scholarly journals Preliminary Forecasting of Rainfall-Induced Shallow Landslides in the Wildfire Burned Areas of Western Greece

Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 877
Author(s):  
Spyridon Lainas ◽  
Nikolaos Depountis ◽  
Nikolaos Sabatakakis
Keyword(s):  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Shallow Landslide ◽  
Exceedance Probability ◽  
Landscape Characteristics ◽  
Burned Areas ◽  
Annual Probability ◽  
Geological Conditions ◽  
Landslide Forecasting ◽  
Western Greece

A new methodology for shallow landslide forecasting in wildfire burned areas is proposed by estimating the annual probability of rainfall threshold exceedance. For this purpose, extensive geological fieldwork was carried out in 122 landslides, which have been periodically activated in Western Greece, after the devastating wildfires that occurred in August 2007 and burned large areas in several parts of Western Greece. In addition, daily rainfall data covering more than 40 years has been collected and statistically processed to estimate the exceedance probability of the rainfall threshold above which these landslides are activated. The objectives of this study are to quantify the magnitude and duration of rainfall above which landslides in burned areas are activated, as well as to introduce a novel methodology on rainfall-induced landslide forecasting. It has been concluded that rainfall-induced landslide annual exceedance probability in the burned areas is higher when cumulative rainfall duration ranges from 6 to 9 days with local differences due to the prevailing geological conditions and landscape characteristics. The proposed methodology can be used as a basis for landslide forecasting in wildfire-affected areas, especially when triggered by rainfall, and can be further developed as a tool for preliminary landslide hazard assessment.

scholarly journals Landscape Design as a Tool to Reduce Soil Salinization: The Study Case of Keriya Oasis (NW China)

2019 ◽  
Vol 11 (9) ◽  
pp. 2578
Author(s):  
Jumeniyaz Seydehmet ◽  
Guang-Hui Lv ◽  
Abdugheni Abliz
Keyword(s):  
Traditional Knowledge ◽  
Land Reclamation ◽  
Sand Dunes ◽  
Soil Salinization ◽  
Landscape Design ◽  
Adaptive Landscape ◽  
Farmland Abandonment ◽  
Landscape Characteristics ◽  
Geological Conditions ◽  
Water Logging

Irrational use and management of water and land are associated with poor hydro-geological conditions causing water logging and salinization problems, possibly leading to farmland abandonment and economic loss. This poses a great challenge to the sustainability of oasis’ and requires desalinization through reasonable landscape design by multiple crossing studies so we collected traditional knowledge by field interviews and literature schemes, except for the modern desalinization approaches by literature, and we found that the salinization problem has been solved by traditional land reclamation, traditional drainage, natural drainage and flood irrigation, locally. It is worth mentioning that the traditional reclamation in salinized areas requires flood water, sand dunes and a salinized pit area; the sand dunes are used to elevate the pit surface, and water is used to leach salt from the soil. Natural drainage (the depth and width are 4–10 m and 50–100 m, respectively) caused by flash flooding has significant benefits to some salinized villages in the range of 3000–5000 m and ancient groundwater drainage systems, such as Karez are supporting the oasis with drainage water for centuries. In addition landscape characteristics, salinization and hydro-geological conditions of the oasis were studied from Landsat image, DEM, literature and field photos. Then based on the gathered information above, a desalinization model was developed to decrease the groundwater table and salt leaching in the water logging landscape. Then according to landscape characteristics, different desalinization approaches were recommended for different landscapes. To address environmental uncertainties, an adaptive landscape management and refinement approach was developed, and acceptance of the model was validated by stakeholder opinion. The results provide guidelines for sustainable desalinization design and highlight the importance of combining traditional knowledge and modern ecological principles in sustainable landscape design.

scholarly journals A susceptibility-based rainfall threshold approach for landslide occurrence

2019 ◽  
Vol 19 (4) ◽  
pp. 775-789 ◽  
Author(s):  
Elise Monsieurs ◽  
Olivier Dewitte ◽  
Alain Demoulin
Keyword(s):  
Landslide Susceptibility ◽  
Rainfall Threshold ◽  
Exceedance Probability ◽  
Equal Weight ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Continental Scale ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

Application of SIGMA model for landslide forecasting in Darjeeling Himalayas

2021 ◽  
Author(s):  
Samuele Segoni ◽  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan
Keyword(s):  
Sigma Model ◽  
Confusion Matrix ◽  
Regional Scale ◽  
Geographical Location ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Integrated System ◽  
Rainfall Thresholds ◽  
Skill Scores ◽  
Landslide Forecasting

<p>SIGMA (Sistema Integrato Gestione Monitoraggio Allerta – integrated system for management, monitoring and alerting) is a landslide forecasting model at regional scale which is operational in Emilia Romagna (Italy) for more than 20 years. It was conceived to be operated with a sparse rain gauge network with coarse (daily) temporal resolution and to account for both shallow landslides (typically triggered by short and intense rainstorms) and deep seated landslides (typically triggered by long and less intense rainfalls). SIGMA model is based on the statistical distribution of cumulative rainfall values (calculated over varying time windows), and rainfall thresholds are defined as the multiples of standard deviation of the same, to identify anomalous rainfalls with the potential of triggering landslides.</p><p>In this study, SIGMA model is applied for the first time in a geographical location outside of Italy, i.e. Kalimpong town in India. The SIGMA algorithm is customized using the historical rainfall and landslide data of Kalimpong from 2010 to 2015 and has been validated using the data from 2016 to 2017. The model was validated by building a confusion matrix and calculating statistical skill scores, which were compared with those of the state-of-the-art intensity-duration rainfall thresholds derived for the region.</p><p>Results of the comparison clearly show that SIGMA performs much better than the other models in forecasting landslides: all instances of the validation confusion matrix are improved, and all skill scores are higher than I-D thresholds, with an efficiency of 92% and a likelihood ratio of 11.28. We explain this outcome mainly with technical characteristics of the site: when only daily rainfall measurements from a spare gauge network are available, SIGMA outperforms other approaches based on peak measurements, like intensity – duration thresholds, which cannot be captured adequately by daily measurements. SIGMA model thus showed a good potential to be used as a part of the local Landslide Early Warning System (LEWS).</p>

scholarly journals Regional landslide forecasting in Piemonte (Italy) and in Norway: experiences from 2013 late spring

2017 ◽  
Author(s):  
Davide Tiranti ◽  
Graziella Devoli ◽  
Roberto Cremonini ◽  
Monica Sund ◽  
Søren Boje
Keyword(s):  
National Level ◽  
Daily Rainfall ◽  
Landslide Hazard ◽  
Weather Type ◽  
Late Spring ◽  
Large Area ◽  
Pressure System ◽  
Weather System ◽  
Landslide Forecasting ◽  
Hazard Assessments

Abstract. A few countries in the world operate systematically national and regional forecasting services for rainfall-induced landslides (i.e. shallow landslides, debris flows and debris avalanches), among them: Norway and Italy. In Norway, the Norwegian Water Resources and Energy Directorate (NVE) operates a landslide forecasting service at national level. A daily national hazard assessment is performed, describing both expected awareness level and type of landslide hazard for a selected warning region. In Italy, each administrative region has its own regional environmental agency (Regional Agency for Environmental Protection, ARPA) that is responsible of the daily landslide hazard assessments and emission of landslide warnings for one or more catchments within the region. One of these agencies, the ARPA Piemonte, is responsible for issuing landslide warnings for the Piemonte region, located in Northwestern Italy. Both services provide regular landslide hazard assessments founded on a combination of quantitative thresholds and daily rainfall forecasts together with qualitative expert analysis. Daily warning reports are published at http://www.arpa.piemonte.gov.it/rischinaturali and www.varsom.no. On spring 2013, the ARPA Piemonte, and the NVE issued warnings for hydro-meteorological hazards due to the arrival of a deep and large low-pressure system, called herein Vb cyclone. This kind of weather system is known to produce the largest floods in Europe. Less known is that the weather type can trigger landslides as well. In this study, we present the experiences acquired in late spring 2013 by NVE and ARPA Piemonte. From 27th April to 19nd May 2013, more than 400 mm rain in Piemonte caused severe floods and diffused landslides. In Norway, the same weather type lasted from 15th May to 2nd June 2013 and brought warm winds with high temperatures that caused intense snow melt over a large area, and brought a lot of rain in the Southeastern Norway, initiating large flood along Glomma river and several landslides. Floods and landslides produced significant damages to roads and railways along with buildings and other infrastructure in both countries.

scholarly journals Assessment of Rainfall Thresholds for Rain-Induced Landslide Activity in North Sikkim Road Corridor in Sikkim Himalaya, India

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Bappaditya Koley ◽  
Anindita Nath ◽  
Subhajit Saraswati ◽  
Kaushik Bandyopadhyay ◽  
Bidhan Chandra Ray
Keyword(s):  
Rainfall Intensity ◽  
Lower Boundary ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Rainfall Data ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Sikkim Himalaya ◽  
North East ◽  
The North

Land sliding is a perennial problem in the Eastern Himalayas. Out of 0.42 million km2 of Indian landmass prone to landslide, 42% fall in the North East Himalaya, specially Darjeeling and Sikkim Himalaya. Most of these landslides are triggered by excessive monsoon rainfall between June and October in almost every year. Various attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity – duration of antecedent rainfall models on global, regional and local scale for triggering of the landslide. This paper describes local aspect of rainfall threshold for landslides based on daily rainfall data in and around north Sikkim road corridor region. Among 210 Landslides occurring from 2010 to 2016 were studied to analyze rainfall thresholds. Out of the 210 landslides, however, only 155 Landslides associated with rainfall data which were analyzed to yield a threshold relationship between rainfall intensity-duration and landslide initiation. The threshold relationship determined fits to lower boundary of the Landslide triggering rainfall events is I = 4.045 D - 0.25 (I=rainfall intensity (mm/h) and D=duration in (h)), revealed that for rainfall event of short time (24 h) duration with a rainfall intensity of 1.82 mm/h, the risk of landslides on this road corridor of the terrain is expected to be high. It is also observed that an intensity of 58 mm and 139 mm for 10-day and 20-day antecedent rainfall are required for the initiation of landslides in the study area. This threshold would help in improvement on traffic guidance and provide safety to the travelling tourists in this road corridor during the monsoon.

scholarly journals Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1195 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Sai Kushal ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Early Warning ◽  
Sigma Model ◽  
Warning System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Integrated System ◽  
Warning Systems ◽  
Rainfall Forecasting ◽  
Rainfall Thresholds

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

scholarly journals Landslides, floods and sinkholes in a karst environment: the 1–6 September 2014 Gargano event, southern Italy

2017 ◽  
Vol 17 (3) ◽  
pp. 467-480 ◽  
Author(s):  
Maria Elena Martinotti ◽  
Luca Pisano ◽  
Ivan Marchesini ◽  
Mauro Rossi ◽  
Silvia Peruccacci ◽  
...  
Keyword(s):  
Southern Italy ◽  
Flash Floods ◽  
Temporal Information ◽  
Karst Area ◽  
Exceedance Probability ◽  
Karst Environment ◽  
Gargano Promontory ◽  
Landslide Forecasting ◽  
Landslide Early Warning ◽  
Torrential Rainfall

Abstract. In karst environments, heavy rainfall is known to cause multiple geohydrological hazards, including inundations, flash floods, landslides and sinkholes. We studied a period of intense rainfall from 1 to 6 September 2014 in the Gargano Promontory, a karst area in Puglia, southern Italy. In the period, a sequence of torrential rainfall events caused severe damage and claimed two fatalities. The amount and accuracy of the geographical and temporal information varied for the different hazards. The temporal information was most accurate for the inundation caused by a major river, less accurate for flash floods caused by minor torrents and even less accurate for landslides. For sinkholes, only generic information on the period of occurrence of the failures was available. Our analysis revealed that in the promontory, rainfall-driven hazards occurred in response to extreme meteorological conditions and that the karst landscape responded to the torrential rainfall with a threshold behaviour. We exploited the rainfall and the landslide information to design the new ensemble–non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the possible occurrence of rainfall-induced landslides and of related geohydrological hazards. The ensemble of the metrics produced by the E-NEP algorithm provided better diagnostics than the single metrics often used for landslide forecasting, including rainfall duration, cumulated rainfall and rainfall intensity. We expect that the E-NEP algorithm will be useful for landslide early warning in karst areas and in other similar environments. We acknowledge that further tests are needed to evaluate the algorithm in different meteorological, geological and physiographical settings.

scholarly journals Failure probability assessment of landslides triggered by earthquakes and rainfall: a case study in Yadong County, Tibet, China

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lixia Chen ◽  
Le Mei ◽  
Bin Zeng ◽  
Kunlong Yin ◽  
Dhruba Pikha Shrestha ◽  
...  
Keyword(s):  
Local Governments ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Permanent Displacement ◽  
Distribution Maps ◽  
Landslide Hazards ◽  
Threshold Distribution ◽  
2015 Gorkha Earthquake ◽  
Seismic Landslide

Abstract Yadong County located in the southern Himalayan mountains in Tibet, China, is an import frontier county. It was affected by landslides after the 2011 Sikkim earthquake (Mw = 6.8) and the 2015 Gorkha earthquake (Mw = 7.8). Casualties and property damage were caused by shallow landslides during subsequent rainfall on the earthquake-destabilized slopes. Existing researches have generally examined rainfall- and earthquake-triggered landslides independently, whereas few studies have considered the combined effects of both. Furthermore, there is no previous study reported on landslide hazards in the study area, although the area is strategically applicable for trade as it is close to Bhutan and India. This study developed a new approach that coupled the Newmark method with the hydrological model based on geomorphological, geological, geotechnical, seismological and rainfall data. A rainfall threshold distribution map was generated, indicating that the southeast part of Yadong is prone to rainfall-induced landslides, especially when daily rainfall is higher than 45 mm/day. Permanent displacement predictions were used to identify landslide hazard zones. The regression model used to calculate these permanent displacement values was 71% accurate. Finally, landslide probability distribution maps were generated separately for dry and wet conditions with rainfall of varying intensities. Results can serve as a basis for local governments to manage seismic landslide risks during rainy seasons.

Spatial and seasonal distribution of rainfall erosivity in Australia

Soil Research ◽  
2002 ◽  
Vol 40 (6) ◽  
pp. 887 ◽  
Author(s):  
Hua Lu ◽  
Bofu Yu
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Seasonal Distribution ◽  
Rainfall Erosivity ◽  
Continental Scale ◽  
R Factor ◽  
Monthly Distribution ◽  
The Mean

Spatially distributed rainfall erosivity and its seasonal distribution are needed to use the revised universal soil loss equation (RUSLE) for erosion risk assessment at large scale. An erosivity model and 20-year daily rainfall data at 0.05° resolution were used to predict the R-factor and its monthly distribution for RUSLE in Australia. Predicted R-factor values were compared with those previously calculated using pluviograph data for 132 sites around Australia. The daily erosivity model was further evaluated for 43 sites where long-term pluviograph data were available. Predicted and calculated monthly distributions of the R-factor were compared for these 43 sites. For the 132 sites where R-factor values were compiled from previous investigations, the model efficiency was 0.81 with root mean squared error (rmse) of 1832 MJ.mm/(ha.h.year), or 47.5% of the mean for the 132 sites. For the additional 43 sites, the coefficient of efficiency was 0.93 with a 12.7 mm rainfall threshold, and 0.94 when all storms were included in the calculations. The rmse was 908 MJ.mm/(ha.h.year), or 28.6% of the mean for the 43 sites with a zero rainfall threshold. The prediction error for monthly distribution of the R-factor was 2.3% with a zero threshold and 2.5% with 12.7�mm threshold. This and previous studies have shown that the daily rainfall erosivity model can be used to accurately predict the R-factor and its seasonal distribution in Australia. Digital maps were produced showing the spatial and seasonal distribution of the R-factor at 0.05° resolution in Australia. These maps have been used to assess rill and sheet erosion rate at the continental scale.

scholarly journals Probabilistic and Economical Design of Roof Top Rainwater Harvesting Tank by Simulation Technique

2018 ◽  
pp. 1-17
Author(s):  
J. Ramachandran ◽  
V. Ravikumar
Keyword(s):  
Rainwater Harvesting ◽  
Design Method ◽  
Daily Rainfall ◽  
Daily Basis ◽  
Annual Runoff ◽  
Balance Model ◽  
Graphical Form ◽  
Exceedance Probability ◽  
Toilet Flushing ◽  
Economical Design

Introduction: Rainwater harvesting is the collection of rainwater directly from the surface(s) it falls on. Rainwater harvesting through collection tank is an effective method. Numerous methods are available for determining the size of the storage capacity required to satisfy a given demand. These methods vary in complexity and sophistication. Methods: The tank design method includes general thumb rule (5% of annual runoff), sequential peak analysis (simulating twice the length of the record), optimization (best one that suits objective criteria), simulation, probabilistic and economical design. Simulation water balance model which works on daily basis, normal probability distribution and economics are used in designing the capacity of tanks and it is presented in a graphical form. The tanks are designed for two different purposes like domestic use and toilet flushing only. Place and Data: Trichy city daily rainfall records from 1951-2011 is used. If a person living in Trichy city wants to construct a rainwater harvesting tank for toilet flushing purpose (6 Nos * 25l = 150l demand per day), the graphs can used. Results: At a chosen exceedance probability (EP) of failure (how much time the tank fails to supply water), the engineer can decide the storage size under a preset deficit rate and also the cost of each tanks (per 1000 l) from the curves generated in this study. These relationships can be used by engineers in the design process.

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