Global flood monitoring with GRACE/GRACE-FO

2021 ◽  
Author(s):  
Milena Latinovic ◽  
Andreas Güntner ◽  
Frank Flechtner ◽  
Michael Murböck ◽  
Andreas Kwas
Keyword(s):  
Large Scale ◽  
Water Storage ◽  
Early Warning Systems ◽  
Daily Basis ◽  
Large River ◽  
Global Scale ◽  
Flood Events ◽  
Grace Data ◽  
Water Redistribution ◽  
German Aerospace

<p>The German Aerospace Center and NASA's joint mission, the Gravity Recovery and Climate Experiment (GRACE) operational from 2002 until October 2017, provided measurements of Earth's gravity field anomalies. Its follow-on mission GRACE-FO, implemented by NASA and GFZ, was launched in May 2018 and continued to give us large-scale measurements of the Earth's gravity variations. These variations in gravity are used to determine anomalies of total water storage (TWSA) which can provide us with insights into global water redistribution on a monthly up to a daily basis.</p><p>Most common natural disasters that still require efficient early warning systems are floods. Floods are causing significant economic and humanitarian losses on a global scale and are triggered by the interaction of different hydro-meteorological processes (e.g. precipitation, sub-surface water storage, snow cover).    </p><p>We aim to explore GRACE and GRACE-FO products' possibilities to detect the water storage dynamics associated with floods in large river catchments. We include analysis of the basins' wetness states before the flood events, which eventually can give us early indicators of flood development. During the GRACE data period, we investigate around 2500 historical floods from the Dartmouth Flood Observatory (DFO). We acquire GRACE data with daily resolution from the latest releases of ITSG and GFZ for the spatial extent of DFO floods and reduce TWSA values by long-term trends and by average seasonal variability. Furthermore, we assess the available river discharge time series, during the GRACE period, obtained from the Global Runoff Data Centre (GRDC) for the flood event separation. We compare GRACE-based water storage anomalies to flood events' characteristics, like peak, volume, and duration. Results show the potential of GRACE-based TWSA to detect large-scale flood events.</p>

scholarly journals A global database of historic and real-time flood events based on social media

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jens A. de Bruijn ◽  
Hans de Moel ◽  
Brenden Jongman ◽  
Marleen C. de Ruiter ◽  
Jurjen Wagemaker ◽  
...  
Keyword(s):  
Social Media ◽  
Real Time ◽  
Early Warning Systems ◽  
Radar Data ◽  
Global Scale ◽  
Early Event ◽  
Warning Systems ◽  
Flood Events ◽  
Global Database ◽  
Use Of Social Media

AbstractEarly event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10,000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE were detected. All results are publicly available on www.globalfloodmonitor.org.

Assessing Detectability of Global Flood Occurrences using Daily and Monthly GRACE/GRACE-FO

2020 ◽  
Author(s):  
Ashraf Rateb ◽  
Alexander Sun ◽  
Bridget Scanlon ◽  
Himanshu Save
Keyword(s):  
Water Storage ◽  
Rate Of Change ◽  
Economic Losses ◽  
Lead Times ◽  
Monthly Precipitation ◽  
Coincidence Rate ◽  
Flood Events ◽  
Total Water ◽  
Climate Signal ◽  
Grace Data

<p> </p> <p>Floods pose a threat to the lives of millions of people globally each year, with economic losses exceeding those of any other natural hazard. Improving flood forecasting with longer lead times can support enhanced risk management strategies and reduce associated socioeconomic losses. The objective of this study was to assess the detectability of floods using newly developed GRACE daily and regular monthly total water storage data. </p> <p>We compared total water storage (TWS) maxima from GRACE and GRACE-FO with flood occurrences from 2002 to 2020. GRACE daily TWS maxima were based on three daily GRACE solutions (UTCSR-RSWM, GFZ-RBF, and ITSG-2018) derived using statistical learning and geophysical models for the GRACE period (2002-2017). Monthly GRACE and GRACE-FO data were based on mascons solutions from UT-CSR and NASA-JPL for 2002-2020. A flood susceptibility index was developed based on the climate signal portion in the TWSA and compared with other flood indices (e.g., standardized precipitation index and streamflow). We evaluated the spatiotemporal coincidence rate of change of the 90th percentile of the daily and monthly precipitation based on the GPM-Imerg and GPCP rainfall data and the corresponding 90th percentile of the daily and monthly TWSA. The coincidence rate between GRACE TWSA maxima and precipitation were also compared relative to actual flood data (~3000 events) from the Dartmouth flood Observatory (DFO) catalog. </p> <p>Preliminary results using precipitation data from GPCP reveal that monthly GRACE/GRACE-FO data have a high predication rate for the monthly maxima precipitation > 90th percentile with a lead time of ~ two months across the tropical rain belt. Assessment against the real flood events shows that the three daily GRACE data perform well for flood events resulting from heavy and monsoonal rain and slightly differ for the events triggered by snowmelt and storm surges. The duration of flood events from GRACE data is generally shorter than the periods reported by DFO. An empirical relationship was derived between floods' duration based on the cause and the expected precursor coincidence rate from daily GRACE data. Further analysis is necessary to evaluate the GRACE precursor rate using different lead times and tolerance windows, quantify the change in rate relative to climate, topography, and soil types, and interpret the different performance GRACE products. This preliminary analysis suggests the high potential for GRACE/GRACE-FO data to extend flood forecast lead times and potentially improve the mitigation strategies</p>

scholarly journals Global modelling of continental water storage changes – sensitivity to different climate data sets

2007 ◽  
Vol 11 ◽  
pp. 63-68 ◽  
Author(s):  
K. Fiedler ◽  
P. Döll
Keyword(s):  
Water Storage ◽  
Global Scale ◽  
Data Sets ◽  
Precipitation Data ◽  
Climate Data ◽  
Satellite Mission ◽  
Full Data ◽  
Data Set ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Since 2002, the GRACE satellite mission provides estimates of the Earth's dynamic gravity field with unprecedented accuracy. Differences between monthly gravity fields contain a clear hydrological signal due to continental water storage changes. In order to evaluate GRACE results, the state-of-the-art WaterGAP Global Hydrological Model (WGHM) is applied to calculate terrestrial water storage changes on a global scale. WGHM is driven by different climate data sets to analyse especially the influence of different precipitation data on calculated water storage. The data sets used are the CRU TS 2.1 climate data set, the GPCC Full Data Product for precipitation and data from the ECMWF integrated forecast system. A simple approach for precipitation correction is introduced. WGHM results are then compared with GRACE data. The use of different precipitation data sets leads to considerable differences in computed water storage change for a large number of river basins. Comparing model results with GRACE observations shows a good spatial correlation and also a good agreement in phase. However, seasonal variations of water storage as derived from GRACE tend to be significantly larger than those computed by WGHM, regardless of which climate data set is used.

ASSESSMENT OF THE SPATIO-TEMPORAL VARIATIONS IN GROUND WATER STORAGE USING DOWN-SCALED GRACE DATA

2018 ◽  
Author(s):  
Hossein Sahour ◽  
◽  
Mohamed Sultan ◽  
Karem Abdelmohsen ◽  
Sita Karki ◽  
...  
Keyword(s):  
Ground Water ◽  
Water Storage ◽  
Temporal Variations ◽  
Grace Data ◽  
Spatio Temporal

scholarly journals Extreme Floods in the Eastern Part of Europe: Large-Scale Drivers and Associated Impacts

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1122
Author(s):  
Monica Ionita ◽  
Viorica Nagavciuc
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Flood Events ◽  
Rainfall Events ◽  
Catchment Areas ◽  
Large Scale Atmospheric Circulation ◽  
Heavy Rainfall Events

The role of the large-scale atmospheric circulation in producing heavy rainfall events and floods in the eastern part of Europe, with a special focus on the Siret and Prut catchment areas (Romania), is analyzed in this study. Moreover, a detailed analysis of the socio-economic impacts of the most extreme flood events (e.g., July 2008, June–July 2010, and June 2020) is given. Analysis of the largest flood events indicates that the flood peaks have been preceded up to 6 days in advance by intrusions of high Potential Vorticity (PV) anomalies toward the southeastern part of Europe, persistent cut-off lows over the analyzed region, and increased water vapor transport over the catchment areas of Siret and Prut Rivers. The vertically integrated water vapor transport prior to the flood peak exceeds 300 kg m−1 s−1, leading to heavy rainfall events. We also show that the implementation of the Flood Management Plan in Romania had positive results during the 2020 flood event compared with the other flood events, when the authorities took several precaution measurements that mitigated in a better way the socio-economic impact and risks of the flood event. The results presented in this study offer new insights regarding the importance of large-scale atmospheric circulation and water vapor transport as drivers of extreme flooding in the eastern part of Europe and could lead to a better flood forecast and flood risk management.

scholarly journals Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Global Scale ◽  
Area Network ◽  
Biophysical Characterization ◽  
Challenges And Opportunities ◽  
Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

scholarly journals Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

2021 ◽  
Vol 13 (11) ◽  
pp. 2220
Author(s):  
Yanbing Bai ◽  
Wenqi Wu ◽  
Zhengxin Yang ◽  
Jinze Yu ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Flood Events ◽  
Test Set ◽  
Source Data ◽  
Flood Disasters ◽  
Temporary Water ◽  
Sentinel 2

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

scholarly journals Combined Block Adjustment for Optical Satellite Stereo Imagery Assisted by Spaceborne SAR and Laser Altimetry Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3062
Author(s):  
Guo Zhang ◽  
Boyang Jiang ◽  
Taoyang Wang ◽  
Yuanxin Ye ◽  
Xin Li
Keyword(s):  
Large Scale ◽  
Satellite Image ◽  
Field Measurements ◽  
High Elevation ◽  
Global Scale ◽  
Stereo Image ◽  
Image Process ◽  
Control Points ◽  
Altimetry Data ◽  
Laser Altimetry

To ensure the accuracy of large-scale optical stereo image bundle block adjustment, it is necessary to provide well-distributed ground control points (GCPs) with high accuracy. However, it is difficult to acquire control points through field measurements outside the country. Considering the high planimetric accuracy of spaceborne synthetic aperture radar (SAR) images and the high elevation accuracy of satellite-based laser altimetry data, this paper proposes an adjustment method that combines both as control sources, which can be independent from GCPs. Firstly, the SAR digital orthophoto map (DOM)-based planar control points (PCPs) acquisition is realized by multimodal matching, then the laser altimetry data are filtered to obtain laser altimetry points (LAPs), and finally the optical stereo images’ combined adjustment is conducted. The experimental results of Ziyuan-3 (ZY-3) images prove that this method can achieve an accuracy of 7 m in plane and 3 m in elevation after adjustment without relying on GCPs, which lays the technical foundation for a global-scale satellite image process.

scholarly journals Planning of technical flood retention measures in large river basins under consideration of imprecise probabilities of multivariate hydrological loads

2009 ◽  
Vol 9 (4) ◽  
pp. 1349-1363 ◽  
Author(s):  
D. Nijssen ◽  
A. Schumann ◽  
M. Pahlow ◽  
B. Klein
Keyword(s):  
Flood Control ◽  
Large River ◽  
Control Measures ◽  
Imprecise Probabilities ◽  
Flood Events ◽  
Modeling Tools ◽  
Statistical Characterization ◽  
Design Floods ◽  
Known Unknowns ◽  
Flood Waves

Abstract. As a result of the severe floods in Europe at the turn of the millennium, the ongoing shift from safety oriented flood control towards flood risk management was accelerated. With regard to technical flood control measures it became evident that the effectiveness of flood control measures depends on many different factors, which cannot be considered with single events used as design floods for planning. The multivariate characteristics of the hydrological loads have to be considered to evaluate complex flood control measures. The effectiveness of spatially distributed flood control systems differs for varying flood events. Event-based characteristics such as the spatial distribution of precipitation, the shape and volume of the resulting flood waves or the interactions of flood waves with the technical elements, e.g. reservoirs and flood polders, result in varying efficiency of these systems. Considering these aspects a flood control system should be evaluated with a broad range of hydrological loads to get a realistic assessment of its performance under different conditions. The consideration of this variety in flood control planning design was one particular aim of this study. Hydrological loads were described by multiple criteria. A statistical characterization of these criteria is difficult, since the data base is often not sufficient to analyze the variety of possible events. Hydrological simulations were used to solve this problem. Here a deterministic-stochastic flood generator was developed and applied to produce a large quantity of flood events which can be used as scenarios of possible hydrological loads. However, these simulations imply many uncertainties. The results will be biased by the basic assumptions of the modeling tools. In flood control planning probabilities are applied to characterize uncertainties. The probabilities of the simulated flood scenarios differ from probabilities which would be derived from long time series. With regard to these known unknowns the bias of the simulations was considered by imprecise probabilities. Probabilities, derived from measured flood data were combined with probabilities which were estimated from long simulated series. To consider imprecise probabilities, fuzzy sets were used to distinguish the results between more or less possible design floods. The need for such a differentiated view on the performance of flood protection systems is demonstrated by a case study.

scholarly journals A pan-African medium-range ensemble flood forecast system

2015 ◽  
Vol 19 (8) ◽  
pp. 3365-3385 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Flood Forecast ◽  
Flood Events ◽  
Forecast System ◽  
Pan African ◽  
Forecasting System

Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

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