The Ongoing Central European Drought Observed with GRACE Follow-On

2020 ◽  
Author(s):  
Eva Boergens ◽  
Andreas Güntner ◽  
Henryk Dobslaw ◽  
Christoph Dahle
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Surface Water ◽  
Central Europe ◽  
Drought Index ◽  
Water Mass ◽  
Water Storage ◽  
Drought Indices ◽  
Severe Drought ◽  
Data Set

<p class="western">In the last three years Central Europe experienced an ongoing severe drought. With the data of the GRACE Follow-On (GRACE-FO) mission we are able to quantify the water deficit of these years. Since May 2018 GRACE-FO continues the observations of GRACE (2002-2017) allowing to compare the most recent drought with earlier droughts in 2003 and 2015.</p> <p class="western">In July 2019 the water mass deficit in Central Europe amounted to -154 Gt, which has been the largest deficit in the whole GRACE and GRACE-FO time series. In November 2018 the deficit reached -138 Gt and in June 2020 -147 Gt. Comparing these deficits to the mean annual water storage variation of 162 Gt shows the severity of the ongoing drought. With such a water mass deficit, a fast recovery within one year cannot be expected. In comparison to this, the droughts of 2003 with a deficit of -55 Gt and of 2015 with a deficit of -111 Gt were less severe.</p> <p class="western">The GRACE and GRACE-FO total water storage data set also allows for analysing spatio-temporal drought patterns. In 2018 the drought was centred in in the South-West of Germany and neighbouring countries while parts of Poland were hardly affected by the drought. In 2018 the drought reached its largest extent only in late autumn. However, the exact onset of drought is not determinable due to missing data between July and October. Both in 2019 and 2020 the centre of the drought is located further East and the months with the largest deficit were July and June, respectively. Also in the later years, the drought was more evenly spread out over the whole of Central Europe.</p> <p class="western">Additionally, we compared the GRACE and GRACE-FO data to an external soil moisture index and to surface water drought indices for Lake Constance and Lake Müritz. To this end, we derive a drought index from the GRACE and GRACE-FO mass anomalies. For the whole time series, the GRACE drought index shows a high congruency to the soil moisture drought index. Overall, the surface water drought index also fits well together with the GRACE drought index. However, the comparison reveals the influence of regional effects on surface waters not observable with GRACE and GRACE-FO.</p>

The Central European droughts of 2018 and 2019 observed with GRACE-Follow-On

2020 ◽  
Author(s):  
Eva Boergens ◽  
Andreas Güntner ◽  
Henryk Dobslaw ◽  
Christoph Dahle
Keyword(s):  
Soil Moisture ◽  
Water Deficit ◽  
Central Europe ◽  
Drought Index ◽  
Water Mass ◽  
Time Lag ◽  
Water Storage ◽  
Summer Drought ◽  
Central European ◽  
Spatio Temporal

<p><span>In this study we investigate the ability of GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) to quantify the two consecutive summer droughts of 2018 and 2019 in Central Europe. The GRACE-FO mission was launched in May 2018 as the successor of GRACE (2002-2017) and thus, allows us to relate the droughts of the last two years to former droughts in 2003 and 2015.</span></p><p><span>The water mass deficit in 2018 was 90±18.5 Gt and in 2019 even 116±18 Gt compared to the long term climatology. These deficits are 60% and 76% of the annual mean variations which is so severe that a fast recovery of the water storage cannot be expected within one year. The drought of summer 2019 was more severe than the European-wide drought of 2003 with a water deficit of 85±16 Gt and had the largest water deficit in the whole GRACE and GRACE-FO time span. </span></p><p><span>GRACE-FO total water storage data also allows the analysis of the spatio-temporal drought patterns. The largest water mass deficit in 2018 was detected in October and centred in South-Western Germany and neighbouring countries. However, the exact onset of the 2018 drought is not determinable due to missing data between July and October. The drought 2019 reached its largest deficit in July and was more evenly spread across Central Europe than the 2018 drought.</span></p><p><span>From the GRACE and GRACE-FO mass anomalies, we derive a drought index which is compared to an external soil-moisture drought index. Over the whole time series between 2002 and 2019 both indices show a high congruence. However, as the two indices </span><span>do not describe</span><span> the same hydrological compartment</span><span>s</span><span> a time lag and a memory effect of TWS </span><span>relative to</span><span> soil-moisture is visible in the comparison. </span></p><p><span>Overall, th</span><span>e presented study proves the successful continuation of GRACE with GRACE-FO and thus the reliability of the observed </span><span>Central European</span><span> summer drought of 2019 as the most extreme water scarcity event since 2002.</span></p>

scholarly journals Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018

2020 ◽  
Author(s):  
Riccardo Tortini ◽  
Nina Noujdina ◽  
Samantha Yeo ◽  
Martina Ricko ◽  
Charon M Birkett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Water ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Water Storage ◽  
Data Set ◽  
Lakes And Reservoirs ◽  
Global Surface ◽  
Surface Water Storage

Abstract. The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring to a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goal is to promote the use of satellite-derived products for the study of large inland water bodies, and to set the stage for the expected availability of products from the Surface Water and Ocean Topography (SWOT) mission, which will vastly expand the spatial coverage of such products, expected from 2021 on. Our general strategy is to estimate global surface water storage changes (ΔV) in large lakes and reservoirs using a combination of paired water surface elevation (WSE) and water surface area (WSA) extent products. Specifically, we use data produced by multiple satellite altimetry missions (TOPEX-Poseidon, Jason-1, Jason-2, Jason-3, and ENVISAT) from 1992 on, with surface extent estimated from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 on. We leverage from relationships between elevation and surface area (i.e., hypsometry) to produce estimates of ΔV even during periods when either of the variables was not available. This approach is successful provided that there are strong relationships between the two variables during an overlapping period. Our target is to produce time series of ΔV as well as WSE and WSA for a set of 347 lakes and reservoirs globally for the 1992–2018 period. The data sets presented are publicly available and distributed via NASA’s Jet Propulsion Laboratory’s Physical Oceanography Distributed Active Archive Center (PO DAAC; https://podaac.jpl.nasa.gov/). Specifically, the WSE data set is available at https://doi.org/10.5067/UCLRS-GREV2 (Birkett et al., 2019), the WSA data set is available at https://doi.org/10.5067/UCLRS-AREV2 (Khandelwal and Kumar, 2019), and the ΔV data set is available at https://doi.org/10.5067/UCLRS-STOV2 (Tortini et al., 2019). The records we describe represent the most complete global surface water time series available from the launch of TOPEX-Poseidon in 1992 (beginning of the satellite altimetry era) to near-present. The production of long-term, consistent, and calibrated records of surface water cycle variables such as the data set presented here is of fundamental importance to baseline future SWOT products.

scholarly journals Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018

2020 ◽  
Vol 12 (2) ◽  
pp. 1141-1151 ◽  
Author(s):  
Riccardo Tortini ◽  
Nina Noujdina ◽  
Samantha Yeo ◽  
Martina Ricko ◽  
Charon M. Birkett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Water ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Water Storage ◽  
Data Set ◽  
Lakes And Reservoirs ◽  
Global Surface ◽  
Surface Water Storage

Abstract. The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring at a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goals are to promote the use of satellite-derived products for the study of large inland water bodies and to set the stage for the expected availability of products from the Surface Water and Ocean Topography (SWOT) mission, which will vastly expand the spatial coverage of such products, expected from 2021 on. Our general strategy is to estimate global surface water storage changes (ΔV) in large lakes and reservoirs using a combination of paired water surface elevation (WSE) and water surface area (WSA) extent products. Specifically, we use data produced by multiple satellite altimetry missions (TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, and Envisat) from 1992 on, with surface extent estimated from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 on. We leverage relationships between elevation and surface area (i.e., hypsometry) to produce estimates of ΔV even during periods when either of the variables was not available. This approach is successful provided that there are strong relationships between the two variables during an overlapping period. Our target is to produce time series of ΔV as well as of WSE and WSA for a set of 347 lakes and reservoirs globally for the 1992–2018 period. The data sets presented and their respective algorithm theoretical basis documents are publicly available and distributed via the Physical Oceanography Distributed Active Archive Center (PO DAAC; https://podaac.jpl.nasa.gov/, last access: 13 May 2020) of NASA's Jet Propulsion Laboratory. Specifically, the WSE data set is available at https://doi.org/10.5067/UCLRS-GREV2 (Birkett et al., 2019), the WSA data set is available at https://doi.org/10.5067/UCLRS-AREV2 (Khandelwal and Kumar, 2019), and the ΔV data set is available at https://doi.org/10.5067/UCLRS-STOV2 (Tortini et al., 2019). The records we describe represent the most complete global surface water time series available from the launch of TOPEX/Poseidon in 1992 (beginning of the satellite altimetry era) to the near present. The production of long-term, consistent, and calibrated records of surface water cycle variables such as in the data set presented here is of fundamental importance to baseline future SWOT products.

scholarly journals Multi-Dimensional Drought Assessment in Abbay/Upper Blue Nile Basin: The Importance of Shared Management and Regional Coordination Efforts for Mitigation

2021 ◽  
Vol 13 (9) ◽  
pp. 1835
Author(s):  
Yared Bayissa ◽  
Semu Moges ◽  
Assefa Melesse ◽  
Tsegaye Tadesse ◽  
Anteneh Z. Abiy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Earth Observation ◽  
Drought Indices ◽  
Severe Drought ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Small Streams ◽  
Upper Blue Nile Basin

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin.

scholarly journals Technical Note: Comparing and ranking soil drought indices performance over Europe, through remote-sensing of vegetation

2010 ◽  
Vol 14 (2) ◽  
pp. 271-277 ◽  
Author(s):  
E. Peled ◽  
E. Dutra ◽  
P. Viterbo ◽  
A. Angert
Keyword(s):  
Soil Moisture ◽  
Drought Index ◽  
Technical Note ◽  
Temperate Climate ◽  
Drought Indices ◽  
Soil Drought ◽  
The Past ◽  
Analysis Technique ◽  
Global Soil ◽  
Warm Temperate

Abstract. In the past years there have been many attempts to produce and improve global soil-moisture datasets and drought indices. However, comparing and validating these various datasets is not straightforward. Here, interannual variations in drought indices are compared to interannual changes in vegetation, as captured by NDVI. By comparing the correlations of the different indices with NDVI we evaluated which drought index describes most realistically the actual changes in vegetation. Strong correlation between NDVI and the drought indices were found in areas that are classified as warm temperate climate with hot or warm dry summers. In these areas we ranked the PDSI, PSDI-SC, SPI3, and NSM indices, based on the interannual correlation with NDVI, and found that NSM outperformed the rest. Using this best performing index, and the ICA (Independent Component Analysis) technique, we analyzed the response of vegetation to temperature and soil-moisture stresses over Europe.

scholarly journals Assessment of spatiotemporal characteristics of agro-meteorological drought events based on comparing Standardized Soil Moisture Index, Standardized Precipitation Index and Multivariate Standardized Drought Index

2020 ◽  
Vol 11 (S1) ◽  
pp. 1-17 ◽  
Author(s):  
Muhammad Imran Khan ◽  
Xingye Zhu ◽  
Muhammad Arshad ◽  
Muhammad Zaman ◽  
Yasir Niaz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Moisture Index ◽  
Drought Characteristics ◽  
Soil Moisture Index ◽  
The Impact ◽  
Standardized Drought Index

Abstract Drought indices that compute drought events by their statistical properties are essential stratagems for the estimation of the impact of drought events on a region. This research presents a quantitative investigation of drought events by analyzing drought characteristics, considering agro-meteorological aspects in the Heilongjiang Province of China during 1980 to 2015. To examine these aspects, the Standardized Soil Moisture Index (SSI), Standardized Precipitation Index (SPI), and Multivariate Standardized Drought Index (MSDI) were used to evaluate the drought characteristics. The results showed that almost half of the extreme and exceptional drought events occurred during 1990–92 and 2004–05. The spatiotemporal analysis of drought characteristics assisted in the estimation of the annual drought frequency (ADF, 1.20–2.70), long-term mean drought duration (MDD, 5–11 months), mean drought severity (MDS, −0.9 to −2.9), and mild conditions of mean drought intensity (MDI, −0.2 to −0.80) over the study area. The results obtained by MSDI reveal the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. The results of this study provide valuable information and can prove to be a reference framework to guide agricultural production in the region.

Towards the removal of model bias from ESA CCI SM by using an L-band scaling reference

2021 ◽  
Author(s):  
Rémi Madelon ◽  
Nemesio Rodriguez-Fernandez ◽  
Robin Van Der Shalie ◽  
Yann Kerr ◽  
Tracy Scalon ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Climate Models ◽  
In Situ Measurements ◽  
Time Data ◽  
Data Set ◽  
Spatial Coverage ◽  
L Band ◽  
Better Than

<p>Merging data from different instruments is required to construct long time data records of soil moisture (SM). This is the goal of projects such as the ESA Climate Change Initiative (CCI) for SM (Gruber et al., 2019), which uses both active and passive microwave sensors. Currently, the GLDAS v2.1 model is used as reference to re-scale active and passive time series by matching their Cumulative Density Function (CDF) to that of the model. Removing the dependency on models is important, in particular for data assimilation applications into hydrological or climate models, and it has been proposed (Van der Schalie et al., 2018) to use L-band data from one of the two instruments specifically designed to measure SM, ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, as reference to re-scale other time series.<br>To investigate this approach, AMSR-2 SM time series obtained from C1-, C2- and X-band observations using LPRM (Land Parameter Retrieval Model) were re-scaled by CDF-matching (Brocca et al., 2011) using different SMAP and SMOS official (SMAP L2 V005, SMOS L3 V300, SMOS NRT V100&V200) and research (SMOS IC V103) SM products as well as the SMAP and SMOS LPRM v6 SM data used by the ESA CCI. The time series re-scaled using L-band remote sensing data were compared to those re-scaled using GLDAS and were evaluated against in situ measurements at several hundred sites retrieved from the International Soil Moisture Network (Dorigo et al., 2011). The results were analyzed as a function of the land cover class and the Koppen-Geiger climate classification.<br>Overall, AMSR-2 time series re-scaled using SMAP L2, SMAP LPRM and SMOS IC data sets as reference gave the best correlations with respect to in situ measurements, similar to those obtained by the time series re-scaled using GLDAS and slightly better than those of the original AMSR-2 time series. These results imply that different SMAP and SMOS products could actually be used to replace GLDAS as reference for the re-scaling of other sensors time series within the ESA CCI. However, one must bear in mind that this study is limited to the re-scaling of AMSR-2 data at a few hundred sites.<br>For a more detailed assessment of the L-band data set to be used for a global re-scaling, it is necessary to investigate other effects such as the spatial coverage or the time series length. SMAP spatial coverage is better than that of SMOS in regions affected by radio frequency interference. In contrast, the length of SMAP time series can be too short to capture the long term SM variability for climate applications in some regions. The CDF of SMOS time series computed from the date of SMAP launch is significantly different to those of the full length SMOS time series in some regions of the Globe. Possible ways of using a coherent SMAP/SMOS L-band data set will be discussed.</p>

scholarly journals Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

2019 ◽  
Vol 11 (23) ◽  
pp. 2742 ◽  
Author(s):  
Tran ◽  
Tran ◽  
Myint ◽  
Latorre-Carmona ◽  
Ho ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Drought Index ◽  
Negative Impact ◽  
Statistical Significance ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Series Data ◽  
Drought Indices ◽  
Drought Severity

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

Sign in / Sign up

Export Citation Format

Share Document