Mean Annual Rainfall Research Articles

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques

Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980–2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann–Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027–2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

Analysis of Trend in Meteorological and Hydrological Time-series using Mann-Kendall and Sen’s Slope Estimator Statistical Test in Akwa Ibom State, Nigeria

The monthly, seasonal, annual, and decadal trends of seven hydro-meteorological variables were analysed for stations in Akwa Ibom State, Nigeria, controlled by the Nigerian Meteorological Agency (NiMet) and the Cross River Basin Development Authority (CRBDA) from 1972 to 2021. At the 5% statistical significance level, the non-parametric Mann-Kendall and Sen's slope estimator techniques were used to detect if there was a positive or negative trend and the magnitude of the trend in hydro-meteorological data. In the present study, there was a significant statistically increasing (positive) trend in mean seasonal and annual rainfall, maximum temperature, minimum temperature, and runoff. However, there was a significant statistically decreasing (negative) trend in average annual relative humidity, solar radiation, and potential evapotranspiration. The magnitudes of the trends were 19.39mm/year, 0.0314oC/year, 0.013oC/year, -0.104%/year, -8.78MJ/m2/year, -1.440mm/year, and 0.028m3/s/year for annual rainfall, maximum temperature, minimum temperature, relative humidity, solar radiation, PET, and runoff, respectively. The rising trends in precipitation, temperature, and runoff in this research area show that this region is subject to climatic variability. The results of the Mann-Kendall and Sen's slope estimator statistical tests revealed the consistency of performance in the detection of the trend for the hydro-meteorological variables.

Groundwater Recharge and Circulation in Dolomitic Aquifer Located in Semi-Arid Region: Evidence from the δ18O and δ2H Record, South Africa

Dolomitic aquifers are regarded as important groundwater storage sites in South Africa. Since these aquifers occur in a semi-arid climatic setting with low rainfall, often characterized by a torrential downpour and high potential evapotranspiration, the occurrence of active recharge is very limited (<5% of mean annual rainfall) as compared with the rainfall amount. The Malmani dolomites that have undergone greenschist metamorphism contain widespread caves and open karst structures at shallow levels, which facilitate groundwater recharge, circulation, storage and spring occurrence. However, the open karst structures receive recharge that passes through fractures in the vadose zone, which regulates the recharge through retardation and mixing processes. The integrated approach involving major ions and stable isotopes of water was applied to understand the recharge mechanism. The cave drip water samples were represented by the δ18O values of −3.95‰ to 3.32‰ and the δ2H values ranging from −11.0‰ to 27.7‰. On the other hand, the rainfall isotope results for δ18O fall between −16.11‰ and 5.38‰, while the δ2H values fall between −105.7‰ and 35.6‰. The most depleted Malapa springs contain δ18O of −5.64‰ and δ2H of −32.4‰. Based on the results, the mixing of water in the vadose zone could be considered as an indicator of the dominance of a slow-diffusive flow process in the aquifer as a result of poor fracture permeability. However, regional groundwater circulation through faults and dykes besides interconnected karst structures helps in generating highly productive karst springs in the region characterized by low rainfall.

Understanding amphidromy in Hawai'i: 'O'opu nākea (Awaous stamineus).

Hawai'i is home to 'o'opu nākea (Awaous stamineus), a culturally significant, endemic, goby that exhibits an amphidromous life cycle characterized by a marine larval stage followed by post-larval recruitment to streams, where they live to become reproductive adults. However, it was recently suggested that their migration to the ocean might not be obligatory, as originally thought. Despite their importance in Hawaiian traditions and the ecology of Hawaiian freshwater ecosystems, we still lack a full understanding of their migratory patterns and life history due to the difficulties in determining the environmental migratory cues that set the timing and location of their migratory paths. This study examined environmental factors, such as mean annual rainfall, streamflow, and water chemistry, to determine if they play a role in whether A. stamineus spend their larval period in the ocean or their entire life cycle in freshwater streams. We sampled A. stamineus (n = 90) from three streams (Kahana, Kahalu'u, and Waimānalo) on the island of O'ahu, Hawai'i that represented the range of hydroclimatic gradient in wet-habitat conditions on the windward side of the island and characterized their migratory pattern using elemental analysis of sagittae, the largest pair of otoliths (calcareous ear structures). Based on otolith strontium:calcium and barium:calcium ratios, we determined if individuals spent their larval period in the ocean or the stream. We found that 100% of individuals displayed clear evidence of marine residence during their larval phase, regardless of the environmental factors the fish experienced. This study highlights the necessity of stream-ocean connectivity for the survival of A. stamineus and emphasizes the importance of stream-mouth conservation and management as it is a critical transition zone in stream-ocean-stream migratory pathways.

Modeling spatial variation in radiata pine slenderness (height/diameter ratio) and vulnerability to wind damage under current and future climate in New Zealand

Wind is a significant disturbance agent in forests that is likely to become more prevalent as plantation forests are increasingly grown at high stand density to maximize yields and carbon sequestration. Although research has used models to characterize plantation wind risk at discrete sites, little research has regionally scaled these predictions under both current and future climate. Stem slenderness, or height/diameter ratio, has been found to be a useful proxy for stand stability in the widely grown species, radiata pine (Pinus radiata), which constitutes 90% of the New Zealand plantation area. Stands with a slenderness that exceed 80 m m–1 are likely to be at risk when exposed to strong winds. In this study, slenderness was derived from 4,004 radiata pine permanent growth monitoring plots, that covered the spatial distribution of the species, and matched with stand density, age and key climatic and edaphic variables derived from surfaces. Using these data, the objectives of this study were to (i) develop a model to predict slenderness using machine learning and partition the impacts of key factors on slenderness, (ii) use this model to spatially predict slenderness throughout New Zealand for three contrasting silvicultural regimes, under current and future climate, and (iii) identify any regions that may be particularly vulnerable to wind risk. The final model of slenderness developed using the random forest algorithm included, in order of importance, stand density, stand age, mean annual air temperature and total annual rainfall. Predictions made on a withheld independent dataset (n = 401) were both unbiased and accurate (R2 = 0.85). Spatial predictions of slenderness under current climate showed wide regional variation with mean slenderness at age 30 years increasing from 62.1 m m–1 for low stand density clearwood regimes to 73.5 m m–1 for medium stand density structural grade regimes and 79.5 m m–1 for high stand density carbon regimes. Mean changes in slenderness were relatively low to 2040 and 2090 within the North Island. In contrast, within the South Island there were moderate increases in slenderness for the three silvicultural regimes that were markedly higher for 2090 than 2040.

Unpacking Occupational Health Data in the Service Sector: From Bayesian Networking and Spatial Clustering to Policy-Making

The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including variography, ordinary kriging, and G clustering, was used to generate national distribution maps for various factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers.

Unravelling the future changes in rainfall erosivity over India under shared socio-economic pathways

The rainfall erosivity index or R-factor is a climate-driven parameter affecting soil erosion. We studied the spatial distribution of rainfall erosivity over India for the historical (1951 – 2014) and two future periods (near future: 2015–2040; far future: 2041–2070) under two shared socio-economic pathways (SSP245 and SSP585). We used 15-minute rainfall datasets from 70-gauge stations to establish a relationship between rainfall erosivity estimated from 15-minute rainfall data and those estimated from 30 and 60-minute rainfall data. Further, we used 30,203 erosive storm rainfall data from 329 stations in India to estimate the daily rainfall erosivity model parameters. After that, annual rainfall erosivity values of 329 stations, nine bio-climatic variables, and a Gaussian Process Regression (GPR) model were used to prepare rainfall erosivity maps for the historical and two future periods. The bias-corrected Coupled Model Intercomparison Project-6 (CMIP6) dataset of 13 models was used for deriving bioclimatic variables and rainfall erosivity estimation for the historical and future periods. We used the GPR model to find the historical period's mean annual rainfall erosivity value as 5225 MJ.mm.ha−1.h−1.year−1. The result shows a +13 % and +15 % increase in the mean rainfall erosivity over India in the near future under SSP245 and SSP585 projected pathways compared to the historical period. The increase in erosivity values in the far future is even higher at +22 % and +31 % under SSP245, and SSP585 projected pathways, respectively. The northwestern and central India is likely to have a pronounced increase in rainfall erosivity in the far future. The change in rainfall erosivity due to climate change might substantially impact India's land and water resources, which is mainly an agrarian economy. The outcome of the present study could help in better planning and management of land and water resources in the upcoming decades.

Cytological Pattern Reveals Genome Downsizing in Cynodon dactylon (L.) Pers along the Longitudinal Gradient

Understanding the cytological pattern of genome size and ploidy level of the bermudagrass (Cynodon dactylon) is vital to explore the evolution pattern and breeding of the species. To study the diversification of the cytological pattern of bermudagrass along the longitudinal gradient, the genome size and ploidy level were measured and explored with the relationship to climate factors. The corresponding ploidy level was verified through the mitotic chromosome counts method. Bermudagrass accessions ploidy level included diploids, triploid, tetraploid, pentaploid and hexaploid with a basic chromosome number of x = 9. The major ploidy level was tetraploid (45%) and aneuploidy was commonly discovered in collected regions. Mean genome size of bermudagrasswas was estimated to be 1.31 pg/1Cx along longitudinal gradient. The 1Cx values of diploid were higher than that of triploid and tetraploid, while the tetraploid had minimum basic genome size. In the current study, we observed that genome downsizing exists in tetraploids of Cynodon dactylon. Tetraploids have a wider distribution than other ploidy levels, especially in arid areas, occupying a relatively high proportion. In addition, at the same ploidy level, genome size was remarkably variable in the current study. The coefficient of determination analysis showed that longitude and mean annual rainfall were significantly correlated to genome size rather than ploidy level. This cytological study will be helpful for further genetic mechanisms and molecular characteristics to landscape adaptation of bermudagrass.

Survival trends (2008–2020) of three tree species in response to elephant impact, environmental variation, and stem wire-netting protection in an African savanna

Reduced levels of the survival of large trees (≥5 m height) in Africa’s savannas are a conservation concern, particularly where large trees co-occur with African elephants (Loxodonta africana). Elephants, as ecosystem engineers, can structurally modify and lesson the savanna large tree component. Wire-netting, which involves wrapping chicken-mesh around a tree’s main stem, has been used as a mitigation method to increase tree survival. We assess the trends in survival of three large tree species of conservation importance, namely Lannea schweinfurthii, Senegalia nigrescens and Sclerocarya birrea, within the Associated Private Nature Reserves (APNR) on the western boundary of the Kruger National Park, South Africa. We consider survival trends linked by both elephant impact, as well as external environmental factors. We conducted four field assessments on 2,758 trees in 2008 (baseline), 2012, 2017, and 2020, where we recorded i) elephant impact levels on each tree, ii) whether the tree had wire-netting, and iii) the tree’s survival status. We then modelled tree survival status as a dependent variable against multiple environmental factors. We found that tree survival was lowest when mean annual rainfall was lowest due to the drought, particularly amongst L. schweinfurthii and S. nigrescens. Wire-netting significantly increased large tree survival in comparison to control trees over the 12-year period, however, this effect decreased after four years if the wire-netting had lost its structural integrity. We illustrate how various environmental factors, in combination with elephant impact, affect large tree survival in an African savanna with a high density of artificial water points. We also provide results on the longest known study on wire-netting as a mitigation method for elephant impact on large trees and provide evidence on how a period of drought may have accelerated large tree decline in a southern African savanna.

Spatio-temporal epidemiology of livestock diseases in the variable semi-arid rangelands of northern Kenya.

Diseases affecting livestock can have significant impacts on the animal, humans and the economy. Participatory epidemiology and spatial analysis were conducted to assess livestock disease problems in Samburu County, northern Kenya. Key informants were selected purposively with the help of local leaders. Among the livestock, goats were identified to have the most economic importance. On the other hand Pestes des Petits Ruminants (PPR), Foot and Mouth Disease (FMD) and Camel Trypanosomiasis diseases were identified to have the highest impact on pastoral livelihood. Spatial analysis indicated that all the disease hotspots were closely related to the distribution of herds during different seasons of the year. Correlations between the mean annual rainfall and selected livestock diseases were significant for East Coast Fever (ECF) (r = - 0.767, p = 0.001, N = 15), Cattle Helminthiasis (r = 0.639, p = 0.010, N = 15), Cattle Anaplasmosis (r = 0.631, p = 0.012, N = 15) and Camel Pox (r = - 0.646, p = 0.044, N = 10). There was a strong relationship between seasonality and livestock disease epidemiology. Disease control efforts should be focused towards the hotspots in the wet season and dry season grazing areas.

Geo-climatic risk factors for chronic rhinosinusitis in southwest Iran.

Chronic rhinosinusitis (CRS) is a prevalent and disabling paranasal sinus disease associated with some environmental factors. In this study, we evaluated the effect of geo-climatic factors on CRS in a region of southwest Iran. The study mapped the residency addresses of 232 patients with CRS who lived in Kohgiluyeh and Boyer-Ahmad province and had undergone sinus surgery from 2014 to 2019. The effects of Mean Annual Humidity (MAH), Mean Annual Rainfall (MAR), Mean Annual Temperature (MAT), maximum MAT (maxMAT), minimum MAT (minMAT), Mean Annual Evaporation (MAE), wind, elevation, slope, and land cover were assessed on the occurrence of CRS using Geographical Information System (GIS). Statistical analysis was performed using univariate and multivariate binary logistic regression. Patients came from 55 points including villages, towns, and cities. In univariate analysis, climatic factors including MAT (OR = 0.537), minMAT (OR = 0.764), maxMAT (OR = 0.63), MAR (OR = 0.994), and MAH (OR = 0.626) were significantly related to CRS occurrence. Elevation (OR = 0.999), slope (OR = 0.9), and urban setting (OR = 24.667) were the significant determinants among geographical factors when analyzed independently. The multivariate analysis found maxMAT (OR = 0.5), MAR (OR = 0.994), elevation (OR = 0.998), and urban (OR = 16.8) as significant factors affecting CRS occurrence. The urban setting is the most critical factor affecting CRS disease. Cold and dry areas and low attitude are the other risk factors for CRS in Kohgiluyeh and Boyer-Ahmad province, southwest Iran.

Trend and homogeneity test analysis for rainfall over a 121-year time period in the desert district of Western Rajasthan, India

Abstract The Thar Desert receives very scarce and scanty rainfall; hence, water is given the top priority and importance in daily life. During the past decades, there has been a variation in the trend of the rainfall, climate, and other atmospheric conditions in the Jaisalmer district. An analytical study has been carried out to investigate this changing environmental phenomenon. Sixteen grids have been selected to study the trend and magnitude of the slope for rainfall using the MMK (Modified Mann–Kendall) test and Sen's Slope estimator. Pettitt's test, SNHT (Standard Normal Homogeneity Test), and Buishand's test have been used to find trend change year in 121-year time series of rainfall. Mean annual rainfall for all grids shows positive values of Kendall's tau and Sen's Slope, which indicate that trend interpretation is rising, and test interpretation is increasing. Most of the results obtained from grids show the year ‘1992’ as the trend-changing year. A significant rise in mean annual rainfall has been noticed after the change of year in the study area. This type of study has not been conducted earlier in the region to identify the rainfall trend and change of year of trend. This study will help policymakers to take necessary action.

The influence of spatial patterns in rainfall on shallow landslides

Understanding how rainfall events influence the pattern and magnitude of landslide response is an important research focus from geomorphological and hazard planning perspectives. Few studies quantitatively relate spatial patterns in rainfall and landslides, largely due to difficulties in acquiring landslide inventories and data on rainfall patterns for individual storm events. However, the increasing availability of frequent, high-resolution satellite imagery and weather radar is overcoming these impediments. Here, we aim to a) identify which factors most influence susceptibility to shallow landslides at the event scale and b) assess how the spatial density of landslides varies in relation to rainfall. We combine landslide inventories spanning study areas located across the upper North Island of New Zealand with rainfall estimates from weather radar to assess the influence of different explanatory variables using a logistic regression model. We found land cover and slope exert the largest influence on landslide susceptibility ahead of intra-event rainfall intensities and pre-event rainfall accumulations. Of the rainfall variables, maximum 12-h rainfall normalised by the 10-y recurrence interval intensity and the 10-d pre-event accumulation normalised by mean annual rainfall had the most influence on susceptibility. Forest cover reduced the sensitivity of landslide spatial density to variations in slope, rainfall, and rock type, in contrast to pasture. We observed a 3.5-fold increase in mean landslide density once the maximum 12-h intensity was ≥25 % above the 10-y recurrence interval intensity for pastoral land on weak sedimentary rocks. This threshold is consistent with the increase in 12-h rainfall by late century under the highest levels of projected warming in New Zealand, which suggests that the landslide response to storm rainfall could be significantly amplified by climate change. Our study demonstrates the insights gained from combining event inventories with spatial rainfall data to better understand factors influencing landslide susceptibility.

Improvement of satellite‐based rainfall product CHIRPS in estimating rainfall erosivity on the Loess Plateau

AbstractSatellite‐based rainfall products have great potential for estimating rainfall erosivity, as they can provide continuous spatiotemporal distribution of precipitation estimates over large areas, especially for monitoring in areas with complex terrain and extreme climates. This paper uses the daily rainfall derived from the satellite‐based CHIRPS product on the GEE platform and Zhang's daily rainfall erosivity model to calculate the rainfall erosivity on the Loess Plateau during the period of 1981–2020. The accuracy of rainfall erosivity for the CHIRPS product is evaluated by comparing the results to estimates from national meteorological stations, and then the calculation formula of rainfall erosivity is optimized to improve the accuracy of rainfall erosivity based on CHIRPS products. The results show that the annual average rainfall of the CHIRPS product at the locations of national meteorological stations in 1981–2020 is 473.7 mm, which is 6.8% higher than that observed by the meteorological stations. The multi‐year average value of daily rainfall and the yearly average rainfall both for days with rainfall larger than or equal to 12 mm are 15.9% and 18.2% greater than those of the meteorological stations, respectively, and eventually resulting in an overestimation of rainfall erosivity on the Loess Plateau by 44.0%. The annual mean rainfall erosivity interpolated based on meteorological stations and calculated from the gridded CHIRPS product in 1981–2020 is 1344.2 MJ·mm/(hm2·h) and 2013.7 MJ·mm/(hm2·h), respectively. This result suggests that rainfall erosivity estimated by gridded CHIRPS product is overestimated by 49.8%, of which 46.1% is due to the overestimation of erosive rainfall in gridded CHIRPS and 3.7% is caused by site density and interpolation. Satellite‐based CHIRPS product is similar to the observations of meteorological stations in total rainfall and trends, but differs in rainfall frequency and intensity, which is an important reason for the difference in rainfall erosivity between satellite‐based rainfall products and meteorological stations. The CHIRPS‐based rainfall erosivity calculated using the optimized parameters reduces the overestimation from 49.8% to 3.2%, greatly reducing the satellite‐based rainfall erosivity estimation error.

Legacy effects of intensified drought on the soil microbiome in a mesic grassland

AbstractThe soil microbiome response to global change drivers remains largely unclassified, with current studies showing contrasting results to several global change drivers. One such driver, drought, is increasing in severity and frequency due to climate change. Further, legacy effects, or long‐lasting impacts after drought has subsided, could have lasting impacts on the soil microbiome with important consequences for ecosystem functioning. Thus, our study aimed to understand how the soil microbiome responds after a severe, prolonged drought and whether legacy effects persist post‐drought. We measured soil microbial community response in a mesic grassland for two years after a four‐year experimental drought in a native tallgrass prairie. The experimental drought was imposed either (1) chronically by reducing each growing season rainfall event by 66% or (2) intensely by completely eliminating growing season rainfall until an ~45% reduction in mean annual rainfall (climatic average) was achieved. The bacterial community showed no legacies in the first season post‐drought in response to either chronic or intense drought but showed an increased abundance of Verrucomicrobia and decreased richness in both treatments in the second growing season after the drought treatments ended. In the first and second post‐drought growing seasons, we found small differences in beta diversity between the control and intense drought treatment for fungal communities but not for the chronic drought treatment. Further, we found that the two main phyla of fungi, Ascomycota and Basidiomycota, showed reduced relative abundance post‐drought in the intense drought treatment. Overall, few legacies in soil microbial communities persisted after a four‐year experimentally induced drought. However, our results show that the nature of the drought—chronic versus intense—can differentially impact fungal versus bacterial short‐term legacies. These results suggest that the soil microbiome is for the most part drought resistant (responds little during drought) and/or resilient (quickly recovers post‐drought, since a study at the same site found that drought impacted the bacterial community) in this mesic grassland.

Temporal and spatial patterns of habitat of Nipponia nippon in China under the background of climate change.

Crested ibis (Nipponia nippon) is one of the endangered species in the International Union for Conservation of Nature. It is of great significance to pay attention to the changes of its suitable habitat in the context of climate change. Based on the geographical distribution data of crested ibis, the MaxEnt model was used to predict the suitable habitat of crested ibis under current scenario and future climate change. The results showed that the prediction accuracy of MaxEnt model was high, with an AUC value of 0.989. The minimum temperature of the coldest month, the mean temperature of the coldest quarter, and the mean annual rainfall were the dominant environmental factors affecting the habitat of crested ibis. Under current climate scenario, the area of moderately and highly suitable area of Chinese crested ibis was 10.65×104 km2, mainly distributed in Shaanxi, Sichuan, Hubei, Henan, and Gansu. In the future, the suitable habitat area of crested ibis would increase significantly under climate change, mainly distributed in Anhui, Chongqing, Guizhou, Jiangsu, Hunan, Shandong, Shaanxi, Jiangxi, Taiwan, Yunnan, Liaoning, and Fujian. In the SSP126 scenario from 2041 to 2060, the suitable habitat area of crested ibis would reach the maximum, being 139.53×104 km2 higher than that of the current climate scenario, accounting for 19.6% of the land area. This study could provide a basis for policy making on the conservation of crested ibis under global climate change.

Rainfall erosivity estimation models for the Tibetan Plateau

Many models have been developed internationally to estimate rainfall erosivity. However, the effectiveness of these pre-existing models has not been verified for the Tibetan Plateau with large spatiotemporal heterogeneity of rainfall due to lack of high-temporal resolution rainfall data. The purpose of this study was to develop models to accurately estimate daily and semi-monthly rainfall erosivity values in the Tibetan Plateau. We used one-minute resolution rainfall data from 1,182 weather stations to develop rainfall erosivity models over the Tibetan Plateau for the first time. The particle swarm optimization (PSO) method was performed to determine the newly developed model parameters. The newly developed models were compared with three internationally common existing models (exponentially seasonal1Exponentially seasonal model denoted Richardson model (1983) that was revised as R =αP1.7265 by Xie et al. (2016).1, cosine2Cosine model denoted Yu and Rosewell model (1996) that was revised as R=0.26861+0.5412cosπ6M-7π6P1.7265 by Xie et al. (2016).2, and exponentially semi-month3Exponentially semi-month model denoted Zhang model (2002) that was expressed as R=α∑j=1mPjβ,α= 21.586β- 7.1891 and β=0.8363+18.177Pd12+24.455Py12.3) to evaluate their effectiveness. Our results showed that (1) the three existing models all overestimated the values of mean annual rainfall erosivity in the Tibetan Plateau because limited rainfall data in the area were used to develop these models; (2) the new models greatly reduced computational error values of mean annual rainfall erosivity (the symmetric mean absolute percentage error (SMAPE) of ≤ 23%), relative to the three existing models (SMAPE of ≥ 40%); (3) the new semi-monthly models (SMAPE of 17.7 – 29.2%) generally outperformed the new daily models (SMAPE of 17.9 – 30.7%) because of their better ability to estimate the values of extreme rainfall erosivity. It is advisable to use the semi-monthly model rather than daily model to compute rainfall erosivity when using universal soil loss equation (USLE) and its derived models to calculate soil erosion. That will benefit to reduce the time-scale conversion errors between event-based and daily data when lacking high-temporal resolution data and using daily rainfall data to analyze the influence of rainfall on soil erosion.

Landslide Susceptibility Mapping Using LiDAR Data: A Case Study of Khao Yai National Park, Thailand

Landslide is the natural problem occur worldwide due to its geological features, climatic characteristics and human activities. With the help of a geographic information system (GIS) and the Analytic Hierarchical Process (AHP) method, this research attempts to develop a map of landslide susceptibility. During the present investigation, a total of ten landslide influencing factors including elevation, slope, curvature, aspect, topographic wetness index (TWI), land cover, lithology, precipitation, distance to the road and drainage, were examined for the present analysis. Using AHP, weights were applied to each factor. The weight over lay approach was used to create the landslide susceptibility map, which was then divided into five classes. According to the research findings of the susceptibility classes, 19.97% of the research 's area was highly susceptible, followed by 61.65% of low susceptible, 17.33% of moderate susceptible, 0.94% of high susceptible, and 0.12% of very high susceptible. The areas with extremely high landslide susceptibility are adjacent to a road system and have a steep slope. The amount of mean annual rainfall is high and lithology belonging to the Jurassic metasediments. The findings for this map showing the research area's vulnerability to landslides in Khao Yai National Park are useful for planners and decision-makers for slope management and future development projects in the area.

Fluvial response to Quaternary hydroclimate in eastern Africa: Evidence from Gona, Afar, Ethiopia

The Busidima Formation in the Afar region, Ethiopia, spans the Quaternary and records the cultural evolution of the genus Homo. Yet, the Middle Pleistocene to Holocene fluvial environments in which early humans lived are undersampled in eastern Africa. This paper examines the stratigraphy, geochronology and paleoenvironments of the newly designated Odele Member of the uppermost Busidima Formation (<152 thousand years ago (ka)), which has received little attention despite representing a critical period in the evolution of early Homo sapiens and its migration out of Africa. The Odele Member is 40–50 m thick and is dated using tephrochronology, radiometric, luminescence, and electron spin resonance techniques. The member spans 151 to 7 ka, defined at the base by the widespread Waidedo Vitric Tuff (WAVT, 151 ± 16 ka modeled age and 95.4% credible interval - C.I.). There are two prominent erosional unconformities in the Odele Member, a lower one after the WAVT deposition with a modeled 95.4% C.I. range of 124–97 ka; and an upper one involving widespread alluvial fan incision commencing between 21.7 and 12.9 ka. The uppermost Odele Member also contains black, organic-rich mats, redox features, reed casts, and freshwater gastropods marking wetter conditions during the terminal Pleistocene and Early Holocene. A black, fine-grained relict soil coeval with the Halalalee paleosol bounds the top of the Odele Member and has mollic and vertic properties, weathering since ∼12 ka. These incision events and prominent paleosol development near/at the top of the Busidima Formation document Middle to Late Pleistocene Awash River incision to its present-day course. Paleo-rainfall estimates suggest that the Early Holocene-age Halalalee paleosol weathered under a climate with mean annual rainfall 10–15% higher than today. A compilation of radiocarbon ages from aquatic gastropods, carbonized wood and charcoal from the upper Odele Member shows wetter and possibly more vegetated conditions during late marine isotope stage (MIS) 3 and the African Humid Period (AHP) that are tightly coupled with precession-driven summer insolation maxima. These key findings suggest that periods of incision, aggregation, and landscape stability in the Odele Member have an orbital precession pacing. The Odele Member revises upward the age of the Busidima Formation to 7 ka, showing that it spans into the Holocene and now includes Middle and Later Stone Age archaeological traditions.

Greenhouse gas emissions and environmental drivers in different natural wetland regions of China

Wetlands sequestrate carbon at the highest rate than any other ecosystems on Earth. However, the spatial and temporal dynamics of GHGs emissions from the wetland ecosystems in China are still elusive. We synthesized 166 publications that contain 462 in situ measurements of GHGs emissions from the natural wetlands in China, and further analyzed the variability and the drivers of GHGs emissions in eight subdivisions of China's wetlands. The results show that the current studies are mainly concentrated in the estuaries, Sanjiang Plain, and Zoige wetlands. The average CO2 emissions, CH4 fluxes and N2O fluxes from Chinese wetlands were 218.84 mg·m−2·h−1, 1.95 mg·m−2·h−1 and 5.8 × 10−2 mg·m−2·h−1, respectively. The global warming potential (GWP) of China's wetlands was estimated to be 1881.36 TgCO2-eq·yr−1, with CO2 emissions contributing more than 65% to the GWP value. The combined GWP values of Qinghai-Tibet Plateau wetlands, coastal wetlands and northeastern wetlands account for 84.8% of GWP of China's wetlands. Correlation analysis showed that CO2 emissions increased with the increasing mean annual temperature, elevation, annual rainfall, and wetland water level, but decreased with soil pH. CH4 fluxes increased with the mean annual temperature and soil water content but decreased with the redox potential. This study analyzed the drivers of GHGs emissions from wetland ecosystems at the national scale, and GWP values of eight wetland subregions of China were comprehensively assessed. Our results are potentially useful for the global GHGs inventory, and can help assess the response of GHGs emissions of wetland ecosystem to environmental and climate change.