scholarly journals Verification of two hydrological models for real-time flood forecasting in the Hindu Kush Himalaya (HKH) region

2021 ◽  
Author(s):  
Karma Tsering ◽  
Manish Shrestha ◽  
Kiran Shakya ◽  
Birendra Bajracharya ◽  
Mir Matin ◽  
...  
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Early Warning Systems ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Good Decision ◽  
Hindu Kush ◽  
Forecast Validation

AbstractThe Hindu Kush Himalayan region is extremely susceptible to periodic monsoon floods. Early warning systems with the ability to predict floods in advance can benefit tens of millions of people living in the region. Two web-based flood forecasting tools (ECMWF-SPT and HIWAT-SPT) are therefore developed and deployed jointly by SERVIR-HKH and NASA-AST to provide early warning to Bangladesh, Bhutan, and Nepal. ECMWF-SPT provides ensemble forecast up to 15-day lead time, whereas HIWAT-SPT provides deterministic forecast up to 3-day lead time covering almost 100% of the rivers. Hydrological models in conjunction with forecast validation contribute not only to advancing the processes of a forecasting system, but also objectively assess the joint distribution of forecasts and observations in quantifying forecast accuracy. The validation of forecast products has emerged as a priority need to evaluate the worth of the predictive information in terms of quality and consistency. This paper describes the effort made in developing the hydrological forecast systems, the current state of the flood forecast services, and the performance of the forecast evaluation. Both tools are validated using a selection of appropriate metrics in measurement in both probabilistic and deterministic space. The numerical metrics are further complemented by graphical representations of scores and probabilities. It was found that the models had a good performance in capturing high flood events. The evaluation across multiple locations indicates that the model performance and forecast goodness are variable on spatiotemporal scale. The resulting information is used to support good decision-making in risk and resource management.

scholarly journals Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

2021 ◽  
Author(s):  
Mukakarangwa Assoumpta ◽  
Daniel Aja
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Lead Time ◽  
Hydrological Modeling ◽  
Model Performance ◽  
Warning System ◽  
Flood Forecasting ◽  
False Alarms ◽  
Time Flow ◽  
Quantitative Precipitation Forecasts

Abstract The absence of a viable flood early warning system for the Sebeya River catchment continues to impede government efforts toward improving community preparedness, the reduction of flood impacts and relief. This paper reports on a recent study that used satellite data, quantitative precipitation forecasts and the rainfall–runoff model for short-term flood forecasting in the Sebeya catchment. The global precipitation measurement product was used as a satellite rainfall product for model calibration and validation and forecasted European Centre Medium-Range Weather Forecasts (ECMWF) rainfall products were evaluated to forecast flood. Model performance was evaluated by the visual examination of simulated hydrographs, observed hydrographs and a number of performance indicators. The real-time flow forecast assessment was conducted with respect to three different flood warning threshold levels for a 3–24-h lead time. The result for a 3-h lead time showed 72% of hits, 7.5% of false alarms and 9.5% of missed forecasts. The number of hits decreased, as the lead time increased. This study did not consider the uncertainties in observed data, and this can influence the model performance. This work provides a base for future studies to establish a viable flood early warning system in the study area and beyond.

scholarly journals Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Vol 21 (9) ◽  
pp. 2753-2772
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach ◽  
Landslide Early Warning

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

scholarly journals Communicating Complex Forecasts for Enhanced Early Warning in Nepal

2019 ◽  
Author(s):  
Mirianna Budimir ◽  
Amy Donovan ◽  
Sarah Brown ◽  
Puja Shakya ◽  
Dilip Gautam ◽  
...  
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Warning System ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Deterministic Models ◽  
Early Action ◽  
Probabilistic Forecasts ◽  
Barriers And Challenges

Abstract. Early warning systems have the potential to save lives and improve resilience. Simple early warning systems rely on real-time data and deterministic models to generate evacuation warnings; these simple deterministic models enable life-saving action, but provide limited lead time for resilience-building early action. More complex early warning systems supported by forecasts, including probabilistic forecasts, can provide additional lead time for preparation. However, barriers and challenges remain in disseminating and communicating these more complex warnings to community members and individuals at risk. Research was undertaken to analyse and understand the current early warning system in Nepal, considering available data and forecasts, information flows, early warning dissemination and decision making for early action. The research reviewed the availability and utilisation of complex forecasts in Nepal, their integration into dissemination (Department of Hydrology and Meteorology (DHM) bulletins and SMS warnings), and decision support tools (Common Alerting Protocols and Standard Operating Procedures), considering their impact on improving early action to increase the resilience of vulnerable communities to flooding.

Using Ground Microtremor Data in Advanced Rockfall Early Warning Systems and Predicting Spatiotemporal Characteristics of Rockfall Hazards

2021 ◽  
Author(s):  
Yi-Rong Yang ◽  
Tzu-Tung Lee ◽  
Tai-Tien Wang
Keyword(s):  
Slope Stability ◽  
Early Warning ◽  
Local Governments ◽  
Lead Time ◽  
Early Warning Systems ◽  
Slope Instability ◽  
Warning Systems ◽  
Spatiotemporal Characteristics ◽  
Stability Of Slopes ◽  
Rockfall Hazards

Abstract Identifying cliffs that are prone to fall and providing a sufficient lead time for rockfall warning are crucial steps in disaster risk reduction and preventive maintenance work, especially that led by local governments. However, existing rockfall warning systems provide uncertain rockfall location forecasting and short warning times because the deformation and cracking of unstable slopes are not sufficiently detected by sensors before the rock collapses. Here, we introduce ground microtremor signals for early rockfall forecasting and demonstrate that microtremor characteristics can be used to detect unstable rock wedges on slopes, quantitatively describe the stability of slopes and lengthen the lead time for rockfall warning. We show that the change in the energy of ground microtremors can be an early precursor of rockfall and that the signal frequency decreases with slope instability. This finding indicates that ground microtremor signals are remarkably sensitive to slope stability. We conclude that microtremor characteristics can be used as an appropriate slope stability index for early rockfall warning systems and predicting the spatiotemporal characteristics of rockfall hazards. This early warning method has the advantages of providing a long lead time and on-demand monitoring, while increasing slope stability accessibility and prefailure location detectability.

Multi-parametric observations of debris-flow initiation at the headwaters of the Gadria catchment (eastern Italian Alps)

2020 ◽  
Author(s):  
Velio Coviello ◽  
Matteo Berti ◽  
Lorenzo Marchi ◽  
Francesco Comiti ◽  
Giulia Marchetti ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Debris Flow ◽  
Early Warning ◽  
Debris Flows ◽  
Lead Time ◽  
Early Warning Systems ◽  
Force Balance ◽  
Italian Alps ◽  
Alarm Systems ◽  
Debris Flow Initiation

<p>The complete understanding of the mechanisms controlling debris-flow initiation is still an open challenge in landslide research. Most debris-flow models assume that motion suddenly begins when a large force imbalance is imposed by slope instabilities or the substrate saturation that causes the collapse of the channel sediment cover. In the real world, the initiation of debris flows usually results from the perturbation of the static force balance that retains sediment masses in steep channels. These perturbations are primarily generated by the increasing runoff and by the progressive erosion of the deposits. Therefore, great part of regional early warning systems for debris flows are based on critical rainfall thresholds. However, these systems are affected by large spatial-temporal uncertainties due to the inadequate number and distribution of rain gauges. In addition, rainfall analysis alone does not explain the dynamics of sediment fluxes at the catchment scale: short-term variations in the sediment sources strongly influence the triggering of debris flows, even in catchments characterized by unlimited sediment supply.</p><p>In this work, we present multi-parametric observations of debris flows at the headwaters of the Gadria catchment (eastern Italian Alps). In 2018, we installed a monitoring network composed of geophones, three soil moisture probes, one tensiometer and two rain-triggered videocameras in a 30-m wide steep channel located at about 2200 m a.s.l. Most sensors lie on the lateral ridges of this channel, except for the tensiometer and the soil moisture probes that are installed in the channel bed at different depths. This network recorded four flow events in two years, two of which occurred at night. Specifically, the debris flows that occurred on 21 July 2018 and 26 July 2019 produced remarkable geomorphic changes in the monitored channel, with up to 1-m deep erosion. For all events, we measured peak values of soil water content that are far from saturation (<0.25 at -20 cm, <0.15 at -40 cm, <0.1 at -60 cm). We derived the time of occurrence and the duration of these events from the analysis of the seismic signals. Combining these pieces of information with data gathered at the monitoring station located about 2 km downstream, we could determine the flow kinematics along the main channel.</p><p>These results, although still preliminary, show the relevance of a multi-parametric detection of debris-flow initiation processes and may have valuable implications for risk management. Alarm systems for debris flows are becoming more and more attractive due the continuous development of compact and low-cost distributed sensor networks. The main challenge for operational alarm systems is the short lead-time, which is few tens of seconds for closing a transportation route or tens of minutes for evacuating settlements. Lead-time would significantly increase installing a detection system in the upper part of a catchment, where the debris flow initiates. The combination of hydro-meteorological monitoring in the source areas and seismic detection of channelized flows may be a reliable approach for developing an integrated early warning - alarm system.</p>

scholarly journals Effective real-time forecasting of inundation maps for early warning systems during typhoons

2018 ◽  
Vol 147 ◽  
pp. 03014
Author(s):  
Jhih-Huang Wang ◽  
Gwo-Fong Lin ◽  
Bing-Chen Jhong
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Reference Points ◽  
Support Vector ◽  
Spatial Expansion ◽  
Warning Systems ◽  
Inundation Maps ◽  
Inundation Depth ◽  
Proposed Model

Accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation during typhoons. In this paper, an effective forecasting model is proposed to yield 1- to 6-h lead-time inundation maps for early warning systems during typhoons. The proposed model based on Support Vector Machine (SVM) is composed of two modules, point forecasting and spatial expansion. In the first module, the rainfall intensity, inundation depth, cumulative rainfall and forecasted inundation depths are considered as model input for point forecasting. In the second module, the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps for spatial expansion. The results show that the proposed model is able to provide accurate point forecasts at each inundation point. Moreover, the spatial expansion module is capable of producing accurate spatial inundation forecasts. Obviously, the proposed model provides reasonable spatial inundation forecasts, and is able to deal with the nonlinear relationships between inputs and desired output. In conclusion, the proposed model is suitable and useful for inundation forecasting.

Increasing Early Warning Lead Time Through Improved Transboundary Flood Forecasting in the Gash River Basin, Horn of Africa

2016 ◽  
pp. 183-200 ◽  
Author(s):  
G. Amarnath ◽  
N. Alahacoon ◽  
Y. Gismalla ◽  
Y. Mohammed ◽  
B.R. Sharma ◽  
...  
Keyword(s):  
River Basin ◽  
Early Warning ◽  
Lead Time ◽  
Flood Forecasting ◽  
Horn Of Africa

Forecast of temperature-attributable mortality at lead times of up to 15 days for a very large ensemble of European regions

2021 ◽  
Author(s):  
Marcos Quijal-Zamorano ◽  
Desislava Petrova ◽  
Xavier Rodó ◽  
Èrica Martinez-Solanas ◽  
Joan Ballester
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Attributable Mortality ◽  
Lead Times ◽  
Warning Systems ◽  
Epidemiological Models ◽  
European Regions ◽  
Large Ensemble ◽  
Weather Forecasts

<p>Implementing adequate health preventing measures is essential for public health decision making, particularly in the current context of rising temperatures. Most of the early warning systems are only based on climate data, and in very few cases they truly model the actual impact of the climate phenomena.</p><p>Here we establish, for the first-time, the theoretical basis for the development of operational heat-health early warning systems that combine climate and health data. We studied the predictability of Temperature Attributable Mortality (TAM) at lead times of up to 15 days for a very large ensemble of European regions. To achieve this goal, we analysed daily counts of all-cause mortality for the period 1998-2012 in 147 NUTS2 regions in 16 European countries, representing more than 400 million people, and daily high-resolution weather forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). We applied epidemiological models for the fitting of the temperature-mortality relationship in each of the regions, accounting for the different vulnerabilities and socio-demographic characteristics existing in Europe. We compared the predictive skill of the temperature and health forecasts on seasons and days with higher mortality risk. </p><p>We conclude that the predictability of temperature can be used to issue skilful forecasts of TAM. In general, the predictability limit of temperature is similar to the one of TAM, which implies that the use of epidemiological models to transform the climate variables into health information does not reduce the lead time limit with significant forecast skill. Nonetheless, the spatial heterogeneity of the predictability lead time for TAM is higher than for temperature, especially in summer, where the complex shape of the temperature-mortality association amplifies the forecast errors. Overall, we find  a nearly-linear relationship between the predictability of temperature and TAM for different seasons and regions, suggesting that future improvements in the predictability of temperature could automatically lead to improvements in the predictability of TAM.</p>

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