Estimation Of Rainfall Threshold Research Articles

Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation

Flood warning systems can provide an effective and low-cost solution to protect urban residents especially in developing countries suffering from severe flood damages caused by weak capacity of flood defense structures. Rainfall Threshold for Flood Warning (RTFW) is an essential key of accurate flood warning system. This study proposed an easy-to-use mathematical equation for RTFW for small urban watersheds based on computer simulations. First, a coupled 1D–2D dual-drainage model was used to simulate the flooded area in nine watersheds of Seoul, Korea corresponding to 540 scenarios of various synthetic rainfall events and watershed imperviousness. Then, the results of the 101 simulations that caused the critical flooded depth (0.25 m–0.35 m) were used to develop the equation that relates the value of RTFW to the rainfall event temporal variability (represented as coefficient of variation or CV) and the watershed imperviousness (represented as NRCS Curve Number or CN). The results suggest that (1) RTFW exponentially decreases as the rainfall CV increases; (2) RTFW linearly decreases as the watershed CN increases; and that (3) RTFW is dominated by CV when the rainfall has low temporal variability (e.g., CV < 0.2) while RTFW is dominated by CN when the rainfall has high temporal variability (e.g., CV > 0.4). For validation, the proposed equation was applied for the flood warning system with two storm events occurred in 2010 and 2011 over 239 watersheds in Seoul. The system showed the hit, false and missed alarm rates at 69% (48%), 31% (52%) and 6.7% (4.5%), respectively for the 2010 (2011) event.

Estimation of rainfall threshold for the early warning of shallow landslides along National Highway-10 in Darjeeling Himalayas

The Darjeeling Himalayan region receives heavy rainfall during the monsoon and as a result is known for having many shallow landslides. These monsoon-induced landslides cause massive disruption to traffic on the National Highway-10 (NH-10), which serves as a life-line for the area. Developing an early warning system for landslide occurrence can contribute to minimize the risk. In the present study, an attempt is made to estimate the rainfall threshold for initiation of landslides which may help in developing an early warning system for rainfall-induced shallow landslides. To work out the threshold for rainfall in triggering landslides, a rainfall intensity-duration model has been developed for the study area. Daily rainfall data were obtained from the India Meteorological Department (IMD) rain gauge stations and adjacent tea gardens along NH-10, whereas landslide event-related information was obtained from the Public Works Department (PWD), newspapers, and earlier publications. The study was carried out separately on four lithostratigraphic units along NH-10 between 2011 and 2018. Over this time, 288 rainfall events were reported which are responsible behind triggering 681 landslides along this road corridor. The overall threshold for the entire NH-10 has been estimated as I = (20.10 ± 1.84)D(−0.45 ± 0.05), where I is the rainfall intensity (mm/d) and D is the event duration (d). The result shows that the area with a slate lithology is more susceptible to landslides than in areas with other rock types. Along the NH-10 corridor in general, over a cumulative rainfall event of 3 or 5 days before the failure, the daily rainfall on the final day is effective in triggering about 17% and 8% of landslides, respectively.

Challenges in urban stormwater management in Chinese cities: A hydrologic perspective

For managing the worsening urban water disasters in China, the Government of China proposed the concept of “Sponge City” in 2013 and initiated the strategy in 30 pilot cities from 2015. Despite the promise of the concept, there have been many challenges in implementing the “Sponge City” program (SCP). In this manuscript, we discuss the hydrology-related challenges in implementing the SCP. In particular, we consider two key challenges: (1) Determination of the “Volume Capture Ratio of Annual Rainfall” (VCRAR), as controlling urban stormwater runoff is one of the core targets of the SCP; and (2) Estimation of a proper rainfall threshold, which influences the layout of green-infrastructures in the SCP to achieve the core VCRAR target. To discuss these challenges, we consider the city of Beijing, the capital of China, as a case study. Our analysis shows that the trade-offs between the investment for the SCP and its potential economic benefits should be considered by undertaking a proper determination of VCRAR. The VCRAR estimated for Beijing from the present analysis is 0.73. This value is more reasonable than the empirical value of 0.80 that is presently used, as it can guarantee the positive rate of return on the investment. We also find that the nonstationary characteristics of rainfall data and their spatiotemporal differences are important for the estimation of the rainfall threshold in SCP. For instance, even using the daily rainfall data over a period of 30 years (1983–2012) in Beijing, as required by the National Assessment Standard, the estimated rainfall threshold of 27.3 mm underestimates the reasonable rainfall threshold that should at least be larger than 30.0 mm. Thus, the former cannot ensure the VCRAR target of 0.80. Based on these results, we offer proper approaches and key suggestions towards useful guidelines for delivering better SCP in the Chinese cities.

Estimation of rainfall thresholds for shallow landslides in the Sierra Madre Oriental, northeastern Mexico

Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico, causing significant damage to infrastructure. In this work, we have studied the connection between rainfall and landslides in the Santa Rosa Canyon, a catchment located in the northeastern Mexico. A landslide database triggered by major storms and hurricanes that have hit the region over the past 30 years was analyzed. A total of 92 rainfall events in the Santa Rosa Canyon were studied to determine the amount of precipitation needed to trigger shallow landslides. For each event the duration (D, in hours) and the cumulated rainfall event (E, in mm) were determined by using historical rainfall data from weather stations located near the study area. We have proposed an ED threshold for rainfall-induced landslides with durations 0.5 < D < 120 hours to address the conditions that trigger these events in the study area. On analyzing the obtained threshold, it has been established that almost 60 mm of a daily rainfall accumulation is required to trigger shallow landslides in the study area. This estimation is consistent with other calculations made for areas close to the Santa Rosa Canyon. Finally, we validated the predictive capability of the threshold with a different set of rainfall data that did not result in landslides performing a straightforward receiver operating characteristic analysis. A good approach was obtained, especially for rainfall events with daily measurements. Results could be used as input information in the design of a landslide early warning system for the northeastern Mexico, and replicated for other landslide prone areas in the region.

An empirical equation to determine the threshold for rainfall-induced landslides developing to debris flows

This study derives an empirical equation to determine the rainfall threshold that triggers landslides and subsequent debris flows. In this research, the contribution of different factors, especially various sediment layer thicknesses, was assessed on the landslide leading to debris flows. Bed slope, sediment thickness, sediment mean diameter, sediment layer length, rainfall intensity, and time of landslide occurrence were selected as the effective factors. Rainfall simulator device was used to create rainfall on the sediment sample. The effect of these factors on the landslide which leads to debris flow occurrence was investigated using dimensional analysis of experimental data. Then, linear and power regression models were tested by 30% of the total experimental data to estimate the rainfall-induced landslide. Determination coefficient (R2), mean absolute relative error (MARE), and Akaike information criterion (AIC) were applied to determine the best equation. Results revealed that landslide often occurs at a rapid rate in the thicker deposits than the thinner one. The evaluation of linear and power equations demonstrated that two-variable power model was the best empirical equation for estimation of rainfall threshold in initiation of landslide and debris flows. The evaluation criteria including MARE, R2, and AIC were estimated 0.044, 0.984, and − 25.93, respectively. In this equation, bed slope and the ratio of the time of landslide occurrence to the sediment layer length were used as the effective factors. This study can be used for assessment of the threshold of rainfall-induced landslide which finally leads to debris flow hazard.

Determination of cumulative rainfall threshold trigger of landslides in Grindulu watershed as an early warning effort

Landslides are one of the natural disasters that cause severe damage to life. The frequency of landslide has increased with global climate change and population growth, so early warning efforts are needed. Rain is a trigger factor for landslides can be used as an approximate model for disaster prevention and mitigation through the estimation of rainfall threshold. The purpose of this study was to determine the characteristics and threshold of rainfall that triggered landslides in the Grindulu watershed. The method used is the Cumulative Rainfall Threshold (CT), this method compares the amount of rainfall for the last 3 days (72 hours) with rainfall 15 days before. In the Grindulu watershed, landslides occur during 3-day cumulative daily rains ranging from 23-464 mm, whereas in the 15 days before the events range between 67-756.5 mm. Estimates of lower threshold rainfall are determined linear so that the equation P3 = 0.4675 P15-46.9 is obtained with the value R 2 = 0.5774The threshold value of rainfall triggers a landslide in the Grindulu watershed when the cumulative rainfall of 3 days is 40 mm and 15 days before the event is 320 mm. The determination of rain threshold is expected to increase community preparedness for landslides.

Probabilistic Rainfall Thresholds for Shallow Landslides Initiation – A Case Study from the Nilgiris District, Western Ghats, India

Rainfall is one of the major causes of landslides/landslips across the globe. The fatalities and damage caused by rainfall induced landslides increased in recent days. The Nilgiris district in Western Ghats part of Tamil Nadu state is one of the very high to severe landslide hazard-prone areas of India. The present study is focused on estimation of rainfall thresholds and the temporal probability of landslides in different landslide-prone slopes in part of The Nilgiris district. The landslide prone areas identified in earlier research are used for the present study. The landslide locations data for the years 1824 to 2018 were collected and a spatial database on landslide inventory was created. A detailed inventory carried out on the 2009 landslides were analysed and used for the calculation of rainfall thresholds. Monthly and Yearly Rainfall data for the years 2000 to 2011 were collected for 37 rainguage stations from various government agencies. Based on the quality and quantity of data, the rainfall thresholds for 14 different locations were estimated viz., Aderly, Coonoor, Coonoor Railway, Governor Sola, Ooty (Near Botanical Garden), Runnymedu, Burliar where the probability of landslide occurrences is high. The temporal probability of landslide was calculated for four years viz., 1, 3, 5 and 10 years. The present study can be used as a key to develop an early warning system in The Nilgiris District.

Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India

The Indian Himalayan locale has been essentially influenced by the increase in the frequency of landslide events. Out of 0.42 million km2 of India’s landmass prone to landslides, 42% falls in the North East Himalaya, especially Darjeeling and Sikkim. The harm due to landslides is massive, causing loss of life, property and agricultural land, thus initiating a dire need for formulating strategies to minimize its impact. There have been many attempts to establish rainfall thresholds on global, regional and local scales which compare analysis at various levels. Rainfall thresholds anticipate landslide occurrence and help in issuing a warning to civil authorities and the general population. However, empirical relations defining the relationship between landslide occurrences and rainfall events in Kalimpong remain unattended. In this paper, rainfall thresholds for landslide occurrence have been ascertained for Kalimpong area of Darjeeling Himalayas, in the Indian province of West Bengal. A threshold for landslide occurrences which describes intensity–duration threshold was estimated using the power law equation. The relationship for the study area is I = 3.52 D−0.41 (I is rainfall intensity (mm/h) and D is duration (h)). Results show that events with a rainfall intensity of 0.95 mm/h with a duration of 24 h have a high risk of slide initiation in this region. It also demonstrates that for 10- and 20-day antecedent rainfall, an intensity of 88.37 and 133.5 mm is required for landslide occurrence in this region. Such data would help in implementing early warning systems that focus on rainfall thresholds and forecasting rainfall measurements. Rainfall thresholds for landslide initiation in Kalimpong can be enhanced with more precipitation and landslide data as and when available.

A probabilistic model for evaluating the reliability of rainfall thresholds for shallow landslides based on uncertainties in rainfall characteristics and soil properties

This study aims to develop a probabilistic rainfall threshold estimation model for shallow landslides (PRTE_LS) in order to quantify its reliability while being affected by uncertainties in the rainfall characteristics and soil properties. The rainfall characteristics include the rainfall duration, depth and storm patterns in which their uncertainties result from temporal variation. The effective cohesion of soil, the unit weight of soil, the angle of internal friction, hydraulic conductivity and hydraulic diffusivity are the soil properties represented as the soil parameters in the TRIGRS model in which uncertainties are attributed to spatial variation. After analyzing the sensitivity of rainfall characteristics and TRIGRS parameters to the estimation of rainfall thresholds, the maximum rainfall intensity, storm pattern and soil parameters (soil cohesion, soil friction angle and total unit weight of soil) are retreated as uncertainty factors used in the model development. The proposed PRTE_LS model is used for the reliability assessment of the issued rainfall thresholds in the Jhoukou River watershed, southern Taiwan, to demonstrate its applicability. The results indicate that the corresponding exceedance probability (i.e., underestimated risk) approximates 0.1 on average. In other words, its reliability reaches 0.9. However, issued rainfall thresholds with high reliability might hardly achieve the goal of early warning because shallow landslides can possibly happen before the actual rainfall amount exceeds the threshold. Consequently, the proposed PRTE_LS model can modify issued rainfall thresholds with the specific occurrence probability under the critical safety factors and warning durations being considered. As a result, the proposed PRTE_LS can not only quantify the reliability of the issued rainfall thresholds, but its results can also be referred to in modifying the issued thresholds in order to enhance the early-warning performance.

Semi-quantitative landslide risk assessment using GIS-based exposure analysis in Kuala Lumpur City

ABSTRACTA semi-quantitative landslide-risk assessment method, which would provide a spatial estimate of future landslide risks in a densely populated area in Kuala Lumpur City, was presented in this study. This work focused on detail risk assessment by identifying the number of elements at risk. A medium-scale analysis was performed using geospatial based techniques. The estimation of rainfall threshold and the landslide hazard map used in the current work are obtained from the previous literature published by the same authors. Subsequently, the vulnerability value was generalized, and then a valid integration between elements at risk and the hazard map was conducted to determine the expected number of elements that would likely be under direct risk. Results showed that the approximate number of predicted affected elements per pixel, as a percentage of the settlement unit, is nearly 50% in residential areas, 35% in commercial buildings, 31% in industrial buildings, 31% in utility areas, and 18% in densely populated areas. Similarly, a significant percentage of predicted losses (27%) were found for the road network. The results showed the capability of the method to approximately predict the number of infrastructure elements and the population density under landslide risk in data-scarce environments.

Susceptibility mapping and estimation of rainfall threshold using space based input for assessment of landslide hazard in Guwahati city in North East India

Abstract. Slopes are the most common landforms in North Eastern Region (NER) of India and because of its relatively immature topography, active tectonics, and intense rainfall activities; the region is susceptible to landslide incidences. The scenario is further aggravated due to unscientific human activities leading to destabilization of slopes. Guwahati, the capital city of Assam also experiences similar hazardous situation especially during monsoon season thus demanding a systematic study towards landslide risk reduction. A systematic assessment of landslide hazard requires understanding of two components, "where" and "when" that landslides may occur. Presently no such system exists for Guwahati city due to lack of landslide inventory data, high resolution thematic maps, DEM, sparse rain gauge network, etc. The present study elucidates the potential of space-based inputs in addressing the problem in absence of field-based observing networks. First, Landslide susceptibility map in 1 : 10,000 scale was derived by integrating geospatial datasets interpreted from high resolution satellite data. Secondly, the rainfall threshold for dynamic triggering of landslide was estimated using rainfall estimates from Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis. The 3B41RT data for 1 hourly rainfall estimates were used to make Intensity-Duration plot. Critical rainfall was estimated for every incidence by analysing cumulative rainfall leading to a landslide for total of 19 incidences and an empirical rainfall intensity-duration threshold for triggering shallow debris slides was developed (Intensity = 5.9 Duration-0.479).

Estimation of rainfall threshold and its use in landslide hazard mapping of Kuala Lumpur metropolitan and surrounding areas

This paper presents rainfall-induced landslide thresholds and predicts landslide hazard in Kuala Lumpur metropolitan city and surrounding areas. Landslide events from 2000 to 2012 were collected. The long and short antecedent rainfall days were prepared for landslide and non-landslide days simultaneously. First, threshold analysis was conducted by using data obtained from rainfall stations located in highly urbanized areas of Kuala Lumpur metropolis. Six rainfall gauges were selected, and the study area was divided into six zones according to rainfall gauges: Taman Desa Station (TD-KL), Genting Klang (GK-KL), LDG Edinburgh Station (LDGE-KL), SG Raya Hulu Langat Station (SRHL-Slg), Puchong Drop Station (PD-Slg), and Bukit Antarabangsa (BTA-Slg). After the threshold analysis was conducted for different periods (10, 15, and 30 days) in each station, reliability index test was conducted to optimize the best threshold that limits the predicted events along the study period for each region. Second, the threshold analysis results were used as input in the Poisson probability model to estimate landslide temporal probability (P T). Third, the spatial probability (P S) analysis was prepared by using the evidential belief function multiplied by the P T to obtain the hazard maps for 1-, 3-, and 5-year scenarios. Finally, a validation process was conducted to test the prediction performance of the resultant hazard map for a 1- and 2-year prediction by using the landslide inventory of 2012 to early 2014, which was not included in the modeling of the hazard map. Results showed a valid correlation between the high and moderate hazardous areas for the six zones. The predicted hazard maps indicated a quantitative assessment of the prone areas and proved to be a valid disaster management tool. The produced hazard maps may play a vital role as input component in risk analysis.

Estimation of rainfall thresholds triggering shallow landslides for an operational warning system implementation

The Piemonte regional warning system service, managed by the Environmental Protection Agency of Piemonte (“ARPA Piemonte” as official Italian acronym), is based on an advanced meteo-hydrological automatic monitoring system, and it is integrated with forecasting activities of severe weather-related natural hazards. At present, a meteo-hydrological chain is operated to provide flood forecasting on the main river pattern. The development of a forecasting tool for shallow landslides triggered by heavy rainfall is presented. Due to the difficulties in modelling shallow landslides triggering in a large and complex area like the Piemonte region, an empirical model is developed on the basis of the correlation between rainfall and previous landslides in historical documents. The research focuses on establishing rainfall thresholds for landslides triggering, differentiating the critical rainfall values through a geological characterisation of the different territories. The period from 1990 to 2002 is considered. A total number of 160 landslides with hourly information and time of triggering are used to calibrate the system. As a first outcome, two different zones have been identified: (1) zones in alpine environments, principally characterised by a bedrock composed of metamorphic rocks, igneous rocks, dolostones or limestones that require high values of critical rainfall and (2) zones in hilly environments, principally characterised by sedimentary bedrock that require low values of critical rainfall. Verification has been performed on a total number of 429 landslides with known date of occurrence. The results show a good agreement with the model with no missed alarms and a very low number of false alarms, thus suggesting an effective operational implementation.