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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 30 July 2020, for manuscripts that are not under consideration for publication elsewhere.

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Co-organized by NH9
Convener: Brunella Bonaccorso | Co-conveners: Carmelo Cammalleri, Athanasios Loukas, Micha Werner
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| Thu, 07 May, 14:00–18:00 (CEST)

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Session materials Download all presentations (228MB)

Chat time: Thursday, 7 May 2020, 14:00–15:45

D210 |
EGU2020-14871
Olivier Prat, Alec Courtright, Ronald Leeper, Brian Nelson, Rocky Bilotta, James Adams, and Steve Ansari

We present an operational near-real time drought monitoring framework on a global scale that uses satellite quantitative precipitation estimates from the NOAA/CDR program (CMORPH-CDR, PERSIANN-CDR). Monthly and daily Standardized Precipitation Indexes (SPI) are computed for various time scales over the entire period of record of the respective datasets. The near-real time availability of CMORPH-CDR permits for a daily update of the global drought conditions starting in 1998, while the longer period of record of PERSIANN-CDR allows to compute global drought conditions since 1983. The SPI sensitivity to different precipitation datasets and to various lengths of record is quantified. Results indicated that both monthly and daily SPIs computed with both CDRs presented the same timing and area for the major droughts episodes over the continental United States as well as for selected drought events around the globe. Furthermore, the difference resulting from the use of the two-parameter Gamma distribution (McKee et al. 1993) and the three-parameter Pearson III distribution (Guttman 1999) is evaluated. The global mapping of the different distribution parameters (2 and 3 parameters respectively for the Gamma and Pearson III distributions) informs us on how to optimally compute the SPI in areas experiencing too much or too little rainfall. Both CMORPH-CDR and PERSIANN-CDR SPIs are evaluated primarily over CONUS where long-term drought monitoring products based on in-situ data exists such as the United States Drought Monitor (USDM) and the nClimGrid derived SPI. A publicly available interactive visualization tool that provides access to global drought information is also presented. The tools is intended to fill some of the drought monitoring information gaps around the globe. A variety of visualization techniques are used to aid in the interpretation of global drought indices while interactive functionality allows users to focus on a specific region and time-scale of interest. Additional information for region specific drought monitoring resources is also provided to help users access regional drought monitoring information.

D211 |
EGU2020-10133
Emanuele Romano, Franco Salerno, Anna Bruna Petrangeli, and Nicolas Guyennon

Central Italy presents numerous factors potentially affecting the precipitation regime: 1) it includes both Tyrrhenian and Adriatic sides, the first exposed to Atlantic perturbation and generally more rainy, the second one possibly exposed also to Balkan streams; 2) due to the short distance between the two coasts (few tens of kilometres), also the more internal areas are prone to the influence of the sea; 3) at the same time, the highest reliefs of the Apennine chain are located in the region; 4) the Northern areas of the Adriatic side experience the influence of the continental climate, due to the Po Valley. The climate framework Central Italy sees in the last thirty years a tendency toward drier conditions and an increasing of drought events, mostly in frequency. To explore the variability in time and space of the precipitation regime in relation to the atmospheric patterns, land rainfall data collected and homogenised trough geostatistical approach over the period 1951-2019 in Central Italy have been analysed in relation to the following indexes: Winter NAO index, East Atlantic-West Russia index, Pacific/North America index, Polar/Eurasia index, Scandinavian index, Artic oscillation index, Western Mediterranean oscillation index. Focus of the analysis (1951-2019) is put on possible common signal between precipitation regime anomalies (on both Tyrrhenian and Adriatic side) and teleconnection patterns, sought through regression analysis and a wavelet and cross-wavelet decomposition. Results indicate that possible influence of some teleconnection patterns (particularly East Atlantic, East Atlantic/Western Russia and NAO) on the precipitation regime is limited to winter and early spring for the Tyrrhenian side, and to summer for the Adriatic side. Moreover, the analysis of the mean wavelet time series-period indicates an increasing in frequency of drought episodes for the last 20 years on both sides of the study area.

D212 |
EGU2020-6317
Rounak Afroz, Ashish Sharma, and Fiona Johnson

The complexity of representing droughts has led to many drought indices being developed. A common aspect for many of these indices, however, is the need to adopt a predefined time period, over which a drought is characterized. Therefore, to declare a catchment as drought-impacted, 6, 12 or 24-month SPI are required. Actual water allocations, however, are required at all times and are thus duration free; a concept well described by the well-known residual mass curve. Here we propose a new framework to characterize drought, termed as the Residual Mass Severity Index (RMSI). As the name suggests, the RMSI defines drought based on the magnitude of the residual mass in any location which is calculated by performing a water balance using a prescribed demand. Demand here is adopted as the median monthly precipitation for the region. Water shortages only become significant when there is a sustained deficit compared to this demand. The above described residual mass is standardized to formulate the RMSI across Australia. The new RMSI has been validated against established drought indices (such as the SPI) to highlight the advantages of a duration-free drought index.

RMSI provides a simple method of assessing sustained and severe drought anomalies which is important with expected increases in water scarcity due to anthropogenic climate change. We demonstrate that RMSI can be used as a tool to evaluate the performance of General Circulation Models (GMCs) in representing the sustainability of water resource systems as a product of resilience, reliability, and vulnerability (RRV) of the system. Future projections of drought from GCMs which perform well in representing RMSI in the RRV context in the historical climate are then compared to drought projections from the full CMIP5 ensemble.

Keywords: Drought, Residual Mass Curve, SPI, RRV, Climate Change, CMIP5 GCMs

D213 |
EGU2020-22510
| Highlight
Mike Hobbins, Amy McNally, Daniel Sarmiento, Timen Jansma, Greg Husak, Will Turner, and James Verdin

A robust definition of drought is as a sustained and impactful surface moisture imbalance between its supply and demand. While the supply aspect has generally long been well characterized by precipitation, the same cannot be said for the demand side, which is a function of atmospheric evaporative demand (sometimes also called potential evaporation, or PET) and surface moisture availability. Traditional drought analyses have neglected evaporative demand entirely or inadequately parameterized it using either its climatological mean or estimates based on temperature. This is primarily due to (i) a deficient understanding of the role that evaporative demand plays in both driving and exacerbating drought, and (ii) a paucity of the data required to fully characterize evaporative demand—temperature, humidity, solar radiation, and wind speed. These deficiencies are particularly acute over data-sparse regions that are also home to drought-vulnerable and food-insecure populations, such as across much of Africa.

There is thus urgent need for global evaporative demand estimates for physically accurate drought analyses and food security assessments such as those operationally conducted by the Famine Early Warning Systems Network (FEWSNET). We need first to improve our understanding of how evaporative demand and drought interact, and then exploit these interactions in drought monitoring and in support of famine early warning.

The US National Oceanic and Atmospheric Administration (NOAA) supports FEWSNET’s food-security monitoring, early warning, and forecast efforts by providing a nearly 40-year long, daily, 0.125-degree, global dataset of Penman-Monteith reference evapotranspiration as a fully physical metric of evaporative demand. This dataset is driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)—an accurate, fine-resolution land-surface/atmosphere reanalysis—and is proving invaluable for examining and attributing hydroclimatic changes and extremes on secular time scales and in ongoing operations. An emerging drought index based on this dataset—the Evaporative Demand Drought Index (EDDI)—represents drought’s demand perspective, and permits early warning and ongoing monitoring of agricultural flash drought and hydrologic drought, both crucial drivers of food insecurity.

Our goal in this presentation is to describe how these needs are increasingly being met by service of evaporative demand data and value-added drought-monitoring and famine early warning products to regional scientists tasked with assessing drought (and famine) risk in food-insecure countries within the FEWSNET framework. We will summarize the development and verification of the evaporative demand dataset and the results of a rigorous decomposition of its temporal variability across Africa. Further, we will highlight the utility of the dataset by examining the attribution of extreme evaporative demand anomalies associated with canonical droughts across the continent (e.g., the 2016 Horn of Africa drought), by using EDDI in early warning, and using the new evaporative demand dataset as an input to established food-security metrics such as GeoWRSI—a geo-spatial, stand-alone implementation of the Water Requirements Satisfaction Index. Together, these analyses should greatly contribute to a more holistic understanding of drought and food-security risk across the continent.

D214 |
EGU2020-5564
Jaime Gaona, Pere Quintana-Seguí, and Maria José Escorihuela

The Mediterranean climate of the Iberian Peninsula defines high spatial and temporal variability of drought at multiple scales. These droughts impact human activities such as water management, agriculture or forestry, and may alter valuable natural ecosystems as well. An accurate understanding and monitoring of drought processes are crucial in this area. The HUMID project (CGL2017-85687-R) is studying how remote sensing data and models (Quintana-Seguí et al., 2019; Barella-Ortiz and Quintana-Seguí, 2019) can improve our current knowledge on Iberian droughts, in general, and in the Ebro basin, more specifically.

The traditional ground-based monitoring of drought lacks the spatial resolution needed to identify the microclimatic mechanisms of drought at sub-basin scale, particularly when considering relevant variables for drought such as soil moisture and evapotranspiration. In situ data of these two variables is very scarce.

The increasing availability of remote sensing products such as MODIS16 A2 ET and the high-resolution SMOS 1km facilitates the use of distributed observations for the analysis of drought patterns across scales. The data is used to generate standardized drought indexes: the soil moisture deficit index (SMDI) based on SMOS 1km data (2010-2019) and the evapotranspiration deficit index (ETDI) based on MODIS16 A2 ET 500m. The study aims to identify the spatio-temporal mechanisms of drought generation, propagation and mitigation within the Ebro River basin and sub-basins, located in NE Spain where dynamic Atlantic, Mediterranean and Continental climatic influences dynamically mix, causing a large heterogeneity in climates.

Droughts in the 10-year period 2010-2019 of study exhibit spatio-temporal patterns at synoptic and mesoscale scales. Mesoscale spatio-temporal patterns prevail for the SMDI while the ETDI ones show primarily synoptic characteristics. The study compares the patterns of drought propagation identified with remote sensing data with the patterns estimated using the land surface model SURFEX-ISBA at 5km.  The comparison provides further insights about the capabilities and limitations of both tools, while emphasizes the value of combining approaches to improve our understanding about the complexity of drought processes across scales.

Additionally, the periods of quick change of drought indexes comprise valuable information about the response of evapotranspiration to water deficits as well as on the resilience of soil to evaporative stress. The lag analysis ranges from weeks to seasons. Results show lags between the ETDI and SMDI ranging from days to weeks depending on the precedent drought status and the season/month of drought’s generation or mitigation. The comparison of the lags observed on remote sensing data and land surface model data aims at evaluating the adequacy of the data sources and the indexes to represent the nonlinear interaction between soil moisture and evapotranspiration. This aspect is particularly relevant for developing drought monitoring aiming at managing the impact of drought in semi-arid environments and improving the adaptation to drought alterations under climate change.

D215 |
EGU2020-22049
Santosh Nepal, Saurav Pradhananga, Narayan Shrestha, Jayandra Shrestha, Manfred Fink, and Sven Kralisch

Soil moisture is an important part of the vegetation cycle and a controlling factor for agriculture. Withstanding the role of agricultural productivity in economic development of a nation, it is imperative that water resources planners and managers are able to assess and forecast agricultural drought. As agricultural drought is related to declining soil moisture, this paper studies the dynamics of soil moisture based drought in the transboundary Koshi river basin in the Himalayan region. By applying the J2000 hydrological model, the daily soil moisture is derived for the whole basin for a 28-year time frame (1980-2007). The soil moisture deficit index (SMDI) is calculated based on a fully distributed spatial representation by considering the derivation from the long term soil moisture on a weekly time scale. In order to analyze the variation of soil moisture drought spatially, the river basin is subdivided into three distinct geographical areas, i.e. Northern Tibet, High and Middle Mountains, and Southern Plain. Further, temporally the SMDI is calculated for four distinct seasons based on wetness and dryness patterns observed in the study area, i.e. monsoon, post-monsoon, winter and pre-monsoon. A multi-site and multi-variable (streamflow at one station and evapotranspiration at three stations) approach was used for the calibration and validation of the J2000 model. Results show that the J2000 model is able to simulate the hydrological cycle of the basin with high accuracy. The model properly represents the winter drought of 2005 and 2006 was the most severe drought in the 28-year time period. Results also show considerable increases in the frequency of pre-monsoon and post-monsoon soil moisture drought in recent years. Severe droughts have had a high frequency in recent years, which is also reflected by an increase of areas that were impacted. In summary, our results show that severity and occurrence of agricultural drought has increased in the Koshi river basin in the last three decades, especially in the winter and pre-monsoon. This will have serious implications for agricultural productivity and for water resources management of the basin.

D216 |
EGU2020-11120
Maria Jose Escorihuela, Pere Quintana Quintana-Seguí, Vivien Stefan, and Jaime Gaona

Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.

Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. 


A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.

Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.

A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.

The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).

In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.

D217 |
EGU2020-11116
Nunzio Romano, Carolina Allocca, Roberto Deidda, and Paolo Nasta

Water balance components depend on annual rainfall amount and seasonality in Mediterranean catchments. A high percentage of the annual rainfall occurs between late fall and early spring and feeds natural and artificial water reservoirs. This amount of water stored in the mild-rainy season is used to offset rainfall shortages in the hot-dry season (between late spring and early fall). Observed seasonal anomalies in historical records are quite episodic, but an increase of their frequency might exacerbate water stress or water excess if the rainy season shortens or extends its duration, e.g. due to climate change. Hydrological models are useful tools to assess the impact of seasonal anomalies on the water balance components and this study evaluates the sensitivity of water yield, evapotranspiration and groundwater recharge on changes in rainfall seasonality by using the Soil Water Assessment Tool (SWAT) model. The study area is the Upper Alento River Catchment (UARC) in southern Italy where a long time-series of daily rainfall is available from 1920 to 2018. To assess seasonality anomalies, we compare two approaches: a “static” approach based on the Standardized Precipitation Index (SPI), and a “dynamic” approach that identifies the rainy season by considering rainfall magnitude, timing, and duration. The former approach rigidly selects three seasonal features, namely rainy, dry, and transition seasons, the latter being occasionally characterized by similar properties to the rainy or dry periods. The “dynamic” approach, instead, is based on a time-variant duration of the rainy season and enables to corroborate the aforementioned results within a probabilistic framework. A dry seasonal anomaly is characterized by a decrease of 241 mm in annual average rainfall inducing a concurrent decrease of 116 mm in annual average water yield, 60 mm in actual evapotranspiration and 66 mm in groundwater recharge. We show that the Budyko curve is sensitive to the seasonality regime in UARC by questioning the implicit assumption of temporal steady-state between annual average dryness and evaporative index. Although the duration of the rainy season does not exert a major control on water balance, we have been able to identify seasonal-dependent regression equations linking water yield to dryness index over the rainy season.

D218 |
EGU2020-2577
Helena Gerdener, Olga Engels, Jürgen Kusche, and Petra Döll

Detecting and quantifying hydrological drought in hindcast plays an important role for understanding its negative impacts on water supply and agricultural systems. Observation of, for example, streamflow or groundwater storage decline would be required for drought detection, but is often restricted due to inaccessibility or irregular spatial and temporal coverage of in-situ data. At this point, hydrological models can help to provide information about surface and subsurface storages. However, models do not perfectly represent reality because they are subject to assumptions and very sensitive to uncertainties of input data.

At larger spatial scales, the gravity satellite mission GRACE (Gravity Recovery And Climate Experiment) offers a possibility to observe total water storage anomalies (TWSA), which also contain surface and subsurface storages. A number of indicators for hydrological drought based on GRACE TWSA have been developed and applied to detect drought events in different parts of the world. But the application of GRACE TWSA data is severely hampered by its sparse spatial resolution of about 300 km and it does not allow to distinguish separately storage declines in different hydrological compartments like snow, soil, groundwater and surface water bodies.

To overcome these limitations of the model and observation data, we developed an assimilation framework that integrates GRACE TWSA into the WaterGAP Hydrological Model (WGHM). The ability of spatially downscaling and disaggregating GRACE data by data assimilation opens up new opportunities for drought detection. We compare and analyze different TWSA-based drought indicators in the period of 2003 to 2016 for South Africa using (i) the WGHM model, (ii) GRACE observations, and (iii) GRACE TWSA integrated into WGHM. Finally, we apply the same methodology to surface and groundwater storage variability from the GRACE data assimilation. We show that the 2016 drought event was mainly related to groundwater deficit, which is more pronounced in the assimilation as compared to the model.

D219 |
EGU2020-21967
Sasin Jirasirirak and Aksara Putthividhya

Drought monitoring and assessment is critical considering the immense costs and impacts Thailand has been experiencing these days.  Deficit in precipitation is typically referred to as meteorological drought.  While deficit in soil moisture (i.e., below average moisture in the soil) is known as agricultural drought.  Hydrological drought corresponds to a deficit in runoff or groundwater resources. Socio-economic drought (also known as anthropogenic drought) refers to water stress intensified by human activities and increase water demands.  Our long-term research in ground observation drought monitoring and assessment has been integrated with remotely sensed precipitation and soil moisture information necessary for the computation of extensively used drought indicators, such as Standardized Precipitation Index (SPI) using widely available satellite-based precipitation products including PERSIANN, TRMM, GSMaP, and IMERG to demonstrate the multidimensional and multi-sectoral impacts of change in rainfall patterns which is directly linked to drought assessment.  Long-term satellite-based soil moisture time series obtained from NASA’s Soil Moisture Active Passive (SMAP) mission have been employed for drought detection from provided near real-time top soil moisture estimates in accordance with The Gravity Recover and Climate Experiment (GRACE) mission.  Preliminary results indicate that multi-sensor multi-satellite remotely sensing data can enhance soil moisture mapping and its long-term spatial and temporal trends match well with change in terrestrial water storage and groundwater storage of the country.   This approach can provide more robust and integrated measure of drought based on wider range of satellite observations such as precipitation, soil moisture, total water storage anomalies, groundwater storage change, offering the opportunities to investigate droughts from different viewpoints. Drought monitoring scheme developed in this work can serve as a supporting tool for water resources and climate change policy making.  It can contribute to improve understanding on potential impacts of climate change, multi-sectoral linkages, multi-scale vulnerability, and adaptation programs.   

D220 |
EGU2020-1491
Henny A.J. Van Lanen, Theresa C. Van Hateren, and Samuel J. Sutanto

Robust sub-seasonal and seasonal drought forecasts are essential for water managers and stakeholders coping with water scarcity. Many studies have been conducted to evaluate the performance of hydrological forecasts, that is, streamflow. Nevertheless, only few studies evaluated the performance of hydrological drought forecasts, e.g. forecast of deficit volumes in river flow, or duration of deficits. The objective of this study, therefore, was to analyse the skill and robustness of meteorological and hydrological drought forecasts at the catchment scale (the Ter and Llobregat rivers in Catalonia, Spain), rather than at a continental or global scale. Meteorological droughts were forecasted using downscaled (5 km) probabilistic seasonal weather reforecasts (ECMWF-SEAS4). These downscaled data were also used to drive the hydrological model (EFAS-LISFLOOD) to produce hydrological drought forecasts, which were derived from time series of simulated streamflow data. This resulted in seasonal hydro-meteorological reforecasts with a lead time up to 7 months, for the time period 2002-2010. These monthly reforecasts were compared to two datasets: 1) droughts derived from a proxy for observed data, including gridded precipitation data and streamflow simulated by the LISFLOOD model; and 2) droughts derived from in situ observed precipitation and streamflow. Results show that the skill of hydrological drought forecasts is higher than the climatology, up to 3-4 months lead time. On the contrary, meteorological drought forecasts, analysed using the Standardized Precipitation Index (SPI), do not show added value for short accumulation times (SPI1 and SPI3). The robustness analysis show that using either a less extreme or a more extreme threshold leads to a large change in forecasting skill, which points at a rather low robustness of the hydrological drought forecasts. Because the skill found in hydrological drought forecasts is higher than the meteorological ones in this case study, the use of hydrological drought forecasts in Catalonia is highly recommended to improve drought risk management. The results of this study have already been implemented by the Catalonian Water Agency to forecast reservoir volumes of two big reservoirs located in the Ter and Llobregat catchments, which supply the majority of water to the Barcelona metropolitan area.

D221 |
EGU2020-16052
Felix Greifeneder, Emilie Crouzat, Mario Fosatti, Gregor Gregoric, Klaus Haslinger, Stanka Klemencic, Zdravko Kozinc, Paolo Mancin, Claudia Notarnicola, Stefan Schneiderbauer, Kerstin Stahl, Massimiliano Zappa, and Marc Zebisch and the ADO Team

Water scarcity and related conflicts are becoming a worrying topic in Alpine regions. Moreover, lowland regions far beyond the Alps suffer from missing water from the Alps. Thus, countries are urged to act on this topic with common strategies. To support this cause, the Interreg Alpine-Space project, Alpine Drought Observatory (ADO), aims to set up a virtual observatory for the monitoring of drought in the entire Alpine region and beyond this, to derive recommendations for improved risk preparedness and efficiency of drought management.

The ADO itself will be a transnational alpine-wide operational system with a web-interface (e.g. WebGIS, periodic reports) to access data and specific impact-oriented indices for monitoring droughts and their impacts. It will provide optimized observations and forecasts for mountainous areas, which could be integrated in existing EU-level monitoring systems (e.g. European Drought Observatory). Monitoring will be based on a fusion of existing approaches (e.g. meteorological drought indices, hydrological drought indices), and newly available information (e.g. remote sensing of snow and soil moisture), to provide an optimized set of drought indices and a common drought classification. One of the further project activities will be the collection and recording of specific drought impacts. This knowledge will help to relate meteo-hydrological indices to concrete, real world effects and thus significantly enhance their applicability for drought monitoring and management.

The ADO will be tested in six case studies in all alpine countries with local partners. The case studies represent different drought issues such as agricultural drought, hydrological drought or drought impact on ecosystems. Out of the case studies, guidelines for an improved drought risk management will be developed. Findings will be upscaled to recommendations for drought governance policies for the Alps. Main beneficiaries of project findings are institutions with decision-making capacities in the field of water management, energy production, and agriculture.

D222 |
EGU2020-20433
Janneke Pouwels, Perry de Louw, Dimmie Hendriks, and Joachim Hunink

In large parts of Europe, the year 2018 is known as an extremely dry year. In the Netherlands this 2018 drought caused over 1 billion euros of economic damage to different sectors like agriculture, nature, industry, shipping, infrastructure and buildings. A large part of economic damage was due to extreme low groundwater levels and large soil moisture deficits. Many streams stopped flowing since groundwater levels were too low to feed the streams. The extreme low rainfall amount, in combination with above average high potential evaporation rates, caused a precipitation deficit of 300 mm in the growing season, which is normally less than 100 mm. In 2019, the year after, the spatial variability of precipitation in the Netherlands was high with only a precipitation deficit in the growing season of a few tens of millimeters in the low-lying western part of the Netherlands. However, in the higher sandy areas in the south and east part of the Netherlands, the precipitation deficit was again extreme and more than 240 mm. For the higher sandy areas this was the second dry year in a row and the question arose what the effect of two consecutive dry years on the water system was and how fast it may recover.

This question has been analyzed by applying an integrated nationwide groundwater and surface water model (De Lange et al., 2014). The model results showed that for the higher sandy areas, groundwater levels and stream discharges were even lower in the second than in the first dry year. In addition, the recovery period of the groundwater system after two extremely dry years was examined by simulating ten "normal" years with average precipitation and evaporation patterns following the two extremely dry years. The model results showed a large spatial variation in groundwater level recovery.  In the first recovery year groundwater levels increased for most of the area, except for the higher-lying sandy areas lacking any surface waters (ditches and streams), like the largest Dutch forest area, the Veluwe. In these slow-responding regional recharge areas, groundwater levels are still dropping. For the central part of the Veluwe, this dropping continues until the seventh recovery year.  The model results showed that two consecutive dry years have a large impact on the water system, and that full recovery of groundwater levels and stream discharges may take 2 to 4 years in most of the sandy areas, yet the recovery of the highest parts may take up to 7 to 8 years.

 

De Lange, W.J., Prinsen, G.F., Hoogewoud, J.C., Veldhuizen, A.A., Verkaik, J., Oude Essink, G.H.P., Van Walsum, P.E.V., Delsman, J.R., Hunink, J.C., Massop, H.Th.L., Kroon T. (2014). An operational, multi-scale, multi-model system for consensus-based, integrated water management and policy analysis: The Netherlands Hydrological Instrument. Accepted for publication in Environmental Modelling & Software

D223 |
EGU2020-2495
| Highlight
A Multi-Scale Study of US Drought Awareness
(withdrawn)
Jonghun Kam
D224 |
EGU2020-973
Vivek Gupta and Manoj Kumar Jain

Droughts are recurring natural phenomena having devastating impacts on the ecosystem and agro-economics in almost every part of the world. The hazard caused by drought costs more than any other natural calamity. Better prediction of droughts may result in better preparedness which, in turn, helps in reducing the hazard caused by droughts. In many parts of the world, drought events have been found to possess a link with the Pacific Decadal Oscillation (PDO). A better understanding of this relationship may help us in improving the drought prediction models. However, for India, the nature of this relation largely remained unexplored. Therefore, to quantify the causal relationship between PDO and droughts in India, a Granger Causality test-based methodology has been explored. Results of the linear Granger Causality test have been compared against a recently developed neural-network-based nonlinear Granger causality-based test for Standard Precipitation Evapotranspiration Index (SPEI) at 3, 6, 9, and 12-month scales.  The results of this study suggest the significant causal teleconnections between PDO and droughts in most parts of India at all analyzed scales. Further, it was found that the nonlinear model is able to capture significant causality for more parts of the country than the traditional linear model.

D225 |
EGU2020-2071
Qiu Shen, Jianjun Wu, Leizhen Liu, and Wenhui Zhao

As an important part of water cycle in terrestrial ecosystem, soil moisture (SM) provides essential raw materials for vegetation photosynthesis, and its changes can affect the photosynthesis process and further affect vegetation growth and development. Thus, SM is always used to detect vegetation water stress and agricultural drought. Solar-induced chlorophyll fluorescence (SIF) is signal with close ties to photosynthesis and the normalized difference vegetation index (NDVI) can reflect the photosynthetic characteristics and photosynthetic yield of vegetations. However, there are few studies looking at the sensitivity of SIF and NDVI to SM changes over the entire growing season that includes multiple phenological stages. By making use of GLDAS-2 SM products along with GOME-2 SIF products and MODIS NDVI products, we discussed the detailed differences in the relationship of SM with SIF and NDVI in different phenological stages for a case study of Northeast China in 2014. Our results show that SIF integrates information from the fraction of photosynthetically active radiation (fPAR), photosynthetically active radiation (PAR) and SIFyield, and is more effective than NDVI for monitoring the spatial extension and temporal dynamics of SM on a short time scale during the entire growing season. Especially, SIFPAR_norm is the most sensitive to SM changes for eliminating the effects of seasonal variations in PAR. The relationship of SM with SIF and NDVI varies for different vegetation cover types and phenological stages. SIF is more sensitive to SM changes of grasslands in the maturity stage and  rainfed croplands  in the senescence stage than NDVI, and it has significant sensitivities to SM changes of forests in different phenological stages. The sensitivity of SIF and NDVI to SM changes in the senescence stages stems from the fact that vegetation photosynthesis is relatively weaker at this time than that in the maturity stage, and vegetations in the reproductive growth stage still need much water. Relevant results are of great significance to further understand the application of SIF in SM detection.

D226 |
EGU2020-2373
Marcelo Zeri, Karina Williams, Eleanor Blyth, Ana Paula Cunha, Toby Marthews, Garry Hayman, and Marcelo Galdos

Monitoring of soil water is essential to assess drought risk over rainfed agriculture. Soil water indicates the onset or progress of dry spells, the start of the rainy season and good periods for sowing or harvesting. Monitoring soil water over rainfed agriculture can be a valuable tool to support field activities and the knowledge of climate risks.

A network of soil moisture sensors was established over the Brazilian North East semiarid region in 2015 with measurements at 10 and 20 cm, together with rainfall and other variables in a subset of locations. The data are currently being used to assess the available water over the region in monthly bulletins and reports of potential impacts on yields.

In this work, we present a comparison of a dataset of observations from 2015 to 2019 with the soil water estimated by the JULES land surface model (the Joint UK Land Environment Simulator). Overall, the model captures the spatial and temporal variability observed in the measured data well, with an average correlation coefficient of 0.6 across the domain. The performance was compared for each station, resulting in a selection of locations with significant correlation.

Based on the regression results, we derive modelled soil moisture for the time span of the JULES run (1979 to 2016). The modeled data enabled the calculation of a standardized soil moisture anomaly (SSMA). The values of SSMA in the period were in agreement with the patterns of drought in the region, especially the recent long-term drought in the Brazilian semiarid region, with significant dry years in 2012, 2013 and 2015. Further analysis will focus on comparisons with other drought indices and measures of impacts on yields at the municipality level.

D227 |
EGU2020-2590
Ying Wang

Based on disaster theory, combined with the standardized precipitation index (SPI) and relative meteorological yield data, meteorological factors and social factors were comprehensively considered to assess the vulnerability of maize (Zea mays) to drought. The probability distribution curve for of the degree of meteorological drought and relative meteorological yield was obtained by using information distribution technology, using a two-dimensional normal information diffusion method to construct the vulnerability relationship between degree of meteorological drought and relative meteorological yield. This resulted in drought vulnerability curves for maize in the eastern part of Northwest China (Gansu, Ningxia, and Shaanxi), and calculations of drought risk for maize in each province. The probability of moderate drought in Shaanxi was relatively high, followed by Gansu and Ningxia. The probability distribution of Gansu was more discrete. The probability of strong meteorological drought in Ningxia was high, followed by Shaanxi and Gansu. Probability distribution of relative meteorological yield for maize in Gansu Province was highly discrete, with thick tailings, large uncertainties, and more extreme values, which were strongly affected by meteorological conditions, followed by Shaanxi and Ningxia. When the degree of meteorological drought was low, the relative meteorological yield of maize increased within 10%. This is because mild drought stress can promote the adaptability of maize to drought and stimulate maize’s overcompensatory effect. With an increased degree of meteorological drought, the relative meteorological yield of maize gradually declined. When the degree of meteorological drought exceeded –2.2, maize was most vulnerable to drought in Shaanxi followed by Ningxia and Gansu. When meteorological drought was < –2.2, maize was most vulnerable to drought in Shaanxi followed by Gansu and Ningxia. Shaanxi had the highest maize drought risk, followed by Gansu and Ningxia. This research had a clear physical background and clear risk connotations. The results provide a data foundation and a theoretical basis for drought prevention and disaster reduction for maize in the study area.

D228 |
EGU2020-3604
Peyman Saemian, Mohammad Javad Tourian, and Nico Sneeuw

Climate change and the growing demand for freshwater have raised the frequency and intensity of extreme events like drought. Satellite observations have improved our understanding of the temporal and spatial variability of droughts. Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) have been observing variations in Earth's gravity field yielding valuable information about changes in terrestrial water storage anomaly (TWSA). The terrestrial water storage vertically integrates all forms of water on and beneath land surface including snow, surface water, soil moisture, and groundwater storage.

Drought indices help to monitor drought by characterizing it in terms of their severity, location, duration and timing. Several drought indices have been developed based on GRACE water storage anomaly from a GRACE-based climatology, most of which suffer from the short record of GRACE, about 15 years, for their climatology. The limited duration of the GRACE observations necessitates the use of external datasets of TWSA with a more extended period for climatology. Drought characterization comes with its own uncertainties due to the inherent uncertainty in the GRACE data, the various post-processing approaches of GRACE data, and different options for external datasets on the other hand.

This study offers a method to quantify uncertainties for the storage-based drought index. Moreover, we assess the sensitivity of major global river basins to the duration of the observations. The outcome of the study is invaluable in the sense that it allows for a more informative storage based drought, including uncertainty, thus enabling a more realistic risk assessment.

D229 |
EGU2020-4013
Sheng-Hsueh Yang, Wen-Hao Leu, Meng-Chen Chen, Jiun-Hue Kuo, and Keh-Chia Yeh

Climate change has gradually affected Taiwan's agricultural environment. The number of raining days has decreased, the rainfall intensity has increased, and the drought time has been prolonged. In addition, with the mountainous terrain of Taiwan, rainfall is not easy to be stored and used. The summer and autumn are rainy seasons, which are prone to flooding disasters, the lack of water in spring and winter causes droughts that cause insufficient agricultural water supply, and the stable supply of food is closely related to people's livelihood needs. Therefore, the research department uses UAV imaging technology to identify agricultural crops, grasp the agricultural crops and water supply needs in the spring and winter seasons, and try to estimate the water demand and distribution water volume as the management basis of agricultural irrigation and drainage. Use long-term meteorological models to estimate rainfall results in the next month, and determine whether there are water shortage characteristics in agricultural crop areas. If there is a water shortage, further use the Internet of Things monitoring technology to monitor the inflow and outflow of agricultural crops in irrigated areas, control and distribute the required water consumption, and then to reduce water supply at night or supplying irrigation water in turn in response to the water shortage during the irrigation period. In the summer and autumn rainfall periods, the Internet of Things technology is also used to observe the water level and flow discharge of the main irrigation waterways, and set the rainfall and water level early warning values to reduce the occurrence of flooding disasters in agricultural areas, and use the immediate hydrological and hydraulic models to forecast the future suggestions such as hourly water level and flow discharge and gate control provide timely information on agricultural disaster warnings. The relevant research area is in the Meinong Agricultural Area (about 4,000 ha) of Farm Irrigation Association of Kaohsiung Taiwan as an example. Through web pages hourly displays Internet of Things information, model analysis results, and disaster prevention early warning results to let the management units understand the actual status of agricultural irrigation areas.

D230 |
EGU2020-4080
Yi Liu

The rapid intensification rate and short duration are currently two different notions in defining flash droughts. Here we make a thorough evaluation on these two approaches and illustrate which approach can provide more accurate information of flash drought and why. Based on the conception of intensification rate, a new method focusing on soil moisture depletion during the onset-development phase is proposed, and its performance in monitoring flash drought events, their onset time and spatial dynamics are compared with those from the short-duration method. Results show that the proposed rapid-intensification approach is superior to the short-duration approach in capturing the continuous evolution process of drought. Since the short-duration approach ignores the change of soil moisture with time, it can hardly ensure the identified flash drought events all have the rapid evolving characteristic. Meanwhile, the miss monitoring for flash drought onset is also observed for the short-duration approach. The unreasonable selections of hydro-meteorological variables and corresponding thresholds, particularly refers to that of temperature, is the main reason for the poor behavior of the short-duration approach.

Chat time: Thursday, 7 May 2020, 16:15–18:00

D231 |
EGU2020-4734
Carmelo Cammalleri, Paulo Barbosa, and Jürgen Vogt

Winter droughts, defined as periods of reduced precipitation and snow accumulation during the cold season, can have significant impacts on the subsequent summer season, especially over areas that strongly rely on stored water resources released during the spring melting.

The Snow Water Equivalent, SWE, represents a reliable means to quantify the amount of liquid water in the snowpack, and its anomalies can be used to evaluate deviations from the amount usually stored. Unfortunately, the use of SWE for operational monitoring of winter droughts is constrained by the limited availability of long time series of ground observations, and the lack of coordinated measuring networks at European continental scale.

Remote sensing data from microwave sensors, therefore, represent a valuable source of continuously-updated SWE data. Products such as the H-SAF (EUMETSAT Hydrology Satellite Application Facility, http://hsaf.meteoam.it/) SNOBS4-H13 are updated in almost near-real time, providing daily maps covering continental Europe and northern Africa. Limitations include data gaps, difficult retrievals over impervious terrain, coarse spatial resolution and a reduced length of the time series.

In this study, we tested the potential inclusion of a drought indicator based on the H-SAF SWE product in the European Drought Observatory (EDO, http://edo.jrc.ec.europa.eu), with the aim to fill the current gap faced over mountainous basins in terms of early warning of spring water deficits.

An analysis of the full dataset collected between 2013 and 2019 highlights how, currently, the main drawback of the product seems to be represented by the limited length of the time series, as well as by the difficulties to capture snow accumulation over some mountainous areas (e.g., Pyrenees) likely due to the coarse spatial resolution. Spatial aggregation at water basin scale was also tested, in order to evaluate the possibility to reduce the effects of some of these limitations.     

D232 |
EGU2020-5049
Stefano Terzi, Janez Sušnik, Sara Masia, Silvia Torresan, Stefan Schneiderbauer, and Andrea Critto

Mountain regions are facing multiple impacts due to climate change and anthropogenic activities. Shifts in precipitation and temperature are affecting the available water influencing a variety of economic activities that still rely on large quantities of water (e.g. ski tourism, energy production and agriculture). The Alps are among those areas where recent events of decreased water availability triggered emerging water disputes and spread of economic impacts across multiple sectors and from upstream high water availability areas to downstream high water demand areas. In order to make our water management systems more resilient, there is a need to unravel the interplays and dependencies that can lead to multiple impacts across multiple sectors. However, current assessments dealing with climate change usually account for a mono sectoral and single risk perspective.

This study hence shows an integrative assessment of multi-risk processes across strategic sectors of the Alpine economy. System dynamics modelling (SDM) is applied as a powerful tool to evaluate the multiple impacts stemming from interactions and feedbacks among water-food-energy economic sectors of the Noce river catchment in the Province of Trento (Italy).

The SDM developed for the Noce catchment combined outputs from physically based models to evaluate water availability and statistical assessments for water demands from three main sectors: (i) apple orchards cultivation, (ii) water releases from large dam reservoirs for hydropower production and (iii) domestic and seasonal tourism activities.

Hydrological results have been validated on historical time series (i.e. 2009-2017) and projected in the future considering RCP 4.5 and 8.5 climate change scenarios for 2021-2050 medium term and 2041-2070 long term. Results show a precipitation decrease affecting river streamflow with consequences on water stored and turbined in all dam reservoirs of the Noce catchment, especially for long-term climate change scenarios. Moreover, temperature scenarios will increase the amount of water used for agricultural irrigation from upstream to downstream. Nevertheless, decreasing population projections will have a beneficial reduction of water demand from residents, counterbalancing the increasing demand from the other sectors.

Finally, the integrated SDM fostered discussions in the Noce catchment on interplays between climate change and anthropogenic activities to tackle climate-related water scarcity.

D233 |
EGU2020-5911
Radu-Vlad Dobri, Liviu Apostol, Lucian Sfîcă, Simona Țîmpu, and Ion-Andrei Niță

Drought can be determined by climatic conditions (atmospheric precipitation, water supply from soil accessible to the plant, moisture and air temperature and wind speed) but is also induced by environmental aspects some of them related to anthropogenic influences.

In order to monitor the drought and its impact for Romania, four indices were analyzed in the present study (SPI (Standardized Precipitation Index), PNI (Percent of Normal Index), DI (Deciles index), and ZSI (Z-score Index)), through Meteorological Drought Monitoring software, using the total daily amount of precipitation for 27 weather stations in Romania, of which 22 stations for the period 1961-2015, 4 stations for the period 1961-2000 and one station for the period 1964-2015.

Preliminary analyzes resulting from the use of these indices were correlated with 18 GWT (Großwettertypen) atmospheric circulation types of daily mean sea level pressure (SLP). This was done using COST733 class software to evaluate the influence of large-scale mechanisms of atmospheric circulation. Also, four teleconnection indices were used, more exactly AO (Arctic Oscillation), NAO (North Atlantic Oscillation), PNA (Pacific-North American Pattern) and AAO (Antarctic Oscillation) that are recognized for their effect on climatic conditions at European scale,  
provided by National Oceanic and Atmospheric Administration (NOAA) – Climate Prediction Center.

Therefore, according to the types of circulation, the amount of precipitation produced in certain areas and implicitly the degree of drought severity is influenced. The types of anticyclonal circulation 13, 16 or 18, for example, which occur on average in 46 (12.7%), 14 (3.9%) , respectively 20 (5.4%) days a year, cause less precipitation as known, compared to the types of cyclonal circulation 1, 2 or 17 for example with an average of 12 (3.2%), 12 (3.2%), respectively 19 (4.3%) days a year.

In terms of drought analysis indices, according to SPI, the entire analysis interval for Iasi, located in the northeast region of Romania, was 6 years of "moderately dry", 5 years of "severely dry", and one year of "extremely dry", unlike Cluj, located in the central western region, with two years of "moderately dry", 3 years of "severely dry" and two years of "extremely dry". In Bucharest, located in the southern region of Romania there were 4 "moderately dry" years and 5 "severely dry" years. In Iasi, according to the ZSI index with the same classifications as the SPI index, there were 3 "moderately drought" years, 7 "severely drought" years and 7 "extreme drought" years, while in Cluj there were 9, 3 and respectively 6 years and in Bucharest 7, 5 and respectively 6 years with the above classification.

According to the PNI index, there were 5 "moderate drought" years in Iasi and Cluj and 6 "moderate drought" years in Bucharest. Also, there were 9 "weak drought" years in Iasi, 3 in Cluj and 5 in Bucharest.

And last but not least, according to the DI index, at all 3 stations there were 5 "extreme drought" years, 6 "severe drought" years and 5 "moderate drought" years.

D234 |
EGU2020-7137
Rares Halbac-Cotoara-Zamfir and Cristina Halbac-Cotoara-Zamfir

Droughts are one of the costlier natural hazards on a year-to-year basis considering their impacts which are generally significant and widespread. Monitoring these events is a vital process in the efforts of predicting, analyzing and recovering after. A clear understanding of droughts and their behavior will improve the resilience of the affected regions and their capacity to recover after such events. It is important to note that the impacts of droughts can be as varied as the causes of droughts. Drought represents the effects of water demands unmet by the available resources. Regional characteristics of a drought, such as covered area, total water deficit, or return time, are very important in determining the phenomenon’s severity.  Worldwide, over 100 drought indices were developed, each of them with specific features and approaching different aspects of drought.

In this article, several drought indices (SPI (Standardized Precipitation Index), RAI (Rainfall Anomaly Index), RDI (Reconnaissance Drought Index) and SPEI (standard index of rainfall drought)) will be used for a critical analysis of drought in the last 50 years (1968 - 2018) in western Romania (Timisoara area). These indices will be calculated using several programs (DRINC, RDIT, CLIC-MD) recently introduced in Romanian research institutions for drought estimation and monitoring. We compared the results for SPI indicating the correlations among drought indices calculated with these programs and emphasized the pros and cons for each of these tools.

We also present the effects of drought in relation with land management from western Romania as well as the main key recommendations from the national strategy on mitigation of drought effects.

D235 |
EGU2020-8119
Bart van den Hurk, Ruud Hurkmans, Fredrik Wetterhal, Ilias Pechlivanidis, and Albrecht Weerts

During dry spells, a large part of the Netherlands depends on water from the IJssel lake, a large surface water reservoir. Water is extracted for a number of purposes, such as irrigation, water quality, shipping and drinking water. Besides local precipitation, the main source of water flowing into the lake is the river IJssel; a distributary of the Rhine. To keep water available for extraction by the surrounding regions, lake levels cannot be allowed to fall more than about 20 cm under the regular summer maintenance level. Prior to the onset of a drought, therefore, it might be desirable to raise lake levels to maintain sufficient water availability during the dry spell. For adequate management of the reservoir, therefore, long-range forecasting of precipitation and river discharge would be extremely helpful. However, meteorological forecast skill is known to be nearly absent for lead times longer than about a month in northwestern Europe. The land surface contains a number of components that may increase forecast skill for Rhine river discharge; examples are the amount of snow in the Alps, groundwater, and soil moisture. We investigate to what extent this is the case and whether the forecast skill of Rhine river discharge forecasts increases with increasing detail in the land surface parameterization of the initial conditions. We collected streamflow reforecasts from various sources: ECMWF SEAS5, EFAS, SMHI-HYPE and a high-resolution distributed hydrological model (WFLOW), forced by ECMWF SEAS5 meteorological forecasts.

D236 |
EGU2020-9724
Carl Hartick, Carina Furusho-Percot, Klaus Goergen, and Stefan Kollet

In 2018, a severe drought occurred in Central and Northern Europe and water security concerns rose in regions where previously water was considered an abundant resource. Followed by another extremely dry year 2019, the meteorological drought developed into a hydrological drought and estimates on the probable evolution of water stores at an interannual time scale over Europe seem required that have the potential to provide informed options for adaptation. Utilizing the Terrestrial Systems Modeling Platform (TSMP) regional Earth system model over the 12km resolution pan-European CORDEX model domain, a probabilistic assessment methodology is proposed based on fully coupled groundwater-to-atmosphere simulations, which provide subsurface water resources anomalies for a water year defined from September to August. For the assessment, the TSMP ensemble is initialized with the surface and subsurface states at the end of a previous water year that is part of a spun up climatology run (here: 1989 to 2019). In an ensuing step, an ensemble of forward simulations is performed, driven by past ERA-Interim reanalysis meteorological boundary conditions until the end of August of the following year. The memory effect of groundwater, which is well-captured in TSMP, in combination with the different, plausible atmospheric states and evolution of the atmospheric forcing from the reanalysis, allows for a probabilistic assessment of the development of water resources in the upcoming year. The novelty is the use of the past meteorological conditions in a fully coupled model to account for the uncertainty of unknown weather conditions at the interannual forecasting time scale. We show that the method provides good results in a hindcast approach of 2018/19 and present the results of the upcoming water year 2019/20.

D237 |
EGU2020-12501
Shuang Xiao, Dioni Cendón, and Bryce Kelly

In most catchments, there is usually inadequate information to build an accurate three-dimensional representation of the sediment type and associated hydraulic properties. This makes it challenging to build a physics-based groundwater flow model that accurately replicates measured fluctuations in the groundwater level, and it also results in considerable uncertainty in forecasting the groundwater level under various climate scenarios. However, in many catchments in Australia, and around the world, there are 100 year-long rainfall and streamflow records. Good groundwater level data sets often date from mid last century, when advances in pumping technology enable high volume groundwater extractions to support irrigated agriculture. For the lower Murrumbidgee alluvial aquifer in Australia, which covers an area of 33,000 km2, we demonstrate that it is possible to train the gradient boosting algorithm to predict the annual change in the groundwater level to within a few centimetres.

The lower Murrumbidgee aquifer, which is up to 300 m thick, is an important but highly stressed aquifer system in Australia. Annually the groundwater level fluctuates many metres due to groundwater withdrawals and occasional flooding.  Some portions of the alluvial aquifer are unconfined and other portions semi-confined. Under current groundwater pumping conditions, groundwater levels decline in the semi-confined portions of the aquifer during extended periods of below average rainfall. In other portions of the catchment, there have been periods of groundwater level rise due to deep drainage beneath irrigated crops.

Despite the catchment size, groundwater levels throughout the region are driven by four primary processes: ongoing river leakage, pumping, deep drainage and occasional flooding. Combined with knowledge of the hydrogeological setting, we successfully used just rainfall, streamflow and annual groundwater withdrawal records to build a gradient boosting model to predict where the groundwater level will rise and fall, in both space and time. Under existing annual pumping rates, the gradient boosting model forecasts that the groundwater level will fall many metres if the catchment has a period of below average rainfall as occurred from 1917 to 1949. This fall in the groundwater level will trigger groundwater access restrictions in some portions of the aquifer.

D238 |
EGU2020-13111
Hyung Jin Shin, Jong Won Do, Jae Nam Lee, Gyumin Lee, and Mun Sung Kang

According to the Korea Meteorological Administration, in 2018, Korea's national average temperature and maximum temperature are the highest in 111 years since meteorological observations (1907.10.1.) The highest value was observed since August 1 at  39.6 in Seoul. Heatwaves represent the number of days with the highest daily temperature above 33 ° C. The number of heatwaves in 18 years totaled 31.5 days. Heatwaves have a particularly significant effect on the growth and death of field crops. Indeed, 18,254 ha of field crops occurred nationwide. Precipitation in 2018 is higher than normal, but precipitation shortages have occurred due to seasonal and regional variations and local droughts due to the lowest precipitation from mid-July to late August. In particular, there were more rains than normal years at the beginning of farming season (March-May) and the end of farming season (October), but the summer agricultural drought occurred due to less precipitation than the average year-end of July-August. The second shortest rainy season (half of the average year) since 1987 and the rainy season was 72% compared to the average year, some of the reservoirs have caused a serious and severe stage. The country recorded the maximum number of rainfall days on 27th during the period of 7.10 ~ 8.5 days and 43 days on Chungnam. This is believed to have affected the drought occurrence by overlapping with the stage of water-forming, which requires the largest amount of water supply for rice growth. In the case of field crops, irrigation facilities are inferior to paddy fields, so field crop growth is directly related to no rainfall days, and droughts such as deterioration of field crops were recorded nationwide during the maximum rainfall period. Since the end of the rainy season, there have been a total of 22,767 ha droughts, iincluding 2,513 ha of paddy field and 20,254 ha of field crops, due to severe shortages of precipitation and damage to crops caused by heat waves. For the 2018 rainfall-based drought frequency analysis, the analysis was based on cumulative precipitation from January to August of 18, and there was a severe shortage of precipitation from mid-July to mid-August, but the cumulative precipitation from January to August is normal. As a result of rainfall-based drought frequency analysis, the drought frequency area was analyzed into two regions for more than 10 years. Based on rainfall in July 2018, drought occurred in most parts of the country due to severe rainfall shortages. For over 200 years, the frequency of drought has been analyzed to 107 counties. As a result of the drought frequency analysis based on the reservoir storage rate in August 2018, there were 45 counties in the drought frequency area for more than 200 years due to the lack of water during the high demand period of rice crop growth period.

This research was supported by a grant(2019-MOIS31-010) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety(MOIS).
D239 |
EGU2020-13511
Jeongeun Won and Sangdan Kim

In drought monitoring, it is very important to select climate variables to interpret drought. Most drought monitoring interprets drought as deficit in precipitation, so drought indices focused on the moisture supply side of the atmosphere have been mainly used. However, droughts can be caused not only by lack of rainfall, but also by various climate variables such as increase in temperature. In this regard, interest in potential evapotranspiration(PET), which is an moisture demand side of the atmosphere, is increasing and a PET-based drought index has been developed. However, complex droughts caused by various climate variables cannot be interpreted as a drought index that only considers precipitation or PET. In this study, we suggest a drought monitoring method that can reflect various future climate variables, including precipitation. In other words, copula-based joint drought index(CJDI), which incorporate standardized precipitation index(SPI) based on precipitation and evaporative demand drought index(EDDI) based on PET, is developed. CJDI, which considers both precipitation and PET, which are key variables related to drought, is able to properly monitor the drought events in Korea. In addition, future Drought severity – duration - frequency curves are derived to project future droughts compared to various drought indices. It is shown that CJDI can be used as a more reasonable drought index to establish the adaptation policy for future droughts by presenting the pattern of future droughts more realistically.

Acknowledgment: This study was funded by the Korea Ministry of Environment (MOE) as Smart Urban Water Resources Management Program. (2019002950004)

Keywords: Climate change; Copula; Drought; CJDI; Drought severity-duration-frequency curve

D240 |
EGU2020-16916
Laura Crocetti, Milan Fischer, Matthias Forkel, Aleš Grlj, Wai-Tim Ng, Adam Pasik, Ivana Petrakovic, Andreas Salentinig, Miroslav Trnka, Benjamin Wild, Espen Volden, and Wouter Dorigo

The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.

Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.

This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).

D241 |
EGU2020-17885
Mahmud Haghshenas Haghighi and Mahdi Motagh

Iran is located in a semi-arid to arid environment and is highly dependent on its groundwater resources for development in its agricultural and industrial sectors. In many aquifers across the country, unsustainable groundwater extraction in the past few decades caused severe groundwater level decline, at locations exceeding 20 m. The country is divided into six major basins. However, neither the water consumption nor renewable water resources are distributed evenly. Quantitative assessment of the groundwater situation in different basins is a piece of crucial information for improving management practices. In this study, we use satellite observations to assess the groundwater situation across Iran.

We observe the terrestrial water storage (TWS) from Satellite gravimetry measurements of Gravity Recovery And Climate Experiment (GRACE). These observations provide a country-scale picture of groundwater variations at a coarse spatial resolution of 500 km. In all six basins, TWS declines during the 15 year lifetime of GRACE from 2002 until 2017. In total, the Equivalent Water Height (EWH) declines as much as approximately 10 cm during this period. Although part of this decline is caused by other components such as surface water or soil moisture, groundwater decline is responsible for the major part.

The compaction of aquifers resulted from the over-extraction of groundwater can be observed as land subsidence on the surface. We analyze ground subsidence for the whole Iran using Interferometric Synthetic Aperture Radar (InSAR) observations of the Copernicus Sentinel-1 satellite and present the first detailed map of compacting aquifers across the country at a high spatial resolution of 100 m. The average rate of displacement, exceeding 30 cm/yr in some areas, reveals hundreds of aquifers across the country are suffering unsustainable groundwater consumption. The distribution of subsidence basins is significantly correlated with the distribution of agricultural regions.

To obtain information on the sustainability of groundwater consumption, we separate the time series of land subsidence into two parts: the short term part as elastic/recoverable component and the long-term part as inelastic/irrecoverable. The ratio between elastic and inelastic elements provides quantitative measurements of aquifer health. Combining the Sentinel-1 subsidence measurements with GRACE observations of groundwater variations gives us new details on how the groundwater is consumed across different basins in the country. The results can have essential implications on the more sustainable management of groundwater resources.

D242 |
EGU2020-18404
Alise Babre, Janis Bikse, Konrads Popovs, Andis Kalvans, and Aija Delina

Even though, droughts usually correspond to smaller latitudes, during the last decade Latvia has faced several long-term drought events. This have caused awareness of more frequent drought episodes in future due to climate change. Accurate and complete meteorological data are required to calculate realistic drought indices and to estimate drought probability in the future.

In places were direct meteorological observation data are absent or dataset is incomplete and not sufficient, data from numerical weather forecasting reanalysis such as ERA5 can be used instead or along with direct observations. Reanalysis provide comprehensive snapshots of conditions at regular intervals over long time periods — often years or decades. The European Center for Medium-Range Weather Forecasts (ECMWF) has released its latest reanalysis product, the ERA5-Land dataset (C3S, 2019).

The goal of this study is to evaluate the ERA5-Land reanalysis product as a substitute for observations in meteorological stations for calculation of drought related indices for time period from 1981 to 2018.  Two meteorological stations more than 200 km apart in Latvia - Riga and Rezekne - were considered. 

Meteoric and agricultural drought indices were calculated with freely available software DrinC (Tigkas et.al., 2013) as well as R – packages spei and pdsi - using monthly mean reanalysis as well as observed meteorological data as input. It is found that meteorological parameters as well as drought indices have high consistency between two data sources. 

References:

Copernicus Climate Change Service (C3S) (2019): C3S ERA5-Land reanalysis. Copernicus Climate Change Service, 12.12.2019.

Tigkas, Dimitris & Vangelis, Harris & Tsakiris, George. (2013). The Drought Indices Calculator (DrinC).

The study is supported by fundamental and applied science research programme, project No.: lzp-2019/1-0165 “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”.

D243 |
EGU2020-18414
Roelof Rietbroek, Helena Gerdener, Olga Engels, and Jürgen Kusche

For many years the satellite gravity mission Gravity Recovery And Climate Experiment (GRACE) provided important insights into a large number of hydrological processes on Earth. However, to derive observations that can be used for hydrological applications many different postprocessing steps, e.g. filtering, replacement of low-degree coefficients, correction for glacial isostatic adjustment, need to be applied. Therefore, the official analysis centers meanwhile also provide postprocessed total water storage anomalies on a grid. Unfortunately, the effect of large earthquakes is usually not reduced. The resulting biases and artefact trends then bias hydrological analysis such as the detection of droughts and their severity.

 

For the first time, we analyze the effect of large earthquake correction on GRACE-derived Drought Severity Index (DSI) over Peninsular Malaysia in the period 2003 to 2016. For this, we perform a time series analysis based on GRACE-derived total water storage anomalies, while estimating, among others, a co-seismic and post-seismic signal using a nonlinear Bayes estimator (Einarsson et al. 2010). Our results show that the earthquake correction has a significant impact on the detected drought severity, suggesting that the earthquake correction may become a standard tool in time series analysis of GRACE and GRACE-Follow-On level-3 data.

D244 |
EGU2020-20389
Toma Rani Saha, Luis Samaniego, Pallav K Shrestha, Stephan Thober, and Oldrich Rakovec

South Asia (SA) is highly vulnerable to extreme climatic events and experiences a wide range of natural hazards such as floods, drought, storms, and sea-level rise.  Droughts are recurrent in SA and its impact on regional agriculture, food storage, and livelihood is enormous. Agricultural droughts have severe consequences on the economy, society, health and water resources sectors. In this work, a state-of-the-art monitoring system of soil moisture drought in SA is developed. This study aims at improving the agricultural drought monitoring system for SA and contributing towards better adaptation solutions in the region. The SA drought monitoring system is inspired by the German Drought Monitor (www.ufz.de/duerremonitor)[1]. First, we implement the mesoscale hydrologic model (mHM, https://git.ufz.de/mhm) to reconstruct daily soil moisture from 1981 to 2019 using a near-real-time precipitation product (CHIRPS version 2, 0.25-degree resolution). Second, the SMI is estimated with a non-parametric kernel-based cumulative distribution function [2] based on mHM’s historic soil moisture reconstruction. The generated SMI maps are classified into five classes based on severity: abnormally dry, moderate drought, severe drought, extreme drought and exceptional drought. Third, we develop the South Asia Drought Monitor (SADM) which is an interactive web-portal (http://southasiadroughtmonitor.pythonanywhere.com/) for the dissemination of the simulated near-real-time drought classes. To achieve maximum dissemination, the daily and monthly SMI fields will be uploaded and published on the SADM portal. The SADM will help to inform decision-makers, the general public, researchers, and stakeholders in the SA. The drought monitoring system will allow the scientific community to conduct micro-level in-depth research and to enable policymakers to formulate proper planning and to take mitigation measures in sectors encompassing energy, health, forestry, and agriculture at local to regional scales.

 

[1] Zink, M., Samaniego, L., Kumar, R., Thober, S., Mai, J., Schäfer, D., Marx, A., 2016: The German drought monitor, Environ. Res. Lett. 11 (7), art. 074002, DOI:10.1088/1748-9326/11/7/074002.

[2] Samaniego, L., Kumar, R. and Zink, M.,2013: Implications of Parameter Uncertainty on Soil Moisture Drought Analysis in Germany, Journal of Hydrometeorology, DOI: 10.1175/JHM-D-12-075.1.

 

D245 |
EGU2020-21408
Depeng Zuo, Siyang Cai, Zongxue Xu, and Hong Yang

Most research on drought assessment adopted historical in-situ observations, however, there has been increased data availability from remote sensing during the recent years. This study utilizes the two sources of data in drought assessment. Using the historical in-situ observations, the spatiotemporal variations of meteorological drought were firstly investigated by calculating the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at 1, 3, 6-month time scales in Northeast China. Using remote sensing data, the combined deficit index (CDI) for agricultural drought assessment was computed based on tri-monthly sum of deficit in antecedent rainfall and deficit in monthly NDVI at land cover type and sub-type levels in the same region. In the end, the agricultural drought calculated by the CDI was evaluated against the deficit in crop yield, as well as deficit in Land Surface Temperature (LST) and Evapotranspiration (ET), in order to verify the applicability of the CDI for agricultural drought assessment in the study region. The results showed that the CDI has better correlations with the SPEI (R2=0.48) than the SPI (R2=0.05) at 3-month scales with weight factor a=0.5 in dry farming areas. The spatial pattern of the CDI showed that the area of agricultural drought increased from July to October. In addition, a significant linear correlation was found between the CDI and anomaly in annual agricultural yield (R2=0.55), and anomaly in monthly land surface temperature (R2=0.42). The results prove that the CDI calculated by remote sensing data is not only a reliable indicator for agricultural drought assessment in Northeast China, but also provides useful information for agricultural drought disaster prevention and mitigation, and water management improvement.

D246 |
EGU2020-21650
Roberto Real-Rangel, Adrián Pedrozo-Acuña, and Agustín Breña-Naranjo

Drought monitoring and forecasting allows to adopt mitigating actions in early stages of an event to reduce the vulnerability of a wide range of environmetal, economical and social sectors. In Mexico, various drought monitoring systems on national and regional scale perform a follow up of these events, such as the Drought Monitor in Mexico, and the North American Drought Monitor, but seasonal drought forecasting is still a pending task. This study aims at fill this gap applying a methodology that uses data derived from a globally available atmospheric reanalysis product and a principal component regression based model oriented to predict drought impacts in rainfed crops associated to deficits in the soil moisture, estimated by means of the standardized soil moisture index (SSI). Using the state of Guanajuato (Center-North of Mexico) as a study case, the model generated yielded RSME values of 0.74 using regional and global hydrological, climatic and atmospheric variables as predictors with a lead-time of 4 months.

D247 |
EGU2020-21684
Hyun-Han Kwon, Jang-Gyeong Kim, Jin-Guk Kim, and Geun-Il Jeon

With a highly regulated river and water distribution system, assessment of water availability under drought stress to meet the current (or future) demand and supply is becoming a challenging issue due to their complexities in the water distribution system. However, coarse spatial and temporal resolution in a general water budget analysis model may be viewed as a limitation to understanding the entire pathway for various combinations of water allocations, return flows, and dam operations. In these contexts, this study aims to develop a Daily-based quasi real-time Water Budget simulation model-Sejong University (DWB-SU) comprised of sub regions (or nodes) that are determined by water-related facilities and water permits. The proposed DWB-SU model is a flexible tool that could be applied to highly regulated river basins.

 

Acknowledgement

This research gratefully acknowledges the financial support provided by Korea Water Resources Corporation (K-water). This work was also supported by the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2019R1A2C2087944)

D248 |
EGU2020-22567
Xiaojing Yang, Hongquan Sun, Zhicheng Su, Juan Lv, and Siyang Cai

Drought is always triggered by long-term deficit of precipitation and extreme temperature, which had led to huge losses of the economy in China. But to what extent have we defined the drought scale in contrast to farmland, river basin, grassland, forests and other types of the underlying surface? It is a vital issue for improving the accuracy of drought monitoring model/system. This study focused on developing an integrated drought assessment model which can reveal the relatively reasonable true drought circumstances and improve the monitoring ability of professional drought monitoring operation system. The object-oriented method was merged into the drought monitoring model. In order to verify the accuracy of the integrated drought assessment model, the model was applied in the professional operated drought monitoring system of Anhui Province. The monitoring system can automatically serve the daily drought monitoring map which directly shows the spatial distribution and intruded area within 4 diverse drought levels. The monitoring results indicated Anhui Province has suffered a persistent drought since the second half of 2019. And the severe drought was released by effective precipitation during August 10-13, which was caused by Typhoon Lekima. Then the drought-prone region and drought severity were consistently increasing until the extensive precipitation process in the whole province during November 26-27. The real drought development and changing phases were accurately detected by the integrated drought assessment model. And the drought impact areas from the model were coincidence with the reported data from local government. Beside that, monitoring results were approved by the local professional personnel and experts for drought monitoring.

D249 |
EGU2020-13396
Juan F. Salazar, Silvana Bolaños, Estiven Rodríguez, Teresita Betancur, Juan Camilo Villegas, and Micha Werner

Many natural and social phenomena depend on the regulation of river flow regimes. Regulation is defined here as the capacity of river basins to attenuate extreme flows, which includes the capacity to enhance low flows during dry periods of time. This capacity depends on how basins store and release water through time, which in turn depends on manifold processes that can be highly dynamic and sensitive to global change. Here we focus on the Magdalena river basin in northwestern South America, which is critical for water and energy security in Colombia, and has experienced water scarcity problems in the past, including the collapse of the national hydropower system due to El Niño 1991-1992. In this basin we study the evolution of regulation and related processes from two perspectives. First, we present a widely applicable conceptual framework that is based on the scaling theory and allows assessing the evolution of regulation in river basins, and use this framework to show how the Magdalena basin’s regulation capacity has been changing in recent decades. Second, we use data from the GRACE mission to investigate variations in water storage in the basin, and identify recent decreasing trends in both terrestrial water storage and groundwater storage. Further we show that temporal and spatial patterns of water storage depletion are likely related to the occurrence of ENSO extremes and pronounced differences between the lower and higher parts of the basin, including the presence of major wetland systems in the low lands and Andean mountains in the high lands. Our results provide insights on how to assess and monitor regulation in river basins, as well as on how this regulation relates to the dynamics of low flows and water storage, and therefore to potential water scarcity problems.

D250 |
EGU2020-13493
Ruud Hurkmans, Chris Geerse, Bastiaan Kuijper, Durk Klopstra, Bas de Jong, Herbert Berger, and Hans van Twuiver

During dry spells, a large part of the Netherlands depends on water from the IJssel lake, a large surface water reservoir. Water is extracted for a number of purposes, such as irrigation, water quality, shipping and drinking water. Besides local precipitation, the main source of water flowing into the lake is the river IJssel; a distributary of the Rhine. During periods of low discharge and low precipitation, water shortages may occur, as the recent summer of 2018 showed. ​We develop a probabilistic model to simulate water availability in the lake during dry spells. We derive marginal distributions of precipitation, open water evaporation, river discharge and water intake from the surrounding region, based on a 101-year simulation of the deterministic Dutch national water model. We assess the plausibility of the resulting extreme tail of the distributions by comparing them to values based on the ECWMF seasonal reforecasting archive, which, when all ensemble members, years and lead times are combined, contains over 4,000 years of data. All correlations between the four terms are modeled using a four-dimensional copula. The resulting distributions of water availability show aggregated water shortages up to extremely dry (return periods in excess of 10,000 years) conditions. Lake level dynamics are, during dry conditions, dominated by high water demand from the surrounding region (caused by lack of local precipitation) and low IJssel river discharges. A coincidence of these two terms causes the most extreme shortages. Because model is conceptually relatively simple, it is able to run a large number of realizations and is potentially highly suitable for, for example, assessment of measure effectiveness.

 

D251 |
EGU2020-18791
Solomon Hailu Gebrechorkos and Justin Sheffield

Extreme drought and floods have a large societal impact if not appropriately monitored and if mitigation/adaptation measures are not developed and modified based on early warning of these events. Currently, there are only a few global seasonal forecast models available with a high temporal (e.g., daily) but coarse spatial resolution (~1°) that can provide early warning operationally. Application of these forecast models for long-term (up to 6-7 months) hydrological forecasting first requires evaluation of their skill against observed data. In this study, five European seasonal forecast models; Copernicus Climate Change Service (ECMWF), UK Met Office, Météo France, Deutscher Wetterdienst (DWD), and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), are used. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Princeton Global Forcing (PGF) available at high spatial and temporal resolution are used as a reference dataset for precipitation and maximum and minimum temperature, respectively. Multiple methods such as correlation, percentage of bias and root mean square error and rainfall onset and cessation are used to evaluate the skill of individual models on daily, monthly, seasonal, and climatological periods. In addition, extreme indices (e.g., consecutive dry and wet days) developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) are used. Finally, a bias-corrected multi-model weighted ensemble forecast is developed as input into a global hydrological model (Variable Infiltration Capacity (VIC)) for seasonal hydrological forecasting in Africa.