Assessing the viability of using GEOS-Forecast Product for Landslides Forecasting−A step toward Early Warning Systems

2020 ◽  
Author(s):  
Sana Khan ◽  
Dalia B. Kirschbaum ◽  
Thomas Stanley
Keyword(s):  
Distribution Function ◽  
Early Warning ◽  
Management Practices ◽  
Early Warning Systems ◽  
Probability Of Detection ◽  
Cumulative Distribution ◽  
Precipitation Forecast ◽  
Precipitation Field ◽  
Extreme Rainfall Events ◽  
Landslide Forecasting

<p>Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time.</p><p>Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system.</p><p>This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS).  GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.</p>

Assessment of Flood Early Warning Systems with Social Media

2021 ◽  
Author(s):  
Thierry Hohmann ◽  
Judit Lienert ◽  
Jafet Andersson ◽  
Darcy Molnar ◽  
Peter Molnar ◽  
...  
Keyword(s):  
Social Media ◽  
Performance Assessment ◽  
Early Warning ◽  
Early Warning Systems ◽  
Probability Of Detection ◽  
Economic Losses ◽  
Warning Systems ◽  
Flood Events ◽  
Spatial Coverage ◽  
De Bruijn

<p><strong>Introduction</strong></p><p>Flood early warning systems (FEWS) can reduce casualties and economic losses (UNEP, 2012). The EC Horizon 2020 project FANFAR (www.fanfar.eu) aims to co-develop a FEWS in West Africa together with stakeholders, predicting streamflow and return period threshold exceedance (Andersson et al., 2020). A Multi-Criteria Decision Analysis (MCDA) indicated, that stakeholders find information accuracy especially important, among a broad set of fundamental objectives (Lienert et al., 2020). Social media have the potential to support accuracy assessment by detecting flood events (Lorini et al., 2019; de Bruijn et al., 2019) due to their large spatial coverage (Restrepo-Estrada et al., 2018). We investigated the potential of social media to assess FANFAR forecast accuracy.</p><p> </p><p><strong>Research Approach</strong></p><p>FANFAR forecasts are based on HYPE, which is a semi-distributed land-cover and sub-catchment based hydrological model (Arheimer et al., 2020). We lumped the forecasted flood risk (FFR) on a country scale and compared it to flood events detected on Twitter, using an algorithm (FEDA) developed by de Bruijn et al. (2019). FEDA detects flood-related tweet bursts based on regionally and temporally adjusted thresholds (de Bruijn et al., 2019). We compared FEDA detected events with floods from the disaster database EM-DAT (https://www.emdat.be/), to find if tweets indicate flooding. We also compared FEDA to the lumped FFR to identify false positives (FP), false negatives (FN), and true positives (TP), from which we deduced the probability of detection (POD) and false alarm rate (FAR). We further calculated the correlation of single flood-related tweets with the lumped FFR and investigated seasonality, lag, and the influence of rainfall.</p><p> </p><p><strong>Findings</strong></p><p>The detailed findings are described in Hohmann (2021). FEDA (i.e., tweets) and EM-DAT events (i.e., floods) mostly occurred in the same period. However, FEDA detected shorter and more frequent events than EM-DAT. In the Upper Niger, POD<sub>FEDA</sub> and FAR<sub>FEDA</sub> (deduced from FEDA) were of similar order of magnitude as the POD<sub>S</sub> and FAR<sub>S</sub> (deduced from streamflow) but were different in the Lower Niger region. This suggests that tweets can be employed additionally to e.g. streamflow timeseries as a complementary way to evaluate accuracy. Correlation analysis between single flood-related tweets and the lumped FFR showed no relationship. We also did not find a systematic influence of seasonality or a lagged response between tweets and FFR. The correlation coefficients between tweets and rainfall ranged from 0.1-0.9, but were mostly non-significant. This suggests that a performance assessment based on single tweets is not (yet) adequate. Also, since FEDA does not differentiate between pluvial and fluvial floods, it is less suited to assess the accuracy of FANFAR. Our findings suggest the need for inclusion of other factors into the performance assessment of FEWSs, such as regional thresholds to identify TP, FP, and FN. Also, rainfall causing pluvial flooding must be considered. Finally, our approach is limited to Twitter. Further research should assess the potential of e.g. Facebook to be included in FEWS performance assessment. The question whether social media, FEWSs, or EM-DAT are correct remains, and is in our opinion best addressed by employing multiple data sources.</p>

Development of a Hydrological Telemetry System in Bago River

2018 ◽  
Vol 13 (1) ◽  
pp. 116-124 ◽  
Author(s):  
Ralph Allen Acierto ◽  
Akiyuki Kawasaki ◽  
Win Win Zin ◽  
Aung Than Oo ◽  
Khon Ra ◽  
...  
Keyword(s):  
Real Time ◽  
Disaster Management ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Probability Of Detection ◽  
Future Climate Change ◽  
Telemetry System ◽  
Production Type

Hydrological monitoring is one of the key aspects in early warning systems that are vital to flood disaster management in flood-prone areas such as Bago River Basin in Myanmar. Thousands of people are affected due to the perennial flooding. Owing to the increasing pressure of rapid urbanization in the region and future climate change impacts, an early warning system in the basin is urgently required for disaster risk mitigation. This paper introduces the co-establishment of the telemetry system by a group of stakeholders. The co-establishment of the system through intensive consultations, proactive roles in responsibility sharing, and capacity building efforts, is essential in developing a base platform for flood forecasting and an early warning system in the basin. Herein, we identify the key challenges that have been central to the participatory approach in co-establishing the system. We also highlight opportunities as a result of the ongoing process and future impact on the disaster management system in the basin. We also highlight the potential for scientific contributions in understanding the local weather and hydrological characteristics through the establishment of the high-temporal resolution observation network. Using the observation at Zaung Tu Weir, Global Satellite Mapping of Precipitation (GSMaP) and Global Precipitation Measurement (GPM) satellite estimates were assessed. Near real-time and standard versions of both satellite estimates show potential utility over the basin. Hourly aggregation shows slightly higher than 40% probability of detection (POD), on average, for both satellite estimates regardless of the production type. Thus, the hourly aggregation requires correction before usage. The results show useful skills at 60% POD for standard GSMaP (GSMAP-ST), 55% POD for near real-time GSMaP (GSMAP-NR), and 46% POD for GPM, at 3-hourly aggregations. Six-hourly aggregations show maximum benefit for providing useful skill and good correspondence to gauge the observation with GSMAP-ST showing the best true skill score (TSS) at 0.54 and an equitable threat score (ETS) at 0.37. While, both final run GPM and GSMAP-NR show lower POD, TSS, and ETS scores. Considering the latency of near real-time satellite estimates, the GSMAP-NR shows the best potential with a 4-hour latency period for monitoring and forecasting purposes in the basin. The result of the GSMAP-NR does not vary significantly from the GSMAP-ST and all GPM estimates. However, it requires some correction before its usage in any applications, for modeling and forecasting purposes.

Recent Advances in Early Warning Systems for Landslide Forecasting

2021 ◽  
pp. 249-260
Author(s):  
Ram Wanare ◽  
Kannan K. R. Iyer ◽  
Prathyusha Jayanthi
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Recent Advances ◽  
Landslide Forecasting

scholarly journals Quantitative comparison between two different methodologies to define rainfall thresholds for landslide forecasting

2015 ◽  
Vol 15 (10) ◽  
pp. 2413-2423 ◽  
Author(s):  
D. Lagomarsino ◽  
S. Segoni ◽  
A. Rosi ◽  
G. Rossi ◽  
A. Battistini ◽  
...  
Keyword(s):  
Early Warning ◽  
State Of The Art ◽  
Test Site ◽  
Early Warning Systems ◽  
The Other ◽  
Threshold Models ◽  
Warning Systems ◽  
Rainfall Thresholds ◽  
Landslide Forecasting ◽  
Validation Procedure

Abstract. This work proposes a methodology to compare the forecasting effectiveness of different rainfall threshold models for landslide forecasting. We tested our methodology with two state-of-the-art models, one using intensity–duration thresholds and the other based on cumulative rainfall thresholds. The first model identifies rainfall intensity–duration thresholds by means of a software program called MaCumBA (MAssive CUMulative Brisk Analyzer) (Segoni et al., 2014a) that analyzes rain gauge records, extracts intensity (I) and duration (D) of the rainstorms associated with the initiation of landslides, plots these values on a diagram and identifies the thresholds that define the lower bounds of the I–D values. A back analysis using data from past events is used to identify the threshold conditions associated with the least number of false alarms. The second model (SIGMA) (Sistema Integrato Gestione Monitoraggio Allerta) (Martelloni et al., 2012) is based on the hypothesis that anomalous or extreme values of accumulated rainfall are responsible for landslide triggering: the statistical distribution of the rainfall series is analyzed, and multiples of the standard deviation (σ) are used as thresholds to discriminate between ordinary and extraordinary rainfall events. The name of the model, SIGMA, reflects the central role of the standard deviations. To perform a quantitative and objective comparison, these two models were applied in two different areas, each time performing a site-specific calibration against available rainfall and landslide data. For each application, a validation procedure was carried out on an independent data set and a confusion matrix was built. The results of the confusion matrixes were combined to define a series of indexes commonly used to evaluate model performances in natural hazard assessment. The comparison of these indexes allowed to identify the most effective model in each case study and, consequently, which threshold should be used in the local early warning system in order to obtain the best possible risk management. In our application, none of the two models prevailed absolutely over the other, since each model performed better in a test site and worse in the other one, depending on the characteristics of the area. We conclude that, even if state-of-the-art threshold models can be exported from a test site to another, their employment in local early warning systems should be carefully evaluated: the effectiveness of a threshold model depends on the test site characteristics (including the quality and quantity of the input data), and a validation procedure and a comparison with alternative models should be performed before its implementation in operational early warning systems.

scholarly journals MIDAS: A New Integrated Flood Early Warning System for the Miño River

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2319 ◽  
Author(s):  
Diego Fernández-Nóvoa ◽  
Orlando García-Feal ◽  
José González-Cao ◽  
Carlos de Gonzalo ◽  
José Antonio Rodríguez-Suárez ◽  
...  
Keyword(s):  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
River Flow ◽  
Warning System ◽  
Early Warning Systems ◽  
Precipitation Forecast ◽  
Mitigation Measures ◽  
Flood Alert ◽  
The Impact

Early warning systems have become an essential tool to mitigate the impact of river floods, whose frequency and magnitude have increased during the last few decades as a consequence of climate change. In this context, the Miño River Flood Alert System (MIDAS) early warning system has been developed for the Miño River (Galicia, NW Spain), whose flood events have historically caused severe damage in urban areas and are expected to increase in intensity in the next decades. MIDAS is integrated by a hydrologic (HEC-HMS) and a hydraulic (Iber+) model using precipitation forecast as input data. The system runs automatically and is governed by a set of Python scripts. When any hazard is detected, an alert is issued by the system, including detailed hazards maps, to help decision makers to take precise and effective mitigation measures. Statistical analysis supports the accuracy of hydrologic and hydraulic modules implemented to forecast river flow and flooded critical areas during the analyzed period of time, including some of the most extreme events registered in the Miño River. In fact, MIDAS has proven to be capable of predicting most of the alert situations occurred during the study period, showing its capability to anticipate risk situations.

Evaluation of gaps in early warning mechanisms and evacuation procedures for coastal communities in Sri Lanka

2020 ◽  
Vol 11 (3) ◽  
pp. 415-433 ◽  
Author(s):  
Chamal Perera ◽  
Darshana Jayasooriya ◽  
Gimhan Jayasiri ◽  
Chameera Randil ◽  
Chaminda Bandara ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Disaster Management ◽  
Early Warning ◽  
Online Survey ◽  
Management Practices ◽  
Secondary Data ◽  
Early Warning Systems ◽  
Coastal Communities ◽  
Content Type ◽  
Questionnaire Surveys

Purpose Even though Sri Lanka has established Early Warning (EW) mechanisms and Evacuation Procedures (EP) for the communities affected by the coastal disasters, there are several gaps, which hinder effective mechanisms in operation of disaster management practices. These gaps affect both the vulnerable communities and relevant authorities involved in the Disaster Management sector. This paper aims to identify and evaluate those gaps while providing adequate solutions. Design/methodology/approach For that, questionnaire surveys were carried out with a sample size of 217 via an online survey (117) among the urban level and interviews and telephone interviews (100) with the village level coastal communities. Data analysis was carried out using statistical analysis of questionnaire surveys and grounded theory was used for in-depth qualitative study. Findings Primary and secondary data obtained from the surveys were categorized under five themes, namely, response to early warning systems, evacuation routes, shelters, drills and training, effect of having a family vehicle, relatives and domestic animals, evacuation of people with special needs and cooperation with local government units. This paper analyses these themes in detail. Originality/value While critically evaluating the gaps in existing early warning mechanisms and evacuation procedures, this paper identifies correlations between some of the gaps and recommendations as well. Input from the international academics were also obtained at different forums and have strengthen the findings to overcome the barriers, which hinder successful mechanisms.

scholarly journals Satellite rainfall products outperform ground observations for landslide forecasting in India

2021 ◽  
Author(s):  
Maria Teresa Brunetti ◽  
Massimo Melillo ◽  
Stefano Luigi Gariano ◽  
Luca Ciabatta ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Early Warning ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Warning Systems ◽  
Landslide Occurrence ◽  
Landslide Forecasting ◽  
Satellite Rainfall ◽  
Global Precipitation ◽  
The Impact

Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important tool to forecast landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide forecasting, to reduce the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide forecasting in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (10 km vs 25 km) and temporal (hourly vs daily) resolution. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g., overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

scholarly journals Quantitative comparison between two different methodologies to define rainfall thresholds for landslide forecasting

2015 ◽  
Vol 3 (1) ◽  
pp. 891-917 ◽  
Author(s):  
D. Lagomarsino ◽  
S. Segoni ◽  
A. Rosi ◽  
G. Rossi ◽  
A. Battistini ◽  
...  
Keyword(s):  
Early Warning ◽  
Extreme Values ◽  
Threshold Model ◽  
Confusion Matrix ◽  
Test Site ◽  
Early Warning Systems ◽  
The Other ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Forecasting

Abstract. This work proposes a methodology to compare the forecasting effectiveness of different rainfall threshold models for landslide forecasting. We tested our methodology with two state-of-the-art models, one using intensity-duration thresholds and the other based on cumulative rainfall thresholds. The first model identifies rainfall intensity-duration thresholds by means of a software called MaCumBA (MAssive CUMulative Brisk Analyzer) (Segoni et al., 2014a) that analyzes rain-gauge records, extracts the intensities (I) and durations (D) of the rainstorms associated with the initiation of landslides, plots these values on a diagram, and identifies thresholds that define the lower bounds of the I−D values. A back analysis using data from past events is used to identify the threshold conditions associated with the least amount of false alarms. The second model (SIGMA) (Sistema Integrato Gestione Monitoraggio Allerta) (Martelloni et al., 2012) is based on the hypothesis that anomalous or extreme values of rainfall are responsible for landslide triggering: the statistical distribution of the rainfall series is analyzed, and multiples of the SD (σ) are used as thresholds to discriminate between ordinary and extraordinary rainfall events. The name of the model, SIGMA, reflects the central role of the SDs in the proposed methodology. To perform a quantitative and objective comparison, these two methodologies were applied in two different areas, each time performing a site-specific calibration against available rainfall and landslide data. After each application, a validation procedure was carried out on an independent dataset and a confusion matrix was build. The results of the confusion matrixes were combined to define a series of indexes commonly used to evaluate model performances in natural hazard assessment. The comparison of these indexes allowed assessing the most effective model in each case of study and, consequently, which threshold should be used in the local early warning system in order to obtain the best possible risk management. In our application, none of the two models prevailed absolutely on the other, since each model performed better in a test site and worse in the other one, depending on the physical characteristics of the area. This conclusion can be generalized and it can be assumed that the effectiveness of a threshold model depends on the test site characteristics (including the quality and quantity of the input data) and that a validation and a comparison with alternative models should be performed before the implementation in operational early warning systems.

The CARE Telematics Network for the Surveillance of Influenza in Europe

1995 ◽  
Vol 34 (05) ◽  
pp. 518-522 ◽  
Author(s):  
M. Bensadon ◽  
A. Strauss ◽  
R. Snacken
Keyword(s):  
Public Health ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Surveillance Networks ◽  
Spanish Flu ◽  
The 1950S ◽  
National Networks ◽  
First Time

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.

Sign in / Sign up

Export Citation Format

Share Document