Analysis Of Rainfall Research Articles

An Investigation on Interdependence Between Rainfall and Temperature in Ekpoma, Edo State, Nigeria

This paper focuses on the interdependence between rainfall and temperature and their joint effect. Rainfall and temperature are vital climatic variables for agricultural productivity and other human activities. Despite the importance of rainfall and temperature, there are difficulties associated with accurate analysis of their joint distribution due to the possibility of interrelationship between the variables. Several studies have been conducted by researchers on the interaction between climatic variables in order to ascertain their effects on the environment because temperatures are observed to be undergoing changes regularly. The analysis of rainfall and temperature for exploratory and visualization purposes is investigated because underlying structures and patterns do form the basis of decisions by government and regulatory agencies. This study employs the statistical approach in investigating the interdependence between rainfall and temperature in Ekpoma, Edo State, Nigeria for a period of five consecutive years from 2016 to 2020 using the Gaussian kernel estimator. The results of the investigations using some statistical indicators establish that there is irregular pattern of rainfall which is occasioned by changes in temperature. The variability of rainfall is mostly prominent in two years which are 2017 and 2019 with 29.43mm and 27.74mm as maximum amount of rainfall respectively. The results also demonstrate that the performance of years with high standard deviations are better than that of low standard deviations. Again, the performance of years with high negative correlation coefficients and high negative covariance of rainfall and temperature is better than years with weak correlations and low covariance.

Patrimonio natural y cultural de cuerpos de agua lagunares. El origen del turismo en Carhué, Buenos Aires, Argentina.

This research addresses the study of heritage as a uniting social factor. It is a case study of the Epecuén lagoon, in Carhué, Buenos Aires, Argentina. The article sets out to analyse the contextualised heri‑ tage activation process, based on an evaluation of the natural and cultural heritage resources by analysing the processes, actors and discourses used in tourism. The characteristics of the water in the lagoon were analysed from field work, rainfall analysis and historical reconstruction. We worked from a symbolic and economic appreciation of the natural and cultural heritage. This tourist territory is the product of the con‑ junction of the inherent qualities of the place, and as valued by a society, making it attractive

Study the seasonal trend analysis and probability distribution functions of rainfall for atmospheric region of Pakistan

It is known that the rainfall is one of the major aspects of the climate change and hydrological processes. Its impact on the resources of water has always been considered very essential. In the countries like Pakistan having severe weather conditions the effect of rainfall can be more crucial. Due to the geographical differences, the impact of sudden rainfall can be more disastrous in Pakistan as in 2011 and 2022 only the few days’ extreme rainfall in summer monsoon disturbed many regions of Pakistan especially southern region of Pakistan and lost hundreds of lives and homes. In addition, there are two methods applied for this study (1) trend analysis and (2) probability distribution functions. Our results revealed that only three cities i.e. Chitral, Skardu and Gilgit having increasing as well as the station Sialkot have increasing trend pattern in only summer rainfall, and other all have shown decreasing trend overall. Furthermore, key extreme weather events over Pakistan have changed in frequency and intensity during the past decades due to significant increase in global warming. The methods and results will be useful to study the historical rainfall data for projecting future rainfall variations impact on hydrological processes due to climate change.•The Data, methods and results of this study can be useful to government officials for protective measures and future developments.•Detecting trend analysis was applied to point out how strong the trend in the rainfall is and whether it is increasing or decreasing.•Additionally, the probability distribution was applied to indicate changed in frequency and intensity during the past decades due to significant variations in rainfall data.

Verification of operational numerical weather prediction model forecasts of precipitation using satellite rainfall estimates over Africa

AbstractRainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate‐related diseases and public health. Numerical weather models (NWMs) are an important component of this work, due to their complete spatial coverage, high resolution and ability to forecast into the future. In this study, the spatio‐temporal skill of short‐term forecasts of rainfall across Africa from 2016 through 2018 is evaluated. Specifically, the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction‐Global Forecast System (NCEP‐GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in situ observations and are commonly used in analysis of African rainfall. Model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics of daily and weekly forecasts show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter‐tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at 1 week aggregation time are improved against daily forecasts.

Analysis and Trend Estimation of Rainfall and Seasonality Index for Marathwada Region

Analysis and Trend Estimation of Rainfall and Seasonality Index for Marathwada Region

宁夏河东沙区刺槐和丝绵木水分利用策略

PDF HTML阅读 XML下载 导出引用 引用提醒 宁夏河东沙区刺槐和丝绵木水分利用策略 DOI: 10.5846/stxb202202100330 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31760236，42161013）；宁夏自然科学基金项目（2022AAC03094） Water use strategies of Robinia pseudoacacia and Euonymus bungeanus in the east sandy land of the Yellow River in Ningxia, China Author: Affiliation: Fund Project: The National Natural Science Foundation of China (31760236,42161013);The National Natural Science Foundation of Ningxia,China(2022AAC03094) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:刺槐和丝绵木混交林是宁夏河东沙区防护林建设的主要模式，了解刺槐和丝绵木的水分利用策略，能为区域植被恢复和防护林林分结构调整提供科学依据。以宁夏河东沙区刺槐（Robinia pseudoacacia）和丝绵木（Euonymus bungeanus）混交林为研究对象，通过监测微气象、树干液流和土壤质量含水量，结合大气降水、土壤水、植物木质部水同位素组成，采用Granier及其校正公式，运用贝叶斯混合模型（MixSIAR）和相似性比例指数（PS）研究2个树种的蒸腾耗水、水分来源和水分利用关系。结果表明：刺槐和丝绵木的蒸腾耗水量在生长季中期较高，前期和后期较小，刺槐的蒸腾耗水量是丝绵木的1.55倍；影响刺槐蒸腾耗水的主要环境因子为饱和水汽压差、太阳辐射、0-40 cm土壤质量含水量和40-120 cm土壤质量含水量；影响丝绵木蒸腾耗水的主要环境因子为饱和水汽压差、太阳辐射、平均气温、0-40 cm土壤质量含水量和40-120 cm土壤质量含水量；蒸腾耗水较高时，刺槐主要吸收利用中层土壤水，丝绵木主要吸收利用浅层土壤水，蒸腾耗水较低时，刺槐主要吸收利用浅层土壤水，丝绵木主要吸收利用中层土壤水；在土壤水分含量较小的月份，刺槐和丝绵木主要通过水分利用空间上的差异来应对干旱，维持自身的蒸腾耗水；土壤水分含量较高的月份，刺槐和丝绵木的相似性比例指数最大，存在较强的水分竞争关系；浅层土壤水分含量较高可能反映了沙区植物根系对土壤水分再分配作用下植物在水分和养分需求之间的平衡。 Abstract:The mixed forest of Robinia pseudoacacia and Euonymus bungeanus is the typical shelterbelt construction mode in the east sandy land of the Yellow River in Ningxia, China. Research on the water use strategies of Robinia pseudoacacia and Euonymus bungeanus could provide scientific basis for restoration regional vegetation and stand structure optimization allocation of shelter forest. In this study, the sap flow of Robinia pseudoacacia and Euonymus bungeanus was monitored continuously to clarify the characteristics of water consumption by transpiration which calculated by Granier and its correction formula, as well as simultaneous records of micro-meteorological factors and gravimetric soil water content. Combined with stable hydrogen and oxygen isotope analysis of rainfall, soil water and plant xylem water, the water source and water use relationship among symbiotic trees were quantitatively analyzed by Bayesian mixture model (MixSIAR) and the proportional similarity index (PS). The results showed that the water consumption of Robinia pseudoacacia and Euonymus bungeanus were higher in the middle growing season, but lower in the early and late growing season. The water consumption of Robinia pseudoacacia was 1.55 times larger than Euonymus bungeanus. The main environmental factors affecting water consumption of Robinia pseudoacacia were vapour pressure deficit, solar radiation, 0-40 cm gravimetric soil water content and 40-120 cm gravimetric soil water content. The main environmental factors affecting water consumption of Euonymus bungeanus were vapour pressure deficit, solar radiation, average temperature, 0-40 cm gravimetric soil water content and 40-120 cm gravimetric soil water content. Robinia pseudoacacia mainly used the middle layer soil water and Euonymus bungeanus mainly used the shallow layer soil water in the season with high water consumption. While the shallow layer soil water and the middle layer soil water were ultilized by Robinia pseudoacacia and Euonymus bungeanus separately in the season with lower tree water consumption. In addition, Robinia pseudoacacia and Euonymus bungeanus was able to deal with drought and maintain their own water consumption mainly through the spatial difference of water use strategy under drought seasons. In the seasons when gravimetric soil water content was higher, the proportional similarity index (PS) of Robinia pseudoacacia and Euonymus bungeanus was the highest, and there was a strong water competition relationship. The higher of shallow layer gravimetric soil water content might reflect a trade-off between water and nutrient requirements of plants under the effect of soil water redistribution by plant roots in the sandy land. 参考文献 相似文献 引证文献

Trend analysis of rainfall using non-parametric method-A case study of Pairi sub-basin of Mahanadi basin

Trend analysis of rainfall using non-parametric method-A case study of Pairi sub-basin of Mahanadi basin

Statistical analysis of rainfall impacts on urban traffic in Bangkok, Thailand

Statistical analysis of rainfall impacts on urban traffic in Bangkok, Thailand

Framers’ Perception of Climate Change and Time-Series Trend Analysis of Rainfall and Temperature in North-Western Ethiopia

Smallholder farmers are the most vulnerable community to climate change in Ethiopia because they are highly dependent on subsistence rain–fed agricultural production system. The aims of this study were to identify the perceptions of farmers on climate change and time series trend analysis of rainfall and temperature in the Gondar Zuria district of northwestern Ethiopia that has been severely affected by climate change stresses. In Ethiopia, studying the time series trends of rainfall and temperature in the context of climate change is crucial. As a result, it is easy to identify and implement better climate change adaptation strategies. Primary data were collected from a randomly selected 121 sample households and purposefully selected ten key informants through questionnaires and interviews. The survey data was analyzed using SPSS software version 21, XLSTAT software and excel spreadsheet. The variability and trend analysis were conducted from downloaded Gridded monthly rainfall and temperature data from free online climate explorer Which is Global Precipitation and Climate Centre (GPCC V7) and the Climate Research Unit (CRU TS 3.23) with 0.5 by 0.5 resolution from 1980 to 2013 respectively. Climate variable data have analyzed by using a precipitation concentration index, anomaly index, coefficient of variation and simple linear regression. Furthermore, the Mann-Kendall test was used to detect the time series trend. The result revealed that the mean maximum temperature had an increasing trend through time significantly. But the trend for mean minimum temperature showed a non-significant increasing trend during the period of 1980–2013. Whereas, the rainfall showed a statistically insignificant decreasing trend in the main rain (summer) season. While there was an inter-annual variability in the amount of Belg season rainfall and it showed an increasing trend. The result revealed that farmers perceive changes in temperature and rainfall over the past 30 years. The Gondar Zuria district is characterized by a semi-arid climate and hence, received low and erratic rainfall during the summer season. Therefore, we recommend possible adaptation strategies designed for climate change particularly rain-fed agriculture economic dependent countries should give emphasis for the increasing trend of temperature and the decreasing and unreliable nature of rainfall. Keywords: Climate change, Mann-Kendall test, rainfall, temperature, farmers perception, Gondar Zuria District DOI: 10.7176/JRDM/90-02 Publication date: January 31 st 2023

Exploring extreme value analysis of rainfall for some nuclear power plant sites in India

Extreme rainfall is an important consideration for safety of nuclear installations. Literature reported estimation of the same for Indian nuclear power plant sites using Gumbel and Frechet distributions, fitted by least square (graphical) method (LSM). In these reports, the goodness-of-fit was examined graphically by plots on probability papers. In this study, the data for six nuclear sites collected from literature were analysed. For this purpose, in addition to Gumbel and Frechet, other distributions were included and the parameters of the distributions were evaluated using Lieblein technique and maximum likelihood estimation, as well as LSM. The goodness-of-fit was evaluated using the quantitative tool: Anderson-Darling test; and qualitative tools: probability plot and quantile plot – which are suitable for evaluating goodness-of-fit of extreme value distributions. Maximum daily rainfall for return periods of 50, 100 and 1000 years were reported from the fitted distributions. Comparison with the respective values (50 and 100 year return period) from literature demonstrated that the extreme rainfall reported for these sites were quite accurate with maximum variation within ten per cent.

Characteristics of Warm Season Heavy Rainfall in Minnesota

Abstract Warm season heavy rainfall in Minnesota can lead to flooding with serious impacts on life and infrastructure. Situated in a transition zone between humid eastern and semiarid western conditions in the United States, Minnesota experiences large spatial variability in precipitation. Previous research has often lacked spatiotemporal detail important for heavy rainfall analysis for Minnesota. This research used Stage-IV hourly precipitation data with 4-km grid spacing during May–September 2004–20 to analyze Minnesota spatial, seasonal, and event-based characteristics. Rain event frequency, accumulation, hours, and intensities were compared for all rain events (>2.5 mm) and heavy rain events (>36 mm). For all rain events, results showed the highest regional median monthly rain event frequency (>6 events) in June and the lowest (<5 events) in September. Median monthly accumulations were largest (∼75 mm) in June, followed by July and August. Monthly total rain event hours at a point peaked around 20 h in May in southeastern Minnesota. Smaller event accumulations occurred more frequently than larger accumulations, and event mean intensities were higher in summertime (June–August) than in May and September for rain events and heavy rain events. Heavy rain event region-based analyses showed monthly peaks for frequency in July–August, accumulation in July, and event hours in June–July and September. Median heavy rain event durations were shorter during June–August than in May and September. Monthly heavy rain event accumulation as a percent of all rain event accumulation was greatest in September (24%). These results establish a foundation for future research into precipitation patterns and trends. Significance Statement Climate analysis has indicated that Minnesota is in a region where increases in heavy rainfall are anticipated for the future. Heavy rainfall in Minnesota has led to flooding with severe adverse impacts. This study addresses a gap in information about heavy precipitation in Minnesota and provides heavy rainfall analyses useful for climate-related planning. Stage-IV hourly precipitation data for the warm season (May–September) during 2004–20 enabled the identification of rain events and heavy rain events, as well as their characteristic frequency, rainfall accumulation, duration, and intensity. The results help establish a baseline for past and future analyses of precipitation patterns and trends. They also build a foundation for future research investigating the weather patterns that lead to heavy rainfall.

Geospatial based runoff estimation and maximum one day rainfall analysis for designing water harvesting structures

Geospatial based runoff estimation and maximum one day rainfall analysis for designing water harvesting structures

Rainfall variability assessment in the hydrological catchment of Addis Ababa

The Intensity-Duration-Frequency (IDF) relationship is a key input for defining the hydrograph and peak flow discharge required for the hydraulic design. The IDF curves describe the relationship between rainfall intensity, rainfall duration, and return period. The objective of this study was to develop an accurate rainfall analysis in the Addis Ababa catchment. To compute reliable IDF curves, long rainfall data at fine time step scale and uniformly distributed are necessary. In Ethiopia, there is limited availability of data at sub-hourly and at spatial scales. To cope with these issues, (1) stochastic disaggregation techniques have been employed to disaggregate daily observed data into sub-hourly data and (2) to guarantee homogeneity in the project area, the merge of satellite data and observed gauged data have been considered. In this study, IDF curves have been computed for the entire hydrological catchment of Addis Ababa (~1430 km2). The results of the study include tabular and graphical presentation of IDF curves under changed climatic conditions, for return periods of 2, 5, 10, 25, 50 and 100 years for the durations of 15,30 min and 1, 3, 6, 12 and 24 hours.

Rainless Day Management using Geographic Information System and Wireless Sensor Networks on Climatology Station Banjarbaru

Information obtained at the Banjarbaru Climatology Station includes seasonal forecasts, rainfall analysis, rainfall properties analysis, rainy day analysis, and monitoring of rainless days in the South Kalimantan region. Monitoring of Rainless Day is a form of data on rainless days that occur in South Kalimantan in sequence. The data is sent by observers to the Banjarbaru Climatology Station office using SMS, WhatsApp, and even by calling the staff of the Banjarbaru Climatology Station so that employees must check every media both SMS, WhatsApp, and telephone to find out the data sent by observers. Related to these problems, a web-based rainless day monitoring application was made. By using the Research and Development (R&D) method to facilitate the management of rainless day data, a geographic information system management system using Google Maps API and MySQL database is built which is integrated with Internet of Things (IoT) technology based on Wireless Sensor Networks (WSN) for data collection in real-time. The system built will provide notifications in the management of areas affected by rainless days in order to deal with drought.

A Case Study on Watershed Study for Flood Mitigation of Belgavi Region, Karnataka, India using ArcGIS

During the monsoon season, many regions in India are prone to floods among which Belagavi district, Karnataka (the study area) is one of the most affected. It has suffered huge lives and property losses. This study focuses on areas of rainfall analysis, watershed area mapping, flood vulnerable areas mapping and possible mitigation measures. DEM analysis is used to understand flood risk assessment. The rainfall data is collected for three taluks which are more prone to flood. These are Athani, Chikodi, and Raibagh in Belgavi regions. The study area was analyzed by generating maps for soil type, lithology, geomorphology, land use, and land cover using ArcGIS software. Maps were obtained for the percentage of land contributing to each attribute. Further flood vulnerability mapping has to be done by creating weightage map considering factors such as drainage density, soil type,, precipitation, land use land cover, slope, and watershed compressed together by weighted overlay analysis, and the flood hazard zone map is developed to classify the areas into zones according to the risk of flooding which helps to suggest various non-structural mitigation measures.

The Impact of Dam Reservoir on Flood Reduction (Case Study Logung Reservoir, Kudus, Central Java)

The Indonesian government targeted building 65 reservoirs and dams in 2015-2022. The purpose is to maintain food security facing the long-term climate crisis, besides reducing the impact of a flood. Logung Reservoir is located on the Logung River, Kudus, Central Java. This earth-fill dam was built to solve the flood and drought problems in Kudus. However, the existence of the Logung Reservoir gives a risk to the downsteam area. The purpose of this research is to study flood reduction and the risk of overtopping, which can cause a dam break, and to calculate the Logung Reservoir spillway's performance in handling floods. This study is based on four main steps: the rainfal-runoff modelling, the extreme rainfall estimation, the hydrograph inflow of the dam estimation, and the evaluation of food reduction. The results of the 1000-year rainfall analysis and PMP for the Logung dam basin were 409 mm and 704 mm, respectively. Rainfall distribution is made using ABM, and effective rainfall is calculated by the SCS method. Land use analysis in the Logung dam watershed obtained a CN-II of 71.4 and a CN-III of 85.2. The rainfall-runoff transformation method used a unit hydrograph. The Nakayasu and Gama-I unit hydrographs show significant differences from the peak discharge, although not for the time base. The flood hydrograph design affects the reservoir performance. The reservoir flood routing results a dumping efficiency of 35% for the 1000-year and 21% for PMF. The 1000-year flood routing shows that there was no risk of overtopping. The highest water level on the PMF flood routing at +94.1. Several solutions can prevent overtopping by reducing inflow discharge through small dams upstream, increasing the green area, or modifying spillway structure.

Statistical modeling of monthly maximum rainfall in Senegal

We provide the first statistical analysis of maximum rainfall in Senegal. The data are from twelve stations spread across Senegal. The generalized extreme value distribution was fitted to maximum rainfall by the method of maximum likelihood. Probability and quantile plots showed that the generalized extreme value distribution provided an adequate fit for all stations. The vast majority of stations do not exhibit significant trends in rainfall. Four of the stations exhibit positive trends in rainfall. Estimates of return levels are given.

Rapid comparison of precipitation data between satellite and in situ observations

The agriculture sector responsible for global food and nutrition security has an urgent need to examine climatic trends so that adaptations can be exercised in advance. Freely available dataset from satellite sources can greatly ease rainfall analysis, especially for smallholder farmers who typically operate under limited resources. Tests to determine their accuracy, however, are so far not deployed in tropical Southeast Asia. We compared in situ observations with dataset from the Global Satellite Mapping of Precipitation (GSMaP) and the Prediction of Worldwide Energy Resources (POWER) in two sites located 180km apart in the tropical Malay Peninsula for 30days. We found that in situ precipitation values were markedly overestimated by GSMaP (34.9-67.5%) and POWER (180.5-289.2%), and the possible reasons are discussed. Nonetheless, we conclude that GSMaP remains the best hope for smallholder farmers and its dataset can still be used under the precaution of error margins determined by the practical method described herein.

GIS-Based Rainfall Analysis Using Remotely Sensed Data in Kirkuk Province, Iraq

This research aims to calculate the rate of rainfall for two consecutive years, 2018 and 2019, to study and analyze the drought periods during these years. The research was conducted in Kirkuk province north of Iraq, by dividing the study area into five regions based on five stations of rainfall records. Two types of data used in investigations; remotely sensed daily precipitations from integrated multi-satellite retrievals and monthly precipitations average rate from NASA power application. The methodology involved the calculation of rainfall rats by three methods; Mean, Thiessen’s polygons, and the Isohyetal technique. Data were analyzed, and final maps were produced using Geographic Information Systems. For the year 2018 the average annual rainfall rate produced by Mean, Thiessen’s polygons, and Isohyetal technique were; 1.376, 1.341, and 1.343 respectively. But, for the year 2019 the average annual rainfall rate produced by Mean, Thiessen’s polygons, and Isohyetal technique were; 0.966, 0.932, and 0.882 respectively. The results showed the scarcity of water and the lack of rainfall during the study years, which has a great impact on the region's exposure to drought, represented by the drying up of rivers.

Rainfall forecast using SARIMA model along the coastal areas of Sindh Province

Rainfall forecasting is critical for economic activities such as agriculture, watershed management, and flood control. It requires mathematical modelling and simulation. This paper investigates the time series analysis and forecasting of the monthly rainfall for the Sindh coastline, Pakistan. The seasonal autoregressive integrated moving average (SARIMA) model was used for the last three decades (1991-2020) and forecasting was done for the next two years. The model is based on the Box Jenkins methodology. The decomposition of time series plots into trend, seasonal and random components showed a seasonal effect. The Augmented Dickey–Fuller (ADF) and Mann–Kendall (MK) tests showed the inherent stationarity of the rainfall data. The best SARIMA models for monthly rainfall were SARIMA (1,0,1)(3,1,1)12 and SARIMA (1,0,1)(1,1,1)12 with Akaike information criterion corrected (AICC) values of 1507 and 1387, respectively. The model predictions indicate that, in the years 2021/22, July will likely have the most rainfall, followed by August and June. The diagnostic statistical test values directed that the adequacy of the models is consistent for projected monthly rainfall forecasts.