Effect Of Antecedent Rainfall Research Articles

A precipitation downscaling framework for regional warning of debris flows in mountainous areas

A timely warning system for debris-flow mitigation in mountainous areas is vital to decrease casualties. However, the lack of rainfall monitoring stations and coarse resolution of satellite-based observations pose challenges for developing such a debris-flow warning model in data-scarce areas. To offer an effective method for the generation of precipitation with fine resolution, a machine learning (ML)-based approach is proposed to establish the relationship between precipitation and regional environmental factors (REVs), including normalized difference vegetation index (NDVI), digital elevation model (DEM), geolocations (longitude and latitude) and land surface temperature (LST). This approach enables the downscaling of 3B42 TRMM precipitation data, providing fine temporal and spatial resolution precipitation data. We use PERSIANN-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) data to calibrate the downscaled results using geographical differential analysis (GDA) before applying the calibrated results in a case study in the Gyirong Zangbo Basin. After that, we calculate the rainfall thresholds of effective antecedent rainfall (Pe)—intraday rainfall (Po) based on the calibrated precipitation and integrate these thresholds into a susceptibility map to develop a debris-flow warning model. The results show that (1) this ML-based approach can effectively achieve the downscaling of TRMM data; (2) calibrated TRMM data outperforms the original TRMM and downscaled TRMM data, reducing deviations by 55% and 57%; (3) the integrated model, incorporating rainfall thresholds, outperforms a single susceptibility map in providing debris-flow warnings. The developed warning model can offer dynamic warnings for debris flows that may have been missed by the original warning system at a regional scale.

A Physically Based Model for the Sequential Evolution Analysis of Rainfall‐Induced Shallow Landslides in a Catchment

AbstractPredicting the occurrence time, volume, distribution and run‐out length of shallow landslides is critical for assessing the volume of debris flow in a catchment. Recent studies suggest that the landslides hydro‐mechanical triggering (LHT) model based on threshold‐based mechanical interactions can adequately predict rainfall‐induced landslides. However, this model assumes that the soil‐rock interface is the only sliding surface and cannot dynamically determine a sliding surface above the soil‐rock interface. Therefore, by calculating the wetting front depth in the shallow soil layer and using the limit analysis method, the most likely sliding surface can be dynamically calculated to improve the model. Through linking the Soil and Water Assessment Tool (SWAT, USDA) model and the depth‐resolved LHT model (D‐LHT), a framework for predicting shallow landslides in a catchment was proposed in this work. This framework considers the effects of antecedent rainfall, the mechanical reinforcement of roots, and the spatial distribution of soil properties on slopes. The D‐LHT model was applied to the Jiangjia gully in Yunnan, China, to adequately predicted the occurrence time, scale and spatial distribution of shallow landslides. In addition, the present and antecedent rainfall effects on the shallow landslide volume were analyzed. The results showed that the average soil moisture content threshold was determined by antecedent and same‐day rainfall, which affect the timing and volume of the shallow landslides. This study provides a new method for predicting shallow landslides. The stability and simplicity of the model make it suitable for early warning systems.

MPM-based mechanism and runout analysis of a compound reactivated landslide

MPM-based mechanism and runout analysis of a compound reactivated landslide

Revised Rainfall Threshold in the Indonesian Landslide Early Warning System

Landslides are one of the most disastrous natural hazards that frequently occur in Indonesia. In 2017, Balai Sabo developed an Indonesia Landslide Early Warning System (ILEWS) by utilizing a single rainfall threshold for an entire nation, leading to inaccuracy in landslide predictions. The study aimed to improve the accuracy of the system by updating the rainfall threshold. We analyzed 420 landslide events in Java with the 1-day and 3-day effective antecedent rainfall for each landslide event. Rainfall data were obtained from the Global Precipitation Measurement (GPM), which is also used in the ILEWS. We propose four methods to derive the thresholds: the first is the existing threshold applied in the Balai Sabo ILEWS, the second and third use the average and minimum values of rainfall that trigger landslides, respectively, and the fourth uses the minimum value of rainfall that induces major landslides. We used receiver operating characteristic (ROC) analysis to evaluate the predictability of the rainfall thresholds. The fourth method showed the best results compared with the others, and this method provided a good prediction of landslide events with a low error value. The chosen threshold was then applied in the Balai Sabo-ILEWS.

On inferring population trends of mobile waterbirds from aerial transect surveys in variable environments

Monitoring waterbird populations in Australia is challenging for reasons of counting logistics, and because population aggregation and dispersion can shift rapidly in response to large spatio-temporal variations in resource availability. The East Australian Waterbird survey has conducted annual, aerial, systematic counts of waterbirds over eastern Australia for almost 40 years. It was designed to monitor waterbird populations using design-based inference though for many species this form of inference appears inadequate in the face of these challenges. Here we develop a state-space model-based Bayesian approach that, in addition to explicitly incorporating process noise and observation uncertainty, uses random effects and rainfall-derived covariates to model the year-to-year variation in the proportion of the total (super) population that is present on surveyed wetlands, and available to be counted. We use this model-based approach to estimate the superpopulation size of 45 waterbird species annually, and model the rate of population increase as a function of antecedent rainfall. The results confirm the strong positive effect of antecedent rainfall on population growth rates for nearly all species, and illustrate that species respond to rainfall differently in terms of habitat use, which influences whether they are present on surveyed wetlands. For many species, the year-to-year variation in the estimated proportion of the population on surveyed wetlands is very high. The results have implications for making inferences on population trends from these data, with the ability to model the year-to-year sampling variation a key requirement before the rate of population increase can be estimated with any precision. This study illustrates how to progress this approach, and infers that under average rainfall conditions, the general trend is for estimated superpopulation rates of increase to be negative, though for only a few species is this occurring with strong belief.

Threshold rainfall ranges for landslide occurrence in Matara district of Sri Lanka and findings on community emergency preparedness

The objective of this research was to identify the effects of antecedent rainfall on the probability of landslide occurrence and to analyze the knowledge of the community on Emergency Preparedness. In this study, recent major landslides events were studied. The rainfall relevant to landslide locations was extracted using a GIS-based interpolation. From the daily rainfall of two days prior to landslide event (i.e. Day 3) till the daily rainfall of landslide day (Day 1), there was an identifiable pattern. There is more than 90% chance for a landslide to occur, when the rainfall of Day 1, or the day of the landslide event, is close to 300 mm or above with a rainfall of ~80 mm or above on the previous day (Day 2); i.e. There is more than 94% chance for a landslide to occur when the total rainfall received on Day 1 and Day 2 exceeds 375 mm. The majority of the events (>94%) had a daily rainfall of ~80 mm or above on Day 3. The cumulative rainfall of the landslide day up to three days prior to the landslide event shows a significant pattern (Day 1-Day 4). Overall, when there is a rainfall event of ~80 mm or over, people need to be warned/cautioned about a potential landslide within the next three days, if the rainfall continues to be high. Social survey shows that during 2017, people were more prepared compared to 2003, but still there are many steps to be taken in terms of preparedness.

Effect of Antecedent Rainfall on Slope Stability- A Case Study of Haflong Hill, North East India

Abstract The accurate determination of factor of safety is important to find out the stability of slopes. Out of the many factors which are responsible for slope failure in hilly areas continuous rainfall, particularly, is one of the major important factor responsible for triggering landslide. Slope is an exposed ground surface that stands at an angle with the horizontal. In this study, a part of the hill of Haflong, the head quarter of Dima Hasao district in the state of Assam, India are chosen. Two types of transient analysis i.e., un-coupled analysis and coupled analysis were carried out. The coupled analysis considers the deformation of the soil surface with time when rain water passes through the soil mass. Three slope angles 30°, 45° and 90° were considered for both the case. The results shows that the factor of safety decreases with increase in the period of rainfall and slope angle, whereas the factor of safety increases slightly during coupled analysis which is due to the combined effect of deformation and seepage.

Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects

Abstract. Many studies have shown that downstream flood regimes have been significantly altered by upstream reservoir operation. Reservoir effects on the downstream flow regime are normally performed by comparing the pre-dam and post-dam frequencies of certain streamflow indicators, such as floods and droughts. In this study, a rainfall–reservoir composite index (RRCI) is developed to precisely quantify reservoir impacts on downstream flood frequency under a framework of a covariate-based nonstationary flood frequency analysis using the Bayesian inference method. The RRCI is derived from a combination of both a reservoir index (RI) for measuring the effects of reservoir storage capacity and a rainfall index. More precisely, the OR joint (the type of possible joint events based on the OR operator) exceedance probability (OR-JEP) of certain scheduling-related variables selected out of five variables that describe the multiday antecedent rainfall input (MARI) is used to measure the effects of antecedent rainfall on reservoir operation. Then, the RI-dependent or RRCI-dependent distribution parameters and five distributions, the gamma, Weibull, lognormal, Gumbel, and generalized extreme value, are used to analyze the annual maximum daily flow (AMDF) of the Ankang, Huangjiagang, and Huangzhuang gauging stations of the Han River, China. A phenomenon is observed in which although most of the floods that peak downstream of reservoirs have been reduced in magnitude by upstream reservoirs, some relatively large flood events have still occurred, such as at the Huangzhuang station in 1983. The results of nonstationary flood frequency analysis show that, in comparison to the RI, the RRCI that combines both the RI and the OR-JEP resulted in a much better explanation for such phenomena of flood occurrences downstream of reservoirs. A Bayesian inference of the 100-year return level of the AMDF shows that the optimal RRCI-dependent distribution, compared to the RI-dependent one, results in relatively smaller estimated values. However, exceptions exist due to some low OR-JEP values. In addition, it provides a smaller uncertainty range. This study highlights the necessity of including antecedent rainfall effects, in addition to the effects of reservoir storage capacity, on reservoir operation to assess the reservoir effects on downstream flood frequency. This analysis can provide a more comprehensive approach for downstream flood risk management under the impacts of reservoirs.

Rainfall threshold for landslide activity in Dazhou, southwest China

Dazhou is one of the landslide-prone areas in southwest China. Since rainfall is the primary factor responsible for slope instability in this region, empirical rainfall thresholds may be effective for landslide warning. Thresholds using the intraday rainfall and the effective antecedent rainfall up to 7 days prior to landslide occurrence have been defined for this area. The study region was divided into 6 alert zones. For each zone, the daily rainfall and landslide data in the monsoon season during the period of 2003–2010 were analyzed, and the logistic regression method was employed to fit the dataset and provide diverse probability thresholds. Subsequently, the optimal threshold was identified by receiver operating characteristic (ROC) analysis. Furthermore, two classes of criteria and the associated indicators were used to determine the three rainfall thresholds for the warning model with four classes of alert (i.e., no warning, moderate warning, high warning, and very high warning). Finally, these thresholds were validated with dataset for the period 2011–2015. It suggests that the optimal threshold derived from ROC analysis, with the probability ranging from 4 to 10%, is suitable for issuing moderate warning, while the probability corresponding to the thresholds for high warning and very high warning varies in the ranges 30–55% and 75–95%, respectively. Performance of these thresholds in the period 2011–2015 is comparable to that in the period 2003–2010, indicating the robustness of them and the potential for practical use.

Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India

Sikkim, one of the Northeastern states of India, is a famous tourism spot in the Himalayas with dynamic population density. This mountainous area receives heavy rainfall and is well known for frequent shallow landslides, especially, Chandmari, which is a village, situated in Gangtok area in East Sikkim. Even though it is well known that rainfall and landslides are correlated, Sikkim lacks a well-established landslide early warning system. Such a system is important in this region because it is one of the highest landslide-prone areas in India. The current research attempts to establish rainfall thresholds as part of developing an efficient landslide early warning system for this region. The rainfall thresholds for landslides are derived based on the daily rainfall data available from India Meteorological Department (IMD) for six stations in Sikkim. Analysis of daily rainfall data and landslide events in this area between the year 1990 and 2017 is performed. An intensity–duration (I–D)-based regional rainfall threshold is derived as I = 43.26 D−0.78 (I = rainfall intensity in mm/day and D = duration in days) for the rainfall-triggered landslides in Sikkim region and a local threshold of I = 100 D−.92 was developed for the Gangtok area. Furthermore, the influence of antecedent rainfall in landslide initiation is explored by considering the daily, 3-day, 5-day, 7-day, and 20-day cumulative rainfall values associated with landslides. The proposed threshold equations and study of the effect of antecedent rainfall on landslides are intended to aid in enhancing the real-time landslide early warning system (R-LEWS) being developed for Sikkim.

Effect of antecedent rainfall on pore-water pressure distribution characteristics in residual soil slopes under tropical rainfall

Effect of antecedent rainfall on pore water pressure distribution characteristics in residual soil slopes under tropical rainfall

The catastrophic 13 November 2015 rock-debris slide in Lidong, south-western Zhejiang (China): a landslide triggered by a combination of antecedent rainfall and triggering rainfall

ABSTRACTOn 13 November 2015, a disastrous rockslide-debris avalanche occurred in the Lidong village of Liandou District, Lishui City (south-western Zhejiang, China), which overwhelmed 27 houses and killed 38 people in the village. The geological survey on the slope revealed that prior to this incident, a rupture zone existed between the highly weathered volcanic rock and the underlying weathered volcanic rock in this slope. The rupture zone has dip direction coinciding with the slope aspect and some of the existing cracks in the surface are likely extended from the surface to the rupture zone. This study summarizes the past history and main characteristics of the landslide, simulates the correlation between the safety factor of the slope and the rainfall level using a physically based triggering model, and analyses the distribution of the effective antecedent rainfall that influences landslide occurrence, showing that the landslide on 13 November 2015 was triggered by the very low intraday rainfall and the 15-day antecedent rainfall. The results demonstrate that for a potential unstable slope with increasingly wet antecedent conditions, very low daily rainfall may be able to trigger landslides on a given day, which is important for improving landslide warning systems based on the rainfall–landslide relationship.

Rainfall intensity-duration thresholds for landslide prediction in South Korea by considering the effects of antecedent rainfall

This study was conducted to determine the rainfall intensity-duration thresholds (ID curves) for landslide prediction by considering the effects of antecedent rainfall. Data for the time and location of landslides that occurred in South Korea from 1999 to 2016 were collected. Overall, 231 landslide histories from 1999 to 2013 were used to determine the rainfall thresholds, and 12 landslide histories from 2014 to 2016 were used to verify the proposed rainfall thresholds. Probabilistic ID curves were proposed to reflect the influence of other factors except rainfall, and ID curves for various inter-event time definitions (IETDs) were suggested to analyze the variation in the ID curves with the effects of antecedent rainfall. The results suggest that the IETD indicates the duration for which the antecedent rainfall affects the ground condition. It was also found that the ID curve for 12 h of the IETD was the most reliable of the verification procedures using the receiver operating characteristic (ROC) plot and threat score (TS).

Dynamic hydrologic modeling using the zero‐parameter Budyko model with instantaneous dryness index

AbstractLong‐term partitioning of hydrologic quantities is achieved by using the zero‐parameter Budyko model which defines a dryness index. However, this approach is not suitable for dynamic partitioning particularly at diminishing timescales, and therefore, a universally applicable zero‐parameter model remains elusive. Here an instantaneous dryness index is proposed which enables dynamic hydrologic modeling using the Budyko model. By introducing a “decay function” that characterizes the effects of antecedent rainfall and solar energy on the dryness state of a basin at a time, I propose the concept of instantaneous dryness index and use the Budyko function to perform continuous hydrologic partitioning. Using the same decay function, I then obtain discharge time series from the effective rainfall time series. The model is evaluated by considering data form 63 U.S. Geological Survey basins. Results indicate the possibility of using the proposed framework as an alternative platform for prediction in ungagued basins.

Assessment and mapping of slope stability based on slope units: A case study in Yan’an, China

Precipitation frequently triggers shallow landslides in the Loess Plateau of Shaanxi, China, resulting in loss of life, damage to gas and oil routes, and destruction of transport infrastructure and farmland. To assess the possibility of shallow landslides at different precipitation levels, a method to draw slope units and steepest slope profiles based on ARCtools and a new method for calculating slope stability are proposed. The methods were implemented in a case study conducted in Yan’an, north-west China. High resolution DEM (Digital Elevation Model) images, soil parameters from in-situ laboratory measurements and maximum depths of precipitation infiltration were used as input parameters in the method. Next, DEM and reverse DEM were employed to map 2146 slope units in the study area, based on which the steepest profiles of the slope units were constructed. Combining analysis of the water content of loess, strength of the sliding surface, its response to precipitation and the infinite slope stability equation, a new equation to calculate infinite slope stability is proposed to assess shallow landslide stability. The slope unit stability was calculated using the equation at 10-, 20-, 50- and 100-year return periods of antecedent effective precipitation. The number of slope units experiencing failure increased in response to increasing effective antecedent rainfall. These results were validated based on the occurrence of landslides in recent decades. Finally, the applicability and limitations of the model are discussed.

Slope stability analysis in the Three Gorges Reservoir Area considering effect of antecedent rainfall

ABSTRACTEffects of different initial conditions of pore water pressure distribution on slope stability are investigated based on rainfall data in the Three Gorges Reservoir Area. A method to incorporate the initial condition of pore water pressure distribution into the slope stability analysis is suggested. Then, sandy and clayey slopes are taken as examples to investigate the effect of antecedent rainfall on slope stability. Results indicate that the influence of antecedent rainfall on the slope stability increases as the saturated permeability coefficient of the soil decreases.

A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products

Summary Although flood forecasting and warning system is a very important non-structural measure in flood-prone river basins, poor raingauge network as well as unavailability of rainfall data in real-time could hinder its accuracy at different lead times. Conversely, since the real-time satellite-based rainfall products are now becoming available for the data-scarce regions, their integration with the data-driven models could be effectively used for real-time flood forecasting. To address these issues in operational streamflow forecasting, a new data-driven model, namely, the wavelet-based non-linear autoregressive with exogenous inputs (WNARX) is proposed and evaluated in comparison with four other data-driven models, viz., the linear autoregressive moving average with exogenous inputs (ARMAX), static artificial neural network (ANN), wavelet-based ANN (WANN), and dynamic nonlinear autoregressive with exogenous inputs (NARX) models. First, the quality of input rainfall products of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), viz., TRMM and TRMM-real-time (RT) rainfall products is assessed through statistical evaluation. The results reveal that the satellite rainfall products moderately correlate with the observed rainfall, with the gauge-adjusted TRMM product outperforming the real-time TRMM-RT product. The TRMM rainfall product better captures the ground observations up to 95 percentile range (30.11 mm/day), although the hit rate decreases for high rainfall intensity. The effect of antecedent rainfall ( AR ) and climate forecast system reanalysis (CFSR) temperature product on the catchment response is tested in all the developed models. The results reveal that, during real-time flow simulation, the satellite-based rainfall products generally perform worse than the gauge-based rainfall. Moreover, as compared to the existing models, the flow forecasting by the WNARX model is way better than the other four models studied herein with the TRMM and TRMM-RT rainfalls at 1–3 days lead times. The results confirm the robustness of the WNARX model with only the satellite-based (TRMM-RT) rainfall (without use of gauge data) to provide reasonably good real-time flood forecasts. The utility of the TRMM-RT solves the real-time flood forecasting issues, since this is the only rainfall product disseminated in real-time. Hence, the WNARX model with the TMPA rainfall products can offer an exciting new horizon to provide flood forecasting and early warning in the flood prone catchments.

선행강우를 고려한 산사태 유발 강우기준(ID curve) 분석

본 연구에서는 국내 산사태 발생을 예측하기 위하여 선행강우의 영향을 고려한 산사태 유발 강우기준(Intensity-Duration, ID curve)을 제안하였다. 1999년부터 2013년까지 국내에서 유발된 202개의 산사태에 대하여, 기상청 강우자료를 바탕으로 산사태 발생 시점 이전의 시강우량 데이터를 수집하고 분석하였다. 선행강우의 영향을 고려하기 위해 강우사상간 시간(Inter event time definition, IETD)을 6, 12, 24, 48, 72, 96시간으로 구분하고, 회귀분석을 통해 강우기준을 제안하였다. 국외의 산사태 유발강우기준과 제안된 유발강우기준을 비교하였으며, 선행강우에 대한 산사태 유발 강우기준의 변화를 분석하였다. 그 결과, 국내의 경우 비교적 낮은 강우강도에서 산사태가 유발되는 것으로 나타났으며, IETD가 증가할수록 산사태 유발강우기준의 기울기가 증가하는 경향이 나타났다. 따라서 단기간의 강우에 대해서는 산사태 유발강우기준(강우강도)이 높아지고, 장기간의 강우에 대해서는 낮아지는 것을 알 수 있었다. 2014년도에 국내에서 발생한 산사태 재해이력을 이용하여 검토한 결과, 본 연구에서 제안된 ID curve가 산사태 유발을 비교적 잘 예측할 수 있는 것으로 나타났다. This study was conducted to suggest a landslide triggering rainfall threshold (ID curve) for landslide prediction by considering the effect of antecedent rainfall. 202 rainfall data including domestic landslide and rainfall records were used in this study. In order to consider the effect of antecedent rainfall, rainfall data were analyzed by changing Inter Event Time Definition (IETD) and IETD based ID curve were presented by regression analysis. Compared to the findings of the previous studies, the presented ID curve has a tendency to predict the landslides occurring at a relatively low rainfall intensity. It is shown that the proposed ID curve is appropriate and realistic for predicting landslides through the validation of proposed ID curve using records of landslides in 2014. Based on this analysis, it is found that the longer IETD, the greater the effect of antecedent rainfall, and the steeper the gradient of ID curve. It is also found that the rainfall threshold (intensity) is higher for the short period rainfall and lower for the long period rainfall.

日本の内陸断層活動に伴う地震地すべりの規模と先行降雨・融雪の関係

日本の内陸断層活動に伴う地震地すべりの規模と先行降雨・融雪の関係

선행강우의 영향에 따른 불포화토의 침투특성 분석

선행강우의 영향에 따른 불포화토의 침투특성 분석