HS4.2

HS4 EDI
Drought and water scarcity: monitoring, modelling and forecasting to improve hydro-meteorological risk management 

Drought and water scarcity are important issues in many regions of the Earth. While the projected increase in the severity and frequency of droughts can lead to water scarcity situations, particularly in regions that are already water-stressed, overexploitation of available water resources can exacerbate the consequences of droughts. In the worst case, this can lead to long-term environmental and socio-economic impacts. Drought Monitoring and Forecasting are recognized as one of three pillars of effective drought management, and it is, therefore, necessary to improve both monitoring and sub-seasonal to seasonal forecasting for droughts and water availability and to develop innovative indicators and methodologies that translate the information provided into effective drought early warning and risk management. This session addresses statistical, remote sensing and physically-based techniques, aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and/or water scarcity. These include, but are not limited to, precipitation, snow cover, soil moisture, streamflow, groundwater levels, and extreme temperatures. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and integrating these with the needs and knowledge of water managers, policymakers and other stakeholders, are further issues that are addressed. The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as in the field of water resources and/or drought risk management, also including drought and water scarcity interrelationship, hydrological impacts, and feedbacks with society. Particularly welcome are applications and real-world case studies in regions subject to significant water stress, where the importance of drought warning, supported through state-of-the-art monitoring and forecasting of water resources availability, is likely to become more important in the future.

Co-organized by NH1
Convener: Brunella Bonaccorso | Co-conveners: Carmelo Cammalleri, Athanasios Loukas, Micha Werner, Yonca CavusECSECS
Presentations
| Wed, 25 May, 13:20–17:52 (CEST)
 
Room B

Presentations: Wed, 25 May | Room B

Chairpersons: Brunella Bonaccorso, Micha Werner
13:20–13:25
Drought monitoring and modelling
13:25–13:35
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EGU22-4944
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ECS
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solicited
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Highlight
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On-site presentation
Jignesh Shah, Vittal Hari, Oldrich Rakovec, Yannis Markonis, Luis Samaniego, Vimal Mishra, Martin Hanel, Christoph Hinz, and Rohini Kumar

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.

13:35–13:42
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EGU22-235
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ECS
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Virtual presentation
Femin C Varghese and Subhasis Mitra

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.

13:42–13:49
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EGU22-947
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ECS
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On-site presentation
Estifanos Addisu Yimer, Bert Van Schaeybroeck, Hans Van De Vyver, and Ann Van Griensven

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.

13:49–13:56
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EGU22-1474
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ECS
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Virtual presentation
Jingyu Lin, Qiu Shen, Jianjun Wu, Leizhen Liu, and Wenhui Zhao

Solar-induced chlorophyll fluorescence (SIF) from the ground, airborne to satellite-based observations has been increasingly used in drought monitoring recently, due to its close relationship with photosynthesis. SIF emissions do respond rapidly to drought, relative to the wide used vegetation indices (VIs, e.g., Normalized Difference Vegetation Index (NDVI)), thus indicating its potential for early drought monitoring. The response of SIF to drought can be attributed to the confounding effects of both physiology and canopy structure. In order to reduce the re-absorption and scattering effects, total emitted SIF (SIFtot) was proposed and served as a better tool to estimate GPP compared with top-of-canopy SIF (SIFtoc) in some studies. However, the response time and response magnitude of SIFtot to drought and its relationships with environmental parameters and soil moisture, that is, the knowledge of drought monitoring using SIFtot remains unclear. Here the continuous ground data of F760toc (SIFtoc at 760 nm) in nadir view that was downscaled to F760tot (SIFtot at 760 nm), surface soil moisture at 20cm soil layer (SM), meteorological and crop growth parameters, were measured from four winter wheat plots with different intensities of drought (well-watered treatment, moderate drought, severe drought and extreme drought) over two months. By analyzing these data, we found that F760tot was indeed more closely related to physiological and was less subjected to canopy structure than that of F760toc, but this relationship was reversed under extreme drought. It was more closely correlated with SM than VIs at short time lags, but weaker at longer time lags. The daily mean values of F760tot were able to distinguish the differences in drought gradients and respond quickly to the onset of drought, especially for the moderate drought, which appears to have the most decrease. These results demonstrate that F760tot has potential for early drought monitoring.

How to cite: Lin, J., Shen, Q., Wu, J., Liu, L., and Zhao, W.: Assessing the potential of the downscaled far-red solar-induced chlorophyll fluorescence from canopy to leaf level for drought monitoring in winter wheat, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1474, https://doi.org/10.5194/egusphere-egu22-1474, 2022.

13:56–14:03
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EGU22-1315
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ECS
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On-site presentation
Pedro Henrique Lima Alencar and Eva Nora Paton

Flash droughts are often characterized as events of rapid and unusually large depletion of root-zone soil moisture, in comparison to average conditions, caused by climatic compound conditions over short periods (weeks). We compared six flash drought identification methods and analysed their functioning using measured data from FLUXNET2015 stations across Central Europe. All methods were implemented in an R package and are available as a Shiny app for the public, where the user can visualise the different results of flash drought identification for each method. An in-depth analysis and cross-comparison of methods for co- and misidentification for cropland sites showed a large degree of synchronicity among them, although some divergence was detected, related to four intrinsic differences in the underlying flash drought definitions associated to each identification method: (1) type of critical variable, (2) velocity of drought intensification, (3) pre-set threshold values for final depletion, and/or (4) minimum length of the duration of flash droughts. To balance strengths and weaknesses of the individual methods, we suggest the use of an ensemble approach for each event identification. To balance such strengths and weaknesses of the individual methods we propose an ensemble approach for event identification, allowing the detection of the current unclearly defined sub-types of flash droughts, related to the different combinations of compound drivers and differences in intensification dynamics.

How to cite: Lima Alencar, P. H. and Paton, E. N.: How do we identify flash droughts? Analysis tool and Central European Croplands analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1315, https://doi.org/10.5194/egusphere-egu22-1315, 2022.

14:03–14:10
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EGU22-3286
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ECS
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Virtual presentation
Bageshree Katneshwarkar and Tsuyoshi Kinouchi

Drought is a complex and multidimensional phenomenon affecting the global population. The widespread impacts of drought propagate through the climatic and hydrological cycle and affect the socio-economic security of the related stakeholders, especially farmers. Countries like India use several indices to determine the severity of the drought for governmental relief and mitigation measures, which is crucial for farmers facing agricultural stress and failures. However, the use of single or several separate drought indices cannot capture the combined effect of principal drivers responsible for the drought, where the effect of groundwater availability for agriculture is often neglected despite its heavy use in irrigation through groundwater extraction. In this study, we focus on the multidimensional response of drought in a single joint index to better capture the spatiotemporal variability in drought severity. The semi-arid region of Marathwada from central India, which frequently faces drought and is infamous for farmer suicides due to agriculture failures is taken as the study area. The response of hydroclimatic variables viz. precipitation, evapotranspiration, soil moisture, surface runoff, and groundwater storage were captured in their respective standardized indices (SPEI, SSI, SRI, and SGI respectively) which were then used to construct the Joint Drought Index (JDI) using two principal methods: 1) Principal Component Analysis (PCA) and 2) Gaussian copula. Both the methods were found to be capable of identifying the severity of the drought along with its onset, duration, and termination. Although individual indices such as SPI can sometimes acknowledge the meteorological response better, the JDI has the potential of capturing the response of multiple hydrological variables together at once for drought monitoring and assessment. During the period between 2003 to 2020, the drought of 2015 was identified as exceptionally severe in both the methods, where copula could better accommodate the severity of every integrated index whereas PCA averages the response of the variables to drought by allocating the weights to each index for each month.

How to cite: Katneshwarkar, B. and Kinouchi, T.: Integration of Multiple Drought Indices for Agriculture Drought Categorization and Impact Assessment in Central India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3286, https://doi.org/10.5194/egusphere-egu22-3286, 2022.

14:10–14:17
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EGU22-3365
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ECS
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Virtual presentation
Shanti Shwarup Mahto and Vimal Mishra

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.

14:17–14:24
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EGU22-4547
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ECS
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Virtual presentation
Hamidreza Mosaffa, Paolo Filippucci, Christian Massari, Luca Ciabatta, and Luca Brocca

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.

14:24–14:31
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EGU22-8811
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Virtual presentation
Harm Nomden, Michel Riemersma, Adrian Zamora, Hidde Kats, Ric Huting, Wouter Engel, and Tomas Quisbert

Bolivia, November 2016: the reservoirs high in the mountains around the city of La Paz completely dried out after an dry year, limiting the supply of (drinking) water to the city. Heavy rationing had to take place over a period of 6 weeks, resulting in social unrest. 

The drinking water company EPSAS is responsible for the water supply to the city and region. Over the period 2016-2030, the number of inhabitants will grow from 1.6 to 2.1 million and the water demand increases with 58% while raw water resources are limited and further constrained (climate change and loss of glaciers). To cope with these conditions, the supply infrastructure will double in size: from 3 to 6 treatment plants and from 14 to 26 reservoirs spread over 9 catchments. Total water storage capacity doubles from 54 hm3 to 110 hm3. Additional stream intakes are constructed to extract water, water can even be pumped over the mountains in dry periods. Water is transported from the reservoirs to plants via pipe lines, channels and free flowing streams.

A continuously changing and expanding reservoir network, signifies an increase in complexity, more choices to be made on a daily and weekly basis by the operational staff, influencing (forecasted) water availability. Since 2016 Royal HaskoningDHV and EPSAS have been working together to develop a Monitoring & Decision Support System which is able to monitor the water availability and the status of the catchments, generate hydrological forecasts and optimize (future) use of available raw water resources.

This is done by:

  • installing a system of 40-60 monitoring stations at all dams and upstream in all catchments - monitoring water levels, discharges, extraction volumes and meteorological variables. New to be constructed telemetry stations will send all data to the control room;
  • developing an operational software system to translate measured variables into water volumes and other indicators; to generate hydrological run-off forecasts using advanced hydrological models; to optimize the distribution of water over the reservoirs and the use of water; and forecast resulting water availability and shortages over the coming 18 months. The system generates forecasts and advices on a daily or weekly basis, as defined by the user.

How to cite: Nomden, H., Riemersma, M., Zamora, A., Kats, H., Huting, R., Engel, W., and Quisbert, T.: Avoiding Day Zero water crisis management La Paz, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8811, https://doi.org/10.5194/egusphere-egu22-8811, 2022.

14:31–14:38
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EGU22-8967
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ECS
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Presentation form not yet defined
Brielle Paladino, Racha El Kadiri, and Henrique Momm

Climate change increases the probability of drought occurrence in many parts of the United States and worldwide. Aquifer response to these drought events vary in space and time. This project seeks to understand the response of aquifers to drought events by quantifying the lag time between meteorological droughts and groundwater droughts using the Standardized Precipitation and Evapotranspiration Index (SPEI) and Gravity Recovery and Climate Experiment (GRACE) derived groundwater storage anomalies. Ten major aquifer systems in the continental United States were selected for analysis: Columbia Plateau, Arizona Alluvial, Snake River Basin, Upper Colorado, Pennsylvanian, Mississippi Embayment, Texas Gulf Coast, Edwards-Trinity Plateau, Floridian, Central California, and the High Plains Aquifer Systems. Groundwater storage anomaly data was derived from GRACE total water storage anomaly data by removing all other hydrologic components using the Global Land Data Assimilation System’s (GLDAS) Community Land Surface Model (CLM) of 1.0-degree spatial resolution monthly datasets. Timeseries on monthly intervals for both the derived groundwater storage and SPEI were created for the period of April 2002 to June 2021. Each selected aquifer system had a meteorological drought occur at least three times during the study period, with a maximum occurrence of fifteen in central California. There is a temporal gap in between the original GRACE mission and the launch of GRACE-Follow on (GRACE-FO) from June 2017 to June 2018, five of the ten selected aquifers had meteorological droughts occur in this gap, which have been excluded. Preliminary results indicate that the lag time between the start of the two types of droughts for these aquifer systems is between zero and one month, while the lag time between the end of these types of droughts is more widely varied, between zero and eight months. As these results are varied, contextualizing them with more in-depth looks at the aquifer system characteristics is important and is the next step in furthering our understanding of aquifer responses to the increasing number of probable drought events.

How to cite: Paladino, B., El Kadiri, R., and Momm, H.: Aquifer Response Lag to Meteorological Droughts from GRACE Satellites, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8967, https://doi.org/10.5194/egusphere-egu22-8967, 2022.

14:38–14:45
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EGU22-13376
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Presentation form not yet defined
Drought monitoring in mountain areas with high resolution precipitation products
(withdrawn)
Gustavo Naumann, Francesco Avanzi, Giulia Ercolani, Denise Ponziani, Hervè Stevenin, Sara Maria Ratto, and Simone Gabellani
Coffee break
Chairpersons: Yonca Cavus, Carmelo Cammalleri
Drought early warning, forecasting and future projections
15:10–15:20
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EGU22-9418
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ECS
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solicited
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On-site presentation
Irina Yu. Petrova, Diego G. Miralles, Florent Brient, Markus Donat, Yeon-Hee Kim, and Seung-Ki Min

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.

15:20–15:27
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EGU22-6639
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ECS
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Virtual presentation
Yumiao Wang and Xing Yuan

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.

15:27–15:34
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EGU22-6492
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ECS
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On-site presentation
Marco Mazzolini, Felix Greifeneder, Giacomo Bertoldi, Daniela Quintero, Mattia Callegari, Klaus Haslinger, and Georg Seyerl

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.

15:34–15:41
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EGU22-6687
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ECS
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Presentation form not yet defined
Yusuke Satoh, Kei Yoshimura, Yadu Pokhrel, Hyungjun Kim, Hideo Shiogama, Tokuta Yokohata, Naota Hanasaki, Yoshihide Wada, Peter Burek, Edward Byers, Hannes Müller Schmied, Dieter Garten, Sebastian Ostberg, Simon Gosling, Julien Boulange, and Taikan Oki

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.

15:41–15:48
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EGU22-7613
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Virtual presentation
Xing Yuan and Yumiao Wang

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.

15:48–15:55
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EGU22-9914
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ECS
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On-site presentation
Yenny Marcela Toro Ortiz, Sonia Raquel Gámiz Fortis, Yolanda Castro Díez, Reiner Palomino Lemus, María Jesús Esteban Parra, and Samir Córdoba Machado

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.

15:55–16:02
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EGU22-10481
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Highlight
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Presentation form not yet defined
Shraddhanand Shukla, William Turner, Greg Husak, Daniel McEvoy, Seydou Tinni, Adoum Alkhalil, Abdou Ali, Bako Mamne, Ibrah Sanda, Kathryn Grace, Emil Cherrington, and Rebekke Muench

Early warning of drought is crucial for mitigation of the most adverse impacts of water and food insecurity to lives and livelihoods. Recent advances in routine production (i.e., weekly) and open access to NMME SubX—subseasonal climate forecasts—provide an unprecedented opportunity to improve drought early warning near the onset and middle of the crop-growing season. Near the onset of a season, subseasonal precipitation forecasts have the potential to provide early indication of delay in rain onset, which, as shown in a recent study (Shukla et al., 2021, PLOS ONE), can be a reliable indicator of agricultural drought development. This is particularly relevant for some of the most food-insecure regions in East Africa. Additionally, subseasonal forecasts have the potential to improve drought forecasting during the middle of the season—several months before the harvests—when they are used in combination with to-date observations. Integration of near-real-time observations with subseasonal climate forecasts can enhance drought detection capabilities by leveraging the skill that is derived from initial conditions (as of middle of the season) and complementing it with the skill of subseasonal climate forecasts. Here, we first describe how onset of the rainy season is a reliable indicator of agricultural droughts. The results indicate that in the administrative units in sub-Saharan Africa, which  have the highest risk of acute food insecurity, a delay of about 20 days in the rainy season onset can double the probability of agricultural droughts. We then describe the results of an analysis examining the performance of subseasonal climate forecasts in identifying the timing of the onset of the rainy season in those administrative units. Next, we describe a SERVIR-AST-supported project, which uses subseasonal climate forecasts to develop a West Africa-focused water-deficit forecasting system in collaboration with AGRHYMET, primarily  for agropastoral usage. Here, we make  use of a widely used crop water balance model, the Water Requirement Satisfaction Index (WRSI), to generate improved forecasts of crop water stress, and hence, crop production outcomes, during the middle of the rainy season in West Africa (June through September). We compare the performance of these forecasts with the forecasts generated using climatology only. Finally, we briefly describe how these subseasonal climate forecasting products are being disseminated, communicated, and used in the focus regions.

How to cite: Shukla, S., Turner, W., Husak, G., McEvoy, D., Tinni, S., Alkhalil, A., Ali, A., Mamne, B., Sanda, I., Grace, K., Cherrington, E., and Muench, R.: Improving early warning of droughts near onset and middle of a growing season, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10481, https://doi.org/10.5194/egusphere-egu22-10481, 2022.

16:02–16:09
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EGU22-10806
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Presentation form not yet defined
Olivier Prat, David Coates, Ronald Leeper, Brian Nelson, Rocky Bilotta, Steve Ansari, and George Huffman

We present an operational near-real time drought monitoring framework on a global scale that uses quantitative precipitation estimates (QPEs) from gridded Satellite Precipitation Products (CMORPH-CDR, IMERG) and in-situ datasets (NClimGrid). The Standardized Precipitation Index (SPI) is computed daily for various time scales from the reprocessed, bias-corrected CMORPH-CDR. The near-real time availability of CMORPH-CDR permits for a daily update of global drought conditions starting in 1998. It provides a global daily SPI at a 0.25x0.25 degree spatial resolution. The global SPI is publicly available via the Global Drought Information System (GDIS) dashboard. The GDIS website includes an interactive map hosted within the NOAA GeoPlatform (ArcGIS Online). It provides 45 layers of drought indices and indicators in addition to the global daily CMORPH SPI (https://gdis-noaa.hub.arcgis.com/pages/drought-monitoring).

The pipeline assembled to produce CMORPH-SPI is extended to IMERG (Integrated Multi-satellitE Retrievals for GPM) to generate a daily global IMERG-SPI at a higher spatial resolution (0.1x0.1deg) from 2000 to the present. The 6-fold increase in spatial resolution comes at a higher computational cost which is alleviated by accessing cloud-scale computing resources such as Microsoft Planetary Computer and Azure that allows to optimize the process and reduce considerably the computation time. Similarly, we use the high resolution gridded in-situ precipitation dataset NClimGrid to generate a daily high resolution NClimGrid-SPI over CONUS (5x5-km). Because of NClimGrid longer period of record, it allows accessing daily drought conditions from 1950 up to the present day.

Comparisons between the generated SPIs (CMORPH-SPI, IMERG-SPI, NClimGrid-SPI) are conducted with a focus on the influence of the different resolutions, sensors characteristics, and SPI formulations (two parameter Gamma distribution: McKee et al. 1993; three parameter Pearson III distribution: Guttman 1999). When possible, an evaluation of the remotely sensed and in-situ SPIs is performed against existing droughts monitoring tools such as the US Drought Monitor (USDM). Finally, we present the results of the implementation of a drought relief module that quantifies the precipitation amount that would be needed (i.e. rainfall deficit) for drought relief as a function of the accumulation period considered.

How to cite: Prat, O., Coates, D., Leeper, R., Nelson, B., Bilotta, R., Ansari, S., and Huffman, G.: Operational Framework for Near-real Time Daily Drought Monitoring Using Global Remotely Sensed Precipitation Products and In-situ Datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10806, https://doi.org/10.5194/egusphere-egu22-10806, 2022.

16:09–16:16
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EGU22-10927
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Virtual presentation
Gulomjon Umirzakov, Renji Remesan, Komiljon Rakhmonov, Sanskriti Mujumdar, and Nurmukhammad Omonov

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.

16:16–16:23
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EGU22-12797
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On-site presentation
Qiqi Gou, Akash koppa, Hylke E. Beck, Petra Hulsman, and Diego G. Miralles

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.

16:23–16:30
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EGU22-11147
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On-site presentation
Aristeidis Koutroulis, Manolis Grillakis, Nicola Crippa, Guang Yang, and Matteo Giuliani

Given the specific nature of the Mediterranean region, water scarcity and documented progressive degradation of groundwater quality poses hazardous environmental, economic and social threats to several Mediterranean countries, with a significantly increased risk of conflicts around the limited availability of water resources. These risks are expected to be further exaggerated with the projected climate drying. Due to continued changes in drivers and pressures, traditional management practices alone are no longer sufficient.

Recent advances in weather and climate modeling research are putting into practice hydroclimatic projections of timescales ranging from sub-seasonal to climatic. Seasonal forecasts can be used for triggering a variety of water management strategies, as for example activating early responses and decisions in order to make water systems more adaptive and resilient to the increasing variability and uncertainty of hydrologic regimes, ultimately facilitating the reduction of drought related risks.

In the premises of the STREAM project, we use projections at the climate timescale to estimate the long-term trends and the changes in the temporal and quantitative variability of the hydrologic conditions in the basin of the Faneromeni reservoir under two concentration scenarios, the RCP 4.5 and RCP 8.5. The reservoir is located in Messara valley in Crete Island, Greece, an area highly water overexploited during the recent decades. We further use several seasonal forecast products provided under the umbrella of the Copernicus C3S programme, for a range of lead time horizons. Scenarios of water inflow and evaporation losses are used to inform the multi-objective operation design for the investigation of the impacts of alternative management policies. Our results are expected to improve the current practices used by the local practitioners for the management of water resources for sustainable water exploitation.

 

This work is supported by the STREAM project funded by the Prince Albert II of Monaco Foundation, grant number 2981 (www.streamflows.eu).

How to cite: Koutroulis, A., Grillakis, M., Crippa, N., Yang, G., and Giuliani, M.: Sub-seasonal to climatic hydrologic predictions for sustainable reservoir management in water-stressed Mediterranean basins, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11147, https://doi.org/10.5194/egusphere-egu22-11147, 2022.

16:30–16:37
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EGU22-11509
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Virtual presentation
Athanasios Loukas and Lampros Vasiliades

Droughts are slow-moving natural hazards that comes with high hazardous impacts on the society. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Traditionally, forecasting techniques have used various time-series and/or machine learning methods. However, the use of deep learning methods has not been tested extensively despite its potential to improve our understanding of drought characteristics. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using a deep feed-forward neural network (DFFN and the temporal forecasts are extended to the spatial dimension using a deep learning approach the Long Short-Term Memory (LSTM) to forecast an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. The temporal input variable determination is achieved with the use of the Gamma test that estimates the minimum mean square error (MSE) that can be achieved when modelling the unseen data using any continuous non-linear models. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, are used for the development and spatiotemporal validation of the hybrid deep learning forecasting model. Several drought characteristics (drought severity and duration, drought category and spatial extent) are analysed to better understand how drought forecasting was improved. Several quantitative temporal and spatial statistical indices are considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes are calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal deep learning forecasting model could be operationally used for predicting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 12-24 months). The above findings could be useful in developing a drought preparedness plan in the region and for drought mitigation purposes.

 

Key words: deep learning, drought, Standardized Precipitation Index, drought forecasting, spatiotemporal droughts, DFNN, LSTM.

How to cite: Loukas, A. and Vasiliades, L.: A spatiotemporal deep learning forecasting model for long-term drought prediction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11509, https://doi.org/10.5194/egusphere-egu22-11509, 2022.

Coffee break
Chairpersons: Carmelo Cammalleri, Micha Werner
Drought impacts and adaptation measures
17:00–17:10
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EGU22-11590
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ECS
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solicited
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Highlight
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Virtual presentation
Marijke Panis, Phuoc Phùng, Bouke Pieter Ottow, and Aklilu Teklesadik

The impacts of drought are complex due to the multidimensionality (intensity, duration, and extent) and slow-onset nature of droughts. To be able to forecast the impact of droughts, one needs to prioritize and disentangle the diversity of impacts. In Zimbabwe, our country of interest, the Zimbabwe Red Cross Society prioritized crop loss, livestock loss, child malnutrition, and stunting. However, no high-quality data with national spatial coverage on these impacts is available. Therefore, it is necessary to use a proxy indicator for these impacts (or one of these impacts). As Zimbabwe is strongly dependent on rainfed- agriculture for its livelihood, our assumption is that a crop yield anomaly can be used as a proxy for crop loss impact. A negative crop yield anomaly derived from global historical yield series was used to determine the drought status (yes or no impact) in April and forms the target or predictand. The meteorological indicators to predict the crop yield are the observed 3-month-averaged El Niño–Southern Oscillation (ENSO) and the observed monthly rainfall from CHIRPS for each lead time. Also, a combination of monthly rainfall and ENSO was used as predictor. Our forecasting ML classification model, XGBoost, is run at lead times of one to seven months and at the livelihood zone/agro-climatic zone level. The entire dataset for 1983-2015 is divided into train (80%), test, and validation sets. Statistical performance is measured with the Probability of Detection and False Alarm Ratio of both the test and validation set. Our findings show the potential of ENSO-based data in forecasting our proxy for drought impact over various lead times. The addition of rainfall does not improve forecast skill. Future research will investigate if additional meteorological- and biophysical predictors such as soil moisture and Vegetation Condition Index improve the forecast skill. Our IBF Trigger Model for drought is currently a sequence of automated tasks that feed into an IBF-Portal with comprehensive visualizations for decision-makers. Both the development of the trigger model and the portal result from close collaborations and co-designs with the Zimbabwe Red Cross Society and its in-country partners.

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.

17:10–17:17
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EGU22-240
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ECS
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On-site presentation
Ruth Stephan, Carsten F. Dormann, and Kerstin Stahl

Even across Europe’s generally water-rich Alpine region the number of reports on negative drought impacts increased recently. The Alpine Drought Impact report Inventory EDIIALPS archives information of more than 3,200 specifically reported impacts with a majority in the last decade underlining the need for region-specific drought monitoring and adaptation strategies. The relation between drought conditions and drought impact occurrence has not been analyzed systematically in this heterogeneous mountain terrain. This study aims to improve such systematic understanding through the analysis of selected drought characteristics and reported impacts. Therefore, we assigned EDIIALPS’ reported impacts as soil-moisture drought impacts (SMD) and hydrological drought impacts (HD) and explored statistically the relation of these two impact groups to the following drought indices: Soil Moisture Anomalies, Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Vegetation Condition Index and Vegetation Health Index. The density of the reported SMD impacts and HD impacts increased clearly, the stronger the index’ value indicates drought conditions - apart from the vegetation indices. However, the correlation tests between reported impacts and indices did not identify explicit linear relations. To capture non-linear effects and differences between reported SMD impacts and HD impacts we applied decision trees using recursive partitioning. This way, we identified the Standardized Precipitation and Evapotranspiration Index to be most important for reported HD impacts and the Soil Moisture Anomalies to be most important for reported SMD impacts. To predict impact occurrence we recommend to model and evaluate a combination of drought indices allowing non-linearities in order to improve drought impact monitoring and early warning.

How to cite: Stephan, R., Dormann, C. F., and Stahl, K.: Explaining reported drought impacts in the European Alpine region with selected drought indices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-240, https://doi.org/10.5194/egusphere-egu22-240, 2022.

17:17–17:24
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EGU22-9827
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ECS
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On-site presentation
Beatrice Monteleone, Iolanda Borzì, Brunella Bonaccorso, and Mario Martina

Drought affects a wide range of economic activities, with agriculture as the worst affected sector by the consequences of such an extreme in many regions of the world. Past studies showed that droughts and heat waves are the weather extremes that significantly reduce cereal production at global level, while there is no evidence on the influence of floods and extreme cold on cereal yields. The projected increase in the severity and frequency of droughts can lead to water scarcity situations in regions that are already water-stressed and to overexploitation of available water resources in other areas.

The way regulators and farmers manage water resources during droughts has effects on agricultural resilience and the increased frequency of drought and water scarcity will require more collaborative partnership-based approaches to water resources and drought management in the next future. The development of quantitative models to establish relationships between water scarcity and crop yield losses can help in understanding in which situations farmers need access to water to avoid high losses.

This study develops crop specific vulnerability curves that establish a relationship between water deficit and yield losses during various crop growth stages (vegetative, flowering and yield formation) and can thus provide useful indication on how to allocate water resources to avoid irreparable yield losses. The case study region is the Po river basin (Northern Italy).

The Po river basin is the largest Italian agricultural area and accounts for 35% of the country’s agricultural production. The basin is characterized by the presence of big cities and wide rural zones. Over the past years the it has been hit by multiple droughts. Ten cities were considered in the analysis, based on maize yield data provided by the Italian National Institute for Statistics (ISTAT).

At first the Agricultural Production System sIMulator (APSIM) crop model was used to simulate maize growth. The model was calibrated and validated over the ten provinces based on ISTAT data. An R² of 0.75 was found for both the steps.

The yield in the absence of any water stress during the entire growing season was computed as the reference yield. Then, the reduced yield for the same season was derived introducing a water stress in a single growth stage by progressively reducing the precipitation amount during that growth stage. The yield reduction was expressed as one minus the ratio between the reduced yield and the reference yield.

The water deficit for each season and each growth stage was derived from APSIM. The relationship between yield reduction and water deficit was plotted to derive the vulnerability curves and data points were fitted to appropriate functions.

In the case of maize, flowering was found to be the most sensitive stage to water deficit, followed by yield formation and vegetative. During the establishment phase the crop never went under water stress in the considered area. Soil texture has also proven to play a role on the response of the crop to the water deficit, particularly in the flowering and yield formation stages.

How to cite: Monteleone, B., Borzì, I., Bonaccorso, B., and Martina, M.: Assessing drought vulnerability of maize production in the Po Valley, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9827, https://doi.org/10.5194/egusphere-egu22-9827, 2022.

17:24–17:31
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EGU22-6356
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Virtual presentation
Godfrey Kafera

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.

17:31–17:38
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EGU22-12413
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On-site presentation
Kedar Ghag, Amirhossein Ahrari, Syed Mustafa, Anandharuban Panchanathan, Toni Liedes, Björn Klöve, and Ali Torabi Haghighi

Globally, the hydro-climatological parameters such as precipitation, temperature, and soil moisture are getting more uncertain and varying regionally as well as seasonally with the changing climate. The Nordic region and the regional agriculture are no exception to this. Recent global studies have projected the increasing trend of precipitation during winter and autumn in Northern Europe. Whereas, the declining trend during spring and summer. The studies further lead to the resulting decline in mean soil moisture that consequently will increase the potential for agricultural drought. Additionally, the summer droughts are already getting highlighted locally as the agriculture in the region experiencing substantial yield losses besides excessive rainfall as a common issue. Therefore, supplemental irrigation, and controlled drainage during water-sensitive growth stages of crops, or crop selection could be potential alternatives and need further investigation. In this study, we present an integrated irrigation and drainage approach (IIDA) based on Water Balance Simulation (WBS) to reduce the negative impact of summer droughts in Nordic agriculture. A WBS is developed in the present study for potato crop fields in Tyrnävä municipal area of Finland to examine the required irrigation or drainage during the cropping season. The model considers precipitation, temperature, and soil water-holding properties as inputs to simulate daily water availability in the crop root zone and provide output as the required amount of either irrigation or drainage or a combination of both for the cropping season from 2000 to 2020. The results showed that around 20% of the mentioned period (2003, 2006, 2018, and 2019), the potato fields required supplemental irrigation between 12-120 mm during the entire season. Furthermore, except for 2009 and 2018, an annual average of 44 mm of drainage was required due to extreme rainfall events. The findings of the study will benefit to increasing the sustainability of agricultural yield in the Nordic region by reducing the negative impact of summer droughts.

How to cite: Ghag, K., Ahrari, A., Mustafa, S., Panchanathan, A., Liedes, T., Klöve, B., and Torabi Haghighi, A.: Integrated irrigation and drainage approach to overcome summer droughts in Nordic conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12413, https://doi.org/10.5194/egusphere-egu22-12413, 2022.

17:38–17:45
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EGU22-12562
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ECS
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On-site presentation
Gaia Roati, Giuseppe Formetta, Silvano Pecora, Marco Brian, Riccardo Rigon, and Hervè Stevenin

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.

17:45–17:52
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EGU22-12814
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ECS
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Virtual presentation
Ashutosh Pati, Smaranika Mahapatra, and Pawan Wable

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.