Rainfall Distribution Patterns Research Articles

The response of soil carbon mineralization losses to changes in rainfall frequency is seasonally dependent in an estuarine saltmarsh

Altered rainfall distribution patterns resulting from climate change have substantial effects on soil carbon (C) cycling in terrestrial ecosystems particularly in water-limited regions. However, how rainfall redistribution affects soil C mineralization (CO2 and CH4 fluxes) in humid regions such as of the coastal saltmarshes remain unclear. We conducted mesocosm experiments in an estuarine saltmarsh in the Yellow River Delta of China, where we simulated three rainfall frequency scenarios (high-frequency, medium-frequency and low-frequency) with the same total rainfall amount in the dry and wet seasons, respectively. Soil CO2 and CH4 fluxes were measured before and after rain frequency treatment during a 40-day period for each season. The decrease in rainfall frequency significantly reduced the mean soil CO2 and CH4 fluxes during the dry season, but had no effect on either flux during the wet season. The seasonal variation in the response of soil C mineralization to rainfall frequency changes could be explained by the changes in antecedent soil water and salinity conditions, soil C substrate, microbial activities and diversity. Thus, the effects of changes in rainfall frequency on soil C mineralization are regulated by season, and should be considered when predicting the future C balance of coastal wetland ecosystems. Furthermore, the shift in precipitation frequency distribution towards increasing heavy rainfall events during the dry season in this region will have a great effect on soil C losses, potentially feeding back into the soil C budget and stability in this estuarine saltmarsh.

Irregularity and time series trend analysis of rainfall in Johor, Malaysia

Due to its effect on weather and its propensity to cause catastrophic incidents, climate change has garnered considerable global attention. Depending on the area, the effects of climate change may vary. Rainfall is among the most significant meteorological factors associated with climate change. In Malaysia, changes in rainfall distribution pattern have led to many floods and droughts events which lead to La Nina and El Nino where Johor is one of the states in southern part that usually affected. Thus, rainfall trend analysis is important to identify changes in rainfall pattern as it gives an initial overview for future analysis. This research aims to evaluate historical rainfall data of Johor between 1991 and 2020. Normality and homogeneity tests were used to ensure the quality of data followed by Mann-Kendall and Sen's slope analysis to determine rainfall trend as the rainfall data is not normally distributed (p > 0.05). Standardized precipitation anomaly, coefficient of variation, precipitation concentration index and rainfall anomaly index were used to identify rainfall variability and intensity while standard precipitation index was used to evaluate drought severity. The lowest annual rainfall recorded was 1725.07 mm in 2016 and the highest was 2993.19 mm in 2007. Annual rainfall and seasonal rainfall showed a declining trend although it is not statistically significant (p > 0.05). Results reveal that Johor experienced extreme wet and dry years, leading to drought and flood incidents. Major floods arose in 2006, 2007, 2008, 2010 and 2011 while driest years occurred in 1997, 1998 and 2016 which led to El Nino phenomenon. March and April were identified as the driest months among all. Thus, the findings from this study would assist researchers and decision-makers in the development of applicable adaptation and mitigation strategies to reduce climate change impact. It is recommended that more data analysis from more stations should be done in the future research study to obtain a clearer view and more comprehensive results.

Study of the Spatiotemporal Distribution Characteristics of Rainfall Using Hybrid Dimensionality Reduction-Clustering Model: A Case Study of Kunming City, China

In recent years, the frequency and intensity of global extreme weather events have gradually increased, leading to significant changes in urban rainfall patterns. The uneven distribution of rainfall has caused varying degrees of water security issues in different regions. Accurately grasping the spatiotemporal distribution patterns of rainfall is crucial for understanding the hydrological cycle and predicting the availability of water resources. This study collected rainfall data every five minutes from 62 rain gauge stations in the main urban area of Kunming City from 2019 to 2021, constructing an unsupervised hybrid dimensionality reduction-clustering (HDRC) model. The model employs the Locally Linear Embedding (LLE) algorithm from manifold learning for dimensionality reduction of the data samples and uses the dynamic clustering K-Means algorithm for cluster analysis. The results show that the model categorizes the rainfall in the Kunming area into three types: The first type has its rainfall center distributed on the north shore of Dian Lake and the southern part of Kunming’s main urban area, with spatial dynamics showing the rainfall distribution gradually developing from the Dian Lake water body towards the land. The second type’s rainfall center is located in the northern mountainous area of Kunming, with a smaller spatial dynamic change trend. The water vapor has a relatively fixed and concentrated rainfall center due to the orographic uplift effect of the mountains. The third type’s rainfall center is located in the main urban area of Kunming, with this type of rainfall showing smaller variations in all indicators, mainly occurring in May and September when the temperature is lower, related to the urban heat island effect. This research provides a general workflow for spatial rainfall classification, capable of mining the spatiotemporal distribution patterns of regional rainfall based on extensive data and generating typical samples of rainfall types.

Seasonal variations in rain cells propagation over Central Africa and association with diurnal rainfall regimes

AbstractThree‐hourly data from two satellite rainfall estimates products, PERSIANN and TMPA, are analysed to document the seasonal patterns of diurnal rainfall distribution over the Congo Basin and neighbouring areas. PERSIANN data for 2001–2017, at a one‐hour time‐scale, are further used to identify rain cells (≥4 mm·h−1) in an attempt to explain the diurnal rainfall variations. Over land areas, an afternoon rainfall maximum is clearly shown, but over much of the region only a minor part of the rains (20%–30%) falls in the wettest 3‐h period. Substantial rains (often 50%–60%) occur in the evening and at night, as a progressively delayed peak from east to west, but a seasonal change is found in the meridional propagation of the peak diurnal rainfall, in a south‐westerly direction in January, and a north‐westerly direction in July. Rain cells have prominent genesis areas west of high terrain, but can develop over most regions, with a peak genesis time slightly ahead the diurnal phase of the rains. The size, mean lifetime and mean rainfall intensity of the rain cells are strongly related to each other and display a semi‐annual cycle not fully in phase with the seasonal cycle of the rains. The mean rain cell propagation speed (6.7 m·s−1) is much lower than in previous studies, which focused on mesoscale convective systems. Rain cells which have a longer lifetime move much faster, the mean speed of those lasting less than 6 h being half that of those lasting at least 24 h. Most (86%) of the mobile rain cells propagate westward, but the meridional component of their propagation shows an annual cycle (southward in austral summer, northward in boreal summer) which matches the mid‐tropospheric winds and explains the seasonal changes in the diurnal rainfall peak.

Rainfall erosivity across Austria's main agricultural areas: Identification of rainfall characteristics and spatiotemporal patterns

Study regionTarget area of this study are the main agricultural production zones of Austria. Most important croplands cover the flat to pre-alpine areas concentrated in the north, east, and southeast of Austria. Study focusThe novelty of our study is the spatiotemporal assessment of rainfall characteristics that drive erosivity at the event level as well as the identification of erosive rainfall distribution patterns within the events. Our assessment approach allows the definition of both typical and extreme erosivity. Long-term and high temporal resolution rainfall datasets were used to apply a clustering approach, seasonal and spatial analyses, and rainfall distributions assessment (isopleths) of the identified rainfall types (clusters). New hydrological insights for the regionThree dominant erosive event-types (clusters) were identified that strongly relate with Austria’s seasonality and complex topography. The most erosive rainfall events (cluster C1) are characterized by a high intensity and short duration. C1 events have the largest occurrence frequency in pre-alpine southern Austria and occur from May to September. Unlike the less erosive and more evenly distributed event types (C2 and C3) the highly erosive C1 events have a pronounced maximum rainfall intensity at the onset of the event.

Assessment of Climate Change Impact on Reservoir Inflow

Abstract: Evaluating the effects of climate change is crucial in regions grappling with water scarcity. The Ujjani dam, a significant structure in Maharashtra, India, erected on the Bhima river in 1980, serves as a vital water source for the agricultural lands downstream in Solapur and Pune districts. Assessing climate change's impact involves analysing data from General Circulation Models (GCMs), which project climate parameters under various emission scenarios but often at a broader scale. Since hydrological models necessitate finer-scale climate data, a downscaling technique is employed to derive localized variables from GCM outputs. This study employs Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques for downscaling. By utilizing projected temperature and rainfall data, three distinct models are developed to predict reservoir inflow: multiple linear regression, artificial neural network, and wavelet neural network. The findings indicate significant alterations in rainfall distribution patterns, with a decline during the monsoon season and an increase post-monsoon

RAINFALL MODELING USING THE GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD

Rainfall is an important parameter in understanding the climate and environment in the Malang Regency area. This research aims to model the distribution of rainfall in this region using the Geographically Weighted Poisson Regression (GWPR) method. GWPR is a spatial statistical approach that allows us to understand changes in inhomogeneous rainfall patterns throughout the Malang Regency area. Rainfall data collected from weather stations over several years was used in this study. We use GWR to study the relationship between various environmental factors, such as topography, vegetation, and land use, and rainfall distribution in Malang Regency. The results of the GWR analysis provide a deeper understanding of the spatial differences in the influence of these factors on rainfall. By applying GWR, we can find out how certain factors contribute to different rainfall patterns in certain regions. Rainfall modeling using the Geographically Weighted Poisson Regression (GWPR) method combines the power of Poisson regression in analyzing calculated data with the advantages of GWR in modeling spatial variability. GWPR allows us to identify and map rainfall distribution patterns that vary in geographic space. The main advantage of GWPR is its ability to provide local adjustments and capture the spatial variability associated with rainfall distribution. The results of the modeling analysis show that the GWPR is better, marked by the smallest AIC value, namely 336.84, compared to the generalized poisson regression model, namely 337.76.

Climate variability, trend, and associated risks: Tana sub-basin, Ethiopia

Abstract The study focused on analyzing the variability and trends of climate parameters in the Tana sub-basin. Various statistical methods and indices were employed to assess precipitation and temperature patterns in the region. The findings indicated a statistically non-significant increasing trend in rainfall across the sub-basin, with values ranging from 1.64 to 5.37 mm/year. In terms of temperature, there was an increasing trend observed, but it was also not statistically significant. The seasonality index ranged between 0.87 and 1.03, indicating different rainfall distribution patterns. In 36.69% of the sub-basin, rainfall occurs in marked seasonal patterns with a long dry season, and the remaining (63.31%) is concentrated in 3 or fewer months, indicating a different rainfall distribution pattern. In addition, the study assessed the precipitation concentration and found that 57.5% of the rainfall data exhibited a strong irregular concentration, 41.5% showed an irregular concentration, and 1% exhibited a moderate concentration. The study underscores the presence of climate variability and trends in the Tana sub-basin, emphasizing the need to align agricultural and water resource management practices with the observed climate variability.

Runoff generation and erosion processes at the rock–soil interface of outcrops with a concave surface in a rocky desertification area

Rock surface flow, originating from outcrop surfaces during rainfall, represents a distinctive form of runoff influencing surface erosion and subsurface soil leakage in karst rocky desertification areas. Despite their significance, the processes governing runoff generation and erosion at the rock–soil interface (RSI) under the influence of rock surface flow remain poorly understood. This study aims to elucidate the generation and transformation of runoff at the RSI, along with their scouring erosion effects influenced by rock surface flow from concave-shaped outcrops. Artificial rainfall experiments were conducted on simulated rock–soil structural units featuring exposed rock surfaces and unexposed RSIs of outcrops with concave shapes. Two driving factors, namely rainfall intensity (R) and the inclination of the rock surface (IRS), were manipulated to investigate the formation and output processes of runoffs and sediments from the surface, RSI, and non-RSI (a site away from the RSI). The presence of an exposed rock surface altered the distribution pattern of rainfall–infiltration–runoff. An increase in the IRS led to a significant decrease in surface runoff (p < 0.05). When the IRS was close to vertical, the rainfall-runoff was primarily in the form of surface runoff; however, for inclinations less than 90°, the primary contributors to rainfall-runoff were the RSI and non-RSI runoffs. Surface soil erosion emerged as the predominant form of soil loss, with runoff yield demonstrating a linear positive correlation with sediment production. Both the IRS and R were identified as significant factors affecting surface runoff and soil erosion. Notably, when the IRS was less than 45°, the total soil loss reached its minimum. Therefore, runoff generation at the RSI led to a reduction in surface runoff and an increase in subsurface runoff, contributing to soil leakage at the RSI. Nevertheless, surface erosion remained the primary mechanism of soil loss.

Analysis of Rainfall Distribution in Kurunegala District, Sri Lanka

Investigating the dynamics of rainfall has become very crucial in managing water resources efficiently for sustainable development. The present study aimed to analyze the rainfall distribution in Kurunegala district. Historical rainfall data collected from four gauging stations were subjected to both mathematical and statistical analysis. In addition, trends of rainfall, probability of exceedance and meteorological drought conditions were studied. Rainfall distribution in the district shows high variations. Bathalagoda records the highest mean annual rainfall of 1843 mm. The corresponding values for Wariyapola, Mediyawa and Siyambalagamuwa are 1629 mm, 1315 mm and 1222 mm, respectively. Rainfall is concentrated only in certain months in a year. Annual rainfall exceedance at 50% probability is 1825 mm at Bathalagoda. The corresponding figures for Wariyapola, Mediyawa and Siyambalagamuwa are 1662 mm, 1284 mm and 1226 mm, respectively. Mediyawa, Wariyapola, and Siyambalagamuwa show a decreasing trend in annual rainfall while Bathalagoda shows an increasing trend. Southwest monsoonal (SWM) and 2nd inter-monsoonal (IM2) rainfalls show a decreasing trend at all gauging stations. Mediyawa and Bathalagoda show a positive trend in both 1st inter-monsoonal (IM1) and Northeast monsoonal (NEM) rainfalls. A negative trend in Maha seasonal rainfall is observed in all regions except Bathalagoda. A positive trend of Yala seasonal rainfall is observed at Mediyawewa and Bathalogoda. Further, severe drought conditions were experienced in the recent years at Wariyapola, Mediyawa, and Siyambalagamuwa. Compared to other regions, rainfall at Mediyawa and Siyambalagamuwa highly deviates from the long-term mean. In the study area, rainfall distribution shows a cyclic pattern over time. However, the amount of rainfall received in the recent years is lower than the amount received in the immediate past decade at all stations except Bathalagoda. Hence, proper management decisions based on rainfall distribution patterns is vital for the efficient management of water resources while guaranteeing sustainable agricultural production in this district.

Moving toward the Biophysical Characterization of the Mangrove Swamp Rice Production System in Guinea Bissau: Exploring Tools to Improve Soil- and Water-Use Efficiencies

The mangrove swamp rice production system (MSRPS) in West Africa faces significant challenges in soil, water, and salinity management, making rice production highly vulnerable to variations in the spatio-temporal distribution patterns of rainfall, which are exacerbated by climate change. This study’s results can provide the initial basis for co-developing strategies with farmers aiming to contribute to the biophysical characterization of the MSRPS, in particular: (i) estimate the water-harvesting efficiency (WLef) of the plots in the north and south of Guinea Bissau (GB); (ii) characterize the unevenness of the bottom of the plots, which leads to salinization spots; and (iii) create soil consistency maps to provide farmers with a tool to prioritize sites with optimal conditions for tillage. The research was conducted between 2021 and 2023 in the study site of Cafine-Cafal in the south and Elalab in the north of GB. Systematic soil sampling in a grid was designed to quantify the soil consistency and plot/ridge areas were determined. Linear models were developed to predict biophysical parameters (e.g., effective planting areas and water-logging depths) and geostatistics were used to create soil consistency maps for each study site. The results show precipitation water-harvesting efficiencies of 15% and 16% for the southern and northern regions, respectively. Furthermore, the plasticity limits of 18.6% for Elalab and 35.5% for Cafine-Cafal show the most appropriate times to start tillage in specific areas of the paddies. This study provides information on the efficient management of tillage and freshwater conservation, providing MSRPS farmers with useful tools to counteract the effects caused by salinity and rainfall variability.

Assessment of Climate Trend, Meteorological Drought and Farmers' Perception to Climate Change and Variability in Hadero Tunto District, Southern Ethiopia

Currently, climate change and variability are a hot issue, and more attention needs to be paid to their impact on Ethiopia. Farmers' perception plays a significant role in successfully implementing of adaptation strategies to reduce climate change impacts. The main objective of this study was to assess climate trends, meteorological drought and farmers' perception of climate change variability in Hadero Tunto district, Kembata Tembaro Zone, Southern Ethiopia. To determine historical climate trends, 30 years of rainfall and temperature data were obtained from the Hawassa branch of the Ethiopian National Meteorological Service, and trend analysis was performed. The multistage sampling technique was used to select 150 farm households to be surveyed. The modified Mann–Kendall and Sens' slope estimator trend tests were applied to detect the statistical significance of the trend as well as the magnitude in the time series data. The standardized precipitation index was computed for short and long-term scales to characterize the moisture content in the study area. Results of the standardized precipitation index revealed that the total number of drought events was higher in spring than summer. Still, the most extreme drought was recorded in summer. The years 1987 and 2015 experienced extreme drought in summer with indices values of -2.89 and -2.03, respectively, while, -2.27 was the spring season of 2003. In the long-term scale, -1.7 was recorded as the severe value. The historical average annual maximum and minimum temperatures both showed a significant upward trend. Historical annual rainfall shows a negligible downward trend from 1987 to 2016. This trend of rising temperatures and changing rainfall threatens smallholders´ agriculture, who are already limited by access to basic equipment and land use challenges. The descriptive result revealed that most people perceived long-term variability in the rainfall amount and distribution pattern and an increasing trend and variability of temperature. Based on these results, the study recommends that agricultural extension services be enhanced to sensitize the farmers about climate change, thus improving their perception. Further, studies at a larger scale to illustrate the associations between farmers' perceptions of climate change with meteorological data to figure out the risk of climate change are also critical

Study of changes in temporal distribution pattern of rainfall at Dapoli station in Konkan region of Maharashtra

Dapoli situated in the Konkan region of Maharashtra is having average annual rainfall of 3587 mm with average number of rainy days 75. The yield of major Kharif season crop rice is affected by the erratic behavior of rainfall. Present study is an attempt to study the rainfall variations at the Dapoli station which will be useful for forecasting the future temporal availability of water. Comprehensive statistical tools were used to investigate trends in averages and monthly rainfall over the station on decadal basis. Forty years (year 1972-2011) daily rainfall data for Dapoli station was used for the analysis. Results of study showed that, decadal mean rainfall depths of June and August were found decreasing and those for September was found increasing. Mean rainfall depth variations for July as well as annual total rainfall were found random. Seven years moving averages showed that, rainfall depths for the months of June, July and August were found decreasing and for month of September was found increasing. In annual rainfall graph a slight decline was observed.

Erratic Rainfall and its Upshot on Rice Production in Kerala

In recent years, rainfall irregularities have affected crop production in Kerala. Rice, a water-reliant crop, is the staple food of people in Kerala. It is challenging to revive paddy if the soil loses even 10 to 20 per cent of its moisture. Due to erratic rains and frequent dry spells, rain-dependent paddy cultivation is nearly impossible in Kerala. Among the three seasons of paddy cultivation in Kerala, the autumn season entirely depends on rainfall. According to 2020-21 Kerala agriculture statistics, the majority of the rice is cultivated under irrigated conditions in the state. This loss in rain-fed paddy cultivation was due to the decline in autumn rice cultivation after the mid-1990s. The study analysed the annual and seasonal rainfall variability and their relationships with autumn rice production in Kerala. The variability and concentration in the annual and seasonal rainfall were analysed using the coefficient of variation and the precipitation concentration index, respectively. The results revealed that the overall rainfall distribution pattern in Kerala was moderate to irregular. The seasonal index was estimated for the monthly rainfall data, and it was found that in June and July, the rainfall was the maximum. Production and productivity of autumn rice showed a high correlation with rains in June and July. The variability in rice production was positively influenced by rainfall and negatively influenced by the standard deviation of precipitation.

Application of relative importance metrics for CMIP6 models selection in projecting basin-scale rainfall over Johor River basin, Malaysia

The most recent set of General Circulation Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used in this work to analyse the spatiotemporal patterns of future rainfall distribution across the Johor River Basin (JRB) in Malaysia. A group of 23 GCMs were chosen for comparative assessment in simulating basin-scale rainfall based on daily rainfall from the historical period of the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS). The methodological novelty of this study lies in the application of relative importance metrics (RIM) to rank and select historical GCM simulations for reproducing rainfall at 109 CHIRPS grid points within the JRB. In order to choose the top GCMs, the rankings given by RIM were aggregated using the compromise programming index (CPI) and Jenks optimised classification (JOC). It was found that ACCESS-ESM1–5 and CMCC-ESM2 were ranked the highest in most of the grid. The final GCM was then bias-corrected using the linear scaling method before being ensemble based on the Bayesian model averaging (BMA) technique. The spatiotemporal assessment of the ensemble model for the different months over the near-future period 2021–2060 and far-future period 2061–2100 was compared with those under Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, SSP3–7.0, and SSP5-8.5. Heterogeneous changes in rainfall were projected across the JRB, with both increasing and decreasing trends. In the near-future and far-future scenarios, higher rainfall was projected for December, indicating an elevated risk of flooding during the end of the North East monsoon (NEM). Conversely, August showed a decreasing trend in rainfall, implying an increasing risk of severe drought. The findings of this study provide valuable insights for effective water resource management and climate change adaptation in the region.

Analisis Risiko Bencana pada kasus Curah Hujan Ekstrem Provinsi Sulawesi Barat dengan Metode Moran's I &amp; Local Indicator Of Spatial Association

High rainfall in an area makes the area vulnerable to various disasters. To reduce the risk of damage caused by various disasters, it is important to look at the pattern of spatial distribution of areas that have a high risk of being affected by extreme rains. Information about distribution patterns of rainfall can assist policy makers in making decisions regarding extreme rain events in West Sulawesi which have the potential to cause various losses. Therefore, spatial analysis is carried out to detect connections between regions so that rainfall distribution patterns can be known. Based on this, this study aims to analyze the characteristics of the spatial distribution of rainfall in each sub-district of extreme rainfall on an annual scale in West Sulawesi. The results showed that there was a significant spatial autocorrelation. This showed that there was a correlation between the rainfall values between districts in West Sulawesi based on the spatial autocorrelation analysis with the Moran index test. Furthermore, the results of the Local Indicator Spatial Autocorrelation (LISA) analysis of rainfall data in each sub-district show that there are 10 sub-districts in 2019, 7 sub-districts in 2020, 13 sub-districts in 2021, and 7 sub-districts in 2022 which are identified as having spatial autocorrelation with a level significance 0.05. From all the results of the analysis carried out, namely the analysis of the moran index and LISA, it was obtained that the areas that have the greatest risk of high rainfall are the Kalukku, Tabang and Pana sub-districts. The research results obtained can be useful as information and evaluation material that is useful for preparing natural hazard mitigation programs and assisting the government in establishing policies based on information on areas that have a high risk of being affected by extreme rainfall.

Analysis of the probability of rainfall in the Fingeshwar Tehsil of the Gariyaband District for crop planning

Rainfall probability analysis improves predictions of the minimum assured rainfall to aid crop planning. An attempt has been made to look into the patterns of rainfall distribution, including weekly, seasonal, and annual rainfall, using data collected from the Fingeshwar tehsil of the Gariyaband district, Chhattisgarh, over a ten-year period (2011-2020). Using the Weibull plotting position function, expected weekly, monthly, seasonal, and yearly rainfall values were calculated for various probability levels. Based on a 10-year yearly average, the data revealed that 1074.4 mm of rain were actually seen, following an average of 52.2 rainy days. A rainfall amount with a 75% probability level predicts 862.9 mm annually. The largest amount of weekly rainfall, 49 mm, was predicted to fall in the 35th week, followed by 32.1 mm in the 25th standard week and the least amount, 0.0 mm, in the 20-22nd, 29, 37, 40-42nd SMW. This prediction was made at a 75% chance level, same like the one before. According to a study of monthly rainfall at 70, 75, and 80% probability levels, the three crucial wet months are July, August, and September, with probabilities of getting a monthly rainfall between 0 and 50 mm. At a 70% probability level, the seasonal rainfall report projects 833 mm for the Kharif season. Thus, it can conclude that the kharif season's activities could start between the 22nd and the 23rd standard week and farmers can properly produce paddy crops in highland areas followed by any rabi crop in rabi season.

Ascribing the capricious weather thes is to the unprecedented july-september 2022 flood hazards in the kumba and mutengene-likomba agglomerations of the south west cameroon coastal plain of cameroon

Recurring flood scenarios are globally, the most endemic, ubiquitous and dramatic natural hazards especially on low-lying and steep sloping areas. The area space covered by the settlements of Kumba, Mutengene and Tiko have these characteristic landscapes and flooding events common but those of July to September August to 2022 were not only record breaking but somewhat enigmatic in nature. This study sought to make an in-depth examination of probable triggers of the hydroclimatic catastrophe. The study adopted the mixed research design combining both quantitative and qualitative techniques. Primary data on the dimension of the flooding scenarios, the drivers were obtained through empirical research methods of direct and indirect field observations of affected areas. Daily, monthly and annual climatic data were sourced from NASA satellite database, and analysed to obtain temporal rainfall intensities and distribution patterns. Change detection of landcover and land uses were established through GIS analysis of satellite images between year 2000 and 2022. Impacts of the flood events were obtained through an exploratory survey of the flood impacted zones. The results revealed the principal trigger mechanism of the cataclysmic floods of July and September 2022 to be the disruption of the natural balance of the landscape linked to combined effects over the years, of intensifying high rainfall concentration within fewer months of the year, and the heightened vulnerability of the landscape due to uncontrolled land cover and land use mutations that have coerced anthropic colonisation of natural water course ways, to the extent that indigenous adaptation measures as raised foundations and sand bag embankments were dwarfed by the flood waters.Far reaching consequences registered were destruction of communication lines, infrastructural facilities, arable and pastoral farmlands and landscape spoliation by landslides. The recommends proper land use planning and improvement of drains systems as the sustainable preventive panacea to such a situation.

Spatiotemporal Variation of Hourly Scale Extreme Rainstorms in the Huang-Huai-Hai Plain and Its Impact on NDVI

This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI’s variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China’s rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques.

Smallholder Farmers’ Coping Strategies to Perceived Climate Change and Variability in Isingiro District, South Western Uganda

Climate change and variability pose a major challenge facing the agricultural sector around the world and trends are feared to intensify by 2050. This is likely to complicate agricultural decisions affecting local communities who depend on the sector for their livelihood. Seasonal changes in weather patterns are projected to reduce food production due to the reduction in average yields of common food crops. With the majority of smallholder farmers in Uganda depending on agriculture for survival, building their adaptive capacity to climate change is vital to ensure household food and income security. This study aimed at establishing the smallholder farmers’ perceptions of climate change and their initiatives to cope with and manage the associated risks. A sample of 126 farming households were randomly selected from three Sub counties in Isingiro District and studied using field observations and questionnaires. Key informant interviews were also conducted with three extension workers. The collected data was analysed using Stata 14 and Excel. The findings revealed that there was increasing awareness of the climate change and variability challenge among smallholder farmers with the increasing need to take on adaptation strategies. The results showed that the major perceived changes were, increased intensity and frequency of droughts (98%), increase in daytime temperatures (92.7%), decrease in rainfall intensity (92.1%) and changes in rainfall distribution patterns (90%). It also showed that different initiatives had been taken by farmers to cope with the changes like changing planting dates (92.1%), growing crop variety mixtures (73.8%) and practising soil and water conservation (62.7%). Based on the findings of this study, smallholder farmers greatly perceived climate change and variability with negative impacts imposed on their farming operations. Although farmers have tried to adapt, there is still a need to support their coping strategies through appropriate policies. This will facilitate increased adoption of adaptation measures, thereby reducing the negative consequences of climate change in future