies get affected due to lack of flow to them resulting in storage depletion. This further transforms into agricultural drought when it affects agriculture. Being difficult to observe on-ground, the drought is generally represented in terms of different hydro-meteorological proxies such as precipitation, temperature, soil moisture, streamflow. This study explored the translation of meteorological drought to vegetation in a drought-prone state of India. For this, the vegetation condition index (VCI) and the widely used Standardized Precipitation Index (SPI) were estimated at the district scale. The VCI was calculated from the MODIS-derived NDVI in Google Earth Engine platform. The in-situ rainfall data was used for SPI estimation at different time scales (3-month, 6-month, and 12-month).  Further, different weightage functions such as rectangular, gaussian, triangular, and circular weightage functions were applied for their performance in estimating SPI and their correlation to VCI. Analysis of the results reveals strong dependence of VCI on SPI at larger time scales such as 6-month and 12-month time scales for the whole year as well as in monsoon season. Further, the SPI estimated using the rectangular weightage function shows a better correlation to VCI followed by the circular weightage functions. 

Key Words: Drought, Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Weightage Function

How to cite: Pati, A., Mahapatra, S., and Wable, P.: Understanding the Drought Situation in a Water-Stressed Region of India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12814, https://doi.org/10.5194/egusphere-egu22-12814, 2022.