Water security is threatened by a growing global population and the associated increase in sectoral water demand. This condition is worsened by the occurrence of droughts and heatwaves, which mainly lead to a reduction in the available water, increasing water scarcity. The resulting threats to water security are expected to become more pertinent when considering that such extreme events are expected to increase both in frequency and severity. Nonetheless, little is known about the responses in sectoral water use during extreme hydroclimatic events.

This research therefore quantifies responses in water use for different sectors (i.e. irrigation, livestock, domestic, energy and manufacturing) during droughts, heatwaves and compound events at global, regional and local scales. To achieve this, the spatial extent, times of occurrence and durations of these hydroclimatic extremes were identified worldwide for the period 1990-2019. Next, sectoral water use responses were evaluated during these extreme events and compared to normal (non-extreme) periods for sectoral water withdrawal or consumption.

Our results show that extreme events affect water use responses differently per sector and region. At a global scale, the overall use of water for domestic and irrigation sectors increased while it decreased for thermoelectric and manufacture sectors during heatwaves. Also, water use response patterns show that irrigation and domestic sectors are prioritized over livestock, thermoelectric and manufacturing on a global level. Furthermore, stronger impacts are found for heatwaves and compound events compared with impacts during droughts. Finally, our analyses show that water use drivers -such as income level, use of alternative water sources, and regulatory water policies- impact the magnitude of change in sectoral water use under these extreme events.

These results set the foundation for the development of a new global sectoral water use model which will allow more accurate quantifications of sectoral water use responses and water scarcity during present and future projected droughts and heatwaves.

How to cite: Cárdenas Belleza, G. A., Bierkens, M. F. P., and van Vliet, M. T. H.: Sectoral water use responses to droughts and heatwaves: analyses from local to global scales from 1990-2019, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1645, https://doi.org/10.5194/egusphere-egu23-1645, 2023.

Agro-climatological droughts have been a dominant driver of various socio-economical losses. However, the association between drought hazards & their socio-economic impacts is still less explored on a global scale. The objective of this study is to understand this linkage by globally analyzing drought hazards and their socio-economic impacts during 2001-2021.

To monitor the agro-climatological drought hazard, we have developed a new combined drought indicator (CDI) integrating satellite and reanalysis model-based four input variables (i.e., precipitation- CHIRPS data, temperature, and soil moisture – ERA5-Land data, normalized difference vegetation index – MODIS data). In CDI, the Principal Component Analysis was applied to combine all the variables. To examine the socio-economic impacts of drought hazard, we used the Geocoded Disaster (GDIS) dataset, which provided the location information of subnational-level drought events. Since GDIS shows the actual impact of drought events on socio-economic conditions, the drought vulnerability at a sub-national level can be quantified by performing a comparative analysis between CDI and GDIS.

Based on CDI, the maximum frequency of severe drought events (> 7) is observed over sub-Saharan Africa, followed by parts of south Asia. During these events, the CDI values ranged between -1.5 to -3, signifying the critical hydrometeorological conditions in the respective region. The comparative analysis shows that the CDI-based drought clusters can represent the GDIS drought events at a statistically significant level. Both CDI and GDIS methods noticed that the parts of Argentina, Brazil, the horn of Africa, western India, and north China are continuously under the grips of severe droughts. In these regions, even less severe agro-climatological (CDI) droughts have caused substantial socio-economical (GDIS) losses making these areas highly vulnerable to drought. In contrast, the outcomes of CDI also indicated extreme drought cases over parts of North America and Europe, but these events were inconsistent with GDIS, meaning that developed countries are less vulnerable to drought.

This study highlighted the importance of GDIS data for accurate drought impact assessment at the subnational level and in validating CDI. The proven subnational level association between CDI and GDIS from this study could help to identify the socio-economically vulnerable areas to drought on a finer scale and priorities the regions that demand more concern. 

How to cite: Kulkarni, S. and Sawada, Y.: Monitoring and Assessment of Global Patterns of Subnational droughts using Combined Drought Indicator and Geocoded Disaster Dataset, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3914, https://doi.org/10.5194/egusphere-egu23-3914, 2023.

Droughts are recurrent phenomena, impacting eco- and socio-systems at varied temporal and spatial scales. Their impact depends on both the severity of the antecedent meteorological conditions and the recovery dynamics of the impacted systems. The drought severity analysis proposed in this study accounts for the ”memory effect” of rainfall accumulation by considering across time the rarity of antecedent precipitation at multi-temporal scales. It applies to rainfall accumulation over a single area. In this presentation, we define the yearly curve of multi-temporal drought rarity by the non exceedance probability of the smallest rainfall accumulations observed that year over a range of accumulation durations. Each rarity curve is thus defined by as many values as the number of durations considered. We apply this concept to droughts in France from 1950 to 2022, with accumulation durations varying from 4 weeks to 260 weeks. We show that the rarity curves are easy tools to summarize how droughts build and persists across time and temporal scales. We use an automatic classification of the curves to discriminate years associated to short- to long-term droughts (basically from half a year to five years). Although the concept is here used for rainfall over a single area, France, it could be applied as well to a set of areas and/or to other drought variables such as discharge or soil moisture. 

How to cite: Blanchet, J., Ainési, B., and Creutin, J.-D.: Multi-temporal drought rarity curves - a yearly classification of meteorological drought severity in France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14961, https://doi.org/10.5194/egusphere-egu23-14961, 2023.

Groundwater plays a unique role in the terrestrial water cycle. It is one of the prime sources of water during periods of severe drought. Depletion of groundwater reaching certain thresholds substantially lead to the degradation of water quality. Among all the hydrological variables, it has a characteristics behavior due to its lagged response to precipitation, evapotranspiration, soil water content variations, and surface water variation due to anthropogenic activities. Groundwater drought has been studied in various regions in the world, which revealed significant correlation among hydrological factors, including precipitation, soil water content, and various terrestrial water storage. Terrestrial water storage variables used for monitoring groundwater drought are total water storage change (TWSC) and groundwater storage change (GWSC). While the TWSC can be estimated from the Gravity Recovery and Climate Experiment (GRACE), GWSC can be estimated from in situ groundwater level within the network of well records using relevant hydrogeological information. Previous studies showed the ability and reliability of GRACE data in groundwater monitoring in the regions under extreme drought. Hydrological model outputs, e.g., the Global Land Data Assimilation System (GLDAS), have been used to derive groundwater drought indicators that reached certain reliability. The present study conducts a systematic investigation on the ability of the GRACE data to reflect the groundwater drought conditions, by comparing in situ groundwater data, TWSC estimated from GRACE (TWSC^GRACE), GWSC estimated from the conjuncture of GRACE and GLDAS (GWSC^GLDAS), Standardized Precipitation Index (SPI), and satellite land surface temperature. Further, by estimating the vadose zone water storage change (VZWC) using TWSC and in situ groundwater data (VZWC^{in situ}), as well as using TWSC and GLDAS (VZWC^GLDAS), we investigate the ability of GRACE and in situ data to monitor the vadose zone water content. Our results show that TWSC^GRACE correlates better with in situ groundwater data as compared to GWSC^GLDAS in all three study areas located in India, Australia, and Belgium, which are some of the hotspots suffering from intensive flash drought in the recent decade. TWSC^GRACE shows stronger correlation and better consistency with SPI and land surface temperature as compared to in situ groundwater data. VZWC^{in situ} correlates well with VZWC^GLDAS but is limited to data availability from the well network. Results from GWSC^GLDAS and VZWC^GLDAS show that hydrological model outputs can serve as replacement or supplement to estimate GWSC and VZWC when in situ groundwater data is significantly missing.

How to cite: Wang, J., Mallick, K., Kuhlmann, N., Matgen, P., Bordas, S., and Pfister, L.: Reinvestigating Groundwater Drought Using In Situ and GRACE Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14106, https://doi.org/10.5194/egusphere-egu23-14106, 2023.

Ireland’s climate is changing with the same pattern as global trends. This has the potential to have significant impacts on precipitation and water availability throughout the country. It is vital to be able to quantify the size of these impacts. One way to do this is by hydrological models tuned for the extremes of interest. This study focuses on the development of a national scale hydrological model calibrated for droughts and low flows across Ireland. A total of 332 catchments have been used to calibrate and validate the national scale model hydrological model using the Modular Assessment of Rainfall-Runoff Models toolbox (MARRMoT) over the chosen 332 catchments. These catchments range in sizes (50km² to 10,800 km²) and all chosen catchments have a minimum of 30 years of data available so that the model calibration and validation can be performed adequately. A few different objective functions focusing on droughts were used in calibration and validation including Kling and Gupta efficiency of discharge KGE(Q) function and logarithmic transformation based KGE. Initial results show that the simulated discharges can reproduce the observed discharges across the majority of catchments and that catchment size and the amount of baseflow are the important factors that influence the accuracy of the simulations.

How to cite: Vengana, S. and O'Loughlin, F.: Developing a national-scale hydrological model for drought monitoring in Ireland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7193, https://doi.org/10.5194/egusphere-egu23-7193, 2023.

Recently, the severity of droughts has been increasing due to climate change. Due to the multifaceted nature of droughts (meteorological, hydrological, economic, ecological, etc.), it affects almost all aspects of community life directly or indirectly, both short and long term. Georgia is characterized by diverse terrain and, accordingly, climatic conditions. Most types of climates are present in Georgia except savanna and tropical forests (from humid subtropical to dry subtropical, and climate of eternal snows and glaciers). Therefore, droughts are expressed differently in this small area (67,900 km²). The complexity of different indices used in drought studies depends on the availability of the used data. The purpose of the study was to analyze the intensity of droughts in the short and long term in the territory of Georgia and their distribution for 1931-2020. In this study, we focused on the widespread Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI).  Both indices were calculated based on the data of more than 100 rain gauges located in the study area for several time-scales including 3, 6, 12 and 24 months covering the period from 1931 to 2020. As SPI uses only precipitation data, evapotranspiration is also taken into account in SPEI, which offers a more complete picture of the background of the diversity of Georgia's climate. Daily temperature (for calculation of ET) and precipitation data are used in the research. We calculated the Pearson correlation, R² and RMSE. The correlation of SPI and SPEI allowed us to determine climate type with decisive role of temperature in assessing droughts. The frequency of severe droughts has increased throughout the country, especially in recent decades. This trend is especially striking in the case of the eastern Georgian lowland. In the example of Eastern Georgia‘s precipitation data, another trend was revealed. Here the correlation of SPI and SPEI was relatively low and decreased as the period increases; for example, the correlation for 12- and 24-month periods was lower than for 3- and 6-month periods. This shows that when assessing droughts in East Georgia, it is crucial to take into account the change in temperature along with the change in precipitation. Therefore, in western Georgia, where there is a humid subtropical climate, it is possible to create an idea about the nature of droughts only by using SPI. In the lowland of Eastern Georgia, where it is unlikely to see the accurate picture with only one index, and it is better to use multivariable indices, where along with precipitation, temperature and other data will be taken into account.

How to cite: Tsitsagi, M., Gulashvili, Z., Bolashvili, N., and Leuchner, M.: Comparison of Meteorological Drought Indices in Georgia (1931-2020), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8917, https://doi.org/10.5194/egusphere-egu23-8917, 2023.

In 2018-2020 water managers in the Netherlands were confronted with extreme drought. This event had a large impact on nature, agriculture, shipping and drinking water supply. To better anticipate dry conditions and improve water management during a drought, up-to-date and accurate information about the meteorological and hydrological situation is crucial. During the 2018 drought it became clear that current information about groundwater levels was scattered across many different organisations. In addition, each organisation had different methods to compare current groundwater levels with historical data to indicate the severity of the drought event. There was a clear need for an uniform indication of drought severity.

We developed an online information portal with up-to-date measurements for precipitation and groundwater levels. To quantify the drought severity, the Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiraton Index (SPEI) and Standardized Groundwater Index (SGI) are determined. The availability of long-term records (30> years) of groundwater observations is limited for most regions in the Netherlands. Therefore, the SGI is based on simulations with a time series model for all locations for the same period (27 years). Time series models are developed for 5818 wells with observations. Several criteria have been applied to evaluate the time series model, for example, a minimum value of the explained variance, resulting in 1931 wells for which SGI values are calculated. We have also compared SGI values directly derived from observations with the SGI values from simulated groundwater levels for locations with longer time periods. This comparison indicated that due to errors or missing values in observations, the SGI values from simulations are more reliable to gain a global overview of the drought situation.

By combining the information on meteorological and hydrological drought in one decision-support system (www.droogteportaal.nl), water managers and stakeholders can now get an up-to-date overview of the current situation. Due to the uniform determination of drought severity, regions within the Netherlands can be compared. This can help to implement targeted water management decisions for adaptation measures for mitigating drought impacts. Part of the information of the portal is also included in the national drought monitor of Rijkswaterstaat (Dutch Ministry of Infrastructure and Water Management). At the moment, the portal gives forecasted information for 7 days, but the data provides an excellent opportunity to include forecasts on longer timescales ((sub-)seasonal) to improve water management.

How to cite: van Huijgevoort, M., Brakkee, E., van den Eertwegh, G., Vonk, E., van Deijl, D., and Bartholomeus, R.: Monitoring of drought in the Netherlands in an online portal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7487, https://doi.org/10.5194/egusphere-egu23-7487, 2023.

Brazil has endured the worst droughts in recorded history over the last decade, resulting in severe socioeconomic and environmental impacts. The country relies heavily on water resources, with 77.7% of water consumed for agriculture (irrigation and livestock), 9.7% for industry, and 11.4% for human supply. Hydropower plants generate about 64% of all electricity consumed. One of the most impacted basins was the Paraná River basin It concentrates a third of the Brazilian population in urban centres such as São Paulo, the largest city in Latin America, thus it is the river basin with the greatest demand in the country. This basin is also the most important in hydropower generation, by the highest install capacity for hydropower; 57 reservoirs in the main steam and its tributaries (Grande, Paranaíba, Tietê, Paranapanema and Iguaçu Rivers), with Itaipu having the largest installed capacity (14,000 MW). This study aimed to advance the state of knowledge regarding hydrological drought patterns in the Paraná River Basin for improved monitoring and forecasting.

Droughts, like all hydrometeorological processes, are multivariate processes, that is, they are the result of the interaction of multiple hydrometeorological, climatic, and anthropogenic variables, among others. Therefore, several studies have shown the need to consider a multivariate approach to analysis and modelling drought events, which allows a better evaluation of the characteristics and conditions of its.

In this study we applied: i) well know univariate drought index: SPI, SPEI and SSFI; ii) a multivariate index, obtained through the Copulas Theory and; iii) potential soil moisture conditions obtained through the Normalized Terrain Model HAND, to understand and characterized hydrological droughts in the Paraná River Basin and Subbasins. We used rainfall data from CHIRPS, streamflow data obtained from the Brazilian National Electrical System Operator (ONS) and the National Water and Sanitation Agency (ANA), the SPEI global drought monitor dataset and HAND MERIT dataset (90 m spatial resolution).

The results show that the hydrological droughts in the last decade of 1981–2021, were the most severe and intense. Among the indices, SPEI, SSFI and the multivariate index, presented the strongest evidence, at time scales of 12, 24, 36 and 48 months. The multivariate index together with HAND information allow us to understand better the process of developing, duration, and recovery of drought events.

How to cite: Cuartas, L. A., Fujita, T., Andrade Campos, J., Cunha, A. P., Berttachi Uvo, C., Broedel, E., and Marengo, J. A.: Understanding the hydrological drought processes in the Paraná River Basin., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16778, https://doi.org/10.5194/egusphere-egu23-16778, 2023.

There is an expected increase in the occurrence and severity of hydrometeorological extremes in many regions worldwide. Current research indicates that despite a positive trend in reducing drought impacts, most regions still need to adapt their monitoring practices to cope with projected drought events effectively. On the other hand, we still need a clear understanding of how a changing climate can modify the hydrological regime of catchments in the future. Therefore, it is essential to understand which drought indicators are relevant to monitoring catchments with different hydrological regimes.

Therefore, this study aims to elucidate which drought indices are required to effectively monitor hydrological drought depending on the catchment’s hydrological regime, using  100 near-natural Chilean catchments with contrasting climatic conditions and hydrological regimes as a case study. For this purpose, different drought indices were computed at different temporal scales: SPI and SPEI at 3, 6, 9, 12, and 24 months; the Empirical Standardised Soil Moisture Index (ESSMI) at 3, 6, and 12 months; and a standardised snow water equivalent index (SSWEI) at 3 and 6 months. State-of-the-art gridded datasets used for computing the drought indices were: CR2MET v2.5 (a Chilean dataset based on ERA5) for precipitation and potential evapotranspiration; ERA5, ERA5-Land, and SMAP (L3 and L4) for soil moisture; and ERA5 and ERA5-Land for snow water equivalent. These indices were evaluated against the Standardised Streamflow Index (SSI) to select indices that are able to effectively monitor hydrological droughts, considering different hydrological regimes. A cross-correlation analysis and an event coincidence were used to assess which index had the highest correlation with SSI. Results showed that the indices and temporal scales used to effectively monitor hydrological droughts changed according to the catchment's hydrological regime. The results of the present work are pivotal for water managers as they provide insights on how the hydrological regime of the catchments should be considered in drought monitoring.

How to cite: Baez Villanueva, O. M. and Zambrano-Bigiarini, M.: Effective hydrological drought monitoring depending on the catchment's hydrological regime, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16949, https://doi.org/10.5194/egusphere-egu23-16949, 2023.

Hydrological models often do not simulate properly streamflow (Q) during droughts, because of a poor representation of the interactions among precipitation deficits, actual evapotranspiration (ET), and terrestrial water storage anomalies (TWSA) during these periods. However, there is little research comprehensively evaluating model skills during droughts of varying intensity in a spatially distributed way. To shed further light into these drops in model skills and step toward more robust models in an anthropogenic era and a changing climate, we evaluated Q, ET, and TWSA simulations during moderate and severe droughts, and we tested if calibrating during a moderate drought could enhance model performances during a severe one. We applied the distributed hydrological model Continuum over the heavily human-affected Po river basin in northern Italy and the period 2010 – 2022. Moreover, we exploited independent ground- and remote sensing-based datasets to evaluate the temporal and spatial variability of Q, ET, and TWSA monthly simulations across the whole basin and 38 sub-catchments. Model performances for Q across the study sub-catchments were comparable during both wet years (2014 and 2020, mean KGE = 0.59±0.32) and moderate droughts (2012 and 2017, mean KGE = 0.55±0.25). Further, Continuum simulated well Q for the basin outlet even during a severe drought (KGE = 0.82 in 2022), while its performances generally decreased among the sub-catchments (mean KGE = 0.18±0.69 in 2022). In general, the model well represented ET and TWSA seasonality over the study area, and a decline in TWSA over the more recent years. Yet, during the severe 2022 drought we detected an increased uncertainty in ET anomalies, especially in human-affected croplands, that could explain the Q performance drop along with an increased anthropogenic disturbance. Including a moderate drought (2017) in the calibration period did not lead to a significant improvement in model skills during the severe event (mean KGE = 0.18±0.63 for Q during 2022), meaning that the severe 2022 drought was fairly unique for the study area both in terms of hydrological processes and human disturbance on them. By unveiling an increase in model uncertainty during a severe drought and possible causes for it, our findings are relevant to assess and possibly enhance model robustness in a changing climate and the anthropogenic era for adequate water management, disaster risk reduction, and climate change adaptation.

How to cite: Bruno, G., Duethmann, D., Avanzi, F., Alfieri, L., Libertino, A., and Gabellani, S.: Parameter transferability of a distributed hydrological model to droughts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-417, https://doi.org/10.5194/egusphere-egu23-417, 2023.

We present a near-real time drought monitoring framework that uses precipitation estimates from a selection of satellite (CMORPH-CDR, IMERG) and in-situ (NClimGrid) gridded precipitation products datasets. The near-real time availability of precipitation datasets allows for the computation of the standardized precipitation index (SPI) over various time scales (30-, 90-, 180-, 270-, 365-, 730-day) and daily update of drought conditions. The three drought products generated: CMORPH-SPI (Global; 1998-present; 0.25°x0.25°degree spatial resolution), NClimGrid-SPI (CONUS; 1951-present; 0.05°x0.05°), and IMERG-SPI (Global; 2000-present; 0.1°x0.1°) are being evaluated focusing on the influence of the sensors characteristics and resolutions, differing period of record, and various SPI formulations. The remotely sensed and in-situ SPIs are also compared against existing droughts monitoring resources and in particular the US Drought Monitor (USDM).

The use of cloud-scale computing resources (Microsoft Azure, Amazon Web Services) reduces considerably the computation time. Gain in computational time and process optimization allow for the implementation of a drought amelioration module that is run conjointly with the daily SPI. The drought conditions derived from the precipitation datasets enable us to estimate the amount of deficit precipitation needed to alleviate drought conditions as a function of drought severity and accumulation periods. The process flexibility also allows for the addition of other variables (i.e. temperature, ET) to develop more complex drought indices.  For instance, daily temperature information available from NClimGrid, is used to compute the Standardized Precipitation-Evapotranspiration Index (SPEI) that is evaluated against NClimGrid-SPI over CONUS.

Finally, we present the effort to transfer the SPI from research to operation (R2O). The global daily SPI derived from CMORPH-CDR is publicly available via the Global Drought Information System (GDIS) dashboard (https://gdis-noaa.hub.arcgis.com/pages/drought-monitoring). The other products developed (NClimGrid-SPI, IMERG-SPI) are expected to be added to the existing portfolio of near-real time drought monitoring capabilities.

How to cite: Prat, O., Coates, D., Wilkins, S., Willett, D., Leeper, R., Nelson, B., Shaw, M., Ansari, S., and Huffman, G.: Near-real Time Daily Drought Monitoring Using an Ensemble of Gridded Precipitation Datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11265, https://doi.org/10.5194/egusphere-egu23-11265, 2023.

Droughts are a significant threat to the agricultural sector in general, and rainfed farming in particular. Therefore, effective and timely responses to manage droughts and their impacts are required so that farming systems can limit the negative effects of droughts on food production. We developed a crop drought index (CDI) by integrating drought hazard and exposure and applied this index at the global scale to evaluate the influence of drought on the exposed rainfed areas for different crops. In an attempt to develop an operational, multisectoral global drought hazard forecasting system, we computed and analyzed CDI for historical periods. We further used bias-corrected seasonal climate forecasts to project the drought development in a 7-month period. The CDI was calculated by using the Global Crop Water Model (GCWM) at a global extent (5 arc-minute resolution) from 1980 to 2020. We compared the drought conditions in specific years to the CDI in the 30-year reference period 1986 to 2015. The CDI was computed for 25 specific crops or crop groups based on the relative deviation of the ratio between actual evapotranspiration (ETa) and potential evapotranspiration (ETp) in a specific year from the long-term mean ratio of ETa/ETp during the crop growing season. To test the skill of the seasonal drought forecasts, CDI computed with bias-corrected ensemble forecasts was compared to simulations with standard ERA5-reanalysis data for the year 2018 when severe drought conditions were observed across Europe and other regions. The skill of the CDI to detect drought impacts was tested for historical years by comparing the time series of the harvested area weighted CDI to detrended yield anomalies for crops and countries with predominantly rainfed production. The results of the comparison with historical yield anomalies showed that the CDI is a good indicator for negative yield anomalies, in particular in regions known to be affected regularly by droughts. The model simulations employing the bias-corrected ensemble forecasts reproduced well the reference drought condition in the year 2018 in countries such as Argentina, Australia, Italy, and Spain but showed little skill to reproduce the severe drought in Western Europe. Data availability constraints also had an impact on the accuracy of historical reconstructions and forecasts. For instance, the hazard and exposure analysis rely on static input data for crop shares and crop calendars, which can impact the results (i.e., as cropping patterns are dynamic and often can change over time). The findings suggest that bias-corrected seasonal ensemble forecasts have a significant potential to enhance seasonal drought forecasts, although the skill of the forecasts varies considerably for specific regions. Further research is needed to analyze this potential across different periods and geographies systematically to increase forecasting system efficiency and minimize processing time before this system can be run operationally. In our study, we hence want to demonstrate the current status of the CDI-based forecasting system and discuss the potential, limitations, and uncertainties of such CDI forecasts for agricultural applications.

How to cite: Abbasi, N., Siebert, S., Döll, P., Kunstmann, H., Lorenz, C., and Eyshi Rezaei, E.: Global Drought Hazard Monitoring in Rainfed Areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8730, https://doi.org/10.5194/egusphere-egu23-8730, 2023.

In Flanders, a cumulative precipitation deficit of no less than 330 mm was calculated during the growing season of 2022 (April - September) (Soil Service of Belgium). This high precipitation deficit reflects the importance and need of additional water supply to meet the water demand and the yield potential of the crop. However, this additional water must be administered as efficiently as possible to avoid water waste, while maximizing yields. For decades, the Soil Service of Belgium already offers paid irrigation advice based on simulations with a soil water balance model calibrated with manual soil samples, and weather data, while considering weather predictions separately. With the rise of affordable, autonomous sensors and IoT (Internet-of-Things) technology, it is possible to monitor the soil moisture in a field online and in real time. The use of these sensors offers opportunities such as data accessibility, model calibration, and optimization of irrigation advice.

Soil moisture model simulations and forecasts alone may be less accurate than in situ soil moisture measurements. However, soil moisture forecasts make it possible to anticipate drought or precipitation forecasts, which makes it easier to plan irrigation in advance. Sensor data alone fall short in this respect, as sensors only provide data on the previous and current soil moisture content, but do not provide information on future soil moisture development. Both approaches can be combined by calibrating the model with sensor data via inverse modelling. In this study, DREAM is used as inverse modelling approach to estimate model parameters, including soil and crop growth parameters, as well as their uncertainty. These parameter distributions result in soil moisture simulations, and, when inserting weather forecasts, predictions, along with their uncertainty. The uncertainty of the calibrated model simulations can be used to determine the probability of the soil moisture dropping below the critical water stress threshold.

When this combined approach is compared to the irrigation advice based on a model alone, the soil moisture is simulated and predicted more accurately, resulting in a more efficient water application, while the crop experiences less stress. In the dry growing season of 2022, for example, a celery trial in Flanders (Research Station for Vegetable Production) saved about 45 mm (21%) of water without sacrificing crop quality and yield. In addition to irrigation yield responses, the approach is also validated in light of parameter estimation, and soil moisture simulations, comparing simulated and measured soil moisture content.

How to cite: Hendrickx, M., Diels, J., Vanderborght, J., and Janssens, P.: Simulating and predicting soil water content by combining soil water balance calculations, weather forecasts and soil sensors with inverse modelling for optimal irrigation advice: A case study in Flanders, 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13028, https://doi.org/10.5194/egusphere-egu23-13028, 2023.

Thailand has already been experiencing an increase in severity and duration of its droughts as a consequence of the changing climate. Developing a reliable drought monitoring and early warning system (DMEWS) is an integral part of strengthening a country’s resilience to droughts. However, for DMEWS to be useful for stakeholders, the indicators they monitor should be translatable to potential drought impacts on the ground and, ideally, inform mitigating actions. Here, we analyse these drought indicator-to-impact relationships in Thailand, using a novel combination of correlation analysis and random forest modelling. In the correlation analysis, we study the link between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for general crop health and forest growth. Our analysis shows that these links vary greatly depending on land use (cropland vs. forest), season (wet vs. dry) and region (north vs. south). The random forest models built to estimate regional crop productivity provided a more in-depth analysis of the crop- and region-specific value of different drought indicators. The results highlighted seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effect was somewhat masked in irrigated regions (North). This new high-resolution knowledge of crop- and region-specific indicator-to-impact links can be used as the basis of targeted mitigation actions in an improved DMEWS in Thailand. In addition, the framework developed here can be applied elsewhere in the Southeast Asia region, as well as other drought-vulnerable areas internationally, in particular those that are data sparse.  

How to cite: Tanguy, M., Barker, L., Eastman, M., Ekkawatpanit, C., Goodwin, D., Hannaford, J., Holman, I., Magee, E., Pardthaisong, L., Parry, S., Rey, D., and Visessri, S.: Understanding drought indicator-to-impact relationships to improve drought monitoring and early warning: Thailand as a case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13769, https://doi.org/10.5194/egusphere-egu23-13769, 2023.

The state of Ceará is located in the semi-arid northeast of Brazil and is characterized by strong inter- and intra-annual variability in precipitation. Thus, droughts and an uncertain water supply threaten the people in one of the most densely populated dryland regions in the world. To store and supply water during dry periods, tens of thousands of dams of various sizes have been built, especially since the end of the 19th century. Only 155 of these reservoirs are systematically monitored and managed. For the remaining reservoirs, there is no systematic monitoring and coordinated water resource management so far. In addition to a comprehensive monitoring, it requires an adapted hydrological modeling and forecasting tool to best manage water resources in Ceará and to reduce the impact of future droughts.

In this project, an innovative system for monitoring and forecasting hydrological dynamics in Ceará was developed in collaboration with the Federal Agency of Hydrology and Meteorology (FUNCEME). This system is based on an integrated use of climate modeling, process-based hydrological modeling, remote sensing, and existing databases. Specifically, the following three complementary products have been developed:

• Satellite-based monitoring of stored water volume in reservoirs: Weekly monitoring of water masks of > 30,000 reservoirs is performed by evaluating and classifying Sentinel-1 scenes. The stored water volume can then be inferred from the area-volume relationship derived using high-resolution TanDEM-X CoSSC DEMs for these reservoirs during explicit drought years (i.e. when reservoirs were empty).
• Modeling and seasonal forecasting of hydrological dynamics using WASA-SED: The process-based hydrological model WASA-SED, developed for semi-arid areas, was adapted and calibrated for the state area of Ceará. Information from satellite-based reservoir monitoring is dynamically assimilated in the simulations. Based on an ensemble of ECHAM4.6 climate simulations (updated monthly), the adapted hydrological model is used to generate seasonal forecasts with six months lead time on streamflow and reservoir filling conditions.
• Web-based visualization of monitoring and forecast results: The results of satellite-based monitoring and dynamic hydrological modeling and forecasting are centrally managed in a database and can be retrieved from there by a web application. The corresponding information is visualized online as maps and graphics and made available to different user groups and decision makers.

How to cite: Vormoor, K., Rottler, E., Schüttig, M., Bronstert, A., Estácio, Á., Rocha, R., de Carvalho Junior, V. N., Guimarães, C., and Martins, E.: Innovative system for monitoring and forecasting hydrological dynamics in semi-arid Ceará, NE-Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13796, https://doi.org/10.5194/egusphere-egu23-13796, 2023.

The Piedmont region (north-western Italy) is located between the Alps and the Mediterranean area, two territories that are recognized as climate hotspot regions, showing amplified climate change signals and associated with environmental, social and economic impacts.
A number of water crisis that affected the Italian territory in the last twenty years exacerbated conflicts in different territories with regard to the priority use of water resource. The recent drought events (2017, 2021, and 2022) have seen areas not normally characterized by this type of phenomenon, such as the Piedmont region, go into crisis, involving all water users and human activities.
In this framework, there is a renewed urgency for improved drought monitoring, forecasting and assessment methods, that will allow for better anticipation and preparation and will lead to better management practices, in order to reduce the vulnerability of society to drought and its subsequent impacts.
As drought can be defined in a number of ways and the determination of drought magnitude and impacts can be quite complex, the top scientific priority and social challenge are the identification of meteo-hydrological precursors of water crises. This will lead from meteo-hydrological drought to socio-economic drought and drive water management and decision-making with a strong scientific basis.
In this work we  focused on the Turin area and after identifying the events that have sent in crisis the drinking water supply sources, the meteorological data and appropriate drought indexes have been analyzed. Critical thresholds and parameters have been identified and a first combined index, for developing an operational chain that can alert water utilities, stakeholders and mayors reasonably in advance, is proposed.

How to cite: Brussolo, E., Ronchi, C., Salandin, A., Cremonini, R., and Barbero, S.: Meteo-hydrological precursors of water crisis in the Turin area: a first forecasting and management chain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5990, https://doi.org/10.5194/egusphere-egu23-5990, 2023.

The spring drought over South Korea has been extensive damage recent years and its forecasting can be important in water management and agricultural industries. However, the drought forecasting is not an easy task because of the difficulty to find predictors to the precipitation predictand. Also, limited hydrological records for applying to complex models such as nonlinear or deep learning models do not produce reliable forecasting results. In the current study, we proposed the drought forecasting approach by exhaustive searching for explanatory variables and a regression model for limited record lengths. At first, the target drought index was set with the accumulated spring precipitation (ASP) obtained by the median of the 93 available weather stations over South Korea. Then, exhaustive searching for predictors was performed with association between the ASP and the differences of two pair combination of the global winter MSLP, say Df4m, for the time lag of the spring seasonal drought. The 37 Df4m predictors were found with high correlation over 0.55. The detected 37 variables were categorized into three subregions. The predictors in the same region contain highly similar to each other. Subsequently, the multicollinearity problem cannot be avoidable. To solve the multicollinearity problem, the Least Absolute Shrinkage and Selection Operator (LASSO) model was applied resulting five Df4m predictors and the good agreement of the forecasting value with the observed value as R²=0.72. Therefore, we concluded that the proposed LASSO model with the exhaustive searching of the global MSLP can be a good alternative to forecast the spring drought over South Korea. The spring drought forecasting with the LASSO model and the Df4m predictors can be extensively used for water managers and water industry.  

How to cite: Lee, T., Kong, Y., Kim, T., and Lee, S.: Exhaustive Searching and LASSO for Reliable Drought Forecasting over South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1431, https://doi.org/10.5194/egusphere-egu23-1431, 2023.

The SUDOE AQUIFER project (http://www.igme.es/aquifer/) aims at capitalizing, testing, diffusing and transferring innovative practices for groundwater monitoring and integrated management.

BRGM has developped the « MétéEAU Nappes » web platform (https://meteeaunappes.brgm.fr/fr) for several years. It enables to visualize the current and future behavior of groundwater bodies in France and to forecast groundwater availability in many monitoring wells which have been modeled using a lumped hydrological model [1].

Although more than 500 wells are monitoring groundwater level in real time in unconfined aquifers in the Adour-Garonne basin (France) (https://ades.eaufrance.fr/), none of these monitoring points have been modeled to enable 6 months groundwater levels forecast. The SUDOE AQUIFER project enables to model ten monitoring points in 2022 and 2023 to forecast groundwater levels using different climatic scenarios. These forecasts are updated on a monthly basis and can be compared to groundwater levels thresholds (piezometric drought thresholds from local authority use-restriction orders [2]).

These groundwater level forecasts are further used to predict groundwater withdrawable volume using a three-dimensional groundwater flow model in the Garonne, Tarn and Aveyron alluvial plain [3]. The main activity of this region is agriculture and the main groundwater use is crop’s irrigation. Groundwater withdrawal is especially important in the summer, and can impact the volume of groundwater reaching the rivers and sustaining their baseflow. This competition in use creates the need to accurately define potential withdrawable volumes.

Combining the lumped hydrological models with a three-dimensional groundwater flow model enables to define the potential withdrawable volume based on (1) the summer climatic scenario chosen by the decision maker, (2) the forecasted groundwater level at the end of the low-water season and (3) the status of the groundwater body (critical, balanced, conservative) to achieve at the end of the low-water season. This decision support tool is developed as a web platform and will be accessible to groundwater managers and decision makers. After choosing the groundwater level forecasted at the start of the irrigation period within 6 scenarios based on different climatic conditions, three potential withdrawable volumes will be defined depending on the status of the groundwater body considered acceptable to obtain at the end of the low-water season. This information can then be communicated to groundwater users.

These innovative practices will be extended to other regions where increase groundwater pressure forces local authority to develop methods and tools to sustainably manage groundwater bodies.

Références bibliographiques :

 [1] Mougin B., Nicolas J., Vigier Y., Bessière H., Loigerot S. (2020). « MétéEAU Nappes » : un site Internet contenant des services utiles à la gestion des étiages. La Houille Blanche, numéro 5, p. 28-36. https://doi.org/10.1051/lhb/2020045

[2] Surdyk N., Thiéry D., Nicolas J., Gutierrez A., Vigier Y., Mougin B. (2022). MétéEAU Nappes: a real-time water-resource-management tool and its application to a sandy aquifer in a high-demand irrigation context. Hydrogeology Journal. https://doi.org/10.1007/s10040-022-02509-1

[3] Le Cointe, P., Nuttinck, V., Rinaudo, JD. (2020). A Tool to Determine Annual Ground-Water Allocations in the Tarn-et-Garonne Alluvial Aquifer (France). In: Rinaudo, JD., Holley, C., Barnett, S., Montginoul, M. (eds) Sustainable Groundwater Management. Global Issues in Water Policy, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-32766-8_13

How to cite: Beranger, S., Le Cointe, P., and Mougin, B.: Groundwater level and withdrawable volume forecasts in the Adour-Garonne basin (France) to enable sustainable groundwater management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6607, https://doi.org/10.5194/egusphere-egu23-6607, 2023.

The consistent impact of droughts and the progressive use of groundwater for the superficial allocation of crops has extremely increased groundwater withdrawal. The rapid economic expansion is increasing water usage and is likely to exacerbate hydrological drought. While global drought intensities are increased by 10–500\% due to human water consumption, the consequences at a regional and global scale are aggravated by changing precipitation patterns, resulting in multi-year droughts and decreased groundwater recharge. 

An essential factor to better understand how human activities affect drought characteristics and development is to quantitatively distinguish natural and human components. At the same time, we see that the recovery from a severe drought is also impacted by catchment characteristics and regional climatology. In this study, we focus on the south American non-Amazon region which has frequently experienced multi-drought periods with severe impacts on surface and groundwater.

We estimate the drought impact on groundwater with the model PCR-GLOBWB2 at a 5 arcmin resolution under natural and human influence. Aggregations of the model output at a catchment level of the groundwater and subsurface partitioned run-off was performed. To determine the influence with and without lateral water flux at high resolution, the flux differences of groundwater components such as baseflow and groundwater recharge were quantified. Finally, the drought termination (DT) framework was applied to understand the recovery response of simulated surface runoff, interflow, and groundwater recharge.

The PCR-GLOBWB2 identifies regions influenced by human impact in the non-Amazon basins, supported by the drought duration, deficit, and groundwater fluxes. The differences in fluxes show an increasing groundwater withdrawal due to irrigated zones, affecting hydrological processes at a catchment and regional scale. The recovery of fluxes during these events consists of a relevant indicator for groundwater behavior due to drought and/or human consumption. We quantified the impact on groundwater resources by addressing the land-use component to understand the variability in water volumes. This study is beneficial to identify groundwater drought vulnerability in regions where observations are lacking and help to predict drought recovery periods, lateral-flux impacts, and characteristics.

How to cite: Vega Briones, J., de Jong, S. M., Sutanudjaja, E., and Wanders, N.: Human and natural drought impacts on groundwater fluxes of non-Amazonian South America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8074, https://doi.org/10.5194/egusphere-egu23-8074, 2023.

The reservoir is a hydroengineering structure to regulate discharge in rivers and store water. It can be used for flood control, water supply, irrigation, power generation, etc. It is also used for physical water management to cope with droughts at the catchment scale. The reservoir operations can have a mitigating and/or enhancing impact on droughts and their propagation from meteorological to agricultural and hydrological drought.

The aim of the study is to assess the role of reservoir operation on drought propagation using the Sulejow and Wiory reservoirs as case studies in the catchments of the Pilica and Kamienna rivers (central Poland), respectively. Two approaches, namely hydrological modelling and the observation-based approaches, were used for the study. In the hydrological modelling method, the naturalised hydrological variables in the post-dam period simulated using the Soil and Water Assessment Tool (SWAT) were compared with the observed values in the same period, while in the observation-based approach, the upstream and downstream hydrological variables such as soil moisture (remote sensing data) and observed river discharge were used. In addition, the SWAT with reservoir was considered by applying the target reservoir release method for simulating the downstream hydrological variables and comparing it with the method without reservoir. The threshold method, based on the parameter transfer method, was applied in the analysis of drought conditions to account for the non-stationarity of the hydro-climatic variables.

Preliminary results suggest that the two approaches are consistent in showing the impact of reservoir operations on the propagation and characteristics of droughts. In addition, the comparative analysis between the reservoirs shows differences based on their purpose. The results of the study can be used to understand the propagation of drought in human-altered watersheds and to appropriately manage water resources for drought mitigation.

Acknowledgements

This work was supported by the HUMDROUGHT (https://humdrought.igf.edu.pl) project carried out at the Institute of Geophysics of the Polish Academy of Sciences and funded by the National Science Centre (contract 2018/30/Q/ST10/00654).

How to cite: Senbeta, T. B., Karamuz, E., Kochanek, K., Napiorkowski, J. J., and Bogdanowicz, E.: Human interventions impacts: the role of reservoir operations on drought propagation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7180, https://doi.org/10.5194/egusphere-egu23-7180, 2023.

Extreme value statistics are well established for floods and are also receiving increasing attention in drought hydrology. They allow the user to characterize the severity of an event by a statistical probability or return period, a concept that is well understood in the scientific, policy, and public arenas. Frequency analysis is usually carried out on the basis of annual extreme event series, which  is straightforward in its application and interpretation. However, in seasonal climates with a warm and a cold season, the low-flows can be generated by different processes, which violates the basic assumptions of extreme value statistics and can lead  to inaccurate conclusions.

Here we assess the value of a mixed distribution approach for low-flows to perform frequency analysis in catchments with a mixed summer/winter regime. We first present the theoretical concept of the mixed probability estimator for low-flows. We then illustrate the characteristics of the model for archetypal low-flow regimes, from pronounced summer and winter regimes to flow mixtures with weak seasonality. We successively evaluate the gain in performance from the mixed distribution model for a range of low flow regimes, based on a comprehensive Austrian dataset. We finally scrutinize the assumption of the mixed probability estimator and review the added value of using an extended, Copula-based  framework. The results show large differences of event return periods, and suggest that the mixed estimator is relevant not only for mountain forelands, but for a much wider range of catchment typologies across Europe. These even include typical summer regimes when only single winter low-flows are mixed in. We conclude that the mixed distribution approaches outperform the conventional frequency estimator and should be used by default in seasonal climates where summer and winter low flows occur.

How to cite: Laaha, G.: The value of mixed distribution approaches for low-flow frequency analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4914, https://doi.org/10.5194/egusphere-egu23-4914, 2023.

In the past two decades, Europe has been hit by major summer heat waves and droughts, with heavy impacts on ecology, economy and civil society.

In addition to increased risk of crop failure, forest fires and danger to human health, extensive dry conditions may lead to riverine low flows and general water scarcity. Low flow conditions can restrict river navigation, hydropower production, and limit water use for power plant cooling and irrigation agriculture. Furthermore, the ecological state of the river is impaired.

To address these challenges, setting up a hydrological model based on a large ensemble climate simulation provides the required data to evaluate the water availability under future heat and drought conditions. Therefore, we create a hydrological large ensemble with 50 realizations for the periods 1990 – 2099 featuring the Water balance Simulation Model (WaSiM). The single-model initial condition large ensemble (SMILE) CRCM5-LE (CRCM5-Large Ensemble) used consists of 50 transient simulations (50 members) of a regional climate model of 150 years each (1950-2099, 7500 model years, hourly time step, 0. 11° spatial resolution) and provides the meteorological forcing data, after bias correction and statistical downscaling to the hydrologic model application scale, for 98 gauges simulated with the WaSiM-ETH water balance model in hydrological Bavaria. Due to the high number of model years, this model chain on the one hand provides a novel way to transfer and assess the non-linear relationships of the natural variability of the climate system within the hydrological system, and on the other hand results in a sufficiently large number of extreme events to conduct a robust statistical analysis.

Based on the modeling results, the dynamics of the low flow situation in Bavaria is mapped for the reference period (1981-2010), spatial patterns of drought are highlighted, and regional correlations are identified. To allow for seasonal comparisons of the negative anomalies of the runoff event, the variable-threshold approach is used. Here, the threshold is defined as the 15th percentile for the 30-day moving average of the discharge value for each day of the year, averaged over the reference period. An undershoot of this threshold for at least 20 days is considered a drought event. The use of climate simulation data allows for an analysis of how these characteristics (intensity, duration, spatial occurrence of the drought event) will change in the future due to climate change. Emphasis is placed on the potential change in the seasonal regime and the associated impacts on river system usage. By accounting for the natural variability of the climate system through the ensemble approach, the results become more robust, particularly with respect to extremes, and strengthen confidence in the change signals that are observed.

Results of these analyses are presented using a representative sample of watersheds for the entire study area, highlighting common features as well as unique characteristics. The evaluations provide important evidence for the basic definition of low-flow events and a robust estimate of how their intensity, frequency, and seasonality changes in the future as a result of climate change impacts.

How to cite: Sasse, A., Böhnisch, A., and Ludwig, R.: Low flow in Bavaria: derivation of drought characteristics and their future development in a hydrological single-model large ensemble., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11800, https://doi.org/10.5194/egusphere-egu23-11800, 2023.

Winter warm spells (WWS) are extreme temperature anomalies that might impact the snowpack. WWS amplify snowmelt and sublimation in mountain regions with uncertain consequences to timing and volume of water resources. Most studies focus on the spring season when snowmelt rates and streamflow response are high. However, winter snowmelt events are important in places where the snowpack and air temperatures are closer to the freezing point during winter, and thus it will become important in other regions in a warmer climate.

This study aims to understand the effect of WWS on snowpack ablation patterns in the mountainous western North America and how they might change under a warmer climate. For this, we use two convection permitting regional climate model simulations to represent historical (2001-2013) and future atmospheric and the surface conditions. The future simulation is performed with a Pseudo Global Warming approach for a high emission scenario (RCP8.5). We verify WWS using gridded maximum daily temperature observation, and winter ablation using snow pillows. Then we characterize WWS and relate them to snowpack ablation.

Although days with ablation during WWS represent a small fraction (8.3%, 0.6 days on average), 55% of total ablation occurs during WWS over regions with significant snowpack (mean peak snow water equivalent over 150 mm). Consistently, a larger ablation rate (53%) is found during WWS than non-WWS events. Total ablation during WWS increases about 157% in a warmer climate; however, the extreme ablation (99^th percentile) rates show slight decrease (5%). Classifying the domain based on its humidity and temperature, we found that ablation rates during WWS in humid regions are larger in a warmer climate than those of the dry regions, which is explained by the differences in the energy balance and the snowpack cold content. WWS predominantly drive snowmelt (93.8%) rather than sublimation (6.2%), which has relevance to water resources such as flood risk, soil moisture, and streamflow response. Furthermore, the median snowmelt rate during WWS found to increase in response to warming by 179% compared to the median sublimation rate (125%). This study provides a comprehensive description of the impact of extreme temperature events and a warmer climate over our changing snowpack. We acknowledge financial support by Centro CRHIAM Project ANID/FONDAP/15130015, and the Anillo project ACT-210080.

How to cite: Scaff, L., Krogh, S., Musselman, K., Harpold, A., Lillo-Saavedra, M., Oyarzún, R., Li, Y., and Rassmusen, R.: Winter Warm Spells and snowpack ablation in western North America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6903, https://doi.org/10.5194/egusphere-egu23-6903, 2023.

Water resources management is becoming increasingly challenging under current climate change. Water utilities need to assess planning adaptation strategies aimed at sustainable water resource exploitation. In this study, we estimate the potential impacts of climate change on hydrological variables and future spring discharge availability. Specifically, we exploit an empirical regressive model based on the statistical relationship between Standardized Precipitation-Evapotranspiration Index (SPEI) and minimum annual spring discharge, in combination with Regional Climate Models (RCMs) provided by the EURO-CORDEX initiative. In this regard, two Representative Concentration Pathways (RCPs) are considered, RCP4.5 (intermediate emissions scenario) and RCP8.5 (high emissions scenario), as well as two future time horizons, namely the near future 2021-2050 and the far future 2041-2070. Then, after bias correction of the so estimated minimum spring discharge values, the curves relating spring discharge and reliability in satisfying water demand are assessed. We carried out our investigation for karst aquifers located in the Italian Apennines, which are used for the water supply system of the city of Rome (Italy) and the surrounding areas, managed by ACEA Ato2, serving over 4 million users. Overall, the results indicate a general decrease in the demand that can be satisfied with high reliability, pointing out significant potential impacts of climate change on water availability on both near and far future. The proposed methodology could be a useful tool for water managers, since it provides a support for planning adaptation measures aimed at minimizing future socio-economic impacts of climate change.

How to cite: Peres, D. J., Palazzolo, N., Mineo, C., Passaretti, S., Boscariol, E., Varriale, A., and Cancelliere, A.: Assessing the impacts of future climate change scenarios on water systems supplied by karst aquifers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2252, https://doi.org/10.5194/egusphere-egu23-2252, 2023.

Over the past decade, the Adige river basin in the Eastern Italian Alps has experienced water scarcity during early spring and late summer, due to a combination of decreased snowmelt, less precipitation, and increasing water demand. This condition has caused tension and disputes between upstream and downstream water users, particularly between hydropower companies in the upstream region (Trentino/South Tyrol) and agricultural users in the downstream areas (Veneto region). The potential for water scarcity impacts to intensify and expand in the future remains a major concern with climate change leading to more frequent warm and snow droughts in the region. Informing the region's administration, institutions, communities, and businesses to manage water scarcity conditions, is essential to prepare and mitigate the potential future impacts. This work aims to explore decision-making options in drought conditions in the Adige river basin, along with the potential impacts of climate change, by exploiting hydrological models for the river basins and for the major reclamation consortium in the area. The study will focus on years with severe drought, such as the 2022 drought period, using simplified decision options and examining how the decisions to meet the water needs of hydropower agencies in the upstream part of the Adige river basin could impact agricultural water use in the downstream part. The analysis will then be repeated in similar conditions, but with the added element of climate change forcing and reduced glacier volumes in the Alps. This study will identify those needs that might not be fulfilled in certain drought scenarios providing valuable insights for decision-makers and supporting the development of effective strategies to prepare and better manage future water scarcity conditions in the region.

How to cite: Shrestha, S., Zaramella, M., Bertoldi, G., Borga, M., Terzi, S., and Massimiliano, P.: Water scarcity and climate change impacts in the Eastern Italian Alps: A case study of the Adige river basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13266, https://doi.org/10.5194/egusphere-egu23-13266, 2023.

Drought is one of the costly natural disasters that affect water resources, agriculture and ecosystems. This study developed a standardized aridity index (SAI) to quantify the short- and long-term drought, and then decipher the climate drivers of the drought on local, regional and continental scales.  The ratio of total precipitation (P) to total potential evapotranspiration (PET) for a given month or multiple months was firstly calculated, and then normalized to calculate the SAI. The contribution of P, PET as well as temperature, solar radiation, wind speed and relative humidity on SAI can be decomposed by apply partial derivation of SAI and PET algorithm (i.e., Penman-Monteith model). The SAI is highly correlated to several frequently used drought indexes.  We also examined the temporal variations and spatial extent of different droughts across the global. The contributions of different climate variables on these droughts were also examined. The spatial distribution of individual droughts and their intensity revealed by SAI are comparable to those calculated using existing drought indexes and drought monitors. For example, the 12-month SAI and other drought indexes all suggested a several drought condition in the central Europe during 2015-2020, which is unprecedented in the past 2,000 years. We found that this drought was firstly initiated by precipitation deficit, but the PET became important in the late years of this drought. On average, the precipitation contributed to 70%, while the PET contributed to another 30% to this multi-year drought. The temperature warming alone contributed to about 20% of the drought intensity.

How to cite: Feng, S. and Trnka, M.: Quantifying the climatic drivers of drought using a standardized aridity index, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10679, https://doi.org/10.5194/egusphere-egu23-10679, 2023.

The predominantly desert region of the Arabian Peninsula (AP), comprising seven nations, is characterized by high temperatures and meager rainfall. Temperature, and dust activity, are exacerbating over the AP. In the current study, we found that drought frequency and severity have increased in the AP over the last two decades compared to the previous five decades. This recent drought intensification is characterized by dominant decadal variability in addition to what appears to be a long-term trend. The current droughts intensification appears to be driven by increased warming over the AP than by a decrease in local precipitation. The Atlantic Multidecadal Oscillation (AMO) cycle is strongly related to decadal drought variability, and the current unprecedented multiyear drought is associated with current positive phase of AMO. We developed a statistical model for future projections that indicates that the frequency and intensity of droughts over the AP are expected to decrease significantly in the coming year.

How to cite: Saharwardi, M. S., Dasari, H. P., Ashok, K., and Hoteit, I.: Drivers of sustained drought over the Arabian Peninsula in recent decades, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3295, https://doi.org/10.5194/egusphere-egu23-3295, 2023.

Although the several studies carried out in the Souss Massa region, in terms of water resources, the assessment of the drought is still understudied, particularly groundwater drought which remains a gap in the previous studies. In this work, meteorological drought is investigated by using the standardized precipitation index (SPI) to shed light on its impact on groundwater drought occurrence. Thereafter, a combination of reliability analysis and standardized water level index (SWI) is used for groundwater risk modeling. Reliability analysis accounts for the safety and the failure of a system regarding loads, which take into account the external effects (withdrawals and recharge), and resistance which accounts for the system's capacity, thereafter values of Groundwater Drought Risk (GDR) and Environmental Hazard Index (EHI) are generated and then spatially distributed to assess groundwater risk for mild, moderate, severe, and extreme droughts for the whole region of Souss-Massa. Results showed a wavering between short dry and wet periods based on SPI, and demonstrated a weak correlation between the SPI and the SWI, hence the upward trend in the SWI is explained by the anthropogenic overexploitation of the aquifer. Furthermore, groundwater drought risk (GDR) values are low in the upper Souss and increase in the middle part and in the Massa basin, where significant effects are potentially expected. Based on the EHI results, it is confirmed that the Massa basin and the middle Souss are susceptible to groundwater drought and its environmental impact and need immediate intervention to properly manage the groundwater resources. This model could be helpful for the policymakers for better planning of water supply by providing useful information about the expected frequency and severity of water shortage in the studied area.

Keywords:
Groundwater drought, Reliability analysis, meteorological drought, anthropogenic activities, Souss Massa basin.

How to cite: Gouahi, S., Hssaisoune, M., Nehmadou, M., Bouaakkaz, B., Boudhair, H., and Bouchaou, L.: Spatio-temporal assessment of groundwater drought risk in the Souss-Massa aquifer: Impacts of climate variability and anthropogenic activity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14822, https://doi.org/10.5194/egusphere-egu23-14822, 2023.

Peru and Ecuador have suffered high economic losses because of extreme events (Floods and Droughts). The analysis of the meteorological droughts and their drivers is of paramount importance for water resources management and risk assessment in these countries. This study aims to characterize the spatiotemporal variability of droughts across Peru and Ecuador over the last four decades (1981-2020) and evaluate the relationship with the ocean-atmospheric circulation patterns. The Rain for Peru and Ecuador (RAIN4PE) gridded precipitation dataset was used to estimate the Standardized Precipitation Index (SPI) at time scales of 3 and 12 months to assess short and long-term droughts, respectively. Droughts were characterized by the number of events, duration, intensity, and severity, and the relationship was evaluated by computing the Pearson correlation to identify the leading oceanic-atmospheric indices: E (Eastern Pacific SST anomalies), C (Central Pacific SST anomalies), PDO (Pacific Decadal Oscillation), SOI (Southern Oscillation Index), MEI2 (Multivariate Enso Index), TPI (Tripole Index for the Interdecadal Pacific Oscillation), TNA (Tropical North Atlantic index), and TSA (Tropical Southern Atlantic Index).

The results show high spatiotemporal variability of drought characteristics with the high frequency of extreme droughts over the southern Peruvian Andes in Peru and the eastward of the Andes in Ecuador. The ranking of the extremeness of drought events based on the areal extent, duration, and intensity identified that three of the four more extreme events match ENSO conditions in Peru (1992/02, 1988/08, 1990/01) and Ecuador (1985/04, 1990/01, 1995/04). Finally, strong relationships between ocean-atmospheric indices and droughts in Peru and Ecuador were identified. Droughts in Peru evidence significant correlations with E, C, and TNA indices. Similarly, droughts in Ecuador show high correlations with E, C, PDO, TPI, and SOI indices. These results provide more insights into the characteristics of droughts and the possible drivers, information that is useful for water resource management decisions and can help as the basis for developing drought forecasts.

How to cite: Vega-Jácome, F., Bronstert, A., Fernandez-Palomino, C. A., and Lavado-Casimiro, W.: Drought characterization across Peru and Ecuador and its relationship with ocean-atmospheric indices, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17360, https://doi.org/10.5194/egusphere-egu23-17360, 2023.

Streamflow drought is addressed as below-normal water availability in large rivers and tributaries. Streamflow drought impacts several sectors, including irrigation, river ecology, hydroelectric potential, financial, and drinking water supply. Analyzing variability in streamflow drought timing and the nonlinear interactions between drought onset and severity is necessary not only for better understanding of drought predictability but also of its temporal change, which aids in developing climate adaptation strategies. Very few studies have assessed the seasonality of streamflow droughts, although a few analyses have been performed focusing on other hydroclimatic extremes, such as extreme precipitation and floods. However, little is known about understanding the shifting behaviour of streamflow drought onset patterns at a local or regional scale. Further, a few studies have assessed the severity of low flows at a global and local scales. However, most of these studies have either considered a constant threshold approach to delineate low-flow episodes or employed sub-seasonal (monthly) temporal scales to access streamflow droughts using standardized indices of precipitation or runoff. However, none of the studies have investigated the non-linear interactions between streamflow drought onset and deficit volume and how these bivariate interactions evolve over time across large river basins. Here we investigate the timing of the streamflow drought onset and its severity (i.e., deficit volume) over 472 catchments that are spatially distributed across 21 Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme events and Disasters to Advance Climate Adaptation (SREX) reference regions in the global Tropics. We identified those catchments with little or no potential anthropogenic influences and were selected based on a detailed quality assessment of continuous streamflow records and their proximity to dam locations. We implemented a daily variable threshold approach with an 80% exceedance probability of the flow record to identify streamflow drought episodes. Moreover, based on large streamflow records, we compare the potential shifts in the seasonality of streamflow droughts in the recent (1994-2018) versus the pre-1990s (1969-1993). We show a strong persistency in the timing of streamflow drought onset in the core monsoon-dominated regions. In the northern hemisphere, the mean onset is observed primarily during August and September, whereas in the southern hemisphere, the onset timing is temporally clustered around November to March. Our proof of concept analysis suggests that North-East South-America is the most vulnerable region, in which an earlier occurrence of drought is compounded by an increasing deficit volume, indicating a drying trend throughout. Furthering this, we investigate the non-linear interactions between drought characteristics, onset time, and severity to decipher the pattern of associations across disparate climate regimes, especially in regions with pronounced seasonal cycles. The obtained insights has important implications for water resources management in tropics, where seasonal climates dominates. The findings can inform drought monitoring, planning and improve drought resilience to multiple climate stressors.

How to cite: Raut, A. and Ganguli, P.: Examining Changes in Nonlinear Interactions of Streamflow Drought Seasonality versus its Severity across Global Tropics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2182, https://doi.org/10.5194/egusphere-egu23-2182, 2023.

Grazing is an important ecosystem process affecting more than a third of the global land surface. However, it is challenging to predict responses of rangelands to changing grazing regimes due to complex interactions between grazers, vegetation and climate. Understanding the multiscaling behavior of vegetation and climate time series can be key to improving grazing and vegetation management in semiarid areas where climate change is heavily affecting vegetation-climate complex systems. 

A grassland plot in central Spain (Madrid) was selected to study this system. This plot was selected based on proximity to a meteorological station and maximum surface covered by grasses. For this plot, reflectance data were collected from MODIS (MOD09A1.006) to study the Normalized Difference Vegetation Index (NDVI). These series, from 2002 to 2020, have a 250 m spatial resolution and 8-days temporal resolution. Daily meteorological precipitation and evapotranspiration were obtained from the closest station from AEMET (Spanish Meteorological Agency). Precipitation was accumulated over 8-days and the aridity index was calculated (accumulated precipitation over accumulated potential evapotranspiration) for every 8-days to match the temporal resolution of NDVI. With these three series (NDVI, precipitation and aridity), multifractal detrended fluctuation analysis was performed, to calculate the persistence (H2) and multifractality. Furthermore, this was also done to these series after shuffling and surrogating them. 

The aridity index showed a high persistent character, while precipitation had a light persistence and NDVI showed no persistence or antipersistence, instead, it had a random character. The aridity index and NDVI displayed a decrease in H2, progressively, when surrogate and shuffle series were used. On the other hand, precipitation showed a higher H2 when the surrogate series was used compared to the original series. The shuffle precipitation series had a lesser value of H2 than the original and surrogate precipitation series. The increase in persistence on the precipitation surrogate series, have been reported in other precipitation series and it may indicate that the year that cause a decrease in persistence in the original series are separated along the original series. 

The most multifractal series was found to be NDVI followed by aridity index and finally precipitation. The multifractality always declined when the surrogate series was used in all series. Moreover, when shuffle series were used multifractality was almost eliminated in NDVI shuffle series, but some was retained for precipitation and aridity index, showing a larger source of multifractality due to the probability density function in these two series, mixing with a long-range correlation source of multifractality (mostly dominant for NDVI). 

Acknowledgements: The authors acknowledge the support of Clasificación de Pastizales Mediante Métodos Supervisados - SANTO, from Universidad Politécnica de Madrid (project number: RP220220C024).

Bibliography:

Baranowski, Piotr, et al. "Multifractal analysis of meteorological time series to assess climate impacts." Climate Research 65 (2015): 39-52.

Sanz, Ernesto, et al. "Generalized structure functions and multifractal detrended fluctuation analysis applied to vegetation index time series: An arid rangeland study." Entropy 23.5 (2021): 576.

Sanz, Ernesto, et al. "Clustering Arid Rangelands Based on NDVI Annual Patterns and Their Persistence." Remote Sensing 14.19 (2022): 4949.

How to cite: Sanz, E., Almeida-Ñauñay, A., Díaz-Ambrona, C. G., Saa-Requejo, A., Ruíz-Ramos, M., Rodriguez, A., and Tarquis, A. M.: Multiscaling behavior of vegetation, precipitation and aridity time series in semiarid grasslands. Persistence and multifractal sources., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6835, https://doi.org/10.5194/egusphere-egu23-6835, 2023.

Semiarid rangelands are one of Africa’s most complex and variable biomes. They are a mosaic of land uses, where extensive livestock is the main economic activity, and agriculture is also crucial. They are highly controlled by the availability of water, e.g., pasture and rainfed crop production. Although the vegetation is adapted to variable climatic conditions and dry periods, the increase in drought intensity, duration, and frequency precipitate their degradation. By integrating Earth Observation data into models, we can evaluate, on the one hand, the vegetation water stress and, on the other, its primary production. This allows us to assess the interaction of both processes, improving our knowledge about the vegetation's behavior in the face of drought.

In this work, we set up an open-source cloud framework to monitor water consumption and primary production interaction over this semiarid mosaic in the long term, to analyze system tipping points. This information can help reduce the uncertainty associated with the public administration and farmers’ decision-making processes. A surface energy balance model, previously validated in the area, was applied to estimate evapotranspiration (ET) from 2000-2020 (monthly, at a 1 km spatial resolution, using MODIS data and global atmospheric reanalysis dataset). The anomalies of evapotranspiration (ET) to reference ET were used as an indicator of drought for the period. The biomass production was estimated by applying an adaptation of the Monteith LUE (light use efficiency) model based on the relationship between plant growth and incident solar radiation. The parameterization of the model corresponded to semi-natural grasslands and crops, and it was applied at a daily scale with 250 m of spatial resolution. The model’s estimation presented an acceptable agreement over the area.

Close links between grassland/crop production and drought events were found and evaluated. 2016 was the worst year regarding the state of the vegetation, followed by 2015, 2003, and 2002, all coincident with drier events (as measured by ET/ETo anomalies). The different production patterns of each patch of vegetation were visible. Although crops were mainly rainfed (probably being irrigated if necessary) and followed the precipitation rates, they were less dependent on rain than grassland. Croplands had higher production peaks during February/March than natural grasslands, although trends were similar. Production rates were much higher than usual during 2004, 2009, and 2017. These vegetation blooms came after a drought where biomass production rates were minimal. A thorough analysis of these results can provide insights to better cope with future droughts.

Acknowledgment: This work has been carried out through the project "DroughT impACt on the vegeTation of South African semIarid mosaiC landscapes: Implications on grass-crop-lands primary production" funded by the European Space Agency in the framework of the "EO AFRICA R&D Facility".

How to cite: González-Dugo, M. P., Muñoz-Gómez, M. J., Nieto, H., Polo, M. J., Dube, T., and Andreu, A.: Vegetation dynamic and drought: South African savanna case study., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5680, https://doi.org/10.5194/egusphere-egu23-5680, 2023.

Agricultural drought refers to a period with declining Soil Moisture (SM) content and consequent crop failure from water stress. SM plays an important role in indicating water stress and thereby identifying agricultural drought. Due to the lack of large scale, fine resolution, and accurate/quality SM many agricultural drought studies are mostly based on ground-based SM observations having limited spatiotemporal variability and cannot be applied for large scale studies. Microwave remote sensing showed capability in estimating geophysical properties like SM and paved the way for a continuous agricultural drought monitoring. European Space Agency (ESA) under Climate Change Initiative (CCI) developed an active-passive multi-satellite merged ESA CCI SM dataset. In this study, ESA CCI SM’s potential in agricultural drought monitoring is explored, by deriving Empirical Standardized Soil Moisture Index (ESSMI) to identify agricultural drought in Indian state of Telangana from 2001 to 2020. Telangana is a severely drought-prone state of India heavily impacted by significant water stress and water shortages due to frequent droughts. This increases the need for accurate agricultural drought characterization in the state. Keeping in mind the necessity of drought monitoring system for Telangana and availability of large-scale satellite soil moisture data from ESA CCI, this present study employs the ESSMI using the non-parametric distribution of ESA CCI SM data, to characterize the agricultural drought in drought prone Telangana. The efficiency of ESSMI in drought monitoring is evaluated by comparing it to the Standardised Precipitation Index (SPI) and Rainfall Anomalies (RFA) calculated from India Meteorogical Department (IMD) daily gridded rainfall data. Both the indices along with the RFA identified 2009 as dry year and 2020 as wet year. Temporal evolution of monthly drought identified by ESSMI showed monthly delayed response when compared with SPI, whereas yearly ESSMI showed consistency with SPI and RFA. Different classes of drought areas identified by ESSMI are compared with SPI which showed near normal and mild dry regions for most of the study period. ESSMI is able to effectively capture near normal to moderate drought events and shows a consistent association with the SPI and RFA both in short and long term (monthly and annual) temporal scale. The study showed the overall performance of ESSMI is reliable for agricultural drought monitoring and can be used to develop effective drought warning and risk management.

How to cite: Palagiri, H. and Pal, M.: Agricultural Drought Monitoring using Satellite based Surface Soil Moisture Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-621, https://doi.org/10.5194/egusphere-egu23-621, 2023.

Recent hot and dry summers in Europe have had a significant impact on forest functioning and structure. In 2018 and 2019, Central Europe experienced two extremely dry and hot summers. These extremes resulted in widespread canopy defoliation and tree mortality. The objective in this study is to create a predictive model for predicting the density of vegetation, as measured by the NDVI index. We predict NDVI at a horizon of a month utilising data from the previous months as input to determine where and when drought impacts are triggered. Such predictive models should take into account both spatial and temporal dependencies between environmental variables and impacts. We hereon focus on Switzerland's forests as a region of interest to leverage high-quality model input layers and applications to typical stakeholder needs. Widely used vegetation indices and mechanistic land surface models are not effectively informed by the full information contained in Earth Observation data and the observed spatial heterogeneity of land surface greenness responses at hillslope-scale resolution. Effective learning from the simultaneous evolution of climate and remotely sensed land surface properties is challenging. Modern deep learning and machine learning techniques, however, have the capacity to generate accurate predictions while also explaining the relationship between climate and its recent history, the position in the landscape, and influences on vegetation. The task is to predict the future NDVI over forest areas, given past and future weather and surface reflectance. Giving future weather predictions as an input to the model, we are going for a 'guided-prediction' approach where the aim is to exploit weather information from forecasting models in order to increase the predictive power of the model - similar to the EarthNet2021 Challenge. Models are fully data-driven, without feature engineering and trained on spatio-temporal datacubes which can be seen as stacked satellite imagery for a specific geo-location and a timestep of past Sentinel 2 surface reflectance, past (observed) and future (forecasted) climate reanalysis, time-invariant information from a digital elevation model, and land cover map. The data pre-processing step includes implementing a customized dataset for drought impact prediction task, and a customized data sampler in order to be able to sample data (scenes) both spatially and temporally. Additional data operations include  aggregation of the weather data, normalization, and data imputation both on the image-level and missing-day level. For the prediction task, we used Convolutional Long-Short Term Memory models. In the temporal domain, models are trained on the period between 2015-2018, and be validated between 05-2019 and 09-2019. For the test period summer months of 2020 and 2021 will be used. However, in the spatial domain, for the sake of testing the generalizability of the model, different regions were used for train, validate and test processes. In order to asses the models performance on the temporal domain, tests with different training and testing window sizes are used. As for evaluating the performance of the model, Mean Squared Error was used. The project will lay the basis for an early warning platform to enable periodically updated near-term drought-impact forecasts.

How to cite: Durukan, E. D., Ledain, S., Brunschwiler, T., Tuia, D., Günther, M., and Stocker, B.: Forest Drought Impact Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts -- A Spatio-Temporal Approach using Convolutional LSTM Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12437, https://doi.org/10.5194/egusphere-egu23-12437, 2023.

How to cite: Ledain, S., Durukan, E. D., Brunschwiler, T., Günther, M., Tuia, D., and Stocker, B.: Forest drought impact prediction based on satellite imagery and weather forecasts - a spatially distributed approach using a recurrent deep neural network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12672, https://doi.org/10.5194/egusphere-egu23-12672, 2023.

Drought is a transitory anomaly, prolonged, characterised by a period with precipitation values lower than normal in a specific area. The initial cause of any drought is a shortage of precipitation (meteorological drought) which leads to a shortage of water resources (hydrological drought) necessary to supply the existing demand. Flash drought is a critical sub-seasonal phenomenon that can be devasted for the ecosystems and, consequently, for general economy and health. There are areas where droughts are more devastating, and Spain is in a medium risk zone. In addition to water supplies, one of the first elements where the effects of droughts are first felt is on vegetation. Recent studies have addressed the relationship between NDVI and drought events. They concluded that, although vegetation activity over large parts of Spain is closely related to the interannual variability of drought, there are clear seasonal differences in the response of the NDVI to drought.

The World Meteorological Organization (WMO) categorises various drought indices into different groups such as (a) meteorology, (b) soil moisture, (c) hydrology, (d) remote sensing and (e) composite or modelled. Within the group of indices that can be defined by remote sensing, it points out some indices as possible predictors or evaluators of drought periods such us the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). However, WMO leaves open the use of other possible VIs for drought prediction or assessment. In the current scenario, where there are multiple vegetation indices that can be used from satellite imagery, the initial objective of the study is to establish, from a set of VIs proposed or not by the WMO, which ones have the highest correlation with drought events in the study area. This correlation will be analysed according to vegetation type  (using the categorisation of the recently published ESA World cover map), in order to attempt to determine the behaviour of the vegetation index under meteorology according to each type. The study area is located in the Extremadura region of western Spain. The mean annual precipitation of the zone ranges from 446 to 1323 mm. The precipitations occur mainly from October to April while June, July and August suffer a significant drought with none or close to zero precipitation amount. The land cover types are mainly forests, agricultural and impervious cover. Regarding the temporal extent, two episodes of severe drought (2005-2006 and 2021-2022) will be studied.

Firstly, the vegetation indices available in open collections like OpenEO or Copernicus Global Land Service will be used. All available indices will be used to create time series to be compared with meteorological time series. Once the correlation is established, it will be analysed according to the type of coverage of the World cover map, in order to establish which index correlates better with drought episodes and thus try to establish the best predictor/evaluator.

How to cite: Quirós, E. and Fragoso-Campón, L.: Response of vegetation indices to drought in western Spain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7644, https://doi.org/10.5194/egusphere-egu23-7644, 2023.

The increase in drought conditions is one of the main consequences of climatic change, that affects both natural and socioeconomic systems. Northern Italy is historically rich in water resources, and one of the most fertile areas in Italy. However, in the last decades drought events increased also here, affecting the hydrological behaviour of the Po River and vegetation growth.

This study aims to quantify the spatial distributions of drought events and identify their effects on vegetation greenness in northern Italy during the 2000-2020 period using MODIS images at 1 km spatial resolution. For this purpose, correlation maps between fields of bi-weekly vegetation indices (NDVI and EVI) and drought indices (SPI and SPEI) were estimated.

The NDVI and EVI indices were extracted from the atmospherically corrected MODIS images and vegetation trends were investigated by mean on the Mann-Kendall test. To analyze drought events, 150 daily precipitation ground station series were collected, aggregated at bi-weekly scale, reconstructed, homogenised and spatialised at 1km resolution by mean of the Universal Kriging with auxiliary variables. Land Surface Temperature (LST), assumed as air temperature, was collected from MODIS images. Pixels with clouds were removed, and the accuracy was determined against the high resolution gridded temperature dataset available for northern Italy. The NDVI-LST space was investigated at yearly scale exploring the link between NDVI and LST for 6000 random points in the study area. Evapotranspiration was estimated by means of the Hargreaves equation and severe and extreme drought episodes were detected by means of drought indices (SPI and SPEI) calculated at 12-, 24- and 36-months aggregation time. Trends were analysed and the main drought events were characterised, identifying the percentage of area under drought, and the magnitude, duration and frequency of droughts. Each pixel was analysed to investigate the impacts of severe and extreme drought events on vegetation properties, and the Pearson’s correlation between NDVI/EVI and SPEI/SPI at different time scales was estimated. Finally, on the basis of the correlation maps and on the CORINE Land Cover 2020, drought impacts on different vegetation communities at medium (12 months) and long (24 and 36 months) time scales were detected as the percentage of vegetation under drought stress.

The study highlights the importance of applying multiple indices to study droughts, since even though positive temperature trends were recorded in northern Italy, in the last two decades the main trigger of droughts is the lack of precipitation. Moreover the western portion of northern Italy was mostly interested by drought intensification. The investigation on drought duration revealed that the longest extreme drought events were detected in the Po Valley, where the strongest negative impacts on vegetation were detected. The results also indicated that first droughts interested herbaceous vegetation, while subsequently affecting also sparse and open forests.

How to cite: Baronetti, A., Menichini, M., and Provenzale, A.: Vegetation response to extreme drought events in northern Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6869, https://doi.org/10.5194/egusphere-egu23-6869, 2023.

Recurring episodes of drought have become a hot topic in recent years in the Flemish pear sector. Damages associated with these episodes increasingly cause economic losses and create uncertainty for fruit growers. This trend is expected to continue in the future, as episodes of drought are likely to increase in frequency, intensity and duration as a result of climate change.

This problem calls for the development of efficient risk management methods, which rely on accurate estimates of the hazard imposed by extreme weather. Therefore, our study aims to quantify the location-specific hazard and impact of past and projected drought episodes on pear orchard vigour and productivity in the region of Flanders (Belgium). The hazard under the recent past climate is characterised based on daily historical meteorological observations (1961-2022) with 5x5 km spatial resolution (Gridded Observational Dataset of the Royal Meteorological Institute of Belgium). The future hazard is determined based on daily regional climate model projections from the CORDEX ensemble (12.5x12.5 km spatial resolution). Climate projections are bias-corrected using Multivariate Quantile Mapping based on a N‐dimensional probability density function transform.

Regional AquaCrop, a spatially distributed modelling system of the field-scale crop growth model AquaCrop¹, is used to calculate the soil water balance on a daily timestep, covering the region of Flanders at a spatial resolution of 12.5x12.5 km. Phenology-dependant thresholds of critical values of the soil water potential are used to analyse the frequency, intensity, duration and timing of drought-related stress episodes for rainfed pear orchards (cv. Conférence). Moreover, changes in the characteristics of potentially damaging episodes of drought under future climates are analysed.

Preliminary findings show an increase in projected frequencies of stress-inducing occurrences under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 for the period 2022-2072 compared to the reference period 1972-2022. Moreover, spatial variation in drought hazards for pear orchards across Flanders points to local environmental factors such as soil type and groundwater depth.

The spatially explicit hazard maps associated with the future climatic conditions resulting from this analysis are useful for decision-making by fruit growers, governments and insurance companies.

¹Raes, D., Steduto, P., Hsiao, T. C., and Fereres, E.: AquaCrop – the FAO crop model to simulate yield response to water: II. Main algorithms and software description, Agron. J., 101, 438–447, https://doi.org/10.2134/agronj2008.0140s, 2009.

How to cite: Bamps, B., Gobin, A., Somers, B., and Van Orshoven, J.: Current and future drought hazards in the Flemish pear sector, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9279, https://doi.org/10.5194/egusphere-egu23-9279, 2023.

Man-made local water supply infrastructure (in particular reservoirs) affects future water availability because it is built specifically to cope with climatic extremes. A system with multiple reservoirs, and therefore more local resilience, will be less vulnerable to climatic change and variability compared to a system with limited local capacity to cope with extremes. Therefore, different regions will see different water availability changes depending on their local infrastructure and capacity to cope with variability or adapt to change. The key questions that are studied in this proposal is the extent and intensity of environmental impact of the water stress. To address the question, this study proposes a multidisciplinary framework that integrates top-down (local inflows) and bottom-up (historical water use categories) factors to quantify the human induced water stress in each reservoir and the overall impact on the system’s resilience (water availability). The human induced water stress in regulated basins (with multiple reservoirs) is tracked by assessing the historical water use categories, which are later used to develop hypothetical water demand scenarios for near-future water stress assessment. Recent studies have shown that by changing water use policies, the system builds up resilience to cope with water stress. Our study explores reservoirs with multiple basins and tracks the policy changes impact on the system regarding the reservoirs orientation in the basin. Furthermore, this study tracks the environmental impact of the socioeconomic drought condition in regulated basins and highlights the changes due to water use policies.

How to cite: Mehran, A. and AghaKouchak, A.: Environmental Vulnerability Assessment of Anthropogenic Droughts in Regulated Basins, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17410, https://doi.org/10.5194/egusphere-egu23-17410, 2023.

Drought is multifaceted, more frequent hydrometeorological phenomena occurring worldwide. The intensity and frequency of droughts are increased with rising trend of global warming. These events significantly impact society which directly linked to agricultural productivity and economy. India witnessed these extreme drought events and have faced serious economic loses. Therefore, more effective, and reliable drought monitoring is essential for its mitigation and to enhance early warning systems. In addition, there are limited studies looking at the sensitivity of solar-induced chlorophyll fluorescence (SIF) to response of meteorological parameters during drought event.   

Therefore, a maiden attempt is taken to understand how terrestrial vegetation response under severe drought event which experienced in 2009 summer monsoon period (June to September) over Indo-Gangetic plain regions in India. We studied the productivity of vegetation over IGP region using solar-induced fluorescence as a proxy. Moreover, we have derived drought indices herein Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSI), and SIF Health Index (SHI). These indices were utilized gridded monthly precipitation, evapotranspiration, soil-moisture, land surface temperature (LST) and solar-induced fluorescence (SIF) datasets from 2001 to 2020 over IGP region. In addition, statistical relationships and trends among these indices are evaluated through the Pearson correlation coefficient and Mann-Kendall test.

Our findings provide promising results by addressing the major drought events over Indo-Gangetic plains in India in terms of intensity and spatial coverage. There is great significance to further understand the application of SIF in agriculture drought detection. The spatio-temporal patterns and trends of standardized precipitation evapotranspiration index (SPEI), and standardized soil-moisture index (SSI), have compared against solar-induced chlorophyll fluorescence health index (SHI) anomaly for short, and mid-term (herein 01, 03 and 06 month time scales) for seasonal drought monitoring. Furthermore, the spatial extent of SPEI, SSI and SHI anomaly well agreed for the 2009 drought event across region.

Overall, SIF can be reliable tool for agricultural drought monitoring in a timely and accurate manner. The resultant water stress leads to reduction in vegetation which reflected changes in SHI anomaly. This showcasing the ability of SIF to provide insight the link between carbon and water during droughts. Furthermore, it will enhance information for stakeholders, interested into future carbon-water cycle studies.

 Keywords: SPEI, SSI, SHI, and agricultural drought.

How to cite: Kumar, V., Chu, H.-J., and Aman, M. A.: Is solar-induced chlorophyll fluorescence derived index much useful in agricultural drought monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4708, https://doi.org/10.5194/egusphere-egu23-4708, 2023.