Landslide Susceptibility Mapping and Their Rainfall Thresholds Model in Tinalah Watershed, Kulon Progo District, Yogyakarta Special Region, Indonesia

Landslide often occurred in Tinalah watershed, Kulon Progo District, every year. The frequency of landslide events is increasing after high rainfall intensity. Some factors control landslides such as slope gradient, land use, geological structure, slope hydrology, and geological condition. This research has an objective to develop the susceptibility map of Tinalah watershed and to identify the rainfall threshold to trigger a landslide. The development of the susceptibility map using frequency ratio method with four parameters including slope, type of rock, land use, and lineament density. The landslide data were collected during the field survey and from regional disaster management authority (BPBD) Kulon Progo. Rainfall data were collected from BMKG and GSMap. Soil analysis also was conducted to develop a numerical model to verify the rainfall threshold value. The result shows a high susceptibility of the landslide area is dominated in Tinalah watershed. The rainfall threshold for the low susceptibility of the landslide zone is I=490.14 D-1.404with 5-7 days antecedent rain. The rainfall threshold for medium susceptibility map is I=164.32D-0,689 3-7 days antecedent rain. Moreover, the rainfall threshold for the high susceptibility of the landslide zone is 111.62 D-0.779, with 2-7 days antecedent rain.

High-resolution NWP forecast precipitation comparison over complex terrain of the Sierras de Córdoba during RELAMPAGO-CACTI.

Sierras de Córdoba (Argentina) is characterized by the occurrence of extreme precipitation events during the austral warm season. Heavy precipitation in the region has a large societal impact, causing flash floods. This motivates the forecast performance evaluation of 24-hour accumulated precipitation and vertical profiles of atmospheric variables from different numerical weather prediction (NWP) models with the final aim of helping water management in the region. The NWP models evaluated include the Global Forecast System (GFS) which parameterizes convection, and convection-permitting simulations of the Weather Research and Forecasting Model (WRF) configured by three institutions: University of Illinois at Urbana–Champaign (UIUC), Colorado State University (CSU) and National Meteorological Service of Argentina (SMN). These models were verified with daily accumulated precipitation data from rain gauges and soundings during the RELAMPAGO-CACTI field campaign. Generally all configurations of the higher-resolution WRFs outperformed the lower-resolution GFS based on multiple metrics. Among the convection-permitting WRF models, results varied with respect to rainfall threshold and forecast lead time, but the WRFUIUC mostly performed the best. However, elevation dependent biases existed among the models that may impact the use of the data for different applications. There is a dry (moist) bias in lower (upper) pressure levels which is most pronounced in the GFS. For Córdoba an overestimation of the northern flow forecasted by the NWP configurations at lower levels was encountered. These results show the importance of convection-permitting forecasts in this region, which should be complementary to the coarser-resolution global model forecasts to help various users and decision makers.

New Zealand weather extremes and climate-related events; a model-based assessment

<p>Extreme weather and climate-related events can have pronounced environmental, economic and societal impacts, yet large natural variability within Earth’s constantly evolving climate system challenges the understanding of how these phenomena are changing. Increasingly powerful climate models have made it possible to study how certain factors, including anthropogenic forcings, have modified the likelihood and magnitude of extreme events. This study examines climate observations, reanalysis fields and model output to assess how weather extremes and climate-related events have changed. Part 1 investigates the detection and attribution of surface climate changes in relation to ozone depletion. Part 2 uses probabilistic event attribution and storyline frameworks to evaluate the role of anthropogenic forcings in altering the risk of extreme 1-day rainfall (RX1D) events for Christchurch, New Zealand in light of an unprecedented rainfall event that occurred in March 2014. Extremely large simulations of possible weather generated by the weather@home Australia-New Zealand (w@h ANZ) model found ozone forcings induced significant changes globally (< 3 hPa) in simulations of mean sea level pressure for 2013. A clear seasonal response was detected in the Southern Hemisphere (SH) circulation that was consistent with prior studies. Ozone-induced changes to average monthly rainfall were not significant in New Zealand with large natural variability and the limitation of one-year simulations challenging attribution to this climate forcing. In Christchurch, model and observational data give evidence of human activity increasing the likelihood and magnitude (+17%) of RX1D events despite significant drying trends for mean total rainfall (-66%) in austral summer. For events similar to that observed during March 2014, the fraction of attributable risk (FAR) is estimated to be 27.4%. This result was robust across different spatial averaging areas though is sensitive to the rainfall threshold examined. Unique meteorological conditions in combination with anomalously high sea surface temperatures (SSTs) in the tropical South Pacific were likely important to the occurrence of this extreme event. These results demonstrate how human influence can be detected in present-day weather and climate events.</p>

New Zealand weather extremes and climate-related events; a model-based assessment

<p>Extreme weather and climate-related events can have pronounced environmental, economic and societal impacts, yet large natural variability within Earth’s constantly evolving climate system challenges the understanding of how these phenomena are changing. Increasingly powerful climate models have made it possible to study how certain factors, including anthropogenic forcings, have modified the likelihood and magnitude of extreme events. This study examines climate observations, reanalysis fields and model output to assess how weather extremes and climate-related events have changed. Part 1 investigates the detection and attribution of surface climate changes in relation to ozone depletion. Part 2 uses probabilistic event attribution and storyline frameworks to evaluate the role of anthropogenic forcings in altering the risk of extreme 1-day rainfall (RX1D) events for Christchurch, New Zealand in light of an unprecedented rainfall event that occurred in March 2014. Extremely large simulations of possible weather generated by the weather@home Australia-New Zealand (w@h ANZ) model found ozone forcings induced significant changes globally (< 3 hPa) in simulations of mean sea level pressure for 2013. A clear seasonal response was detected in the Southern Hemisphere (SH) circulation that was consistent with prior studies. Ozone-induced changes to average monthly rainfall were not significant in New Zealand with large natural variability and the limitation of one-year simulations challenging attribution to this climate forcing. In Christchurch, model and observational data give evidence of human activity increasing the likelihood and magnitude (+17%) of RX1D events despite significant drying trends for mean total rainfall (-66%) in austral summer. For events similar to that observed during March 2014, the fraction of attributable risk (FAR) is estimated to be 27.4%. This result was robust across different spatial averaging areas though is sensitive to the rainfall threshold examined. Unique meteorological conditions in combination with anomalously high sea surface temperatures (SSTs) in the tropical South Pacific were likely important to the occurrence of this extreme event. These results demonstrate how human influence can be detected in present-day weather and climate events.</p>

Temporal changes in the debris flow threshold under the effects of ground freezing and sediment storage on Mt. Fuji

Debris flows are one of the most destructive sediment transport processes in mountainous areas because of their large volume, high velocity, and kinematic energy. Debris flow activity varies over time and is affected by changes in hydrogeomorphic processes in the initiation zone. To clarify temporal changes in debris flow activities in cold regions, the rainfall threshold for the debris flow occurrence was evaluated in Osawa failure at a high elevation on Mt. Fuji, Japan. We conducted field monitoring of the ground temperature near a debris flow initiation zone to estimate the presence or absence of seasonally frozen ground during historical rainfall events. The effects of ground freezing and the accumulation of channel deposits on the rainfall threshold for debris flow occurrence were analyzed using rainfall records and annual changes in the volume of channel deposits since 1969. Statistical analyses showed that the intensity–duration threshold during frozen periods was clearly lower than that during unfrozen periods. A comparison of maximum hourly rainfall intensity and total rainfall also showed that debris flows during frozen periods were triggered by a smaller magnitude of rainfall than during unfrozen periods. Decreases in the infiltration rate due to the formation of frozen ground likely facilitated the generation of overland flow, triggering debris flows. The results suggest that the occurrence of frozen ground and the sediment storage volume need to be monitored and estimated for better debris flow disaster mitigation in cold regions.

The Determination of Rainfall Threshold Triggering Landslides Using Remote Sensing

Landslide is one of the natural disasters that can cause a lot of loss, both material and fatalities. Banjarnegara Regency is one of Central Java Province regencies where landslides often occur due to the region's topography and high intensity rainfall.. Therefore, it is necessary to determine the threshold of rainfall that can trigger landslides to be used as an early warning for landslides. The rainfall data used for the threshold is daily and hourly rainfall intensity from remote sensing data that provides complete data but relatively rough resolution. So that remote sensing data need to be re-sampled. The remote sensing data used is CMORPH satellite data that has been re-sampled for detailing existing information of rainfall data. The resampling method used is the bilinear method and nearest neighbor by choosing between the two based on the highest correlation. Threshold calculation using Cumulative Threshold (CT) method resulted equation P3 = 7.0354 - 1.0195P15 and Intensity Duration (ID) method resulted equation I = 1.785D-0305. The peak rainfall intensity occurs at the threshold of 97-120 hours before a landslide occur.

Mechanisms of Initiation, Runout, and Rainfall Thresholds of Extreme-Precipitation-Induced Debris Flows

The frequency of unprecedented extreme precipitation events is increasing, and consequently, catastrophic debris flows occur in regions worldwide. Rapid velocity and long-runout distances of debris flow induce massive loss of life and damage to infrastructure. Despite extensive research, understanding the initiation mechanisms and defining early warning thresholds for extreme-precipitation-induced debris flows remain a challenge. Due to the nonavailability of extreme events in the past, statistical models cannot determine thresholds from historical datasets. Here, we develop a numerical model to analyze the initiation and runout of extreme-precipitation-induced runoff-generated debris flows and derive the Intensity-Duration (ID) rainfall threshold. We choose the catastrophic debris flow on 6 August 2020 in Pettimudi, Kerala, India, for our analysis. Our model satisfactorily predicts the accumulation thickness (7 m to 8 m) and occurrence time of debris flow compared to the benchmark. Results reveal that the debris flow was rapid, traveling with a maximum velocity of 9 m/s for more than 9 minutes. The ID rainfall threshold defined for the event suggests earlier thresholds are not valid for debris flow triggered by extreme precipitation. The methodology we develop in this study is helpful to derive ID rainfall thresholds for debris flows without historical data.

LANDSLIDES RAINFALL THRESHOLDS APPROACHES FOR THE WESTERN GHATS INDIA AND THE NORTH CAUCASUS (Russia)

Knowing the duration, intensity and amount of precipitation triggering landslides is of great importance for landslide risk management. Global, regional and local studies carried out by the researchers revealed that the rainfall-induced landslides occur after rainfall exceeding a certain threshold value. The rainfall threshold is the minimum intensity or duration of rainfall required to initiate the landslide. The Rainfall threshold can be estimated from the daily rainfall data which is collected from the rainguage. The methodology used by Jaiswal and Van Westen and ITC Netherland are used for the present study. Daily data from particular rainguage station closer to the landslide location were considered. The 5Days Antecedent (5-AD) rainfall for each year from daily rainfall of landslide events calculated (for 5 days AD, add the previous 5 days of daily rainfall). Then the daily rainfall and the corresponding 5AD rainfall for the all the landslide event in the same period will be plotted. The relation could be presented as a straight line with negative slope of the type RT = p*R5AD + c, where p is the slope and c is the intercept. The present study is focused on the assessment of precipitation thresholds for landslides on different slopes prone to the landslides in Russia and India, which are characterized by very different geological, geomorphological and meteorological conditions. In this article, the main attention is paid to precipitation threshold criteria as the main driver of landslides in India compared to the North Caucasus, in order to find out the contribution of various factors to the processes of landslides for the development of an early warning system. In order to form the landslide inventory map of the territory of the North Caucasus, we used the data of the Information Bulletins on the state of the subsoil of the North Caucasian Federal District for 2019-2020. For all events, there is information about the genetic types of hazardous exogenous processes, activation factors, consequences and damage. Area of Dagestan was selected. Calculations were made for landslides and rockfalls. One can see that slopes of both lines are nearly same p=-0.11-0.12, while landslides need twice a precipitation more than rockfalls. Comparison with rain thresholds for India had shown that for the territory of Russia requires by an order of less total precipitation and precipitation per day. Perhaps, here it is necessary to take into account the contribution of other factors as well. This work is the first stage, and research will be continued. Subsequently, the influence of other factors on the formation of landslides and rockfalls will also be studied (the influence of earthquakes, man-made impacts, etc.) according to the data of various geophysical methods.