Analysis Of Rainfall Data Research Articles

Analysis of Empirical Rainfall Data Covering 1979-2020 as a Guide to Agriculture and Water Resources Management in Ondo State, Nigeria

The aim of this paper was to evaluate daily empirical rainfall data covering 1979-2020 as a guide to agriculture and water resources management in Ondo, State, Nigeria by collecting data from the Nigerian Meteorological Agency, Abuja, using various standard methods. Results showed that, the area observed rainfall throughout the year with March (86.4 mm) as the rainfall onset, November (56.4 mm) was the cessation, double maximum rainfall occurred in July (254.2 mm) and September (282.1 mm) with the climatological mean of 1752.2 mm. Also, the percentage (%) difference rainfall showed seven (7) positive and four (4) negative occurrences. The highest positive % difference occurred in September (10.4), while the lowest occurred in July (2.6). The highest negative % difference was -26.5 (November), while the lowest was -5.9 (August). The stations deviation from zonal average monthly rainfall showed that, Igbokoda, Ilaje LGA deviated in all the months negatively, while Okitipupa, Okitipupa LGA and Oka-Akoko, Akoko South West LGA deviated positively in eleven (11) months each. The study concluded that, rainfall varied and had declined generally over the study area. Hence, it was recommended that, the Seasonal Climate Prediction (SCP) by Nigerian Meteorological Agency (NiMet) should be down-scaled early to serve as a guide to farmers and hydrologists; irrigation should be encouraged; hybrid crops should be cultivated and more water reservoirs should be constructed.

ANALISIS BANJIR SUNGAI MENGGUNAKAN MODEL HEC-RAS DI WILAYAH DAS TABANIO KABUPATEN TANAH LAUT PROVINSI KALIMANTAN SELATAN

This study aims to determine the amount of water runoff and water depth in the Tabanio Watershed, Tanah Laut Regency. The study was conducted using the HEC-RAS software to generate a flood simulation model. Data analysis included Digital Elevation Model for National Scale (DEMNAS) data, flood discharge data, rainfall data, river bathymetry, and tidal data. DEMNAS data was obtained from the website of the National Geospatial Information Agency (BIG), rainfall data was obtained from the Center for Hydrometeorology and Remote Sensing (CHRS) website. Rainfall data analysis involves selecting the maximum daily rainfall value to determine the flood discharge. The hourly flood discharge is calculated using the Synthetic Unit Hydrograph method with the Nakayasu approach. River bathymetry data is processed as additional data for DEM, with interval recording done every 0.2 seconds. Tide data was obtained through prediction based on the location constant closest to the research area and analyzed using the Pasut.exe software for a period of 6 days. Based on the results of flood analysis research using the HEC-RAS model in the Tabanio Watershed, Tanah Laut Regency, South Kalimantan Province, it can be concluded that the model results show the water depth and area of water runoff at maximum discharge. as follows: less than 0.5 meters with a very low category covering an area of 4,168.86 hectares, 0.51 to 1.5 meters with a low category covering an area of 6,417.79 hectares, 1.51 to 2.5 meters with a medium category covering an area of 1,987, 25 hectares, 2.51 to 3.5 meters with a high category of 741.47 hectares, and above 3.51 meters with a very high category of 113.69 hectares. So that the total water runoff reaches 13,429.05 hectares.

ОПРЕДЕЛЕНИЕ ТРЕНДОВ КОЛИЧЕСТВА ОСАДКОВ ДЛЯ ЧЕТЫРЕХ ГОРОДОВ ШТАТА АССАМ (ИНДИЯ)

In time-series analysis, several non-parametric and parametric techniques are available to identify a trend. However, due to the lesser sensitivity of the non-parametric methods towards the outliers, they are often given priority over parametric methods for analysing the trend. In this study, the significance of the trend in the rainfall data of the four cities; Guwahati, Tezpur, North Lakhimpur, and Dibrugarh in Assam is examined using the non-parametric Mann-Kendall method. The non-parametric Sen’s estimator method is used to estimate the magnitude of the significant trends for the four cities. The rainfall trend has been studied based on monthly, yearly and seasonal observations. The results clearly show that there is no trend in the cities except for Dibrugarh, where an increasing rainfall trend is observed within 5% significance level. Based on the seasonal rainfall analysis, it is noted that the pre- and post-monsoon precipitation has a significant increasing trend, while no trend is seen during the monsoon period, which indicates an absence of significant change in the rainfall magnitude during the monsoon for the study period. The analysis of the monthly rainfall data has reveal a significant trend for July in the cities of Guwahati and Tezpur, which are closely located, while the significant trend May is seen in the city of North Lakhimpur. However, for Dibrugarh, all months have shown significant trends at 5% except for September, November, and December. The analysis has shown that there is a significant change in the rainfall trend for the non-monsoon period, indicating the change in the rainfall pattern, while the overall trend for the monsoon period remains the same. This change of rainfall pattern during the non-monsoon period may be an impact of the climate change, which requires detailed studies. The overall analysis for the four sites over the period under review has shown a significant trend in rainfall only for Dibrugarh.

Vector Generalized Additive Models for Extreme Rainfall Data Analysis: A case study in South Korea

Extreme value theory (EVT) is the branch of statistics concerned with inference about the tails of the distribution function. Accurate estimation of the year return levels of climate extremes is a critical step in the projection of future climate and in engineering design for disaster response. The study of non-stationary behavior in the extremes is important to analyze data in environmental sciences, climate, finance, or sports. The largest order statistics is an extension of the block maxima approach that is used in extreme value modeling. However, in Korea, the scientific literature usually has used the method based on fitting a distribution to an annual maximum. We focus on daily rainfall amounts data sets collected from the Korea Meteorological Administration for Seoul, Busan and Daegu, from 1961 to 2020. First, we fit the GEV df to block maxima data, i.e., annual maximum rainfall. Second, we use the top 10 annual precipitation events. We model, in which both mean and scale parameters of extreme value distribution are modeled as linear or smooth functions of covariate, YEAR, using the vector generalized linear models (VGLMs) and vector generalized additive models (VGAMs).

Analisis Risiko Bencana pada kasus Curah Hujan Ekstrem Provinsi Sulawesi Barat dengan Metode Moran's I & 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.

A Bayesian non-asymptotic extreme value model for daily rainfall data

Motivated by the analysis of extreme rainfall data, we introduce a general Bayesian hierarchical model for estimating the probability distribution of extreme values of intermittent random sequences, a common problem in geophysical and environmental science settings. The approach presented here is derived under a set of assumptions different than those used in traditional extreme value (EV) theory. A hierarchical model structure is adopted for capturing the underlying variability in the yearly distribution of event magnitudes and occurrences, which are described through a latent temporal process. Focusing on daily rainfall extremes, the structure of the proposed model lends itself to incorporating prior geo-physical understanding of the rainfall process. By means of an extensive simulation study, we show that this methodology can significantly reduce quantile estimation uncertainty with respect to Bayesian formulations of traditional asymptotic EV methods, particularly in the case of relatively small samples. On the other hand, a negative bias is observed in estimated rainfall quantiles. We evaluate the tradeoff between bias and variance for extreme rainfall quantiles and show that the approach proposed here can be advantageous depending on the sample size available for training. The benefits of the approach are illustrated with an application to a large data set of 479 long daily rainfall historical records from across the conterminous United States. By comparing measures of in-sample and out-of-sample predictive accuracy, we find that the model structure developed here, combined with the use of all available observations for inference, can improve robustness with respect to overfitting to the specific sample in case of short datasets.

Statistical Analysis and Distribution Modeling of Rainfall Data in Flood-Prone Regions of Karnataka State

This research paper presents a comprehensive analysis of southwest monsoon rainfall data in flood-prone regions of Karnataka State, employing suitable statistical techniques and data distribution modeling. The detailed analysis of the dataset helped in understanding rainfall patterns, which is essential in identifying flood risks in Karnataka.The analysis includes the computation of descriptive statistics, exploration of data stationarity and the application of distribution models to characterize rainfall patterns. Maximum likelihood estimator has been used to estimate the parameters involved in the data distribution modelling. Further, hypothesis tests were conducted to assess the goodness-of-fit of these distribution models. These models are found to be suitable for the dataset in comparison with the Gaussian models.

Climatic-Geomorphological Investigation of the World's Wettest Areas around Cherrapunji and Mawsynram, Meghalaya (India)

This research paper comprehensively examines the climate and geomorphological features of Cherrapunji and Mawsynram, aiming to understand the factors and environmental implications of their extreme precipitation. The study investigates climatic patterns, identifies geomorphological characteristics, and explores the factors influencing the occurrence of heavy rainfall in these areas, and displays unique rainfall patterns with high precipitation levels and notable spatio-temporal variation influenced by topographic interactions. Trend analysis reveals stable rainfall conditions over the past 122 years. The shift of the world's wettest place from Cherrapunji to Mawsynram in recent decades have been attributed to various factors such as geographical location, geomorphology-local topography, LULC-human influence, rain shadow effect, and orographic lifting effects. Cherrapunji recorded maximum rainfall of 24.55 thousand mm, while Mawsynram received 26 thousand mm of rainfall in the last century. The analysis of long-term rainfall data indicates distinct dry and wet seasons, with recent trends (2000-2020) suggesting a decline in rainfall for both locations. Furthermore, extreme value analysis techniques are employed to estimate maximum rainfall for different return periods, offering insights into extreme rainfall events. The return period of one day's highest rainfall of 1340.82 mm is about 100 years. The findings contribute to our understanding of climate change impacts, support sustainable development practices, and inform strategies for water resource management and erosion mitigation in similar geographic contexts. This research enhances our knowledge of these unique regions and their significance within the broader context of global climate systems.

IDENTIFICATION OF MESOSCALE CONVECTIVE COMPLEX (MCCS) AT SOUTHERN PAPUA FOR THE PERIOD OF DECEMBER 2017 – NOVEMBER 2022

One factor contributing to rainfall in a certain region is the presence of convective cloud growth. Extensive convective clouds can trigger extreme weather conditions and the occurrence of Mesoscale Convective Complex (MCC) phenomena. MCCs phenomena are characterized by circular cloud cover with eccentricity ≥ 0.7, core cloud area ≥ 50,000 km², cloud cover area ≥ 100,000 km², and cloud top temperature ≤ -52 °C or 221 K. This study aims to identify the spatial and diurnal of MCC in the Southern Papua and for the period of December 2017 to November 2022, as well as to assess its impacts on rainfall during MCC events. Satellite data from Himawari-8 in IR channel was used to analyze MCCs criteria, which were then plotted using a geographic visualization software. Rainfall data in GSMaP was processed using GrADS to display rainfall values. The data processing procedure is carried out using an algorithm based on the study conducted by Maddox. The results of the spatial distribution analysis showed that there were 20 MCC events that occurred during the 5-year period, with dominant growth in inland areas near mountainous and high-topography regions. Seasonally, MCC events predominantly occurred during the MAM period and were least frequent in the JJA period. The diurnal distribution revealed that MCC events had a nocturnal life cycle, forming during the nighttime until early morning. Analysis of the GSMaP rainfall data indicated that the dominant rainfall intensity caused by MCC events was heavy rain (20 – 50 mm/hr).

Perceptions and Adaptation Practices of Agropastoralists to Rainfall and Temperature Changes in Mali

Many people in African countries derive their livelihoods from agriculture and livestock. Therefore, unfavourable environmental and climatic conditions render them more vulnerable to increasing food insecurity and poverty rates. However, few studies have investigated how farmers’ adaptation strategies affect farm productivity and household food security in the Sahelian region, notably Mali. This study aims to examine the impacts of climate change agro-pastoralists and to analyse the adaptation strategies in Mali. For this purpose, an analysis of daily rainfall and temperature data from 1960 to 2020 over Bamako, Ségou and Sikasso station was carried out using Instat+ v3.36, Rstudio, XLSTAT and Rclimdex software. Field surveys were conducted among 355 agropastoralists in three regions of Mali to assess the impacts and adaptation strategies of agropastoralists. The analysis of climate data showed a downward trend in overall rainfall. As for the temperature, it shows an upward trend over the series from 1960 to 2020 at the station of Ségou, Sikasso and Bamako.
 Agropatoralists use adaptation strategies such as organic fertilizers, changing planting dates and growing of short duration maize varieties to mitigate against the negative effects of climate change. The use of organic fertilizers and short-duration use of improved varieties promotes agricultural productivity and food security. We conclude that building agropastoralists adaptive capacity tends to reduce their vulnerability to climate change by increasing crop yields and food security.

ZONING OF FLOOD PRONE AREAS IN SINGKAWANG CITY DUE TO RAINFALL

Singkawang City is a coastal and hilly city. Flooding incidents have caused considerable material damage, displacement of people, and loss of lives. Floods in Singkawang occur almost yearly. In general, the floods in Singkawang are caused by natural factors, exceptionally high rainfall. Considering the significant impact of floods on the development of Singkawang City, it is necessary to establish flood vulnerability zoning based on rainfall in the city.An analysis is conducted on parameters that influence flood incidents, including the slope of the land, elevation, soil type, land cover, and hydrological analysis of rainfall data to create a flood vulnerability zoning map in Singkawang City. Then, an overlay is performed on the four parameters that affect floods, resulting in a map showing the distribution of inundation due to the maximum planned flood discharge (Q) generated from the hydrological analysis based on rainfall.The flood vulnerability map analysis results based on 4 data parameters that affect flood events obtained that the flood-safe area in Singkawang City covers an area of 14863.97 ha (27.04%). In contrast, areas not prone to floods cover 24571.74 ha (44.70%). Areas vulnerable to floods cover 11476.22 ha (20.87%), and regions highly vulnerable to floods cover 3567.28 ha (6.49%). Based on the results of hydrological analysis based on rainfall, it is obtained that the area of Singkawang City mostly experiences inundation due to the maximum planned flood discharge occurring at a 2-year interval in the Small Semelagi River Zone (353.5 ha), at a 5-year gap in the Small Semelagi River Zone (327.4 hectares), at a 10-year gap in the Selakau River Zone (401.4 ha), at a 20-year interval in the Selakau River Zone (473.1 ha), at a 50-year interval in the Selakau River Zone (545.9 ha), and at a 100-year gap in the Selakau River Zone (617.7 ha).

Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ^{circ }C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region.

Timing and spatial selection bias in rapid extreme event attribution

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating “trigger event”, whose occurrence corresponds to a stopping rule. Intuition suggests that naïvely including the trigger event in a standard fit in which it is the final observation will bias its importance downwards, and that excluding it will have the opposite effect. In either case the stopping rule leads to bias recently discussed in the statistical literature (Barlow et al., 2020) and whose implications for climate event attribution we investigate. Simulations in a univariate setting show substantially lower relative bias and root mean squared error for estimation of the 200-year return level when the timing bias is accounted for. Simulations in a bivariate setting show that not accounting for the stopping rule can lead to both over- and under-estimation of return levels, but that bias can be reduced by more appropriate analysis. We also discuss biases arising when an extreme event occurs in one of several related time series but this is not accounted for in data analysis, and show that the estimated return period for the “trigger event” based on a dataset that contains this event can be both biased and very uncertain. The ideas are illustrated by analysis of rainfall data from Venezuela and temperature data from India and Canada.

Impact of Climate Change on Paddy Farming in the Village Tank Cascade Systems of Sri Lanka

Consequences of global climate change are predicted to increase risks to crop production in the future. However, the possible broader impact of climate change on social-ecological systems still needs to be evaluated. Therefore, the present study focuses on one such globally important agricultural social-ecological system referred to as the Village Tank Cascade System (VTCS) in the dry zone of Sri Lanka. The VTCS has considerable potential to withstand seasonal climate variability mainly through continuous supply of water by the village tank storage throughout the year. The current study aimed to investigate trends of climate variability and possible impacts on paddy production in the North and North-central VTCS zone. Observed and projected rainfall and temperature data were analysed to evaluate the past variability trends (1970 to 2020) and model future (up to 2100) scenarios of climate variability and trends. Long-term observed rainfall and temperature data (1946 to 2020) were analysed to identify possible anomalies. The Maximum Entropy (MaxEnt) model has been used to predict the situation of future paddy farming (2050 and 2070) under two climate scenarios (RCP4.5 and RCP8.5) of the Intergovernmental Panel on Climate Change (IPCC). Six variables that would affect paddy growth and yield quality were used alongside the average monthly rainfall and temperature of two Global Climate Models (MIROC5 and MPI-ESM-LR). Climate suitability for two paddy cultivation seasons (Yala and Maha) were predicted for current and future climate scenarios. The findings revealed that observed and projected climate changes show considerable deviation of expected rainfall and temperature trends across the VTCS zone. Temperature exhibits warming of approximately 1.0 °C during the declared Global Warming Period (1970 to 2020) in the study area. In addition, there is a trend of significant warming by 0.02 °C/year, RCP4.5 and 0.03 °C/year, RCP8.5 from 1950 to 2100. Rainfall (1970–2020) shows high interannual variability but trends were not significant and less discernible. However, long-term projected rainfall data (1950–2100) analysis detected a significant (p = 0) upward trend (2.0 mm/year, RCP4.5 and 2.9 mm/year, RCP8.5), which is expected to continue up to the end of this century. Further, the study revealed some shifts in temperature towards higher values and positive anomalies in rainfall affecting seasonality and the likelihood of more extreme occurrences in the future, especially during the Maha cultivation season. The MaxEnt model predicts the following under future climate scenarios: (i) spatio-temporal shifts (conversions) in climate suitability for paddy farming in the VTCS zone; (ii) substantial low and moderate suitability areas that are currently suitable will remain unchanged; (iii) up to 96% of highly suitable and 38% of moderately suitable paddy growing areas in the VTCS zone will be at risk due to a decline in future climate suitability; and (iv) expansion of lower suitability areas by approximately 22% to 37%, due to conversion from moderate suitability areas. The study provides evidence that the continuous warming trend with increasing variability in rainfall and shifting seasonality could increase the vulnerability of future paddy farming in the VTCS. Thus, findings of this study will help planners to make more targeted solutions to improve adaptive capacity and regain the resilience to adjust the paddy farming pattern to deal with predicted climate variability and change.

Frequency Analysis of Rainfall Data

Abstract: The intensity of precipitation plays a crucial role in hydraulic engineering when creating hydraulic structures and evaluating their impact. To design hydraulic projects such as drainage networks, bridges, and road culverts, Intensity-DurationFrequency (IDF) curves are used. These curves are heavily influenced by project planning, design, and operation in the field of water resources engineering and are also used in various flood structures. In this study, the frequency of rainfall intensity duration was determined in Kamti, Khari, catchments, Nagpur Maharashtra, from 1996 to 2019. Three different frequency analysis methods, including log-normal, Gumbel, and log-Pearson type III distribution, were applied to fit the corresponding curves. Analyzing the precipitation data is of great importance in this field..

Distribution System Dewatering in Coal Mining at PIT Sena Sungai Lilin District, Musi Banyuasin Regency, South Sumatra Province

The purpose of this research is to calculate the total design discharge of water entering the mining site in the sena pit of PT Putra Muba Coal (PMC), design the appropriate shape and dimensions of the sump to accommodate the amount of water entering the mining site and calculate the number of pumps needed to remove water entering the mine to the settling pond. The source of water entering the mine area comes from rainwater entering the mine opening, water runoff from the rainwater catchment area in the area around the mine opening, and groundwater rise. Based on the results of the analysis of rainfall data for 2013-2022, a rainfall plan of 77, 225 mm/day, rainfall intensity of 9.714 mm/hour with a rainfall return period of 2 years was obtained. The rainfall catchment area at the research site is 72,936 m2 with a discharge generated of 0.03 m3 / second and groundwater discharge of 0.00812 m3 / second. To remove the water entering the mine, it is flowed naturally into the sump with dimensions of 50 m length, 14 m top width, 13 m bottom width, and 5 m depth. The dimensions of the open channel are planned with a channel width of 32 cm, a flow depth of 28 cm, a wet cross-sectional area of 13.6 cm2, a wet circumference of 97 cm, a hydraulic radius of 9.25 cm, and a channel length of 233m. The water in the sump is pumped into an open channel. The pump used has a maximum discharge of 150 m3 / hour and a total pump head of 11.8712 m with a pipe diameter of 6 inches.

An investigation of the changing patterns of rainfall in the Indravathi subbasin utilizing the Mann-Kendall and Sen’s slope methods

In hydro-meteorological studies, precipitation is an important parameter that is utilized in irrigation system design and management, as well as agricultural planning. Under the effects of climate change, precipitation is predicted to alter, which will have an impact on sustainable development. Using rainfall data for the Indravathi subbasin, Godavari basin, from 1998 to 2016, the spatial variability and temporal trend of precipitation were examined over the region, which has a humid tropical climate. Using non-parametric tests like Mann-Kendall (MK) and Sen’s slope approach, analysis for trend detection was conducted. The test data were loaded into ArcGIS software, which then performed monthly, seasonal, and annual analyses of the spatial and temporal trends of rainfall. According to an analysis of the monthly rainfall trends, February had the largest spatiotemporal declining trend in rainfall, while September had the highest spatiotemporal increasing trend. An analysis of seasonal rainfall data revealed a considerable increase in the tendency for summer rainfall over nearly 80% of the basin. Data on annual precipitation revealed an increase in annual precipitation for the basin’s central eastern sections. The findings of this research may help the decision makers and stakeholders make the most efficient use of hydrological resources by providing insight on the effects of climate change and climatic changes on precipitation patterns in the Indravathi subbasin.

Agroclimatic Risks and Endogenous Knowledge of Farmers in the Guidimouni Basin (South-Central Zinder, Niger)

Agriculture is one of the most vulnerable sectors to the effects of climate change. The objective of this study is to analyze the impacts of rainfall variability on agricultural activities in the Guidimouni basin. The methodology used is based on the processing and analysis of daily rainfall data (Mirriah and Gouré stations from 1961 to 2019). It is complemented by a survey on perceptions of agroclimatic risks, impacts and strategies among a sample of 150 farmers in four villages. The results show that farmers have a good understanding of climate risks (80%) and are developing resilience strategies. These range from the intensification of irrigated crops (40%) to the use of natron (58%). In addition to these responses, the processing of products, particularly cassava (86%), and seasonal migration to Nigeria (28%) were also considered.

Change analysis of All India and regional rainfall data series at annual and monsoon scales

AbstractRainfall characteristics are changing due to several reasons and change/trend detection is required. Literature survey reveals many relevant studies whose outcomes are divergent, possibly because different data series and different methodologies have been applied. This paper presents a critical appraisal of past studies and methodologies for trend analysis. Results of trend analysis of Indian rainfall data are presented. Data for all of India and for five homogenous regions (North-West, Central North-East, North-East, West Central, and Peninsular India) for 1871–2016 were used. The Pettitt change point test, regression, Mann-Kendall (MK), and Wavelet Decomposition were used to study different aspects of changes. Results of the change point test showed that most rainfall series had change points around 1957–65, possibly due to large-scale land use, cultivation, irrigation, and industrial changes in this period. Generally, rainfall for most homogenous regions and sub-divisions show falling trends; some are statistically significant. Series was also decomposed by the wavelet method. Approximate and detailed components of some decomposed series showed a significant declining trend. This work has focused on the magnitude of rainfalls; trends in rainfall intensities are also important. It is necessary to establish denser observation networks to collect short-term data and analyze.

Rainfall and Streamflow Trends in the Upper <em>Maduru Oya</em> Sub-watershed in Sri Lanka: A Comparative Study Using Different Statistical Methods

Analysis of historical data on rainfall and streamflow provides useful information for water resources management in a river basin. The objective of this study was to assess the variations and trend analysis of rainfall and streamflow data in the upper Maduru Oya sub watershed in Sri Lanka during a 30 year period (1989 October to 2019 September) using daily rainfall data from two gauging locations (Padiyathalawa and Bibile) and daily average streamflow data from one gauging location (Padiyathalawa). Descriptive statistics were used to assess the variations. The Mann Kendal Trend Test (MK test) and Innovative Trend Analysis method (ITA) were used to analyze the trend, and Sen’s Slope Estimator was used to assess the magnitude of the trend. Based on the MK test, most of the rainfall trends obtained are in an increasing direction, and streamflow trends are in a decreasing direction. Rainfall trends found in Padiyathalawa are not statistically significant. Annual, First Inter Monsoon, March, and May time scales of rainfall showed significant upward trends at Bibile (p<0.1) while streamflow in January, April, and September showed significant negative trends (p<0.1) at Padiyathalawa. The results displayed by all three tests were in very good agreement for rainfall. ITA results showed similar trend types of 77%, 95% and 41% at Padiyathalawa and Bibile rainfall and streamflow, respectively. A significant positive (ρ<0.01) strong and lower correlation between the trend value of the ITA and the Z value of the MK test were found for rainfall and streamflow, respectively. Changes in rainfall are not sufficient to explain the observed trends in streamflow. Therefore, future research should be directed at the causal aspects of streamflow change in this watershed.