Rainfall Frequency Research Articles

Advanced Techniques of Flood Forecasting, Flood Inundation Mapping and Flood Prioritization of Panam River Basin

Floods have been quite possibly the most troubling natural disasters in history, causing significant casualties, loss of life, and collateral destruction. Also, we see a shift in the frequency of rainfall every year due to climate change, which exacerbates flooding. Flood forecasting is essential to provide early warning to the people of flood-prone areas, provide enough time for preparedness, and reduce the damage to lives and properties. The flood inundation simulation is critical in presenting potential impending flooding in the study region. Furthermore, flood prioritization plays a key role in better watershed management. Looking at the present scenario, the machine learning methods like neural networks and fuzzy logic contribute profoundly to the headway of flood forecast frameworks giving better execution and financially savvy arrangements. The flood forecasting models can be developed using ANN, ANFIS and fuzzy logic. The comparative study between the developed models can also be carried out by determining different evaluation parameters. For flood forecasting using ANFIS, it is found that the coefficient of correlation values ranges from 0.85 to 0.95. In order to regulate the extent of the flooded area and the depth of the flooded water, HEC-RAS efficiently develops a flood inundation map. Flood inundation maps can be used to know the regions which are more or less vulnerable to flooding hazards.

Modelling extreme rainfall events in Kigali city using generalized Pareto distribution

AbstractExtreme rain events have caused numerous issues and have had a significant impact on agriculture, human activities, ecology, infrastructure, and casualties. The theory of extreme values has been widely applied in extreme precipitation modelling and a variety of other fields. This paper employs the generalized Pareto distribution, which has been widely used to analyse extreme climates, in conjunction with the peak over thresholds approach to investigate exceedances. The occurrence of intense rainfall events in Kigali city causes severe damage to human properties, infrastructure damage, people injuries, loss of life, and other various harmful consequences. Early detection of extreme rainfall in Kigali aids in the development and implementation of strategies and measures to mitigate the negative effects of extreme rainfall before it occurs. The aim of this research is to estimate the frequency and magnitude of intense rainfall events in Kigali. The daily rainfall data from Kigali Airport station collected by Rwanda Meteorological Agency from 1990 to 2019 were applied. The results showed that as the return periods increased, so did the return levels, implying that the intensity and frequency of rainfall in Kigali will increase in the future. The model's goodness was tested, and the study suggests a model that has a non‐negative shape parameter (ξ) to be good. The study's findings are extremely important for understanding the occurrence of these events and also serve as a tool for decision‐making and the development of policies aimed at mitigating the effects of such events.

Close Linkage of the South China Sea Summer Monsoon Onset and Extreme Rainfall in May over Southeast Asia: Role of the Synoptic-Scale Systems

Abstract Based on multiple datasets and methods, this study reveals a close relation between the South China Sea summer monsoon (SCSSM) onset and the occurrence frequency of extreme rainfall over Southeast Asia (including the Indochina Peninsula, Hainan Island, and the Philippines) in May. Specifically, an early SCSSM onset tends to be accompanied by a higher chance of extreme rainfall over Southeast Asia, while a late SCSSM onset is likely to coincide with less heavy rainfall in May. It is suggested that the amplitude of tropical synoptic-scale systems may play an important role in the linkage between the SCSSM onset and the extreme rainfall over Southeast Asia. Accompanying an early SCSSM onset, anomalous cyclonic circulation appears over the SCS and Philippine Sea, which tends to increase the quantity of midtroposphere moisture via moisture advection and Ekman pumping. Besides, the barotropic energy conversion (from mean kinetic to eddy kinetic energy) associated with this anomalous cyclone will facilitate the development of the synoptic-scale systems. The vertical easterly wind shear accompanying an early SCSSM onset will confine the synoptic-scale wave train to the lower troposphere, which is also favorable for its growth. Once these synoptic-scale systems develop, the baroclinic process (from eddy available potential energy to eddy kinetic energy) will further promote their development. As such, the intensity of synoptic-scale systems is much stronger in early than late SCSSM onset years. The increased moisture and vigorous synoptic-scale systems further lead to an increase in the occurrence of extreme rainfall over Southeast Asia.

ROOT DISTRIBUTION OF MATURE OIL PALMS IN MINERAL AND PEAT SOILS AND ITS IMPLICATION ON FERTILISER PLACEMENT

The correct placement of fertilisers is critical, as losses can be very significant especially in regions of high and frequent rainfall, non-terraced slopes, sandy textured soils and peat. To maximise uptake efficiency, fertilisers should be evenly broadcast over the soil surface area that contains the highest density of feeder roots. In order to determine the latter, an investigation was undertaken to ascertain the biomass and distribution of roots of various ages of mature oil palms planted in both mineral and second generation peat soils. Results of the study indicate that if site factors are not limiting, palm age and past fertiliser placement history are two major factors influencing oil palm root development and distribution. In mature well decomposed peat, although roots could be found growing 4 m from palm bole, actual root biomass per unit volume of soil was low. Only 21 per cent of the feeder roots of 8-year-old palms were found growing outside the weeded palm circles (WPC). Even within the latter, approximately 50 per cent were concentrated within a radius of 1 m from the palm bole. The low bulk density and high porosity of peat appears to discourage roots from growing beyond this distance. In mineral soils, there was a consistent and gradual increase in root spread beyond the WPC, with palm age. Feeder root distribution beyond a 2 m radius ranged from as low as 26 per cent in 6-year-old palms planted on terraces to 41 per cent for 8-year-old palms established in non-terraced soils. Only palms older than 10 years of age had root biomass greater than 50 per cent beyond this radius. In all sites, there was an increase in primary and secondary root biomass with soil depth and a linear decrease in feeder root biomass down the soil profile. Soil chemical analysis indicated that apart from palm age, the horizontal and vertical distribution of feeder roots was strongly influenced by soil fertility gradients created by past fertiliser placement history. As fertilisers were previously applied entirely in the WPC for younger palms (<8 years), there was a significant decline in soil fertility with increasing distance from the palm bole and increasing soil depth. The majority of feeder roots were concentrated within the 2 m radius from the palm bole and in the top 20 cm of soil, where nutrient levels were the highest. In older palms (>10 years) where fertilisers had been broadcast over frond heaps in the interpalm spaces and interrows, feeder root biomass was higher outside the WPC, as soil fertility gradients were less apparent. Taking into account the root distribution patterns of oil palm in relation to palm age, specific recommendations on fertiliser placement for oil palm grown on mineral and peat soils are made, so as to improve fertiliser uptake and utilisation.

Comprehensive Impacts of Climate Change on Rice Production and Adaptive Strategies in China.

The rice production system is one of the most climate change sensitive agro-ecosystems. This paper reviews the effects of current and future climate change on rice production in China. In recent decades, thermal resources have increased during the rice growing season, while solar radiation resources have decreased, and precipitation heterogeneity has increased. The increasing frequency of high-temperature stress, heavy rainfall, drought, and flood disasters may reduce the utilization efficiency of hydrothermal resources. Climate change, thus far, has resulted in a significant northward shift in the potential planting boundaries of single- and double-cropping rice production systems, which negatively affects the growth duration of single-, early-, and late-cropping rice. Studies based on statistical and process-based crop models show that climate change has affected rice production in China. The effects of climate change on the yield of single rice (SR), early rice (ER), and late rice (LR) were significant; however, the results of different methods and different rice growing areas were different to some extent. The trend of a longer growth period and higher yield of rice reflects the ability of China’s rice production system to adapt to climate change by adjusting planting regionalization and improving varieties and cultivation techniques. The results of the impact assessment under different climate scenarios indicated that the rice growth period would shorten and yield would decrease in the future. This means that climate change will seriously affect China’s rice production and food security. Further research requires a deeper understanding of abiotic stress physiology and its integration into ecophysiological models to reduce the uncertainty of impact assessment and expand the systematicness of impact assessment.

Effect of Rainfall Pattern on the Tea Production in Bangladesh: An Analysis of Socio-economic Perspectives

In Bangladesh, tea is a cash crop that significantly benefits both our economy and food security. Hence, the study aimed to assess the present pattern of tea production and the impacts of rainfall patterns on tea yield and made tea quality. The empirical data was collected from ten tea gardens covering three tea-producing administrative divisions and six districts of Bangladesh. The cost of production and profit-loss were examined to link with rainfall distribution. Likewise, the daily income of permanent workers, the job market of the contractual workers, and the workers' associated welfare were critically analyzed to understand the social impact. The results showed that the Rangpur division faces incremental dryness; in contrast, the Sylhet division exhibited a steady pattern with a high frequency of continuous heavy rainfall. The rainfall pattern of the Chottogram division was highly unpredictable and oscillated. Long spell drought and heavy showers followed by flash floods affect tea yield. According to respondents' perception, the rainfall pattern showed changing behaviours that are not favourable for tea production. The emergence of new pests and deterioration of soil characteristics were reported. Bangladesh enjoyed the most pleasant weather for tea production in 2019. The cost of production increases with the adverse weather, which increases the loss for the growers. Profit loss of growers is a determinant of the welfare of the workers impacting their livelihoods. The organic tea garden is not affected by climatic events. Consequently, the socio-economic conditions of the organic garden are safeguarded. Hence, the study recommends following organic farming to cope with climate change. Differently, it was reported that the intermediaries' hegemony in the market chain is more problematic than climate change. Policy initiatives are warranted to correct the market and to establish gardeners' and consumers' rights.

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow.

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

Risk assessment of multi-disaster in Mining Area of Guizhou, China

Increased slope and soil instability due to rainfall infiltration and artificial exploitation is a significant cause of multi-disaster in mining areas. Many kinds of research have been conducted to evaluate the susceptibility of single geological disaster species such as landslides, collapses, ground subsides, and debris flows. However, due to the coupling effect of different stages, the evaluation system index's construction is often disregarded during mining-induced multi-disaster risk assessments. Hence, we propose a relatively complete evaluation index system to assess the multi-disaster risk that considers the assessment processes of disaster susceptibility, hazard, and vulnerability in mining areas. The procedure for the proposed method includes three main steps. In step 1, we analyze the spatial probability assessment due to topographic, geological, and geomorphic indicators. In step 2, the hazard assessment indexes system and multi-disaster models are established using different rainfall probability periods, considering the annual maximum daily rainfall. In step 3, the vulnerability assessment method of multi-disaster was proposed according to the calamity environment and considering hazard distribution and bearing body characteristics. The risk evaluation system for multi-disaster mining areas is established based on the previous three steps. We validated the proposed model by analyzing mining exploitation in Panguan town, China. Our model classifies the likelihood of multi-disaster occurrence in mining areas according to five risk levels: very high, high, moderate, low, and very low. The multi-phase risk assessment map is drawn with different rainfall frequencies (10%, 5%, 2%, and 1%, respectively), providing reasonably scientific guidance on the mining region's emergency response capacity.

Adoption of Sobol’s analysis method improved the application of a coupled primary and secondary infection grape downy mildew model in northern China

Grape downy mildew is a major biotic constraint to grapevine production worldwide, and its impact is influenced by environmental conditions and varietal susceptibility. Here, we proposed a grape downy mildew model to optimize plant disease management and its application on northern Chinese grapevine areas. A primary and a secondary infection model of P. viticola were integrated to estimate the date of symptoms on set and disease incidence, to give the firSt brEakout and incidEnce of grape Downy Mildew model (SEE_DM). The experimental data for model calibration were collected on two grapevine varietals with high and moderate susceptibility to downy mildew grown in a multiyear (2009, 2011–2019) and multisite (Beijing, Shenyang and Yantai) trial. A model sensitivity analysis (Sobol analysis) drove the selection of the subset of relevant parameters to be adjusted in calibration. The model adequately reproduced the observed downy mildew incidence, obtaining high R2 (0.89), Nash-Sutcliffe modelling efficiency (0.72), and low RMSE (9–16%). The model correctly estimated the disease onset date in all conditions but two (late by 5 and 13 days), demonstrating to be a valid tool to take timely decision to limit the time course of the disease. The model was then projected in three sites under varying temperature and rainfall to analyses their effects on the trends of grape downy mildew incidence, using a variety with moderate susceptibility. This study highlights that Northern China is gradually becoming more suitable for grape downy mildew infections, along with warmer temperatures and more frequent rainfall.

Backwater Effect of Clogging of Aquatic Plants at Fine-Particle Screens on Inland Flooding in Okayama

In low-lying Okayama city, Japan, the heavy rainfall frequency and intensity have recently increased, subjecting the city to inland flooding. Another factor increasing the inland flooding risk is fine-particle screen clogging by aquatic plants at drainage pump stations, which obstructs drainage and causes backwater. In this study, water level simulations were conducted in drainage pump station channels to clarify the inland flooding risks with and without aquatic plant clogging-induced backwater at fine-particle screens. In the Urayasu West Drainage Pump Station channels, without backwater, no inland flooding occurred under an initial water level of 70% of the channel depth and a 40 m3/s discharge. However, when backwater deeper than 0.2 m occurred under the same conditions, inland flooding occurred, indicating an increased inland flooding risk associated with backwater. Additionally, we conducted an aquatic plant distribution survey in the main Okayama city channels and proposed six priority control sections based on sections with thriving aquatic plants. Although no previous inland flooding studies have considered aquatic plant clogging-induced backwater at fine-particle screens, aquatic plants cause clogging problems and drainage obstructions at water control facilities worldwide. Therefore, this study reveals the importance of conducting water level simulations and distribution surveys in areas other than Okayama city.

Roles of Multi‐Scale Orography in Triggering Nocturnal Convection at a Summer Rainfall Hotspot Over the South China Coast: A Case Study

AbstractThe northern mountainous area of the Pearl River Delta (PRD) is a center of convection and heavy rainfall of coastal southern China during the summer monsoon season. Although there are frequent nighttime convective activities and rainfall over the region, the key dynamical and physical processes responsible for the nocturnal convection initiation (CI) are complex and not yet well documented. In this study, high‐resolution reanalyses from the Variational Doppler Radar Assimilation System (VDRAS) were used to explore the triggering mechanisms of a representative nocturnal convection event that occurred over the northern PRD on August 14, 2014. Results showed that the enhanced prevailing low‐level southerly winds, deflected easterly winds caused by the blocking of multi‐scale orography, and northern downslope winds associated with an orographic cooling effect were crucial for generating low‐level convergence and strong updrafts before CI. The low‐level convergence and moisture flux in the boundary layer started to increase at 00:20 local solar time (LST), and by the CI time (01:00 LST), the convergence intensity more than doubled. The intensification of low‐level convergence and moisture transport was strongest before 00:40 LST and was dominated by a zonal component that was closely related to the deflected easterly winds. Using Doppler radar data assimilation, this is the first study to reveal how the deflected flows and the thermally induced downslope winds combine with prevailing monsoonal winds to affect a nocturnal CI over the complex mountainous region of the South China coast.

Transition of Large‐Scale Environmental Conditions and Characteristics of Four Rainfall Types Observed by S‐PolKa During the MJO‐1 Active Phase of DYNAMO/CINDY/AMIE

AbstractAnalyses of National Center for Atmospheric Research (NCAR) S‐PolKa dual‐polarization radar data and ERA5 reanalysis fields indicate gradual changes in convection characteristics and large‐scale environmental conditions during a central Indian Ocean Madden‐Julian Oscillation (MJO) event observed by the DYNAMO/CINDY/AMIE field campaigns in late October 2011 (MJO‐1). Examination of four rainfall types (isolated convective cores, convective, mixed, and stratiform) reveals a transition of convection characteristics (i.e., areal coverage and depth) between distinct 5‐day environmental periods at the beginning and end of this ∼2‐week MJO active phase. A shift toward less frequent rainfall covering less of the radar domain for all four rainfall types occurs when large‐scale lower‐tropospheric dry air advects into the region with the westerly wind burst (WWB). Drier and warmer lower‐free‐tropospheric conditions associated with the WWB contribute to increased large‐scale surface‐based convective inhibition (CIN), surface‐based convective available potential energy (CAPE), and cloud base heights. A thermodynamic budget analysis indicates reduced surface heat fluxes contribute to the increased surface‐based CAPE. Greater CAPE at the end of MJO‐1 coincides with deeper 50‐dBZ convective echoes, while decreased 10‐dBZ depth for all rainfall types corresponds in time with WWB‐related dry‐air advection. Increased (decreased) reflectivity values in the lower‐level vertical reflectivity distribution of convective (stratiform) precipitation indicate increased (decreased) convective (stratiform) intensities when the WWB is present. The opposite depth changes for convective echoes and opposite shifts in convective and stratiform precipitation intensities underscore how the WWB can have differing impacts at different reflectivity thresholds and stages of the deep convection lifecycle.

Farmers' perceptions of climate change and its impact on gum Talha (Acacia seyal var. seyal) production in Bahar Alarab locality, East Darfur State, Sudan

Acacia seyal var. seyal is an essential source of income to farmers in the Bahar Alarab locality in Sudan. Farmers’ perceptions of current climate conditions and their subsequent repercussions on Gum Talha (Acacia seyal var. seyal) production remains poorly investigated and understood. To fill this gap, a survey was carried out within six villages at Bahar Alarab locality in East Darfur State, Sudan, and a total of 391 randomly selected farmers were included in the study. Moreover, rainfall and temperature data over 30 years (1988 – 2020) were analysed. The results revealed that climate change signs were perceived by farmers based on the size of gum production (27.4%), early falling of leaves (26.1%), decline in production (24.3%), and persistent insect attack (22.3%). Moreover, farmers also reported increases (65%) and decreases (19%) in temperature. In contrast, 46.5% of farmers reported that rainfall deficiency was a decreasing factor of production. Accordingly, 69% of respondents perceived an increase in temperature, whereas 49.4% reported a decrease in rainfall frequency. Additionally, the results showed that there was long-term variability in temperature over the past three decades. The results of multinomial logistic regression highlighted that household size is an important factor contributing to the increasing trend of temperature. In addition, the growth and productivity of acacia trees were found to be determinants of farmers’ perception of temperature and precipitation change over the past 30 years. The study suggests a tailored policy that could reduce climate-induced impacts on gum Talha productivity and increase farmers’ gain to avoid poverty in this locality.

Risk Assessment of Soil Erosion Using a GIS-Based SEMMA in Post-Fire and Managed Watershed

A large wildfire occurred due to strong winds and dry climates in the Gangwon province of South Korea. Thereafter, floods and sediment damage were caused by Typhoon Mitag in the burned areas. This study was an attempt to quantitatively evaluate the risk of soil erosion in wildfire areas using the Soil Erosion Model for Mountain Areas (SEMMA) based on GIS, which was developed in South Korea. The model required the integration of maps of the main factors involved, i.e., rainfall erosivity, vegetation index, soil erodibility, and slope length and steepness. According to the model simulation results, high erosion rates of over 100 t/ha were concentrated within the wildfire areas. Sediment yields from the study watershed, including the wildfire areas, were estimated to be 40.33 t/ha for the 30-year frequency of rainfall, which is similar to those of the typhoon. The high risk of erosion was predominantly observed in the upper mountains, which are characterized by steep slopes, silt loam, and shallow soil depths within the wildfire areas. Urgent and excessive logging of burned trees further increased the risk of erosion. However, various treatment strategies were implemented to control soil erosion and sediment transport from the post-fire watershed. This study confirmed that temporal and spatial BMPs should be selected and enforced to reduce sediment disasters in wildfire areas.

Long Term Microscale Decadal Analysis of Coastal Rainfall Pattern: An Indication of Microclimatic Variation in South India

There are drastic variations in the intensity and the pattern of rainfall and temperature globally. The variations thus reflect the variation in the microclimatic signatures. The current study aims to assess the climatic changes in the coastal region of Ramanathapuram using the monthly rainfall and temperature average along the coastal region. The microscale rainfall and temperature trend have been analysed for three decades (30 years), representing 1990 to 2019. The study was divided into three different periods: first (1990-1999), second (2000-2009), and the third decade (2010-2020). The study infers that 467 km2 of the area receives an average of 676 mm of rain in decade-I. In decade-II, 39 km2 of the area is noted with 637.6 mm rainfall, 48 km2 of the study area is reported to have 821 mm rainfall, and only 29 km2 area receives normal rainfall of 992 mm, but 351 km2 receives “excess” rainfall of above 1000 mm. In decade-III, less than 750 mm rainfall is recorded at 202 km2, with 43 km2 recorded “deficit” rainfall, and 24 km2 area received an “excess” rainfall. The variation in the amount of rain during the recent period was observed. Similarly, the temperature was comparatively elevated during the Ist Decade (1990-1999). The intensity of rain events, number of rainy days and frequency of rainfall reflected variations. The land surface temperatures also indicated minor variations and increased inter-monsoonal periods. The variation in the rainfall amount, the shift in monsoonal periods and the change in temperature indicate the microclimatic variation in the region.

Characterization and frequency analysis of long-term maximum rainfall from São Martinho, Santa Catarina, Brazil

The study of the frequency of intense rainfall is important for agricultural and environmental planning and the dimensioning of drainage works. This study aimed to characterize and determine the relationships of intensity, duration, and frequency of long-term rainfall in São Martinho, Santa Catarina. The series of annual maximums lasting from one to ten days, observed in the period from 1977 to 2020, were determined. The probability distributions Gumbel, GEV, Log-Normal with two parameters, Log-Normal with three parameters, Pearson type III, and Log-Pearson Type III were evaluated. The parameters were estimated by the method of moments, maximum likelihood method, method of L-Moments, and for the Gumbel distribution, the Chow method was used. The Kolmogorov-Smirnov, Anderson-Darling, and Filliben fitting tests were used, and for the selection of the distribution, the standard error of the estimate was also used. The Log-Normal distribution with three parameters was selected for series lasting one, five and six days. The Pearson III distribution was selected for a duration of three and four days and the GEV distribution for the other five series. The adjusted IDF equation allows the estimation of rainfall intensity with duration from24 hours to 240 hours and a return period ranging from 2 to 100 years.

Seedling responses to soil moisture amount versus pulse frequency in a successfully encroaching semi-arid shrub.

Rainfall timing, frequency, and quantity is rapidly changing in dryland regions, altering dryland plant communities. Understanding dryland plant responses to future rainfall scenarios is crucial for implementing proactive management strategies, particularly in light of land cover changes concurrent with climate change. One such change is woody plant encroachment, an increasing abundance of woody plants in areas formerly dominated by grasslands or savannas. Continued woody plant encroachment will depend, in part, on seedling capacity to establish and thrive under future climate conditions. Seedling performance is primarily impacted by soil moisture conditions governed by precipitation amount (quantity) and frequency. We hypothesized that (H1) seedling performance would be enhanced by both greater soil moisture and pulse frequency, such that seedlings with similar mean soil moisture would perform best under high pulse frequency. Alternatively, (H2) mean soil moisture would have greater influence than pulse frequency, such that a given pulse frequency would have little influence on seedling performance. The hypotheses were tested with Prosopis velutina, a shrubnative to the United States that has encroached throughout its range and is invasive in other continents. Seedlings were grown in a greenhouse under two soil moisture treatments, each which was maintained by two pulse frequency treatments. Contrary to H1, mean soil moisture had greater impact than pulse frequency on seedling growth, photosynthetic gas exchange, leaf chemistry, and biomass allocation. These results indicate that P. velutina seedlings may be more responsive to rainfall amount than frequency, at least within the conditions seedlings experienced in this experimental manipulation.

How drought tolerant are tropical woody crop species – turgor loss points for the five most common species in the emerging landscapes on the Malaysian Peninsular

Drought events are increasing with climate change and threaten the future of industrial plantations in Southeast Asia. Drought tolerance has been well investigated for oil palm plantations. A comparative study on the tolerance of different woody crop species is still missing. We assessed the leaf turgor loss point of five common woody crop species using osmometry. The aim was to assess possible future risks in terms of the species´ drought tolerance. The research was carried out in Simpang Pertang, Malaysia. We collected botanical material from five species, namely Acacia mangium, Hevea brasiliensis, Tectona grandis, Mangifera indica, and Elaeis guineensis. To assess the adaptation of the local tree species pool, we measured the turgor loss point of the 65 most abundant tree species in the Pasoh forest reserve for comparison. Measured species showed a wide range of drought tolerance, ranging from -2.56 MPa to -1.15 MPa and -2.46 MPa to -0.88 MPa for crop species and forest species, respectively. Under the current precipitation regime, there is less concern about the cultivation success of these species in the study area. With the predicted changes in rainfall intensity and frequency, a reduction in the productivity of woody crop plantations can be expected.

Spatial runoff estimation under different land uses and rainfall frequencies: case of flood-prone Dechatu River Catchment, Dire Dawa, Ethiopia

Spatial runoff estimation under different land uses and rainfall frequencies: case of flood-prone Dechatu River Catchment, Dire Dawa, Ethiopia

New Insights into Ice Avalanche-Induced Debris Flows in Southeastern Tibet Using SAR Technology

Drastic climate change has led to glacier retreat in southeastern Tibet, and the increased frequency and magnitude of heavy rainfall and intense snow melting have intensified the risk of ice avalanche-induced debris flows in this region. To prevent and mitigate such hazards, it is important to derive the pre-disaster evolutionary characteristics of glacial debris flows and understand their triggering mechanisms. However, ice avalanche-induced debris flows mostly occur in remote alpine mountainous areas that are hard for humans to reach, which makes it extremely difficult to conduct continuous ground surveys and optical remote sensing monitoring. To this end, synthetic aperture radar (SAR) images were used in this study to detect and analyze the pre-disaster deformation characteristics and spatial evolution in the Sedongpu Basin and to detect changes in the snowmelt in the basin in order to improve our understanding of the triggering mechanism of the ice avalanche-induced debris flows in this region. The results revealed that the maximum average deformation rate in the basin reached 57.3 mm/year during the monitoring period from January 2016 to October 2018. The deformation displacement in the gully where the ice avalanche source area was located was intimately correlated with the summer snowmelt and rainfall and was characterized by seasonal accumulation. Clear acceleration of the deformation was detected after both the most recent earthquake and the strong rainfall and snowmelt processes in the summer of 2018. This suggests that earthquakes, snowmelt, and rainfall were significant triggers of the Sedongpu ice avalanche-induced debris flows. The results of this study provide new insights into the genesis of the Sedongpu ice avalanche-induced debris flows, which could assist in disaster warning and prevention in alpine mountain regions.