scholarly journals The Selection of Rain Gauges and Rainfall Parameters in Estimating Intensity-Duration Thresholds for Landslide Occurrence: Case Study from Wayanad (India)

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1000 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan ◽  
Samuele Segoni
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Rainfall Event ◽  
Future Application ◽  
Extreme Rainfall Event ◽  
Selection Of

Recurring landslides in the Western Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most extreme rainfall parameters performed better than the other approaches. The results are useful in understanding the sensitivity of Intensity–Duration threshold models to some boundary conditions such as rain gauge selection, the intensity definition and the strategy of subdividing the area into independent alert zones. The results were discussed with perspective on a future application in a regional scale Landslide Early Warning System (LEWS) and on further improvements needed for this objective.

Exploration of WRF simulations of extreme rainfall in Egypt

2020 ◽  
Author(s):  
Ying Liu ◽  
Yiheng Chen ◽  
Otto Chen ◽  
Jiao Wang ◽  
Lu Zhuo ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Extreme Rainfall ◽  
Rainfall Event ◽  
Cumulus Convection ◽  
Extreme Rainfall Event ◽  
Optimal Model ◽  
Convection Scheme ◽  
Rainfall Distribution

<p>This research evaluates the performance of the Weather Research and Forecasting model (WRF-ARW, version 4.0) in simulating a regional extreme rainfall event over the Alexandria region of Egypt. Different domain configurations, spin-up times and physical schemes are explored to work out appropriate settings for using WRF in the region. Alexandria is an important economic region of the West Nile Delta that faces a growing climate crisis (e.g. rising temperature, rising sea level, increasing flooding) in recent decades, whilst inadequate coverage of in-situ rainfall observations (radars and rain gauges) makes the development of a hydrological early warning system very difficult. Although some researchers have conducted many WRF studies in countries with rich hydrological data, such as the United States and the United Kingdom, there are not many studies in exploring the ability of WRF to reproduce extreme weather events in countries with insufficient data like Egypt. Therefore, we carry out WRF sensitivity studies of an extreme rainfall event (occurred on 04 November 2015) in the Alexandria region to find out the optimal model configurations for Egypt and other similar areas.</p><p> </p><p>In this study, WRF was tested in five scenarios with different types of configurations. The model sensitivity was evaluated for: (1) domain size, (2) number of vertical levels, (3) horizontal resolution (nesting ratio), (4) spin-up times, (5) physical parameterisation schemes (MP, PBL, CU). During the entire screening process, the best configuration identified in each scenario will be adopted as the corresponding configuration in the following scenarios. All simulations used the newly developed ERA5 reanalysis dataset as the forcing data. Model simulations were verified at high temporal and spatial resolutions against the Global Precipitation Measurement data (GPM data). Seven objective verification metrics (POD, FBI, CSI, FAR, RMSE, MBE and SD) were used to calculate the performance of WRF simulations to identify the likely optimal model configurations.</p><p> </p><p>The sensitivity study shows that the rainfall distribution and magnitude are most sensitive to the spin-up time and physical schemes (especially the cumulus convection scheme). It is observed that the improvement of WRF's reproducibility of rainfall intensity may be accompanied by a decrease in the reproducibility of rainfall distribution. The most recommended configurations include three-level nesting (D01 80x80; D02 112x112; D03 88X88), 58 vertical levels, 1:3:3 (31.5, 10.5 and 3.5km) grid ratio, 48h spin-up time, WSM6 microphysics scheme, MYJ planetary boundary layer scheme, and Grell-Freitas cumulus convection scheme. Its hitting rate is 0.818, the false alarm rate is 0.088 and the rainfall mean bias error is -1.639. The knowledge gained in this study provides a useful foundation for developing a flood early warning system by linking WRF with WRF-Hydro.</p>

Updating and tuning a regional-scale landslide early warning system

Landslides ◽  
2012 ◽  
Vol 10 (1) ◽  
pp. 91-97 ◽  
Author(s):  
D. Lagomarsino ◽  
S. Segoni ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Landslide Early Warning

scholarly journals Establishment of urban waterlogging pre-warning system based on coupling RBF-NARX neural networks

2020 ◽  
Vol 82 (9) ◽  
pp. 1921-1931
Author(s):  
Ming Wei ◽  
Lin She ◽  
Xue-yi You
Keyword(s):  
Neural Networks ◽  
Early Warning ◽  
Early Warning System ◽  
Low Impact Development ◽  
Warning System ◽  
Extreme Rainfall ◽  
Area At Risk ◽  
Risk Area ◽  
Rainfall Process ◽  
Flooding Duration

Abstract The optimal layout of low-impact development (LID) facilities satisfying annual runoff control for low rainfall expectation is not effective under extreme rainfall conditions and urban waterlogging may occur. In order to avoid the losses of urban waterlogging, it is particularly significant to establish a waterlogging early warning system. In this study, based on coupling RBF-NARX neural networks, we establish an early warning system that can predict the whole rainfall process according to the rainfall curve of the first 20 minutes. Using the predicted rainfall process curve as rainfall input to the rainfall-runoff calculation engine, the area at risk of waterlogging can be located. The results indicate that the coupled neural networks perform well in the prediction of the hypothetical verification rainfall process. Under the studied extreme rainfall conditions, the location of 25 flooding areas and flooding duration are well predicted by the early warning system. The maximum of average flooding depth and flooding duration is 16.5 cm and 99 minutes, respectively. By predicting the risk area and the corresponding flooding time, the early warning system is quite effective in avoiding and reducing the losses from waterlogging.

scholarly journals Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system

2018 ◽  
Vol 18 (3) ◽  
pp. 807-812 ◽  
Author(s):  
Samuele Segoni ◽  
Ascanio Rosi ◽  
Daniela Lagomarsino ◽  
Riccardo Fanti ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Preliminary Investigation ◽  
Regional Scale ◽  
Back Analysis ◽  
Warning System ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

Abstract. We communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well.

scholarly journals SELECTION OF THE MOST EFFECTIVE BIOLOGICAL EARLY WARNING SYSTEM, BASED ON AHP AND REMBRANDT ANALYSIS

2019 ◽  
Vol 1 ◽  
pp. 95-102
Author(s):  
Joanna Chmist ◽  
◽  
Krzysztof Szoszkiewicz ◽  
Mateusz Hämmerling ◽  
◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Biological Early Warning System ◽  
Selection Of

scholarly journals Strengthening multi-hazards early warning system in the pacific through BMKG-UNESCAP collaboration pilot projects

2018 ◽  
Vol 229 ◽  
pp. 04019
Author(s):  
Asteria S. Handayani ◽  
Marjuki ◽  
Agie Wandala Putra ◽  
Urip Haryoko ◽  
Nurhayati ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Gap Analysis ◽  
Solomon Islands ◽  
Regional Scale ◽  
Warning System ◽  
Pilot Projects ◽  
Implementation Phase ◽  
The Pacific ◽  
High Level

On the period of September to December 2017, three pilot projects were implemented in Tonga, Papua New Guinea, and Solomon Islands aiming to strengthen the multi-hazards early warning system in the respective countries through close collaboration between the Indonesian Agency for Meteorology Climatology and Geophysics (BMKG) and United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). Main activities during the implementation phase were tailored based on gap analysis and risk assessments conducted beforehand. Thus, installation of high-resolution numerical weather, ocean wave, and climate prediction and forecasting tools were chosen to fill in the assessed gaps. These activities were incorporated with capacity building activities and high-level meetings with related stakeholders in disaster risk management using the concept of Fast-Leveraging-Easy-Economical-Sustain (FLEES). All three pilot projects had successfully proven to achieve their objectives by improving the capacities of National Meteorological Services in those three countries to produce multi-hazards early warning in higher resolution at a regional scale for disaster management in their respective countries.

scholarly journals Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2113 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Deekshith Pothuraju ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Antecedent Rainfall ◽  
Rainfall Forecasting ◽  
Study Region ◽  
Landslide Occurrence ◽  
South Indian ◽  
Landslide Early Warning

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D-0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

scholarly journals Technical Note: An operational landslide early warning system at regional scale based on space–time-variable rainfall thresholds

2015 ◽  
Vol 15 (4) ◽  
pp. 853-861 ◽  
Author(s):  
S. Segoni ◽  
A. Battistini ◽  
G. Rossi ◽  
A. Rosi ◽  
D. Lagomarsino ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Limited Area ◽  
Rainfall Thresholds ◽  
Rain Gauges ◽  
Starting Time

Abstract. We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity–duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds. To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently. An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface. The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

scholarly journals Brief communication: Using punctual soil moisture estimates to improve the performances of a regional scale landslide early warning system

2017 ◽  
Author(s):  
Samuele Segoni ◽  
Ascanio Rosi ◽  
Daniela Lagomarsino ◽  
Riccardo Fanti ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Back Analysis ◽  
Warning System ◽  
Threshold Value ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

Abstract. We improved a state-of-art RSLEWS (regional scale landslide early warning system) based on rainfall thresholds by integrating punctual soil moisture estimates. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are never expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods were substituted by soil moisture thresholds. A back analysis demonstrated that both approaches reduced consistently false alarms, while the second approach reduced missed alarms as well.

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