scholarly journals 30-year record of Himalaya mass-wasting reveals landscape perturbations by extreme events

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua N. Jones ◽  
Sarah J. Boulton ◽  
Martin Stokes ◽  
Georgina L. Bennett ◽  
Michael R. Z. Whitworth
Keyword(s):  
Extreme Rainfall ◽  
Return Periods ◽  
Monsoon Precipitation ◽  
Mass Wasting ◽  
Short Term ◽  
Gorkha Earthquake ◽  
Empirical Relationships ◽  
Nepal Himalaya ◽  
Rainfall Events ◽  
Hazard Management

AbstractIn mountainous environments, quantifying the drivers of mass-wasting is fundamental for understanding landscape evolution and improving hazard management. Here, we quantify the magnitudes of mass-wasting caused by the Asia Summer Monsoon, extreme rainfall, and earthquakes in the Nepal Himalaya. Using a newly compiled 30-year mass-wasting inventory, we establish empirical relationships between monsoon-triggered mass-wasting and monsoon precipitation, before quantifying how other mass-wasting drivers perturb this relationship. We find that perturbations up to 5 times greater than that expected from the monsoon alone are caused by rainfall events with 5-to-30-year return periods and short-term (< 2 year) earthquake-induced landscape preconditioning. In 2015, the landscape preconditioning is strongly controlled by the topographic signature of the Gorkha earthquake, whereby high Peak Ground Accelerations coincident with high excess topography (rock volume above a landscape threshold angle) amplifies landscape damage. Furthermore, earlier earthquakes in 1934, 1988 and 2011 are not found to influence 2015 mass-wasting.

scholarly journals 30-year record of Himalaya mass-wasting reveals landscape perturbations by extreme events

2021 ◽  
Author(s):  
Joshua Jones ◽  
Sarah Boulton ◽  
Georgina Bennett ◽  
Martin Stokes ◽  
Michael Whitworth
Keyword(s):  
Peak Ground Acceleration ◽  
Extreme Rainfall ◽  
Return Periods ◽  
Mass Wasting ◽  
Short Term ◽  
Gorkha Earthquake ◽  
Empirical Relationships ◽  
Ground Acceleration ◽  
Rainfall Events ◽  
Hazard Management

In mountainous environments, quantifying the drivers of mass-wasting is fundamental for understanding landscape evolution and improving hazard management. Here, we quantify the magnitudes of mass-wasting caused by the Asia Summer Monsoon (ASM), extreme rainfall and earthquakes in the Nepal Himalayas. Using a newly compiled 30-year mass-wasting inventory, we establish empirical relationships between monsoon-triggered mass-wasting and ASM precipitation before quantifying how other mass-wasting drivers have perturbed this relationship. We find that perturbations up to 5 times greater than that expected from the ASM alone are caused by rainfall events with 5 to 30 year return periods and short-term (< 2 year) earthquake-induced landscape preconditioning. In 2015, the landscape preconditioning induced perturbation is found to be strongly controlled by the topographic signature of the Gorkha earthquake, whereby high Peak Ground Acceleration (PGA) coincident with excess topography (rock volume above a landscapes threshold angle) amplifies landscape damage.

Testing the impacts of wildfire on hydrological and sediment response using the OpenLISEM model

2021 ◽  
Author(s):  
Jinfeng Wu ◽  
João Pedro Nunes ◽  
Jantiene E. M. Baartman
Keyword(s):  
Water Discharge ◽  
Extreme Rainfall ◽  
Fire Occurrence ◽  
Return Periods ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Erosion Risk ◽  
Sediment Yields ◽  
Splash Erosion ◽  
Event Based

&lt;p&gt;Wildfires have become a major concern to society in recent decades because increases in the number and severity of wildfires have negative effects on soil and water resources, especially in headwater areas. Models are typically applied to estimate the potential adverse effects of fire. However, few modeling studies have been conducted for meso-scale catchments, and only a fraction of these studies include transport and deposition of eroded material within the catchment or represent spatial erosion patterns. In this study, we firstly designed the procedure of event-based automatic calibration using PEST, parameters ensemble, and jack-knife cross-validation that is suitable for event-based OpenLISEM calibration and validation, especially in data-scarce burned areas. The calibrated and validated OpenLISEM proved capable of providing reasonable accurate predictions of hydrological responses and sediment yields in this burned catchment. Then the model was applied with design storms of six different return periods (0.2, 0.5, 1, 2, 5, and 10 years) to simulate and evaluate pre- and post-wildfire hydrological and erosion responses at the catchment scale. Our results show rainfall amount and intensity play a more important role than fire occurrence in the catchment water discharge and sediment yields, while fire occurrence is regarded as an important factor for peak water discharge, indicating that high post-fire hydro-sedimentary responses are frequently related to extreme rainfall events. The results also suggest a partial shift from flow to splash erosion after fire, especially for higher return periods, explained by a combination of higher splash erosion in burnt upstream areas with a limited sediment transport capacity of surface runoff, preventing flow erosion in downstream areas. In consequence, the pre-fire erosion risk in the croplands of this catchment is partly shifted to a post-fire erosion risk in upper slope forest and natural areas, especially for storms with lower return periods, although erosion risks in croplands are important both before and after fires. This is relevant, as a shift of sediment sources to burnt areas might lead to downstream contamination even if sediment yields remain small. These findings have significant implications to identify areas for post-wildfire stabilization and rehabilitation, which is particularly important given the predicted increase in the occurrence of fires and extreme rainfall events with climate change.&lt;/p&gt;

scholarly journals Effect of Extreme Rainfall Intensity on Matric Suction and Ground Movement

2020 ◽  
Vol 195 ◽  
pp. 01022 ◽  
Author(s):  
Muhammad Hazwan Zaki ◽  
Mastura Azmi ◽  
Siti Aimi Nadia Mohd Yusoff ◽  
Muhd Harris Ramli ◽  
Mohd Azril Hezmi
Keyword(s):  
Rainfall Intensity ◽  
Extreme Rainfall ◽  
Matric Suction ◽  
Ground Movement ◽  
Laboratory Studies ◽  
Mass Wasting ◽  
Significant Drop ◽  
Rainfall Events ◽  
Hourly Rainfall ◽  
Rain Simulator

Increased intensity of rainfall events due to extreme climate change has led to the substantial increase in the occurrence of disasters, especially in a tropical-climate country such as Malaysia. Rainfall-induced landslide has become one of the most common types of disasters, and its triggering factors are still uncertain and impossible to predict. In this study, the effect of extreme rainfall intensity on groundwater behaviour is addressed through laboratory-scale testing. The adopted rainfall intensity is 60 mm/h, which was the heaviest hourly rainfall intensity recorded in Sarawak on 3rd January 2016 and 80 mm/h, which was the corresponding value recorded in Penang on 10th October 2016. The simulation is conducted on four cases. The simulated rainfall exhibits a duration of 6 h. In addition, the overall trend of the matric suction measurement and soil moisture in all cases is discussed on the basis of the results obtained from laboratory studies. After the rain simulator stopped, the matric suction decreases, and it remains stagnant, followed by a significant drop in the reading. For all cases, failure occurs, albeit at different times with different volumes of mass wasting.

scholarly journals Bivariate Modelling of a Teleconnection Index and Extreme Rainfall in a Small North Atlantic Island

Climate ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 86
Author(s):  
Luis Angel Espinosa ◽  
Maria Manuela Portela ◽  
João Dehon Pontes Filho ◽  
Martina Zelenakova
Keyword(s):  
Climate Variability ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Extreme Rainfall ◽  
Small Island ◽  
Return Periods ◽  
Rainfall Events ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

This paper explores practical applications of bivariate modelling via copulas of two likely dependent random variables, i.e., of the North Atlantic Oscillation (NAO) coupled with extreme rainfall on the small island of Madeira, Portugal. Madeira, due to its small size (∼740 km2), very pronounced mountain landscape, and location in the North Atlantic, experiences a wide range of rainfall regimes, or microclimates, which hamper the analyses of extreme rainfall. Previous studies showed that the influence of the North Atlantic Oscillation (NAO) on extreme rainfall is at its largest in the North Atlantic sector, with the likelihood of increased rainfall events from December through February, particularly during negative NAO phases. Thus, a copula-based approach was adopted for teleconnection, aiming at assigning return periods of daily values of an NAO index (NAOI) coupled with extreme daily rainfalls—for the period from December 1967 to February 2017—at six representative rain gauges of the island. The results show that (i) bivariate copulas describing the dependence characteristics of the underlying joint distributions may provide useful analytical expressions of the return periods of the coupled previous NAOI and extreme rainfall and (ii) that recent years show signs of increasing climate variability with more anomalous daily negative NAOI along with higher extreme rainfall events. These findings highlight the importance of multivariate modelling for teleconnections of prominent patterns of climate variability, such as the NAO, to extreme rainfall in North Atlantic regions, especially in small islands that are highly vulnerable to the effects of abrupt climate variability.

scholarly journals Addressing Climate Internal Variability on Future Intensity-Duration-Frequency Curves at Fine Scales across South Korea

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2828
Author(s):  
Manh Van Doi ◽  
Jongho Kim
Keyword(s):  
South Korea ◽  
Time Series Data ◽  
Extreme Rainfall ◽  
Internal Variability ◽  
Series Data ◽  
Return Periods ◽  
Precipitation Frequency ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Data Length

Designing water infrastructure requires information about the magnitude and frequency of upcoming rainfall. A limited range of data offers just one of many realizations that occurred in the past or will occur in the future; thus, it cannot sufficiently explain climate internal variability (CIV). In this study, future relationships among rainfall intensity (RI), duration, and frequency (called the IDF curve) are established by addressing the CIV and tail characteristics with respect to frequency. Specifically, 100 ensembles of 30-year time series data were created to quantify that uncertainty. Then, the tail characteristics of future extreme rainfall events were investigated to determine whether they will remain similar to those in the present. From the RIs computed for control and future periods under two emission scenarios, following are the key results. Firstly, future RI will increase significantly for most locations, especially near the end of this century. Secondly, the spatial distributions and patterns indicate higher RI in coastal areas and lower RI for the central inland areas of South Korea, and those distributions are similar to those of the climatological mean (CM) and CIV. Thirdly, a straightforward way to reveal whether the tail characteristics of future extreme rainfall events are the same as those in the present is to inspect the slope value for the factor of change (FOC), mFOC. Fourthly, regionalizing with nearby values is very risky when investigating future changes in precipitation frequency estimates. Fifthly, the magnitude of uncertainty is large when the data length is short and gradually decreases as the data length increases for all return periods, but the uncertainty range becomes much greater as the return period becomes large. Lastly, inferring future changes in RI from the CM is feasible only for small return periods and at locations where mFOC is close to zero.

scholarly journals Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 267 ◽  
Author(s):  
Gabriele Franch ◽  
Daniele Nerini ◽  
Marta Pendesini ◽  
Luca Coviello ◽  
Giuseppe Jurman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Extreme Rainfall ◽  
Superior Performance ◽  
Short Term ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Severe Thunderstorms ◽  
Extreme Rain ◽  
Novel Method ◽  
Rain Rates

One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms. While deep learning nowcasting models have recently shown to provide better overall skill than traditional echo extrapolation models, they suffer from conditional bias, sometimes reporting lower skill on extreme rain rates compared to Lagrangian persistence, due to excessive prediction smoothing. This work presents a novel method to improve deep learning prediction skills in particular for extreme rainfall regimes. The solution is based on model stacking, where a convolutional neural network is trained to combine an ensemble of deep learning models with orographic features, doubling the prediction skills with respect to the ensemble members and their average on extreme rain rates, and outperforming them on all rain regimes. The proposed architecture was applied on the recently released TAASRAD19 radar dataset: the initial ensemble was built by training four models with the same TrajGRU architecture over different rainfall thresholds on the first six years of the dataset, while the following three years of data were used for the stacked model. The stacked model can reach the same skill of Lagrangian persistence on extreme rain rates while retaining superior performance on lower rain regimes.

scholarly journals Modeling of short duration extreme rainfall events over Lower Yamuna Catchment

MAUSAM ◽  
2022 ◽  
Vol 63 (3) ◽  
pp. 391-400
Author(s):  
MEHFOOZ ALI ◽  
SURINDER KAUR ◽  
S.B. TYAGI ◽  
U.P. SINGH
Keyword(s):  
Short Duration ◽  
Goodness Of Fit ◽  
Extreme Rainfall ◽  
Return Periods ◽  
Goodness Of Fit Test ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Kolmogorov Smirnov ◽  
Level Of Significance ◽  
Made In

Short duration rainfall estimates and their intensities for different return periods are required for many purposes such as for designing flood for hydraulic structures, urban flooding etc. An attempt has been made in this paper to Model extreme rainfall events of Short Duration over Lower Yamuna Catchment. Annual extreme rainfall series and their intensities were analysed using EVI distribution for rainstorms of short duration of 5, 10, 15, 30, 45 & 60 minutes and various return periods have been computed. The Self recording rainguage (SRRGs) data for the period 1988-2009 over the Lower Yamuna Catchment (LYC) have been used in this study. It has been found that EVI distribution fits well, tested by Kolmogorov-Smirnov goodness of fit test at 5 % level of significance for each of the station.

scholarly journals Selection of Hydrological Probability Distributions for Extreme Rainfall Events in the Regions of Colombia

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1397 ◽  
Author(s):  
Óscar E. Coronado-Hernández ◽  
Ernesto Merlano-Sabalza ◽  
Zaid Díaz-Vergara ◽  
Jairo R. Coronado-Hernández
Keyword(s):  
Frequency Analysis ◽  
Probability Distributions ◽  
Extreme Rainfall ◽  
Return Periods ◽  
Extreme Rainfall Events ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Moments Method ◽  
Engineering Projects ◽  
Selection Of

Frequency analysis of extreme events is used to estimate the maximum rainfall associated with different return periods and is used in planning hydraulic structures. When carrying out this type of analysis in engineering projects, the hydrological distributions that best fit the trend of maximum 24 h rainfall data are unknown. This study collected maximum 24 h rainfall records from 362 stations distributed throughout Colombia, with the goal of guiding hydraulic planners by suggesting the probability distributions they should use before beginning their analysis. The generalized extreme value (GEV) probability distribution, using the weighted moments method, presented the best fits of frequency analysis of maximum daily precipitation for various return periods for selected rainfall stations in Colombia.

Extreme Rainfall Events in the Hawaiian Islands

2009 ◽  
Vol 48 (3) ◽  
pp. 502-516 ◽  
Author(s):  
Pao-Shin Chu ◽  
Xin Zhao ◽  
Ying Ruan ◽  
Melodie Grubbs
Keyword(s):  
Return Period ◽  
Heavy Rainfall ◽  
Hawaiian Islands ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Generalized Extreme Value Distribution ◽  
Economic Losses ◽  
Return Periods ◽  
Extreme Rainfall Events ◽  
Rainfall Events

Abstract Heavy rainfall and the associated floods occur frequently in the Hawaiian Islands and have caused huge economic losses as well as social problems. Extreme rainfall events in this study are defined by three different methods based on 1) the mean annual number of days on which 24-h accumulation exceeds a given daily rainfall amount, 2) the value associated with a specific daily rainfall percentile, and 3) the annual maximum daily rainfall values associated with a specific return period. For estimating the statistics of return periods, the three-parameter generalized extreme value distribution is fit using the method of L-moments. Spatial patterns of heavy and very heavy rainfall events across the islands are mapped separately based on the aforementioned three methods. Among all islands, the pattern on the island of Hawaii is most distinguishable, with a high frequency of events along the eastern slopes of Mauna Kea and a low frequency of events on the western portion so that a sharp gradient in extreme events from east to west is prominent. On other islands, extreme rainfall events tend to occur locally, mainly on the windward slopes. A case is presented for estimating return periods given different rainfall intensity for a station in Upper Manoa, Oahu. For the Halloween flood in 2004, the estimated return period is approximately 27 yr, and its true value should be no less than 13 yr with 95% confidence as determined from the adjusted bootstrap resampling technique.

scholarly journals Extreme Rainfall and Flooding over Central Kenya Including Nairobi City during the Long-Rains Season 2018: Causes, Predictability, and Potential for Early Warning and Actions

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 472 ◽  
Author(s):  
Mary Kilavi ◽  
Dave MacLeod ◽  
Maurine Ambani ◽  
Joanne Robbins ◽  
Rutger Dankers ◽  
...  
Keyword(s):  
Risk Management ◽  
Early Warning ◽  
Flood Risk ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Flood Risk Management ◽  
Lead Times ◽  
Short Term ◽  
Rainfall Events

The Long-Rains wet season of March–May (MAM) over Kenya in 2018 was one of the wettest on record. This paper examines the nature, causes, impacts, and predictability of the rainfall events, and considers the implications for flood risk management. The exceptionally high monthly rainfall totals in March and April resulted from several multi-day heavy rainfall episodes, rather than from distinct extreme daily events. Three intra-seasonal rainfall events in particular resulted in extensive flooding with the loss of lives and livelihoods, a significant displacement of people, major disruption to essential services, and damage to infrastructure. The rainfall events appear to be associated with the combined effects of active Madden–Julian Oscillation (MJO) events in MJO phases 2–4, and at shorter timescales, tropical cyclone events over the southwest Indian Ocean. These combine to drive an anomalous westerly low-level circulation over Kenya and the surrounding region, which likely leads to moisture convergence and enhanced convection. We assessed how predictable such events over a range of forecast lead times. Long-lead seasonal forecast products for MAM 2018 showed little indication of an enhanced likelihood of heavy rain over most of Kenya, which is consistent with the low predictability of MAM Long-Rains at seasonal lead times. At shorter lead times of a few weeks, the seasonal and extended-range forecasts provided a clear signal of extreme rainfall, which is likely associated with skill in MJO prediction. Short lead weather forecasts from multiple models also highlighted enhanced risk. The flood response actions during the MAM 2018 events are reviewed. Implications of our results for forecasting and flood preparedness systems include: (i) Potential exists for the integration of sub-seasonal and short-term weather prediction to support flood risk management and preparedness action in Kenya, notwithstanding the particular challenge of forecasting at small scales. (ii) We suggest that forecasting agencies provide greater clarity on the difference in potentially useful forecast lead times between the two wet seasons in Kenya and East Africa. For the MAM Long-Rains, the utility of sub-seasonal to short-term forecasts should be emphasized; while at seasonal timescales, skill is currently low, and there is the challenge of exploiting new research identifying the primary drivers of variability. In contrast, greater seasonal predictability of the Short-Rains in the October–December season means that greater potential exists for early warning and preparedness over longer lead times. (iii) There is a need for well-developed and functional forecast-based action systems for heavy rain and flood risk management in Kenya, especially with the relatively short windows for anticipatory action during MAM.

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