Mean Monthly Rainfall Research Articles

Application of coupled MIKE SHE/MIKE HYDRO on streamflow dynamics under climate change in Lake Tana sub basin, Abay Basin, Ethiopia

AbstractWater availability and quality are fluctuating due to climate change, which has disastrous effects on life. Modeling climate change impact on streamflow in the Lake Tana sub‐basin (LTSB) in selected watersheds was the main goal of the research. This research is unique in that it applies the coupled MIKE SHE/MIKE HYDRO model technique, which has not been applied before to investigate changes in streamflow due to climate change in LTSB. Streamflow in the LTSB was forecasted using the MIKE SHE/MIKE HYDRO model from seven ensembles of GCMs under two emission scenarios (SSP2–4.5 and SSP5–8.5) between 2041 and 2,100. According to the calibration and validation results in the period of 1985–2007, MIKE SHE performs well while simulating streamflow in LTSB. During calibration and validation, the Nash‐Sutcliffe efficiency (NSE) values in all watersheds were greater than 0.8, except for the Ribb watershed calibration, where it was 0.75. The mean monthly changes in temperatures indicate an incremental tendency in the next decades. According to the simulation result, the monthly mean rainfall for SSP2–4.5 and SSP5–8.5 will increase from 0.05 to 61.9% and 2.02 to 46.26% in the 2050s and 8.7 to 44.38% to 6.44 to 66.24% in the 2080s, respectively. Gilgle Abay, Gumara, and Ribb watersheds will lose 56.2, 56.65, 57.01% of their mean monthly streamflow in the 2050s, according to SSP2–4.5. For the Gilgle Abay, Gumara, and Ribb watersheds, streamflow will increase by 6.7% to 21.94%, 6.08% to 23.26%, and 6.51% to 22.88%, accordingly, in the 2050s under SSP5–8.5. Increased rainfall in the watershed could result in floods in the already‐flooded Gumara and Ribb flood plains. The government and community should implement coping mechanisms to lessen the impact of climate change like early warring and planting more trees. Overall, the study showed that it is expected that conditions related to streamflow may change in the future due to climate alteration. By offering a better understanding of current and future climates, the integrated modeling system developed can assist environmentalists, hydrologists, and policymakers in water management and policy intervention.

An integrated modeling approach for estimating monthly global rainfall erosivity

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha−1 h−1 month−1 in June–August over the Northern Hemisphere and ~ 700 MJ mm ha−1 h−1 month−1 in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Evaluation of the Trends of Climate Variability During Different Crop Seasons in Dera Ghazi Khan

Climate change is profoundly impacting ecosystems and disrupting agricultural systems by altering temperature and precipitation patterns, affecting crop yields and food production. This study evaluates the trends of climate variability during different crop seasons in Dera Ghazi Khan, Pakistan, focusing on temperature and rainfall fluctuations from 1991 to 2020. Climatic data were collected and analyzed, including monthly average temperature and rainfall. The analysis of mean monthly decadal temperature in the study area during the Rabi crop season showed significant fluctuations ranging from 20.04°C, 20.5°C, and 19.73°C in three corresponding decades. The mean monthly seasonal temperature was calculated 20.1°C. Similarly, during the Kharif crop season, notable variations across the mean decadal temperature ranging from 34°C to 34.91°C with mean seasonal temperature 34.6°C. Overall, in both seasons during the past three decades +0.1°C was observed with an annual increase of 0.003°C. All these values were found statistically significant. Mean monthly rainfall also exhibited remarkable variability, with statistically significant differences observed for most months in both Rabi and Kharif seasons, except for November and December in the Rabi season and June and October in the Kharif season. Overall, rain has decreased in the last 30 years to -0.4mm during Rabi and -3.7mm during Kharif seasons. The literature suggests that minor changes in climatic factors remarkably impact crop production. These findings highlight the dynamic nature of climatic conditions in the region and underscore the importance of understanding and adapting to climate change for sustainable agricultural practices.

Impact of urban land use on mean and heavy rainfall during the Indian summer monsoon

Abstract. Northern India has undergone intense urbanization since the middle of the 20th century. The impact of such drastic land-use change on the regional weather and climate remains to be assessed. In this work, we study the impact of the modification of land use – from vegetation to urban – on the Indian summer monsoon rainfall as well as on other meteorological variables. We use the regional Meso-scale Non-Hydrostatic (Meso-NH) model coupled with an urban module (the Town Energy Balance model) to perform monthlong sensitivity simulations centered around Kolkata, the most urbanized area in northeastern India. Paired simulations, one with and another without urban settings, have been performed to identify the impacts related to urbanization through both thermodynamic and kinetic effects. We find that the perturbation induced by urban land use enhances the mean rainfall over the model domain, principally by intensifying the convective activity through thermodynamic perturbation, leading to a 14.4 % increase in the monthly mean rainfall. The urban area also induces a 15.0 % rainfall increase during two modeled periods of heavy precipitation caused by low-pressure systems. In addition, the modeling results demonstrate that the urban area not only generally acts as a rainfall enhancer, particularly during nighttime, but also induces the generation of a specific storm in one modeled case that would not have formed in the absence of the urban area. The initiation of this storm over the city was primarily due to the urban terrain's disturbance of the near-surface wind flow, leading to a surge in dynamically produced turbulent kinetic energy (TKE). The thermal production of TKE over the nighttime urban boundary layer, on the other hand, serves as a contributing factor to the storm formation.

Geo-statistical analysis of climatic variability and trend detection in the Hindu Kush region, North Pakistan.

This paper is an effort of geo-statistical analysis of rainfall variability and trend detection in the eastern Hindu Kush region located in the north-west of Pakistan. The eastern section of the HK region lies in the western part of Pakistan. Exploring rainfall variability and quantifying its trend and magnitude is one of the key indicators among all climatic parameters. In the study area, Pakistan Meteorology Department (PMD) has established seven meteorological stations: Drosh, Chitral, Dir, Timergara, Saidu Sharif, Malam Jabba, and Kalam. Daily, mean monthly, and mean annual rainfall time series data for all the met stations were geo-statistically analyzed in the GIS environment for detecting monthly and annual variability in rainfall, variability, and trend detection. Mann-Kendall (MK) and Theil-Sen's slope (TSS) statistical tests were applied to rainfall data. Initially, the MK test was applied for detection of trends and TSS test was used to quantify the change in magnitude. The results indicate that the rainfall variability in intensity and trend pattern detection. The analysis confirms that an extremely significant rainfall trend in the case of mean annual rainfall was predicted at Dir and Malam Jabba meteorological stations. Opposite to this, at Kalam and Chitral stations, a less significant rainfall trend was noted. In a similar context, no prominent rainfall trend has been found at Drosh, Timergara, and Saidu Sharif meteorological stations. Likewise, using TSS, an extremely negative variation in the magnitude of rainfall was verified at Kalam and Malam Jabba. However, a noteworthy positive change in rainfall magnitude has been noted at Dir and Saidu Sharif meteorological stations. The findings of this research have the potential to assist the decision and policy makers and academicians to think truly and conduct more scientific research studies to mitigate climate change.

Characterization of the 2012 and 2013 Metro Manila “Enhanced Habagat” Heavy Rainfall Events

The strong southwest monsoon episodes in August 2012 and 2013, locally referred to as the “Enhanced Habagat ” 2012 and 2013, respectively, resulted in rainfall that exceeded the monthly mean rainfall of the affected regions along western Luzon, including Metro Manila. The prolonged heavy rainfall events were the result of tropical cyclone enhancement of the monsoon winds. The synoptic environment in the two events was characterized by the deepening of the Asian monsoon trough depicted by the zonally-oriented and eastward-extended 1000 hPa isobar. The deep troughs were caused each year by a combination of tropical cyclones located to the northeast of the Philippines – Typhoon Haikui in 2012 and Severe Tropical Storm Trami in 2013, and remnant tropical cyclones in the northern South China Sea region that formed several days prior to the enhanced Habagat episodes. The monsoon trough induced a low-level westerly jet in the South China Sea toward the western Luzon region that transported a narrow stream of moisture-laden air mass to Metro Manila and surrounding areas. Consequently, heavy precipitation ensued. Analysis showed the remnant lows are as important as the enhancing tropical cyclones Haikui and Trami in inducing the westerly jets. Removal of the enhancing tropical cyclones in numerical model simulations still showed a relatively deep monsoon trough that led to heavy rainfall along western Luzon. In addition, the intrusion of low potential vorticity area to the eastern and northern flanks of the tropical cyclones facilitated the strengthening of the steering ridge that resulted in their westward and slow translational motion, making the events last for several days and exacerbating the impacts. Furthermore, the Madden-Julian Oscillation possibly serves as a precursor to heavy rainfall events. Compounded with a populous megacity, understanding the mechanisms that lead to these extreme hazards is vital to future forecasting and disaster risk management of similar events.

THE APPLIED OF FUNCTIONAL DATA ANALYSIS TO COMPARISON 100 TIMES SIMULATED MONTHLY RAINFALL USING SOME TWO PARAMETERS DISTRIBUTION

Three probability models of mothly rainfall such as Gamma, Weibull and Log Normal distribution are evaluated in terms of their ability to reproduce the mean statistics derived from 100 times simulation of monthly rainfall in the Pekanbaru City, Indonesia. One of the important studies is to investigate and understand the simulate mean monthly rainfall patterns that occur throughout the year. To identify the pattern, it requires a rainfall curve to represent monthly observation of rainfall received during the year. Functional data analysis (FDA) methods are capable to convert discrete data into a function that can represent the rainfall curve and as a result, try to describe the hidden patterns of the rainfall. This study is focused on investigating 100 curve average monthly rainfall simulatated by three different quantile functions using the FDA. The mean and standard deviation of FDA for average monthly precipitation are obtained. Through these two statistics a the confidence interval curves of the mean curve are presented represent 95% pointwise confidence intervals. In this study, most of the monthly average rainfall from the actual data were around the FDA mean and the monthly average rainfall was within the FDA confidence interval. In this study, 100 times monthly rainfall simulations using the quantile function of the gamma and log normal distributions found that the mean FDA can capture most of the mean monthly rainfall from historical data, and within the FDA interval. The contradictory results shown by the monthly rainfall simulations using the Weibull distribution, most of the monthly average rainfall historical data cannot be captured by the FDA mean and are outside the FDA confidence interval. Based on the Mean Absolute Error (MAE) value of the average monthly rainfall of historical data and the monthly average of the FDA, it can be concluded that the gamma distribution can produce simulated rain better than the log normal distribution.

Interrelationship between Rainfall Index and Nifty Index: An Empirical Study

The proposed study is an attempt to determine whether a relationship exists between rainfall index and NSE Nifty index. The study used the monthly mean rainfall data and monthly closing price of Nifty index. The study applied Augmented Dickey-Fuller (ADF) test, correlation analysis, the GARCH (1,1) model, and the Granger Causality test to analyse the interrelationship. The results of correlation matrix show that there is no interrelationship between the two variables. The GARCH (1,1) model found that the NSE Nifty index is not affected by the rainfall index and Granger Causality test displays that rainfall index does not Granger Cause the Nifty index. According to the authors’ knowledge, this is the first empirical study to determine the interrelationship between the rainfall index and the Nifty Index over a longer period of time.

Implementation of a Rainfall Normalization Module for GSMaP Microwave Imagers and Sounders

This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due to differences in sensor specifications and retrieval algorithms. The basic idea of the MMN module is to calibrate target PMW sensors with reference sensors (the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) using the cumulative distribution function (CDF) of the rain rate. Differences between the CDF and normalization table for MWSs are greater than MWIs due to different rain retrieval algorithms. More (less) MWS rainfall is detected over the ocean (land) than GMI rainfall. Matchup rainfall data between GMI and a target PMW sensor are compared to evaluate MMN performance. The monthly mean rainfall and mean bias error were improved for almost all PMW sensors. This study leaves open the possibility for further inter-calibration and improvement of rain detection and heavy rainfall retrievals.

Spatial-Temporal Changes of Carbon Source/Sink in Terrestrial Vegetation Ecosystem and Response to Meteorological Factors in Yangtze River Delta Region (China)

As an important part and the core link of a terrestrial ecosystem, terrestrial vegetation is the main means for human to regulate climate and mitigate the increase in atmospheric CO2 concentration. The Yangtze River Delta (YRD) region is an urban agglomeration with the strongest comprehensive strength among developing countries (China). In the context of global climate change, a rapid, comprehensive, and detailed understanding of the spatio-temporal characteristics and variation tendency of the net ecosystem productivity (NEP) of vegetation and its response to climate during the rapid development of the YRD region is important for protecting ecological land, strengthening land management, and optimizing urban planning. The monthly mean temperature and rainfall data from 63 meteorological stations, the MODIS net primary productivity product, and a land cover product in the YRD region were used to estimate the NEP from 2000 to 2019 based on the soil respiration model, and the correlation between NEP and meteorological factors (such as temperature and rainfall) was analyzed. The results showed that: (1) From 2000 to 2019, the carbon sink area was much larger than the carbon source area in terrestrial vegetation in the Yangtze River Delta, the mean NEP of the vegetation ecosystem in the past 20 years was 253.2 g C·m−2·a−1, and the spatial distribution presented a trend that was higher in the south and lower in the north, higher in the east and lower in the west, and that gradually increased from northwest to southeast; moreover, the NEP of mountain areas was generally higher than that of river courses and urban surroundings. The interannual fluctuation of NEP was small, but presented a slightly increasing trend, and the interannual variation of NEP was significantly correlated with the maximum NEP in this region. (2) The carbon sink capacity of different vegetation cover types was (from strong to weak): forestlands > grasslands > wetlands ≈ croplands. (3) The area with the NEP change rate (θslope) > 0 accounted for 69.0%; however, there was certain spatial difference, the proportions of the areas with θslope < 0 were (from large to small) 14.50% (Zhejiang Province, China), 9.10% (Anhui Province, China), 6.65% (Jiangsu Province, China), and 0.79% (Shanghai, China). In terms of the individual changes of these provinces and municipalities, Shanghai > Zhejiang Province > Jiangsu Province ≈ Anhui Province. (4) There was a correlation between NEP and the annual mean temperature and annual precipitation in some regions.

Farmers’ perceptions of climate change, long-term variability and trends in rainfall in Apac district, northern Uganda

BackgroundClimate change poses severe threats to smallholder farmers' agricultural livelihoods and food security in Sub Saharan Africa. Understanding long-term rainfall trends of variability and extremes at local scales and perceptions regarding long-term changes in climate variables is important in planning appropriate adaptation measures to climate change. This paper examines the perception of farmers in Apac district regarding long-term changes in climate variables and analyzes the trend of occurrence in seasonal and annual rainfall in Apac district, northern Uganda. A cross-sectional survey design was employed to collect data on farmers' perceptions regarding long-term changes in climate from 260 randomly selected smallholder farmers’ households across two sub-counties in Apac district by administering semi-structured questionnaires in February 2018. Monthly rainfall data sets from the Uganda Meteorological Authority (UMA) for the period 1980 to 2019 for Apac district were also used to analyze trends of occurrences in seasonal and annual rainfall in the study area. The non-parametric Sequential Mann–Kendall (SMK) tests were employed at a 5% significance level to detect mean seasonal rainfall trends and abrupt change points.ResultsThe majority of the respondents (87%) perceived a decrease in precipitation over the past 39 years. The plot of forward regression u(ti) values and backward regression u’(ti) values showed interactions indicating rainfall trends, rainfall lower and upper limits and abrupt change points in the different cropping seasons. Analysis of historical series of mean monthly and annual rainfall showed an abrupt change in rainfall in March, April, May (MAM) season in 1982. Although the September, October and November (SON) season did not show a significant abrupt change, there was a significant (p < 0.05) increase in rainfall above the upper limit from 1994 to date.ConclusionThe mean seasonal rainfall for MAM and SON cropping seasons in Apac district were highly variable from different time points within the past 39 years (1980–2019), while June, July, and August (JJA) did not realize a significant change in rainfall within the same study period that the two cropping seasons (MAM and SON) in the district experienced remarkable variations in rainfall. This, therefore, provides a basis for the government to strengthen the provision of an effective climate tailored agricultural advisory service to aid farmers’ adaptation planning at the local level and to assist smallholder farmers and land-use managers in developing effective adaptation management strategies to the effects of climate change.

General circulation models for rainfall simulations: Performance assessment using complex networks

Reliable assessment of the impact of climate change on hydrology in a region requires proper selection of General Circulation Models (GCMs). The present study introduces the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall and selecting an ensemble of the best-performing GCMs. The performance of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating monthly rainfall over India is assessed. The observed (interpolated and gridded rainfall provided by the India Meteorological Department) rainfall data and GCM-simulated rainfall data over the period 1961–2014 at a spatial resolution of 1° x 1° (a total of 288 grids) are studied. The GCMs are evaluated for two cases: (1) Case 1 – whole year rainfall; and (2) Case 2 – summer monsoon rainfall (June–September). The clustering coefficient of the rainfall network is used as a network measure to evaluate the ability of the GCMs in simulating rainfall. For rainfall network construction, each grid is considered as a network. The phase space reconstruction concept is used to reconstruct the single-variable rainfall time series in a multi-dimensional phase space to represent the rainfall dynamics. The optimal dimension for reconstruction is determined using the false nearest neighbor (FNN) algorithm. For network construction, each reconstructed vector is considered as a node, and the connections between them, identified using a distance threshold for the reconstructed vectors in the phase space, serve as the links. For each of the 288 grids, the GCMs are ranked based on the difference in the clustering coefficient between the observed and GCM-simulated rainfall networks. The group decision-making (GDM) approach is employed to identify the ensemble of the best-performing GCMs for the entire study area considering all the grids. The results suggest different models perform well for the whole-year rainfall and summer monsoon rainfall. For the whole-year rainfall, the models CMCC-CM2-HR4, GFDL-ESM4, CMCC-ESM2, EC-Earth3-AerChem, and CMCC-CM2-SR5 rank as the top five. For the summer monsoon rainfall, FIO-ESM-2-0, E3SM-1-1, CESM2-FV2, CMCC-CM2-HR4, and CMCC-CM2-SR5 perform the best. Considering the rank of each model for both the cases, the best-performing models are identified to be CMCC-CM2-HR4, CMCC-CM2-SR5, CMCC-ESM2, FIOESM-2-0, and E3SM-1-1. The rainfall simulated from these models also show close resemblance to the observed rainfall, especially in terms of monthly mean rainfall. Therefore, even among the many GCMs that show good agreement with the monthly mean observed rainfall, the clustering coefficient-based analysis helps to narrow down the models for the ensemble of best-performing GCMs.

Determining the Best Rainwater Harvesting System in Al-Muthanna Governorate, Iraq

Rainwater harvesting is one of the available solutions to overcome water scarcity in arid and semi-arid regions with highly variable rainfall and unexpected periods of drought or floods. This study aims to identify the best rainwater harvesting system in Al-Muthanna governorate using Remote Sensing (RS) and Geographic Information System (GIS) techniques. Landsat 8 images were used to produce the land use map which shows five different classes: water (0.2%), bare soil (82.11%), built-up (15.71%), forest (0.27%), and farmland and grass (1.71%). The results revealed that the rainwater harvesting system can be applied only in the north and north-eastern parts of the study area which consists of residential and agricultural areas and has a maximum monthly mean rainfall range of (85.32-157.21) mm. Rooftops, semi-circular bunds, and ponds are the most suitable systems for rainwater harvesting in Al-Muthanna. The rooftops system can provide 7000-13,500 litres of water for domestic purposes. Furthermore, if the water quality was low, it could be also used in agriculture practices or to irrigate home gardens.

Analysis of rainfall and temperature trend and variability of the Tordzie Watershed

Climate change is universally occurring but probable fluctuations are not anticipated to be uniform worldwide; rather, there may be local differences. Thus, there is a need to investigate the variations at the local and watershed levels to comprehend the magnitude of the change. This study was conducted to determine the trend of rainfall and assess its dependability. In this study, mean monthly and annual rainfall was analysed to determine the variability in magnitude over the period 1984-2014 at different stations. The non-parametric trend detector, Mann-Kendall test (MK) and trend magnitude detector, Sen’s Slope (SS) were used to reveal the trend and magnitude of rainfall and temperature, respectively. The largest increasing slope of significant annual precipitation occurred in November with a magnitude of 1.68 mm/y in Tordzinu. October recorded the most reliable rainfall at Kpetoe with CV of 0.3. The highest maximum monthly rainfall occurred in May, 2005 at Tordzinu with magnitude of 522.2mm for the period under review. The minimum and maximum temperature increased by 0.20C and 0.80C respectively at Kpetoe and also 0.20C and 0.50C at Tordzinu per decade respectively.

Spatiotemporal analysis of environmental and physiographic factors related to malaria in Bareilly district, India.

ObjectivesThe aim of this study was to explore the spatiotemporal clustering of reported malaria cases and to study the effects of various environmental and physiographic factors on malaria incidence in Bareilly district, Uttar Pradesh, India.Methods Malaria surveillance data were collected from the state health department and cleaned into an analyzable format. These data were analyzed along with meteorological, physiographic, and 2019 population data, which were obtained from the Indian Meteorological Department, National Aeronautics and Space Administration web portal, the Bhuvan platform of the Indian Space Research Organization, and the 2011 Census of India.ResultsIn total, 46,717 malaria cases were reported in Bareilly district in 2019, of which 25.99% were Plasmodium vivax cases and 74.01% were P. falciparum cases. The reported malaria cases in the district showed clustering, with significant spatial autocorrelation (Moran’s I value=0.63), and space-time clustering (p<0.01). A significant positive correlation was found between monthly malaria incidence and the monthly mean temperature (with a lag of 1−2 months) and rainfall (with a lag of 1 month). A significant negative correlation was detected between the elevation of blocks (i.e., intermediate-level administrative districts) and annual malaria reporting.Conclusion The presence of space-time clustering of malaria cases and its correlation with meteorological and physiographic factors indicate that routine spatial analysis of the surveillance data could help control and manage malaria outbreaks in the district.

The Leading Mode and Factors for Coherent Variations among the Subsystems of Tropical Asian Summer Monsoon Onset

AbstractPrevious studies on the Asian summer monsoon (ASM) onset mainly focused on each monsoon subsystem. Mainly based on the monthly mean rainfall and low-level winds in May, this study investigated the dominant onset mode from the perspective of the entire tropical ASM region, which reveals the coherent features among the regional-scale onsets. The results of multivariate empirical orthogonal function (MV-EOF) analysis indicate that the MV-EOF1 presents reduced rainfall and anomalous low-level easterly winds at 850 hPa over the tropical ASM region in May during its positive phase. The corresponding principal component (PC1) is highly correlated with the local monsoon onset dates over the Arabian Sea, the Bay of Bengal, the Indo-China Peninsula, and the South China Sea, where the mean monsoon onsets occur in May. The only exception is the Indian subcontinent, where the mean monsoon onsets occur in June. The results indicate that the leading mode captures the synchronized variation of monsoon onset over most Asian monsoon subsystems, which exhibits remarkable interannual and interdecadal changes. The factors that modulate the coherent variation of the tropical ASM onset are further examined. The simultaneously delayed ASM onset tends to occur during the easterly phase of the 30–80-day oscillation, the decaying phase of El Niño, and the positive phase of Pacific decadal oscillation (PDO). The 30–80-day oscillation serves as a background condition for the synchronized delayed or advanced ASM onset. El Niño–related sea surface temperature anomalies modulate the tropical ASM onset mode by modulating the tropical Walker circulation and inducing an atmospheric Rossby wave response. The PDO affects the tropical ASM onset mode mainly via the equatorial Rossby wave response and the extratropical Rossby wave train.

Long-term rainfall projection based on CMIP6 scenarios for Kurau River Basin of rice-growing irrigation scheme, Malaysia

Rainfall is a vital component in the rice water demand model for estimating irrigation requirements. Information on how the future patterns are likely to evolve is essential for rice-growing management. This study presents possible changes in the future monthly rainfall patterns by perturbing model parameters of a stochastic rainfall using the change factor method for the Kerian rice irrigation scheme in Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were employed to project rainfall from 2021 to 2080. The results show that the stochastic rainfall generator performed well at preserving the statistical properties between simulated and observed rainfall. Most scenarios predict the increasing trend of the mean monthly rainfall with only a few months decreasing in April and May occurring in off (dry) season. The future patterns 2051–2080 show a significant increasing trend during main (wet) season compared to the near future period (2021–2050). The projected future rainfall under SSP1-2.6 and SSP2-4.5 are higher than SSP5-8.5 from January to July, and December but lower from August to November. The projected annual rainfall will significantly increase toward 2080 during the main-season but uniform during the off-season except under SSP5-8.5, which is significantly decreasing. The output results are essential for rice farmers and water managers to manage and secure future rice irrigation water under the impact of future climate change. The projected changes in rainfall on the river basin demand further study before concluding the impact consequences for the rice sector.Article highlightsThe rainfall generator performs well in simulating future rainfall based on an ensemble of five different GCMs considering three different scenarios emission (low, medium, and high) caused by greenhouse gas and radiative forcing.The future rainfall projection predicted that off (dry) season would become wet, and main (wet) season would become wetter due increase in monthly and annual rainfall.The outcomes of this paper are beneficial for rice farmers and water managers of the study area to manage their rice cultivation and water release from the reservoir schedules to avoid losses due to flood and drought.

INTER RELATIONSHIP BETWEEN RAINFALL INDEX AND NIFTY INDEX: AN EMPIRICAL STUDY

The proposed study is an attempt to determine whether a relationship exists between rainfall index and NSE Nifty index. The study used the monthly mean rainfall data and monthly closing price of Nifty index. The study applied Augmented Dickey-Fuller (ADF) test, correlation analysis, the GARCH (1,1) model, and the Granger Causality test to analyse the interrelationship. The results of correlation matrix show that there is no interrelationship between the two variables. The GARCH (1,1) model found that the NSE Nifty index is not affected by the rainfall index and Granger Causality test displays that rainfall index does not Granger Cause the Nifty index. According to the authors’ knowledge, this is the first empirical study to determine the interrelationship between the rainfall index and the Nifty Index over a longer period of time.

Spatio-Temporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone, Northwest Ethiopia.

Introduction:Malaria is a life-threatening acute febrile illness which is affecting the lives of millions globally. Its distribution is characterized by spatial, temporal, and spatiotemporal heterogeneity. Detection of the space-time distribution and mapping high-risk areas is useful to target hot spots for effective intervention.Methods:Time series cross sectional study was conducted using weekly malaria surveillance data obtained from Amhara Public Health Institute. Poisson model was fitted to determine the purely spatial, temporal, and space-time clusters using SaTScan™ 9.6 software. Spearman correlation, bivariate, and multivariable negative binomial regressions were used to analyze the relation of the climatic factors to count of malaria incidence.Result:Jabitenan, Quarit, Sekela, Bure, and Wonberma were high rate spatial cluster of malaria incidence hierarchically. Spatiotemporal clusters were detected. A temporal scan statistic identified 1 risk period from 1 July 2013 to 30 June 2015. The adjusted incidence rate ratio showed that monthly average temperature and monthly average rainfall were independent predictors for malaria incidence at all lag-months. Monthly average relative humidity was significant at 2 months lag.Conclusion:Malaria incidence had spatial, temporal, spatiotemporal variability in West Gojjam zone. Mean monthly temperature and rainfall were directly and negatively associated to count of malaria incidence respectively. Considering these space-time variations and risk factors (temperature and rainfall) would be useful for the prevention and control and ultimately achieve elimination.

Evidence of Climate Changes in a Tropical Rainforest: Case Study Kakamega Tropical Rainforest

Aims: To assess trends in rainfall and temperature as evidence of climate change in the Kakamega tropical forest ecosystem over the last 30 years (1980-2010).&#x0D; Study Design: Secondary data on temperature, rainfall and other climate related phenomena that have occurred within the Kakamega forest ecosystem over the last 30 years were obtained from the Kenya Metrological Department (KMD), Kenya Agriculture and Livestock Research Organization (KALRO), and local metrological stations and analysed to assess trends. This data was supplemented with data from questionnaires and structured interviews.&#x0D; Place and Duration of Study: The study was carried out among forest adjacent communities in the Kakamega tropical forest ecosystem between April and December 2020.&#x0D; Methodology: Data on awareness and preparedness for climate change was collected using structured questionnaires, focused group discussions (FGD) and structured interviews. Three villages in the east, west, north and south of the forest that are within a 2km radius of the forest were randomly selected for sampling. From the 52,729 households, 397 households were randomly chosen from each of the three selected villages. Questionnaires targeted family heads in all the selected households. The questionnaire sought first-hand information on climate change awareness and impacts of climate change among the forest-adjacent communities. Each questionnaire comprised of open and closed-ended questions. Focused Group Discussion involved specialized groups of women, village elders, local administrators, CBOs working in the ecosystem, among others. Further information was obtained using key informants. A total of forty-eight (48) key informants were randomly selected for discussion. The interview method involved key personnel working within the forest ecosystem which included staff from KWS, County environmental officers, National Environment Officers, Agriculture officers at the national and county levels. The interviews focused on preparedness, mitigative and adaptive capacity to climate change by the forest adjacent communities.&#x0D; Results: Analysis of the mean rainfall trends over the last 30 years (1980-2018) reveal that, mean monthly rainfall ranged from a monthly minimum 36mm (2012) to a maximum mean of 402.30mm (2018). Similarly, analyses of the mean monthly rainfall reveal dissimilarities in amounts of rainfall in each year over the last 30 years. The mean monthly rainfall fluctuations appear to increase overtime. Analysis of temperature records for the last 30 years (1990-2018) revealed a maximum mean daily temperature of 27.720C, a minimum temperature of 25.35⁰C and a maximum of 31.96⁰C with a range of 25⁰C - 30⁰C. From 2005 to 2018, higher temperatures (above 30⁰C) are evident. When a moving cumulative mean, using data on the minimum temperature over the last 30 years (1982 – 2018) was calculated, a forecasted trend gave a mean minimum temperature of 14.41⁰C, a minimum temperature of 12.33⁰C and a maximum temperature of 18.67⁰C. In the year 1990, the minimum temperature rose from 150C to 18⁰C. The forecasted temperature for 2019 also follows the same trend, with temperatures now stabilizing above 30⁰C. Majority of the respondents (96.7%, n=290) were aware of changes in climatic conditions now, compared to previous years. Most of the respondents had moderate (53%, n=159) to high (30.7%, n=92) knowledge about changes in climate. Majority of the respondents (57.7%, n=173 ) affirmed that information on climate change was obtained from broadcast media. Majority of the respondents were very concerned about climate change 91.7% (n=275) suggesting that they understood the seriousness of the changing climate. Further analyses of the data revealed that many of the respondents were aware that natural causes (31%, n=93), human activities (34.3%, n=103) and a combination of both (34.7%, n=104) were the main causes of climate change, while many of the respondents (58%, n=174) had experienced some extreme weather events in the last five years.&#x0D; Conclusion: There is a clear evidence of climate changes in the Kakamega forest ecosystem as observed from the rising temperatures and variability in precipitation. Most people adjacent to the forests are aware of climate changes and its effects. The main sources of information are the electronic media. The increase in temperature may be due to deforestation, urbanization and agricultural activities.