HS4.2

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

Drought and water scarcity are important issues in many regions of the Earth. While an increase in the severity and frequency of droughts can lead to water scarcity situations, particularly in regions that are already water-stressed, overexploitation of available water resources can exacerbate the consequences of droughts. In the worst case, this can lead to long-term environmental and socio-economic impacts. It is, therefore, necessary to improve both monitoring and sub-seasonal to seasonal forecasting for droughts and water availability and to develop innovative indicators and methodologies that translate the information provided into effective drought early warning and risk management. This session addresses statistical, remote sensing and physically-based techniques, aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and/or water scarcity. These include, but are not limited to, precipitation, snow cover, soil moisture, streamflow, groundwater levels, and extreme temperatures. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and explaining them to water managers, policymakers and other stakeholders, are further issues that are addressed. The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as in the field of water resources and/or risk management; interested in monitoring, modelling and forecasting drought and water scarcity, and in analyzing their interrelationships, hydrological impacts, and the feedbacks with society. Particularly welcome are applications and real-world case studies in regions subject to significant water stress, where the importance of drought warning, supported through state-of-the-art monitoring and forecasting of water resources availability is likely to become more important in the future. Contributors to the session are invited to submit papers to the Special Issue (SI) entitled "Recent advances in drought and water scarcity monitoring, modelling, and forecasting", to be published in the open-access journal Natural Hazard and Earth System Sciences (https://www.natural-hazards-and-earth-system-sciences.net/special_issues/schedule.html). Submission is open until 31 December 2020, for manuscripts that are not under consideration for publication elsewhere.

Co-organized by NH1
Convener: Brunella Bonaccorso | Co-conveners: Carmelo Cammalleri, Athanasios Loukas, Micha Werner
vPICO presentations
| Fri, 30 Apr, 13:30–17:00 (CEST)

vPICO presentations: Fri, 30 Apr

Chairpersons: Brunella Bonaccorso, Athanasios Loukas
13:30–13:35
DROUGHT MONITORING AND CHANGE DETECTION
13:35–13:45
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EGU21-6329
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ECS
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solicited
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Highlight
Irene Garcia-Marti, Marijn de Haij, Hidde Leijnse, Jan Willem Noteboom, Aart Overeem, and Gerard van der Schrier

Recent studies indicate that global warming changes the global hydrological cycle and may trigger drought or expand and deepen existing drought conditions at our planet. During the summer of 2018 the Netherlands experienced extreme drought conditions, matching the previous drought record from 1976. This climatic extreme has been monitored using a cumulative metric based on the difference between (potential) evaporation and precipitation. In an effort to provide exhaustive drought monitoring facilities, the Netherlands Meteorological Service (KNMI) developed a drought monitor based on the Standard Precipitation Index (SPI) using 40 years of daily rainfall (1971-2010) from our official network of rain gauges for calibration. The daily SPI maps help decision makers to assess the status of meteorological drought in the Netherlands, thus enabling preventive measures mitigating its negative impacts on different socio-economic sectors. 

In the past two decades our global society has witnessed the advent of new technological and scientific advances that have reshaped the way we collect weather observations. Increasing numbers of citizens are joining the effort of monitoring the weather by installing citizen weather stations (CWS) in private spaces (e.g., home, schools), thus conforming novel sources of weather data. In 2015, the KNMI joined as a partner the Weather Observations Website (WOW) consortium, a citizen science initiative promoted by the UK Met Office bringing together weather enthusiasts all around the world. WOW-NL CWS have collected 100+ million observations between 2015-2019. However, it is still unclear how to use this remarkable volume of observations, or what is the added value (e.g., economic, operational, research) they provide with respect to the official network. 

In this ongoing work, we combined the newly developed SPI drought monitor with WOW observations from the Netherlands to obtain an ‘SPI-WOW’ indicator. Our goal is threefold: 1) illustrating how to turn WOW-NL data into operational value; 2) assessing the possibility of providing higher resolution drought maps including WOW-NL rainfall data; 3) enable the possibility for underrepresented regions to obtain (relevant) local drought metrics. 

We extracted 12 million precipitation observations for 2019 and, for each day of the year, we computed the daily rainfall accumulations for the previous 30 days (i.e., SPI-1). Note that the precipitation observations are not quality-controlled (QC). The calibrated model is tested with these newly created rainfall accumulations to obtain the SPI-WOW values. Our preliminary results compare the official vs alternative values of SPI at the location of each WOW-NL CWS. For each month we observe a moderate positive correlation, and there are CWS in the network capable of providing measurements close to the official ones. Further work to achieve the above-mentioned goal should include a) the application of a QC to the rainfall data to remove the outliers beforehand; b) thoroughly comparing the values of both networks in space and time across different scenarios; c) identifying the WOW-NL stations providing the best SPI metrics and its characteristics; d) assess the inclusion of radar data for the hi-res maps.

How to cite: Garcia-Marti, I., de Haij, M., Leijnse, H., Noteboom, J. W., Overeem, A., and van der Schrier, G.: Enhanced capability to monitor drought using citizen weather stations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6329, https://doi.org/10.5194/egusphere-egu21-6329, 2021.

13:45–13:47
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EGU21-374
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ECS
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Highlight
Manuela Irene Brunner, Daniel L. Swain, Eric Gilleland, and Andrew W. Wood

Droughts can seriously challenge water management if they have large spatial extents. These extents may change in a warming climate along with changes in underlying hydro-meteorological drivers. Therefore, we ask (1) how streamflow drought spatial extent has changed over the period 1981-2018 in the United States, (2) which physical drivers govern drought spatial extent, and (3) whether/how the importance of these drivers has changed over time. We analyze temporal changes in streamflow drought extents and their drivers using drought events extracted for 671 catchments in the conterminous United States using a variable threshold-level approach. Drought spatial extents are determined as the percentage of catchments affected by drought during a certain month. Then, important drivers are identified by determining the spatial percentage overlap of the area under streamflow drought with precipitation droughts, temperature anomalies, snow-water-equivalent deficits, and soil moisture deficits. Finally, the spatial extent and overlap time series are used in a trend analysis to determine changes in drought spatial extent and to identify changes in the importance of different variables as drivers of drought spatial extent. Our analyses show that (1) drought spatial extents have increased, mainly because of increases in the extent of small droughts; (2) drought extents overall substantially overlap with soil moisture deficits and the relationship of drought to precipitation and temperature varies seasonally; (3) the importance of temperature as a driver of drought extent has increased over time. We therefore conclude that continued global warming may further increase the probability of spatially compounding drought events, which requires adaptation of regional drought management strategies.

 

How to cite: Brunner, M. I., Swain, D. L., Gilleland, E., and Wood, A. W.: Spatial extent of hydrological drought in the United States: changes and hydro-meteorological drivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-374, https://doi.org/10.5194/egusphere-egu21-374, 2020.

13:47–13:49
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EGU21-1786
Ben Livneh, Parthkumar Modi, Joseph Kasprzyk, Brooke Ely, and Benet Duncan

Seasonal water supply predictions offer critical information to aid in planning and mitigation of drought impacts. In many northern and montane systems, spring snow information has been shown to be the most important predictor of seasonal drought, since in these systems snow water storage can exceed that of man-made reservoirs. However, a warmer future portends for less precipitation falling in the form of snow, which challenges current prediction methods. This presentation focuses on evaluating physical and statistical techniques for seasonal water supply prediction in snow-fed systems under both historical and future climate conditions with the goal of identifying regions and methods where predictions are likely to remain skillful under future warming. Initial results using downscaled hydrologic projections over the western U.S. indicate that snow information contributes less predictive skill to drought forecasts over roughly two thirds of snow dominated regions by the middle of this century. Significantly greater resilience to warming is seen higher elevation zones (p<0.01) and for prediction methods that include non-snow predictors such as soil moisture. To understand the impact of non-stationary snow conditions on future drought predictions, we conduct a series of idealized experiments to evaluate the relative importance of secular trends versus changing variability of both snow and seasonal climate conditions. This presentation is part of a larger research effort seeking to identify alternatives to snow-based streamflow predictions to advance future drought predictability.

How to cite: Livneh, B., Modi, P., Kasprzyk, J., Ely, B., and Duncan, B.: Evaluating seasonal drought prediction in snow-fed systems past, present, and future: towards identifying resilient prediction techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1786, https://doi.org/10.5194/egusphere-egu21-1786, 2021.

13:49–13:51
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EGU21-2330
David J. Peres, Alfonso Senatore, Paola Nanni, Antonino Cancelliere, Giuseppe Mendicino, and Brunella Bonaccorso

Regional climate models (RCMs) are commonly used for assessing, at proper spatial resolutions, future impacts of climate change on hydrological events. In this study, we propose a statistical methodological framework to assess the quality of the EURO-CORDEX RCMs concerning their ability to simulate historic observed climate (temperature and precipitation). We specifically focus on the models’ performance in reproducing drought characteristics (duration, accumulated deficit, intensity, and return period) determined by the theory of runs at seasonal and annual timescales, by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to the Sicily and Calabria regions (Southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the former Regional Hydrographic Offices. The density of the measurements is considerably greater than observational gridded datasets available at the European level, such as E-OBS or CRU-TS. Results show that among the models based on the combination of the HadGEM2 global circulation model (GCM) with the CLM-Community RCMs are the most skillful in reproducing precipitation and temperature variability as well as drought characteristics. Nevertheless, the ranking of the models may slightly change depending on the specific variable analysed, as well as the temporal and spatial scale of interest. From this point of view, the proposed methodology highlights the skills and weaknesses of the different configurations, aiding on the selection of the most suitable climate model for assessing climate change impacts on drought processes and the underlying variables.

How to cite: Peres, D. J., Senatore, A., Nanni, P., Cancelliere, A., Mendicino, G., and Bonaccorso, B.: A statistical framework for evaluating EURO-CORDEX simulations and derived drought characteristics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2330, https://doi.org/10.5194/egusphere-egu21-2330, 2021.

13:51–13:53
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EGU21-3389
Hadri Abdessamad, Saidi Mohamed El Mehdi, and Boudhar Abdelghani

During the last few decades, the frequency of drought has significantly increased in Morocco especially for arid and semi-arid regions, leading to a rising of several environmental and economic issues. In this work, we analyse the spatial and temporal relationship between vegetation activity and drought severity at different moments of the year, across an arid area in the western Haouz plain in Morocco. Our approach is based on the use of a set of more than thirty satellite Landsat images data acquired for the period from 2008 to 2017, combined with the Standardized Precipitation Index (SPI) at different time scales and Standardized water-level Index (SWI). The Mann-Kendall and Sen’s slopes methods were used to estimate SPI trends and the Pearson correlation between NDVI and SPI were calculated to assess the sensitivity of vegetation types to drought. Results demonstrated that SPI experienced an overall upward trend in the Chichaoua-Mejjate region, except for 3-months time scale SPI in summer. The vegetation activity is largely controlled by the drought with clear differences between seasons and timesclaes at which drought is assessed. Positives correlations between the NDVI and SPI are dominant across the entire study area except in June when almost half of correlations is negative. More than 80% of the study domain exhibit a correlation exceeding 0.4 for SPI3 and SPI6 in March. Importantly, this study stresses that the irrigation status of land can introduce some uncertainties on the remote sensing drought monitoring. A weak correlation between the SPI and the SWI was observed at different time-scale. The fluctuations of the piezometric levels are strongly affected by the anthropogenic overexploitation of aquifers and proliferation of irrigated plots.

How to cite: Abdessamad, H., Mohamed El Mehdi, S., and Abdelghani, B.: Monitoring multiscale drought using remote sensing in a Mediterranean arid region., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3389, https://doi.org/10.5194/egusphere-egu21-3389, 2021.

13:53–13:55
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EGU21-4962
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ECS
Md Anarul Haque Mondol, Xuan Zhu, David Dunkerley, and Benjamin J. Henley

The nature and characteristics of drought are not like a flood, cyclone or storm surge since droughts cannot easily be tracked and are difficult to quantify as a distinct event. In this study, we examined the characteristics of meteorological drought occurrence and severity using the Effective Drought Index (EDI), including the drought events, drought chronology, onset and ending of drought, consecutive drought spells, drought frequency, intensity and severity, using North-Bengal of Bangladesh as a case study. The rainfall and temperature dataset of Bangladesh Meteorological Department (BMD) for the study region throughout 1979-2018 is utilised. The trends of drought are detected by using the Mann-Kendall test and Sen Slope estimation. We evaluated the performance of EDI using the Standardized Precipitation Index (SPI), historical drought records and rice production. The study finds that seasonal and annual droughts have become more frequent in all seasons except pre-monsoon. In addition, the largest decrease in seasonal EDI is found in the monsoon in both Teesta floodplain and Barind tract. In decades prior to the late 2000s, a drought spell typically started between March to May (± 15 days) and ended with the monsoonal rainfall in June/July. In the years since the last 2000s, monsoon and post-monsoon droughts spells have significantly increased. Overall, the peak intensities of droughts are higher in the Barind tract than in the Teesta floodplain, and the frequency and severity of moderate to severe drought are increasing significantly in the Barind tract. Though EDI is strongly correlated with the SPI index, EDI and rice production have a non-linear relationship and are not significantly correlated. Hence, this research suggests that there are other significant influences on yield rather than just climatological drought (e.g. irrigation, lack of technology and management etc.).

How to cite: Mondol, M. A. H., Zhu, X., Dunkerley, D., and Henley, B. J.: Can Effective Drought Index (EDI) successfully characterise meteorological drought and seasonal agricultural losses?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4962, https://doi.org/10.5194/egusphere-egu21-4962, 2021.

13:55–13:57
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EGU21-5757
Renata Romanowicz, Emilia Karamuz, Jaroslaw Napiorkowski, and Tesfaye Senbeta

Water balance modelling is often applied in studies of climate and human impacts on water resources. Annual water balance is usually derived based on precipitation, discharge and temperature observations under an assumption of negligible changes in annual water storage in a catchment. However, that assumption might be violated during very dry or very wet years. In this study we apply groundwater level measurements to improve water balance modelling in nine sub-catchments of the River Vistula basin starting from the river sources downstream. Annual and inter-annual water balance is studied using a Budyko framework to assess actual evapotranspiration and total water supply. We apply the concept of effective precipitation to account for possible losses due to water interception by vegetation. Generalised Likelihood Uncertainty Estimation GLUE is used to account for parameter and structural model uncertainty, together with the application of eight Budyko-type equations. Seasonal water balance models show large errors for winter seasons while summer and annual water balance models follow the Budyko framework. The dryness index is much smaller in winter than in summer for all sub-catchments. The spatial variability of water balance modelling errors indicate an increasing uncertainty of model predictions with an increase in catchment size. The results show that the added information on storage changes in the catchments provided by groundwater level observations largely improves model accuracy. The results also indicate the need to model groundwater level variability depending on external factors such as precipitation and evapotranspiration and human interventions. The modelling tools developed will be used to assess future water balance in the River Vistula basin under different water management scenarios and climate variability.

How to cite: Romanowicz, R., Karamuz, E., Napiorkowski, J., and Senbeta, T.: Effects of climate change and human interactions on water balance dynamics in the River Vistula basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5757, https://doi.org/10.5194/egusphere-egu21-5757, 2021.

13:57–13:59
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EGU21-7445
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ECS
Felix Greifeneder, Klaus Haslinger, Georg Seyerl, Claudia Notarnicola, Massimiliano Zappa, and Marc Zebisch

Soil Moisture (SM) is one of the key observable variables of the hydrological cycle and therefore of high importance for many disciplines, from meteorology to agriculture. This contribution presents a comparison of different products for the mapping of SM. The aim was to identify the best available solution for the operational monitoring of SM as a drought indicator for the entire area of the European Alps, to be applied in the context of the Interreg Alpine Space project, the Alpine Drought Observatory.

The following datasets were considered: Soil Water Index (SWI) of the Copernicus Global Land Service [1]; ERA5 [2]; ERA5-Land [3]; UERRA MESCAN-SURFEX land-surface component [4]. All four datasets offer a different set of advantages and disadvantages related to their spatial resolution, update frequency and latency. As a reference, modelled SM time-series for 307 catchments in Switzerland were used [5]. Switzerland is well suited as a test case for the Alps, due to its different landscapes, from lowlands to high mountain.

The intercomparison was based on a correlation analysis of daily absolute SM values and the daily anomalies. Furthermore, the probability to detect certain events, such as persistent dry conditions, was evaluated for each of the SM datasets. First results showed that the temporal dynamics (both in terms of absolute values as well as anomalies) of the re-analysis datasets show a high correlation to the reference. A clear gradient, from the lowlands in the north to the high mountains in the south, with decreasing correlation is evident. The SWI data showed weak correlations to the temporal dynamics of the reference in general. Especially, during spring and the first part of the summer SM is significantly underestimated. This might be related to the influence of snowmelt, which is not taken into account in the two-layer water balance model used to model SM for deeper soil layers. Low coverage in the high mountain areas hampered a thorough comparison with the reference in these areas.

The results presented here are the foundation for selecting a suitable source for the operational mapping of SM for the Alpine Drought Observatory. The next steps will be to test the potential of MESCAN-SURFEX and ERA5-Land for the downscaling of ERA5 to take advantage of the low latency of ERA5 and the improved spatial detail of the other two datasets.  

Literature:

[1]  B. Bauer-marschallinger et al., “Sentinel-1 : Harnessing Assets and Overcoming Obstacles,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 1, pp. 520–539, 2019, doi: 10.1109/TGRS.2018.2858004.

[2]  H. Hersbach et al., “ERA5 hourly data on single levels from 1979 to present.” Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2018.

[3]  Copernicus Climate Change Service, “ERA5-Land hourly data from 2001 to present.” ECMWF, 2019, doi: 10.24381/CDS.E2161BAC.

[4]  E. Bazile, et al., “MESCAN-SURFEX Surface Analysis. Deliverable D2.8 of the UERRA Project,” 2017. Accessed: Jan. 11, 2020. [Online]. Available: http://www.uerra.eu/publications/deliverable-reports.html.

[5]  Brunner, et al.: Extremeness of recent drought events in    Switzerland: dependence on variable and return period choice, Nat. Hazards Earth Syst. Sci., 19, 2311–2323, https://doi.org/10.5194/nhess-19-2311-2019, 2019.

How to cite: Greifeneder, F., Haslinger, K., Seyerl, G., Notarnicola, C., Zappa, M., and Zebisch, M.: Assessing the options for the operational mapping of the soil moisture content in the European Alps, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7445, https://doi.org/10.5194/egusphere-egu21-7445, 2021.

13:59–14:01
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EGU21-9483
Dominik Michel, Martin Hirschi, and Sonia I. Seneviratne

Climate projections indicate an increasing risk of dry and hot episodes in Central Europe, including in Switzerland. However, models display a large spread in projections of changes in summer drying, highlighting the importance of related observations to evaluate climate models and constrain projections. Land hydrological variables play an essential role for these projections. This is particularly the case for soil moisture and land evaporation, which are directly affecting the development of droughts and heatwaves in both present and future.

The recent 2020 spring as well as 2015 and 2018 summer droughts in Switzerland have highlighted the importance of monitoring and assessing changes of soil moisture and land evaporation, which are strongly related to drought impacts on agriculture, forestry, and ecosystems. The country was affected by major drought and heatwave conditions in 2015 and 2018. While the meteorological conditions started to recover at the end of the summer, the soil moisture conditions (and runoff) continued to be anomalously low for most of the fall. This illustrates the decoupling between meteorological drought and soil moisture drought conditions related to the intrinsic memory of the soil.

The only Switzerland-wide soil moisture monitoring programme currently in place is the SwissSMEX (Swiss Soil Moisture Experiment) measurement network. It was initiated in 2008 and comprises 19 soil moisture measurement profiles at 17 different sites (grassland, forest and arable land). Since 2017, seven grassland SwissSMEX sites were complemented with land evaporation measurements from mini-lysimeters.

First, a quality assessment and inter-comparison of the in-situ soil moisture and land evaporation observations at 12 grassland sites revealed substantial discrepancies between different sensor types in terms of absolute values and data availability. A standard procedure for processing and interpreting the SwissSMEX data is thus being established. Second, analyses have been carried out comparing the SwissSMEX measurements with gridded remote-sensing and reanalysis products that provide near real time soil moisture data. In particular, the European Space Agency (ESA) Climate Change Initiative (CCI) surface soil moisture product (ESA-CCI soil moisture) as well as the new ECMWF reanalysis ERA5 are considered. The seasonal evolution of the soil moisture anomalies (with respect to the long-term mean) show for 2020 two pronounced phases of dryness. These are consistently represented in SwissSMEX in-situ observations and ERA5. Also the other recent drought events of 2015 and 2018 show a similar temporal evolution in both datasets. The response of ESA-CCI surface soil moisture is less pronounced, more variable and also dependent on the measurement methodology, i.e., active or passive microwave remote sensing.

These first analyses provide useful insights in order to provide near-real time monitoring, enhance process understanding at the national scale and a better preparedness for future droughts.

How to cite: Michel, D., Hirschi, M., and Seneviratne, S. I.: Integrative soil moisture monitoring in Switzerland for a better preparedness for projected drying trends, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9483, https://doi.org/10.5194/egusphere-egu21-9483, 2021.

14:01–14:03
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EGU21-10228
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ECS
Trupti Satapathy, Meenu Ramadas, and Jörg Dietrich

Among natural hazards, droughts are known to be very complex and disastrous owing to their creeping nature and widespread impacts. Specifically, the occurrence of agricultural droughts poses a threat to the productivity and socio-economic development of countries such as India. In this study, we propose a novel framework for agricultural drought monitoring integrating the different indicators of vegetation health, crop water stress and soil moisture, that are derived from remote sensing satellite data. The drought monitoring is performed over Odisha, India, for the period 2000-2019. Soil moisture and land surface temperature datasets from GLDAS Noah Land Surface Model and surface reflectance data from MODIS (MOD09GA) are used in this study. We compared the utility of popular indices: (i) soil moisture condition index, soil moisture deficit index and soil wetness deficit index to represent the soil moisture level; (ii) temperature condition index, vegetation condition index and normalised difference water index to indicate vegetation health; (iii) short wave infrared water stress to represent crop water stress condition. Correlation analyses between these indices and the seasonal crop yields are performed, and suitable indicators are chosen. The popular entropy weight method is then used to integrate the indices and develop the proposed composite drought index. The index is then used for monitoring the agricultural drought condition over the study area in drought periods. The proposed framework for week- to month-scale monitoring have potential applications in identification of agricultural drought hotspots, analysis of trends in drought severity, and drought early warning for agricultural water management.

How to cite: Satapathy, T., Ramadas, M., and Dietrich, J.: A novel agricultural drought monitoring framework using remote sensing products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10228, https://doi.org/10.5194/egusphere-egu21-10228, 2021.

14:03–14:05
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EGU21-12550
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ECS
Rubina Ansari and Giovanna Grossi

Global warming and anthropogenic activities have significantly altered the hydrological cycle and amplified the extreme events (floods and droughts) in many regions of the world, with associated environmental, economic, and social losses. For effective hydro extremes hazards management, it is significant to understand how climate change influences the occurrence, duration, and severity of the regional dryness/wetness conditions (droughts/floods). The present study was carried out over Upper Jhelum Basin (UJB) in Pakistan which lies in the western Himalaya, a most effected mountainous range by Climate Change. Firstly, a suitable gridded precipitation dataset was selected/chosen among various datasets (APHRODITE, CHIRPS, ERA5, PGMFD, MSWEP) through spatio-temporal comparison against in situ data at monthly, seasonal, and annual scale. Secondly, selected gridded data was adjusted for biases using linear (Linear scaling-LS, Local intensity scaling-LOCI) and nonlinear (Power transformation-PT and Distribution mapping-DM) statistical methods. Finally, standardized precipitation index (SPI) at multiple time scale was used to analyses dryness/wetness conditions in the Upper Jhelum Basin over a 35-year period (1981–2015). Results show the higher capability of ERA5 data to represent the UJB precipitation patterns with correlation coefficient (r=0.79) and normalized standard deviation (nSD=1.1), despite of overestimation especially during peak months. Regarding precipitation bias adjustment, all methods were able to correct the mean values while LOCI and DM take advantage over other two methods to correct wet-day probability and precipitation intensity. The SPI analysis at different time scales showed that wet periods occurred more in the first half of the study period, but at later years, drying periods ranging from moderate to severe continue to be seen with increasing frequency. A strong change in dry/wet conditions was observed around years 1997/1998. This change may be the result of the strongest El Nino event (1997-98) occurred in the history. However, further studies are still needed to check whether there is only a large multi-decadal variation or dry conditions will prevail in future. Overall, these findings would assist to better understand the changing pattern of extreme events with climate variability and help water resources managers to develop basin wide appropriate mitigation and adaptation measures to combat climate change and its consequences. 

How to cite: Ansari, R. and Grossi, G.: Evolution of Dryness/Wetness conditions and their characteristics (duration and severity) across Upper Jhelum Basin, Pakistan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12550, https://doi.org/10.5194/egusphere-egu21-12550, 2021.

14:05–14:07
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EGU21-12906
Musab Mbideen and Balázs Székely

Remote Sensing (RS) and Geographic Information System (GIS) instruments have spread rapidly in recent years to manage natural resources and monitor environmental changes. Remote sensing has a vast range of applications; one of them is lakes monitoring. The Dead Sea (DS) is subjected to very strong evaporation processes, leading to a remarkable shrinkage of its water level. The DS is being dried out due to a negative balance in its hydrological cycle during the last five decades. This research aims to study the spatial changes in the DS throughout the previous 48 years. Change detection technique has been performed to detect this change over the research period (1972-2020). 73 Landsat imageries have been used from four digital sensors; Landsat 1-5 MSS C1 Level-1, Landsat 4-5 TM C1 Level-1, Land sat 7 ETM+ C1  Level-1, and Landsat 8 OLI-TIRS C1 Level. After following certain selection criteria , the number of studied images decreased. Furthermore, the Digital Surface Model of the Space Shuttle Radar Topography Mission and a bathymetric map of the Dead Sea were used. The collected satellite imageries were pre-processed and normalized using ENVI 5.3 software by converting the Digital Number (DN) to spectral radiance, the spectral radiance was converted to apparent reflectance, atmospheric effects were removed, and finally, the black gaps were removed. It was important to distinguish between the DS lake and the surrounding area in order to have accurate results, this was done by performing classification techniques. The digital terrain model of the DS was used in ArcGIS (3D) to reconstruct the elevation of the shore lines. This model generated equations to detect the water level, surface area, and water volume of the DS. The results were compared to the bathymetric data as well. The research shows that the DS water level declined 65 m (1.35 m/a) in the studied period. The surface area and the water volume declined by 363.56 km2 (7.57 km2/a) and 53.56 km3 (1.11 km3/a), respectively. The research also concluded that due to the bathymetry of the DS, the direction of this shrinkage is from the south to the north. We hypothesize that anthropogenic effects have contributed in the shrinkage of the DS more than the climate. The use of the DS water by both Israel and Jordan for industrial purposes is the main factor impacting the DS, another factor is the diversion of the Jordan and Yarmouk rivers. Our results also allow to give a prediction for the near future of the DS: the water level is expected to reach –445 m in 2050, while the surface area and the water volume is expected to be 455 km2 and 142 km3, respectively. 

How to cite: Mbideen, M. and Székely, B.: An assessment of the Dead Sea level change using remotely sensed data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12906, https://doi.org/10.5194/egusphere-egu21-12906, 2021.

14:07–14:09
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EGU21-13186
Ponnambalam Rameshwaran, Ali Rudd, Vicky Bell, Matt Brown, Helen Davies, Alison Kay, and Catherine Sefton

Despite Britain’s often-rainy maritime climate, anthropogenic water demands have a significant impact on river flows, particularly during dry summers. In future years, projected population growth and climate change are likely to increase the demand for water and lead to greater pressures on available freshwater resources.

Across England, abstraction (from groundwater, surface water or tidal sources) and discharge data along with ‘Hands off Flow’ conditions are available for thousands of individual locations; each with a licence for use, an amount, an indication of when abstraction can take place, and the actual amount of water abstracted (generally less than the licence amount). Here we demonstrate how these data can be used in combination to incorporate anthropogenic artificial influences into a grid-based hydrological model. Model simulations of both high and low river flows are generally improved when abstractions and discharges are included, though for some catchments model performance decreases. The new approach provides a methodological baseline for further work investigating the impact of anthropogenic water use and projected climate change on future river flows.

How to cite: Rameshwaran, P., Rudd, A., Bell, V., Brown, M., Davies, H., Kay, A., and Sefton, C.: Hydrological modelling and anthropogenic water use, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13186, https://doi.org/10.5194/egusphere-egu21-13186, 2021.

14:09–14:11
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EGU21-13800
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ECS
Emmanuel Likoya, Cathryn Birch, Sarah Chapman, and Andrew Dougill

The societal relevance of droughts in Africa underscores the need for improved understanding of the atmospheric processes that drive them. This study examined drought characteristics across Malawi, and the associated atmospheric circulation patterns, in observations, reanalysis and global climate models. Droughts were identified using the Standardised Precipitation and Evapotranspiration Index (SPEI) for the period 1965 to 2018. Atmospheric circulation patterns during droughts were examined and the main moisture fluxes into Malawi were identified. Despite differences in the frequency, and events being asynchronous at times, droughts exhibited characteristics that were statistically similar between northern and southern Malawi. Droughts in both regions were associated with anomalous circulation that typically worked to diminish moisture advection and thus convection. Differences in the structure of the anomalies were indicative of differences in mechanisms associated with droughts in the north and south of Malawi. Three main moisture flux pathways were identified, and categorized as northeasterly, southeasterly, and northwesterly, each with a unique correlation structure with precipitation and global SSTs. Positive and negative biases of varying magnitudes were noted for drought and rainfall characteristics across the range of CMIP5 models. Such biases can be attributed to biases in moisture fluxes whose variability was found to be a key driver of summer precipitation variability across Malawi. Despite biases in moisture fluxes and their influence on precipitation biases, the majority of models exhibited moisture flux-precipitation correlations consistent with observations and reanalysis. Results from the study highlight the extent to which climate models are reliable in simulating droughts and therefore of value in developing narratives of climate variability essential for long-term development planning.

How to cite: Likoya, E., Birch, C., Chapman, S., and Dougill, A.: Austral Summer Droughts and their Driving Mechanisms in Observations and Present-day Climate Simulations across Malawi, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13800, https://doi.org/10.5194/egusphere-egu21-13800, 2021.

14:11–14:13
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EGU21-15208
Maral Habibi, Wolfgang Schöner, and Iman Babaeian

Snow plays a significant role in surface runoff, groundwater resources, and as an important temporary reservoir for winter precipitation. On the other hand, extreme floods can arise when high melt rates in catchments zone combine with torrential rain at the same time, therefore snowmelt quantities are important for the management of lakes.

This study aims at investigating snow drought characteristics in the catchment area of Lake Urmia, which has recently been faced with the issue of drought and declining water levels. To provide an overview of drought intensity for the last 40 years 1981-2020, the Standardized Snow Melt and Rain Index (SMRI), which accounts for rain and snowmelt deficits, was applied and spatial variations of snow drought were assessed. The index was used in drought analysis based on the ERA5 dataset for the whole study area under three-time scales including the 3-, 6- and 12-month. After determining the dry and wet periods, historical characteristics of droughts were identified, and spatial distribution maps of droughts were plotted.

Results show that during the last years, snow drought events were more frequent, severe, and affected a larger area which shows a spatial spread of drought events. According to the snow index results, most extreme events have happened in the Zarine Rud and Simine Rud sub-catchments which play a key role in increasing the groundwater resources of the Basin. With proper management, these resources can be properly used for lake revitalization.

 

Keywords: Snow droughts, SMRI, Drought characteristics, Urmia lake, Climate extreme

 

How to cite: Habibi, M., Schöner, W., and Babaeian, I.: Spatial and temporal assessment of Snow drought characteristic for 1981 to 2020 on Urmia lake catchment scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15208, https://doi.org/10.5194/egusphere-egu21-15208, 2021.

14:13–14:15
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EGU21-15470
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ECS
Markus Adloff, Michael Bliss Singer, David McLeod, Katerina Michaelides, Nooshin Mehrnegar, Eleanor Hansford, Chris Funk, and Dann Mitchell

Rural communities in the Horn of Africa Drylands (HAD) rely on the availability of soil moisture for crop growth and groundwater for drinking water supply for people and livestock. Recent negative trends in March-May rainfall (‘long rains’) have decreased soil moisture with negative consequences for the livelihoods in HAD communities, who have become increasingly vulnerable to multi-season droughts affecting crops and livestock. These increasingly common failed ‘long rains’, propagate into agricultural drought, causing famines, and lead to major humanitarian intervention across HAD. However, the links between seasonal rainfall (‘long rains’ and ‘short rains’ in October-December) and regional groundwater storage in HAD have not been explored. We examined trends in seasonal rainfall from various gridded datasets alongside an analysis of total water storage (TWS) from GRACE satellite data. Multiple rainfall datasets corroborate declining ‘long rains’ and increasing ‘short rains’, and a 3-hr (MSWEP) dataset reveals the disproportionate contribution of extreme rainfall to totals within both seasons. We also found that TWS generally increased across the HAD region between 2002 and 2017, and that the GRACE TWS signal is primarily composed of groundwater storage changes for this region, rather than trends in soil moisture. We then found that groundwater storage variability correlates strongly with seasonal rainfall on interannual and decadal scales, and it is particularly correlated with extreme rainfall in both rainy seasons. We highlight the importance of increasingly large Indian Ocean Dipole events in dominating extreme rainfall and correspondingly high TWS and groundwater recharge within the October-December rainy season. While groundwater recharge in HAD by high-intensity rainfall is generally high for the March-May rainy season, it is increasing for the ‘short rains’ season. These findings raise the possibility that increasing groundwater availability across HAD could be exploited to offset the ‘long rains’ decline, potentially mitigating their climate change impacts on soil moisture, crops, and drinking water supplies.

How to cite: Adloff, M., Singer, M. B., McLeod, D., Michaelides, K., Mehrnegar, N., Hansford, E., Funk, C., and Mitchell, D.: Groundwater storage in the Horn of Africa drylands dominated by seasonal rainfall extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15470, https://doi.org/10.5194/egusphere-egu21-15470, 2021.

DROUGHT MODELING AND FORECASTING
14:15–14:25
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EGU21-9426
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ECS
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solicited
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Highlight
Christian Poppe Teran, Bibi Naz, Roland Baatz, Harrie-Jan Hendricks-Franssen, Nikolaos Nikolaidis, and Harry Vereecken

Hydrological extremes in Europe, such as droughts, are expected to increase in frequency and severity with advancement of climate change. The consequences for ecosystem functioning and processes, including biomass production and evapotranspiration, have not yet been thoroughly mapped. 

Ecosystem water use efficiency (WUE) describes the amount of carbon assimilated as biomass per unit of water. WUE was examined for various case studies and global assessments, yet disagreements in the methodologic approach and uncertainties hinder generic understanding of WUE variability. As a link between the carbon cycle, water cycle and vegetation states, disclosure of WUE courses across European ecosystems enables important estimates of past, present and future ecosystem dynamics. 

Here we generated a long-term and high resolution observational and reanalysis data-set of WUE over Europe by interpolation of high level observation products (GLASS, CRU TS v4) and reanalysis data sets (ERA5-Land, COSMO-REA6, ESSMRA) to a 3 x 3km grid. This European Drought and Water Use Efficiency data-set (EDWUE) contains variables for calculating WUE using three different approaches, as well as indicators of meteorological and agricultural droughts.  

Drought effects on WUE will be analyzed to investigate the sensitivity of ecosystem processes to extreme weather conditions at regional and local scale by comparison of WUE and drought indices time-series. Spatiotemporal analyses of the EDWUE data-set across European ecosystems will discover differences in patterns and potential trends of WUE between regions and decode the dependencies on ecosystem composition, geographical characteristics, and climate and occurring weather extremes. Intercomparisons between the different WUE calculations will allow to draw conclusions on the roots of particular WUE dynamics.

How to cite: Poppe Teran, C., Naz, B., Baatz, R., Hendricks-Franssen, H.-J., Nikolaidis, N., and Vereecken, H.: The Effect of Droughts on Ecosystem Water-Use-Efficiency in Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9426, https://doi.org/10.5194/egusphere-egu21-9426, 2021.

14:25–14:27
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EGU21-218
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ECS
Gilbert Hinge and Ashutosh Sharma

Droughts are considered as one of the most catastrophic natural disasters that affect humans and their surroundings at a larger spatial scale compared to other disasters. Rajasthan, one of India's semiarid states, is drought inclined and has experienced many drought events in the past. In this study, we evaluated different preprocessing and Machine Learning (ML) approaches for drought predictions in Rajasthan for a lead-time of up to 6 months. The Standardized Precipitation Index (SPI) was used as the drought quantifying measure to identify the drought events. SPI was calculated for 3, 6, and 12-month timescales over the last 115-year using monthly rainfall data at 119 grid stations.  ML techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Linear Regression (LR), were used to evaluate their accuracy in drought forecasting over different lead times. Furthermore, two data processing methods, namely the Wavelet Packet Transform (WPT) and Discrete Wavelet Transform (DWT), have also been used to enhance the aforementioned ML models' predictability. At the outset, the preprocessed SPI data from both the methods were used as inputs for LR, SVR, and ANN to form a hybrid model. The hybrid models' drought predictability for a different lead-time was evaluated and compared with the standalone ML models. The forecasting performance of all the models for all 119 grid points was assessed with three statistical indices: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). RMSE was used to select the optimal model parameters, such as the number of hidden neurons and the number of inputs in ANN, and the level of decomposition and mother wavelet in wavelet analysis.  Based on these measures, the coupled model showed better forecasting performance than the standalone ML models. The coupled WPT-ANN model shows superior predictability for most of the grid points than other coupled models and standalone models.  All models' performance improved as the timescale increased from 3 to 12 months for all the lead times. However, the model performance decreased as the lead time increased.  These findings indicate the necessity of processing the data before the application of any machine learning technique. The hybrid model's prediction performance also shows that it can be used for drought early warning systems in the state.

How to cite: Hinge, G. and Sharma, A.: Comparison of wavelet and machine learning methods for regional drought prediction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-218, https://doi.org/10.5194/egusphere-egu21-218, 2020.

14:27–15:00
Break
Chairpersons: Carmelo Cammalleri, Micha Werner
15:30–15:32
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EGU21-1305
Elizaveta Felsche and Ralf Ludwig

There is strong scientific and social interest to understand the factors leading to extreme events in order to improve the management of risks associated with hazards like droughts. Recent events like the summer 2018 drought in Germany already had severe und unexpected impacts, e.g. forest fires and crop failures; in order to increase preparedness robust prediction tools are  urgently required. In this study, machine learning methods are applied to predict the occurrence of a drought with lead times of one to three months. The approach takes into account a list of thirty atmospheric and soil variables as predictor input parameters from a single regional climate model initial condition large ensemble (CRCM5-LE). The data was produced the context of the ClimEx project by Ouranos with the Canadian Regional Climate Model (CRCM5) driven by 50 members of the Canadian Earth System Model (CanESM2) for the Bavarian and Quebec domains.

Drought occurrence was defined using the Standardized Precipitation Index. The training and test datasets were chosen from the current climatology (1955-2005) for the Munich and Lisbon subdomain within the CRCM5-LE. The best performing machine learning algorithms managed to obtain a correct classification of drought or no drought for a lead time of one month for around 60 % of the events of each class for the both domains. Explainable AI methods like feature importance and shapley values were applied to gain a better understanding of the trained algorithms. Physical variables like the North Atlantic Oscillation Index and air pressure one month before the event proved to be of high importance for the prediction. The study showed that better accuracies can be obtained for the Lisbon domain, due to the stronger influence of the North Atlantic Oscillation Index on Portugal’s climate.

How to cite: Felsche, E. and Ludwig, R.: Applying machine learning for drought prediction using a large ensemble of climate simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1305, https://doi.org/10.5194/egusphere-egu21-1305, 2021.

15:32–15:34
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EGU21-2377
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ECS
Jaime Gaona, Pere Quintana-Seguí, and Maria José Escorihuela

Droughts in the Iberian Peninsula are a natural hazard of great relevance due to their recurrence, severity and impact on multiple environmental and socioeconomic aspects. The Ebro Basin, located in the NE of the Iberian Peninsula, is particularly vulnerable to drought with consequences on agriculture, urban water supply and hydropower. This study, performed within the Project HUMID (CGL2017-85687-R), aims at evaluating the influence of the climatic, land cover and soil characteristics on the interactions between rainfall, evapotranspiration and soil moisture anomalies which define the spatio-temporal drought patterns in the basin.

The onset, propagation and mitigation of droughts in the Iberian Peninsula is driven by anomalies of rainfall, evapotranspiration and soil moisture, which are related by feedback processes. To test the relative importance of such anomalies, we evaluate the contribution of climatic, land-cover and geologic heterogeneity on the definition of the spatio-temporal patterns of drought. We use the Köppen-Geiger climatic classification to assess how the contrasting climatic types within the basin determine differences on drought behavior. Land-cover types that govern the partition between evaporation and transpiration are also of great interest to discern the influence of vegetation and crop types on the anomalies of evapotranspiration across the distinct regions of the basin (e.g. forested mountains vs. crop-dominated areas). The third physical characteristic whose effect on drought we investigate is the impact of soil properties on soil moisture anomalies.

The maps and time series used for the spatio-temporal analysis are based on drought indices calculated with high-resolution datasets from remote sensing (MOD16A2ET and SMOS1km) and the land-surface model SURFEX-ISBA. The Standardized Precipitation Index (SPI), the EvapoTranspiration Deficit Index (ETDI) and the Soil Moisture Deficit Index (SMDI) are the three indices chosen to characterize the anomalies of the corresponding rainfall (atmospheric), evapotranspiration (atmosphere-land interface) and soil moisture (land) anomalies (components of the water balance). The comparison of the correlations of the indices (with different time lags) between contrasting regions offers insights about the impact of climate, land-cover and soil properties in the dominance, the timing of the response and memory aspects of the interactions. The high spatial and temporal resolution of remote sensing and land-surface model data allows adopting time and spatial scales suitable to investigate the influence of these physical factors with detail beyond comparison with ground-based datasets.

The spatial and temporal analysis prove useful to investigate the physical factors of influence on the anomalies between rainfall, evapotranspiration and soil moisture. This approach facilitates the physical interpretation of the anomalies of drought indices aiming to improve the characterization of drought in heterogeneous semi-arid areas like the Ebro River Basin.

How to cite: Gaona, J., Quintana-Seguí, P., and Escorihuela, M. J.: Physical factors of influence in the interactions between rainfall, evapotranspiration and soil moisture driving the spatio-temporal evolution of drought patterns in the Ebro Basin (NE Spain)., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2377, https://doi.org/10.5194/egusphere-egu21-2377, 2021.

15:34–15:36
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EGU21-3255
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Highlight
Shraddhanand Shukla, Kathryn Grace, Abdou Ali, Daniel McEvoy, William Turnet, Adoum Alkhalil, Seydou Tinni, Issaka Lona, Ibrah Sanda, Emil A Cherrington, Rebekke Muench, and Greg Husak

West Africa (WA) is prone to food insecurity due to climate-, economic-, conflict-related shocks, as well as high population growth and lack of proper adaptation strategies. As per the USAID’s Famine Early Warning Systems Network, which uses Integrated Phase Classification to classify acute food insecurity (AFI), between 2011 and early 2020, several parts of WA reported the “Stressed” phase of AFI >30% of the time. Food security and livelihood in the region relies substantially on rainfed farming and small-scale water holes. Droughts lead to water deficits resulting in adverse impacts on food production, human and livestock health and agricultural labor opportunities, leading to or worsening of food insecurity. Thus far, the focus of climate, drought outlooks and their impacts, to support food insecurity early warning in this region has mainly been on the seasonal scale (i.e., 3-6 months in future) forecasts whereas use of subseasonal scale (2-4 weeks in future) forecasts has been negligible. Recent advances in routine production (i.e. weekly) and open access to subseasonal forecasts provide an unprecedented opportunity to improve the existing climate services in the region by focusing on the impacts of subseasonal climate characteristics on food insecurity in the region. Here we report on an ongoing project with the AGRiculture HYdrology and METeorology Regional Centre (SERVIR’s WA Hub) that aims to develop a subseasonal water deficit forecasting system to support food insecurity early warning in the region. The presentation will describe  (i) the results of an ongoing analysis examining the influence of subseasonal climate characteristics (e.g. monthly climate variability, length of dry or wet spell) on food insecurity, as measures by different food insecurity indicators (such as vegetation index, food insecurity reports and household level health and malnutrition reports) and (ii) the major accomplishments towards implementation of the water deficit forecasting system, including development and evaluation of prototype products, (iii) capacity building and stakeholder engagement activities with National Meteorological and Hydrological Services across the region. 

How to cite: Shukla, S., Grace, K., Ali, A., McEvoy, D., Turnet, W., Alkhalil, A., Tinni, S., Lona, I., Sanda, I., Cherrington, E. A., Muench, R., and Husak, G.: Forecasting agropastoral water deficits in West Africa to support food insecurity early warning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3255, https://doi.org/10.5194/egusphere-egu21-3255, 2021.

15:36–15:38
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EGU21-3599
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ECS
Daissy Herrera and Edier Aristizábal

Drought is one of the most critical hydrometeorological phenomena in terms of impacts to society because it affects soil water content, and consequently, crop production and human diets, in some cases under critical conditions, drought produces starving and people migration. Although Colombia is a tropical country, there are areas of the territory that have periods of drought that cause important economic damages such as fires, death loss in cattle, reduction of the capacity to supply water to persons, impacts to agriculture and fish farming.

Due to recent advances in terms of spatial and temporal resolutions of remote sensing and Artificial Intelligence techniques, it is possible to develop Automatic Learning Models supported on historic information. In this research was built a classifier  Random Forest (RF) and Bagged Decision Tree Classifier (DTC) model to predict, spatial and temporal drought occurrence in Colombia, using remote sensing data as land surface temperature, precipitation, soil water contentl, and evapotranspiration, and macro climatic variables information as ONI, MEI and SOI.  It was used the Standardized Precipitation Index (SPI) with 3-month time scale, that allows identifying agricultural drought events. The results showed that Random Forest provides the best outcomes. In terms of recall and precision, RF produced 0.84 and 0.59 and DTC brought a 0.8 and 0.33, respectively, to predict drought. The above, evidence that models could overestimate the number of times where drought occurs, in contrast with normal or humid conditions. On the other hand, False Positive and False Negative rates are important facts for measuring the development of models. In this case, the FP and FN rates are 7.5% and 2% for RF and 21% and 2.5% for DTC respectively, that means that both models made fewer mistakes predicted real drought events, but had more errors forecasting real normal or humid condition, especially, DTC model. RF can provide a better performance predicting drought and normal/humid conditions in contrast with DTC. The implementation of the developed model can allow governmental entities assessment and monitor agricultural drought over time. Taking, in consequence, actions to mitigate the impacts of droughts in their territories.

How to cite: Herrera, D. and Aristizábal, E.: Artificial Intelligence and machine learning model for spatial and temporal prediction of drought in the Colombia Caribbean region., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3599, https://doi.org/10.5194/egusphere-egu21-3599, 2021.

15:38–15:40
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EGU21-3952
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ECS
Samuel J. Sutanto and Henny A. J. Van Lanen

Streamflow drought forecasting is a key element of contemporary Drought Early Warning Systems (DEWS). The term streamflow drought forecasting, rather than streamflow forecasting, however, has created confusion within the scientific hydro-meteorological community, as well as in operational weather and water management services. Streamflow drought forecasting requires an additional step, which is the application of a drought identification method to the forecasted streamflow time series. The way, how streamflow drought is defined, is the main reason for this misperception. The purpose of this study, therefore, is to provide a comprehensive overview of the application of different drought identification approaches to forecast streamflow drought, incl. its characteristics, such as drought occurrence, timing, duration, and deficit volume, across the pan-European river network and for the Rhine River in more detail. In this study, the implications of different approaches for forecasting streamflow drought are elaborated using the extreme 2003 drought in Europe, as an example. The forecasted 25 ensemble streamflow data with 7-month lead time (LT) were obtained from the LISFLOOD hydrological model fed with seasonal meteorological forecasts from the European Centre for Medium-range Weather Forecasts system 5 (ECMWF SEAS 5). Streamflow droughts were analyzed using the daily and monthly Variable Threshold methods (VTD and VTM), daily and monthly Fixed Threshold methods (FTD and FTM), and the Standardized Streamflow Index with 1-month accumulation period (SSI-1). Our results clearly show that streamflow drought characteristics derived with different approaches deviate, which are partly associated with different climate regions across Europe. Using the forecasts initiated in July 2003 for LT=7-month, first, the daily drought approaches forecast more drought events than the monthly approaches. Second, the VT droughts (VTD and VTM), incl. SSI-1 forecast a lower number of drought occurrences than the FT droughts (FTD and FTM), which highlights the importance of taking seasonality into account. Overall, the FT approaches predict a longer drought duration, earlier drought timing, and higher drought deficit volume in many European rivers than the VT approaches. The characteristics of SSI-1 drought, in general, are close to what is being identified by the VTM approach. A detailed analysis of the drought forecasts for the Rhine River indicates that the number of drought events derived from the median of ensemble members can be predicted relatively well, but with lower skill for other drought characteristics. The use of monthly-aggregated forecasted flow data (e.g. VTM, FTM, and SSI) seems to be the best practice for seasonal drought forecasts because it will alleviate the drought forecast skill. The monthly drought threshold approaches, however, will forecast higher drought duration and deficit volume than using daily datasets. The choice of the drought identification method when forecasting streamflow drought, ultimately rests with the end-users and we need to realize that there is no one drought identification approach that fits all needs.

How to cite: Sutanto, S. J. and Van Lanen, H. A. J.: Application of multi drought definitions to forecast streamflow drought across Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3952, https://doi.org/10.5194/egusphere-egu21-3952, 2021.

15:40–15:42
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EGU21-4496
Konstantinos Mammas, Demetris F. Lekkas, and Ilias Pechllivanidis

The Standardized Precipitation Index (SPI) is one of the most popular indices for characterizing the meteorological drought on a range of time scales.  To date, SPI has been thoroughly used to monitor and predict drought in the precipitation signal and to further support early warning and climate services. While many studies focus on the performance improvement of drought models, there is to our knowledge no reference around the correct computation of SPI in a drought forecasting setting. As SPI is typically computed on the entire data set, prior to model-validation, bias is introduced to both the training and validation sets. This stems from the fact that the distribution parameters of the index are estimated using observations from the validation and test sets leading to information leakage. Here, we propose a modified calculation of SPI oriented for forecasting applications by measuring the bias introduced to the SPI values in the training set. Moreover, we propose the best practice for calculating the SPI during model-validation and encapsulate these in a drought forecasting framework. The proposed framework is further demonstrated using a 50-year data set from Sweden. Our findings suggest that the amount of bias introduced to the training sets increases with increased SPI scale, significantly affecting in some cases more than 80% of the available basins.

How to cite: Mammas, K., Lekkas, D. F., and Pechllivanidis, I.: A framework to quantify bias for improved drought forecasting , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4496, https://doi.org/10.5194/egusphere-egu21-4496, 2021.

15:42–15:44
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EGU21-6041
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ECS
Katherine Kowal, Louise Slater, and Anne Van Loon

Seasonal forecasts provide an opportunity to enhance drought preparedness in the Central American Dry Corridor (CADC). Evaluation of seasonal precipitation predictions within the CADC is important because rainfall affects many local livelihoods, and rainfall predictability in this region may be low due to its complex climate and terrain. In this presentation, SEAS5, a leading seasonal forecasting system produced by the European Centre for Medium-Range Weather Forecasting, is evaluated for the accuracy of its precipitation predictions across the CADC relative to the GPCC gridded precipitation dataset derived from rain-gauge data. A few studies have assessed its predecessor (System 4) in Central America but none have assessed SEAS5 in the CADC. SEAS5 predictions of rainfall mean, variability, and extremes are evaluated using one- to seven- month lead-times over 1981-2016 and for known historical droughts. Results show that SEAS5 precipitation forecasts often perform best during July and August, two important months for crop growth because they occur during the mid-summer dry period, which separates the wet season into distinct phases. Elevation seems to have an influence, although alone it does not explain variations in forecast skill across the region. SEAS5 overpredicts rainfall in greater quantities at high elevation. This analysis showcases promising forecast skill of relevance to agricultural forecasting and could be expanded on in future work by evaluating skill of other drought indicators.

How to cite: Kowal, K., Slater, L., and Van Loon, A.: Evaluation of SEAS5 Precipitation Forecasts in the Central American Dry Corridor, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6041, https://doi.org/10.5194/egusphere-egu21-6041, 2021.

15:44–15:46
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EGU21-6499
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ECS
Lu Tian, Sarah Ho, Markus Disse, and Ye Tuo

Drought propagation processes interlink closely with the water cycle, which has so far been mostly investigated without tracking temporal propagation across multiple types of drought. Central Asia, including Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, and Xinjiang (China) areas, is one of the most drought-prone areas in the world and extremely vulnerable to water scarcity. Understanding the multiple pathways of drought propagation over time in Central Asia is necessary for food security, human health, and poverty alleviation. In this study, we quantify the propagation time and track the details of temporal propagation processes of drought across the atmosphere, the geosphere, and the hydrosphere. The standardized indices are calculated using variables directly related to each type of drought: precipitation (SPI), evapotranspiration (SEDI), soil moisture (SSI), and runoff (SRI)1, 2. The drought propagation processes are divided into the development stage and recovery stage. The propagation time at different stages between multiple types of drought is calculated by the Pearson correlation coefficient (p<0.05)3 and run theory method4. Besides, the potential influencing factors on the temporal propagation are explored from the meteorological, land cover, and water management aspects. As the main results, the propagation time in winter is longer than in summer. And topography has a significant impact on drought propagation time. These key findings could further benefit the early warning of drought and facilitate the drought mitigation-adaptation in both Central Asia and other continents.

Keywords: Temporal propagation, Central Asia, Influencing factors, Land cover

1. Gevaert, A. I.; Veldkamp, T. I. E.; Ward, P. J., The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models. Hydrol. Earth Syst. Sci. 2018, 22, (9), 4649-4665.
2. Barella-Ortiz, A.; Quintana-Segui, P., Evaluation of drought representation and propagation in regional climate model simulations across Spain. Hydrol. Earth Syst. Sci. 2019, 23, (12), 5111-5131.
3. Barker, L. J.; Hannaford, J.; Chiverton, A.; Svensson, C., From meteorological to hydrological drought using standardised indicators. Hydrol. Earth Syst. Sci. 2016, 20, (6), 2483-2505.
4. Wu, J. F.; Chen, X. H.; Yao, H. X.; Liu, Z. Y.; Zhang, D. J., Hydrological Drought Instantaneous Propagation Speed Based on the Variable Motion Relationship of Speed-Time Process. Water Resour. Res. 2018, 54, (11), 9549-9565.

How to cite: Tian, L., Ho, S., Disse, M., and Tuo, Y.: The Temporal Propagation Processes of Multiple Types of Drought in Central Asia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6499, https://doi.org/10.5194/egusphere-egu21-6499, 2021.

15:46–15:48
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EGU21-7122
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ECS
Helena Gerdener, Kerstin Schulze, Olga Engels, Jürgen Kusche, Hannes Müller Schmied, Christoph Niemann, Sebastian Ackermann, and Petra Döll

The frequency and severity of drought increase in many regions of the world, which emphasizes the need for sufficient research to better monitor and trigger management plans. An important role hereby plays hydrological drought, because it affects water supply and crop yields that are necessary to ensure food security. Typically, hydrological drought detection is based on in-situ observations of fluxes or storages at the surface. However, this neglects the fact that drought might occur in multiple storages with different timing and severity.  The use of subsurface storage, e.g. groundwater, is rare because the available in-situ well level monitoring is irregularly distributed in space and time and access might be restricted, for example due to national security reasons or problems in converting them to storage estimates.

The satellite mission Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO offer a great possibility to observe the total water storage, i.e. the sum of surface and subsurface storages, on a global scale from space. However, GRACE is restricted to monthly data on a spatial resolution of about 300 km and the vertical sum of the storages. Hydrological models present another possibility to derive global storage information with a finer spatial (~50km), temporal and vertical resolution than GRACE but they do not perfectly represent the reality because they are underlying assumptions and are affected by uncertainty of forcing data. Therefore, to enable downscaling of GRACE while improving the models realism, the GRACE measurements are assimilated into a hydrological model.

In previous works we used a framework that assimilates GRACE into the WaterGAP Global Hydrological Model (WGHM) regionally or basin-wise. In this work we present a new framework that globally assimilates GRACE on a 4 degree grid with full uncertainty information from 2003 to 2018. The framework enables to assimilate about 95% of the global WGHM land surface except Greenland. With regard to vertical and spatial resolution the performance of model, observation and assimilation is compared. Global GRACE based drought indicators are applied and its development in the different compartments of surface water, soil and groundwater is analyzed to identify new insights into the propagation of drought. We expect that by including GRACE we derive new information especially for groundwater droughts, which might reveal time lags and a different severity as compared to surface water droughts for some regions.

How to cite: Gerdener, H., Schulze, K., Engels, O., Kusche, J., Müller Schmied, H., Niemann, C., Ackermann, S., and Döll, P.: Analyzing the propagation of drought through water storages using global scale GRACE-based data assimilation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7122, https://doi.org/10.5194/egusphere-egu21-7122, 2021.

15:48–15:50
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EGU21-10169
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ECS
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Highlight
Paul Voit

Being responsible for about 70% of the world’s freshwater use, agricultural irrigation practices have a strong impact on water budgets in dryland environments and will increase to do so, as an increase in irrigated areas worldwide is expected. In semi-arid catchments, irrigation can account for a substantial proportion of the water budget, especially during the dry season. Consequently, due to the limited water resources, these catchments rely on adequate water management practices. Water withdrawal from groundwater, river flow or reservoirs for irrigation purposes alter the overall hydrological balance. Being aware of such important impacts on the regional (meso-scale) water budget, hydrological models should improve their capability to account for them, including typical operational data availability and constraints. Thus, the answers on water management issues should be addressed, such as, how do these withdrawals alter the rivers’ flow regime and water yield? How do they affect sustainability of regional water resources, both in a seasonal and long-term time scale? Can public irrigation data be used to improve the performance of a catchment model?

To account for this particular anthropogenic interference with the hydrological cycle a novel irrigation module is introduced to improve meso-scale hydrological models’ performance for such hydro-climatic conditions. We implemented this module into WASA-SED, a hydro-sedimentological model tailored for semi-arid catchments on the meso-scale, now enabling to account for irrigation practices in the modelling process. The module allows to represent water abstraction from different sources (ground water, river, reservoirs), inter- and intra- basin transfers and seasonality of irrigation schemes. As a test case, a semi-arid catchment with excellent irrigation data in the Rio Sao Francisco basin, Brazil, was chosen to investigate exemplarily the impact of irrigation operations on the low river flows in the dry season. Using publicly available irrigation data as input for this module, it could be shown, that including irrigation practices into the modelling process helps to improve the model’s performance.

Furthermore, modelling results can be used to estimate the real water withdrawal rates, as there is uncertainty about how much water the users actually withdraw, because irrigation data from the Brazilian authorities shows the maximum withdrawal rates, as defined in contracts for water use for river water, but not the actually used water rates, which might be different (less or sometimes even more) than the contracts’ maximum rates. Whether the users withdraw more or less water than officially granted is uncertain. The model’s results can be used to estimate realistic withdrawal rates as well as to predict further irrigation potential in the given catchment. Likewise, the effect of exploiting different sources for irrigation water (i.e., rivers, reservoirs, and groundwater) can be analysed in terms of their reliability and effect on the river system.

How to cite: Voit, P.: How much water remains? Incorporating publicly available irrigation data to improve meso-scale hydrological model performance in dryland environments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10169, https://doi.org/10.5194/egusphere-egu21-10169, 2021.

15:50–15:52
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EGU21-10886
Dimmie Hendriks, Pieter Hazenberg, Jonas Gotte, Patricia Trambauer, Arjen Haag, Gennadii Donchyts, and Frederiek Sperna Weiland

An increasing number of regions and countries are confronted with droughts as well as an increase in water demand. Inevitably, this leads to an increasing pressure on the available water resources and associated risks and economic impact for the water dependent sectors. In order to prevent big drought impacts, such as agricultural damage and food insecurity, timely and focused drought mitigation measures need to be carried out. To enable this, the detection of drought and its sector-specific risks at early stages needs to be improved. One of the main challenges is to develop compound and impact-oriented drought indices, that make optimal use of innovative techniques, satellite products, local data and other big data sets.

Here, we present the development of a Next Generation Drought Index (NGDI) that combines multiple freely available global data sources (eg. ERA5, MODIS, PCR-GLOBWB) to calculate a range of relevant drought hazard indices related to meteorological, hydrological, soil moisture and agricultural drought (eg. SPI, SPEI, SRI, SGI, VCI). The drought hazard indices are aggregated at district level, while considering the percentage area exposure of the drought impacted sector (exposure). In addition, the indices are enriched with local and national scale drought impact information (eg. online news items, social media data, EM-DAT database, GDO Drought news, national drought reports). Results are presented at sub-national scales in interactive spatial and temporal views, showing the combined drought indices and impact data.

The NGDI approach is being tested for the agricultural sector in Mali, a country with a vulnerable population and economy that faces frequent dry spells which heavily impact the functioning of the important agricultural activities that sustain a large part of the population. The computed drought indices are compared with local drought data and an analysis is made of the cross-correlations between the indices within the NGDI and collected impact data.

We aim at providing the NGDI information to a broad audience as well as co-creation of further NGDI developments. Hence, we would like to reach out to interested parties and identify collaboration opportunities.

How to cite: Hendriks, D., Hazenberg, P., Gotte, J., Trambauer, P., Haag, A., Donchyts, G., and Sperna Weiland, F.: Development of a Next Generation Drought Index: combining multiple global and local data sources to enable detection of sector-specific drought impacts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10886, https://doi.org/10.5194/egusphere-egu21-10886, 2021.

15:52–15:54
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EGU21-14019
Antonio Francipane, Elisa Arnone, and Leonardo Valerio Noto

Artificial reservoirs are one of the main water supply resources in the Mediterranean areas; their management can be strongly affected by the problems of drought and water scarcity. The reservoir water level is the result of the hydrological processes occurring in the upstream catchment, which, in turn, depend on meteorological variables, such as rainfall and temperature. It follows that a reliable forecast model of the meteorological forcing, along with a reliable water balance model, could enhance the correct management of a reservoir. With regard to the rainfall/temperature forecast model, the use of forecast climate data in the mid-term may provide further support for the future water level estimation of reservoirs.

From the perspective of the water balance model, instead, among the approaches used to predict the water levels for the next future, those based on data-driven methods have been demonstrated to be particularly capable of correctly reproducing the correlation between a dependent variable (e.g., water level, volume) and some covariates (e.g., temperature, precipitation).

This study describes the preliminary results of a novel application that exploits the Seasonal Forecast (SF) data, produced at the European Centre for Medium-Range Weather Forecasting (ECMWF), within a data-driven model aimed to predict the reservoir water volume at mid-term scale, up to 6 months ahead in four reservoirs of the Sicily (Italy) here considered as a case study. For each case, a NARX (Nonlinear AutoRegressive network with eXogenous inputs) neural network is calibrated to reproduce the monthly stored water volume starting from the monthly precipitation and mean monthly air temperature variables.

Preliminary results showed that the NARXs have the capability to reproduce the water levels in the investigated period (January 2017 - April 2020), including the variations during more or less dry periods. All this despite the SF data have not been previously treated with downscaling and/or bias correction techniques.

How to cite: Francipane, A., Arnone, E., and Noto, L. V.: Combining a data-driven approach with seasonal forecasts data to predicting reservoir water volume in the Mediterranean area., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14019, https://doi.org/10.5194/egusphere-egu21-14019, 2021.

15:54–15:56
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EGU21-16147
Abror Gafurov, Olga Kalashnikova, Uktam Adkhamov, Akmal Gafurov, Adkham Mamaraimov, and Djafar Niyazov

Central Asia is facing a water shortage due to the negative impacts of climate change and demographic development. Water resources in this region originate mainly in the mountains of Pamir and Tian-Shan due to snow-and glacier melt. However, a limited observation network is available in these mountain systems and many are malfunctioning. Thus, the region needs new innovative methods to forecast seasonal and sub-seasonal water availability to ensure better water resources management and mitigate hydro-meteorological risks.

In this study, we present the results of our efforts for many years to develop a forecasting tool and implementation in the region. Since the region has limited observed meteorological data, we use primarily remote sensing data on snow cover for this purpose. We apply the MODIS snow cover data that is processed, including cloud removal, using the MODSNOW-Tool. We have applied this tool, which can be used to monitor snow cover in an operational mode and forecast water availability for the vegetation period but also for the monthly scale using the multiple linear regression method.

Our results show that snow is important in most of the river basins and can also be used as a single predictor to forecast seasonal water availability. Especially, in remote areas with limited observations, this approach gives a possibility of forecasting water availability for different time period. Besides seasonal hydrological forecast, the MODSNOW-Tool was also used to forecast water availability for upcoming months. The validity of forecasts were tested against observed discharge for the last 20 years and mostly above 70 % verification was achieved. Additionally to remote sensing based snow cover data, observed meteorological information was also used as predictors and improved the validity of forecast models in some river basins.

The implementation of the MODSNOW-Tool to improve the hydrological forecast was done for 28 river basins in Central Asia that are located in the territories of five post-Soviet countries Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan.  The MODSNOW-Tool was also implemented at the National Hydrometeorological Services (NHMS) of each post-Soviet country.

How to cite: Gafurov, A., Kalashnikova, O., Adkhamov, U., Gafurov, A., Mamaraimov, A., and Niyazov, D.: Seasonal to sub-seasonal hydrological forecast in Central Asia to improve water management and mitigate hydrometeorological risks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16147, https://doi.org/10.5194/egusphere-egu21-16147, 2021.

15:56–15:58
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EGU21-16318
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ECS
Ronnie Javier Araneda-Cabrera, María Bermudez, and Jerónimo Puertas

Hydrological droughts can trigger socioeconomic disruptions which can cause large impacts on societies. Therefore, their forecast is extremely important for early warning and disaster mitigation. Several modelling approaches have been developed for this purpose, and models based on machine learning have become popular during the last decades. In this study, we evaluated the performance of five commonly used models in drought forecasting: Autoregressive Integrated Moving Average (ARIMA), multiple linear (MLM), Random Forest (RF), K-nearest Neighbours (KN) and Artificial Networks (ANN) models.
The study site was the Paute River basin (4816.56 km2) located in the mid-high and eastern part of the Ecuadorian Andes. The "Daniel Palacios" dam, the oldest of 4 strategic dams and which alone generates 35% of Ecuador's national energy demand, was considered the lowest point in the basin. The basin is susceptible to hydrological droughts, which have led to power shortages in the past (e.g., in 1995, 1999 and 2009).
As hydrological drought predictand we used the monthly Standardized Streamflow Index (SSI) accumulated at 1-, 3-, 6- and 12-months, while the predictor variables were precipitation, temperature, river flow and the climatic indices associated with the El Niño-Southern Oscillation, ENSO (El Niño 1+2, El Niño 3, El Niño 4, and El Niño 3.4, which are strongly related to droughts in Ecuador). Data were obtained from the National Institute of Meteorology and Hydrology (INAMHI) and the Climate Prediction Centre of NOAA.
The models were evaluated for lead times of 1, 3, 6 and 12 months through the determination coefficient (R2), the Nash-Sutcliff efficiency (NSE) and the Root Mean Square Error (RMSE). The models' capability to distinguish between occurrence and non-occurrence of droughts (here defined as SSI≤-1) was assessed with a receiver operating characteristic (ROC) diagram. Models were validated using the expanding window (walk forward approach) as a back testing strategy starting as calibration and validation periods Aug/1984-Dec/2010 and Jan/2011-Dec/2019 (75 and 25% of the data, respectively).
As expected, SSI at longer aggregations can be predicted more accurately than at shorter ones with all models. This fact is explained because the former ones more effectively reduce the noise than the latter due to the increase in filter length. The greater the lead time, the less reliable the prediction. Considering a lead time of 1 month, the best model was the ANN for SSI-1 and SSI-3 (R2=0.64, ROC=0.67; and R2=0.78, ROC=1.00 respectively), and the MLM model for SSI-6 and SSI-12 (R2=0.86, ROC=0.66, and R2=0.96, ROC=0.99 respectively). However, very similar performances were obtained in the latter cases by the ANN model (R2=0.86, ROC=0.66 R2=0.91 and ROC=0.75 respectively) and the ARIMA model (R2=0.83, ROC=0.98 R2=0.93 and ROC=0.99 respectively). ARIMA models showed large errors for lead times longer than 1 month, so an ANN model is recommended. However, to maximize their potential, further research could explore modifications of the ANN architecture or the input data. Results indicate that these models can be used to forecast hydrological droughts in the Paute river basin and can be used to support reservoir operation decisions.

How to cite: Araneda-Cabrera, R. J., Bermudez, M., and Puertas, J.: Short-term hydrological drought forecasting in the Paute river basin, Ecuadorian Andes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16318, https://doi.org/10.5194/egusphere-egu21-16318, 2021.

15:58–17:00