scholarly journals Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3264
Author(s):  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Jacopo Dari ◽  
Alessia Flammini
Keyword(s):  
Climate Change ◽  
Least Squares ◽  
Trend Analysis ◽  
Time Resolution ◽  
Linear Trend ◽  
Rainfall Data ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Climatic Trend ◽  
Sen’S Method

The main challenge of this paper is to demonstrate that one of the most frequently conducted analyses in the climate change field could be affected by significant errors, due to the use of rainfall data characterized by coarse time-resolution. In fact, in the scientific literature, there are many studies to verify the possible impacts of climate change on extreme rainfall, and particularly on annual maximum rainfall depths, Hd, characterized by duration d equal to 24 h, due to the significant length of the corresponding series. Typically, these studies do not specify the temporal aggregation, ta, of the rainfall data on which maxima rely, although it is well known that the use of rainfall data with coarse ta can lead to significant underestimates of Hd. The effect of ta on the estimation of trends in annual maximum depths with d = 24 h, Hd=24 h, over the last 100 years is examined. We have used a published series of Hd=24 h derived by long-term historical rainfall observations with various temporal aggregations, due to the progress of recording systems through time, at 39 representative meteorological stations located in an inland region of Central Italy. Then, by using a recently developed mathematical relation between average underestimation error and the ratio ta/d, each Hd=24 h value has been corrected. Successively, commonly used climatic trend tests based on different approaches, including least-squares linear trend analysis, Mann–Kendall, and Sen’s method, have been applied to the “uncorrected” and “corrected” series. The results show that the underestimation of Hd=24 h values with coarse ta plays a significant role in the analysis of the effects of climatic change on extreme rainfalls. Specifically, the correction of the Hd=24 h values can change the sign of the trend from positive to negative. Furthermore, it has been observed that the innovative Sen’s method (based on a graphical approach) is less sensitive to corrections of the Hd values than the least-squares linear trend and the Mann–Kendall method. In any case, the analysis of Hd series containing potentially underestimated values, especially when d = 24 h, can lead to misleading results. Therefore, before conducting any trend analysis, Hd values determined from rainfall data characterized by coarse temporal resolution should always be corrected.

scholarly journals Effect of climate change in paddy production and adaptation strategies in Tharu communities of Dang district, Nepal

2018 ◽  
Vol 1 (1) ◽  
pp. 62-75
Author(s):  
Pradip Raj Poudel ◽  
Narayan Raj Joshi ◽  
Shanta Pokhrel
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Time Series Data ◽  
Sampling Technique ◽  
Adaptation Strategies ◽  
Primary Data ◽  
Series Data ◽  
Rice Varieties ◽  
Cross Sectional ◽  
Maximum Rainfall

A study on effects of climate change on rice (Oryza sativa) production in Tharu communities of Dang district of Nepal was conducted in 2018A.D to investigate the perception and major adaptation strategies followed by Tharu farmers. The study areas were selected purposively. Cross-sectional data was collected using a household survey of 120 households by applying simple random sampling technique with lottery method for sample selection. Primary data were collected using semi-structured and pretested interview schedule, focus group discussion and key informants interview whereas monthly and annual time series data on temperature and precipitation over 21years (1996-2016) were collected from Department of Hydrology and Meteorology, Kathmandu as secondary data. Descriptive statistics and trend analysis were used to analyze the data. The ratio of male and female was found to be equal with higher literacy rate at study area than district. Most of the farmers depended on agriculture only for their livelihood where there was large variation in land distribution. Farmers had better access to FM/radio for agricultural extension information sources. The study resulted that Tharu farmers of Dang perceived all parameters of climate. Temperature and rainfall were the most changing component of climate perceived by farmers. The trend analysis of temperature data of Dang over 21 years showed that maximum, minimum and average temperature were increasing at the rate of 0.031°C, 0.021°C and 0.072°C per year respectively which supports the farmers perception whereas trend of rainfall was decreased with 7.56mm per year. The yearly maximum rainfall amount was increased by 1.15mm. The production of local indigenous rice varieties were decreasing while hybrid and improved rice varieties were increasing. The district rice production trend was increasing which support the farmer’s perception. The study revealed that there were climate change effects on paddy production and using various adaptation strategies to cope in Dang district.

scholarly journals Examining Yam Production in Response to Climate Change in Nigeria: A Co-Integration Model Approach

2020 ◽  
Vol 9 (4) ◽  
pp. 42
Author(s):  
Cynthia W. Angba ◽  
Richard N. Baines ◽  
Allan J. Butler
Keyword(s):  
Climate Change ◽  
Error Correction ◽  
Trend Analysis ◽  
Linear Trend ◽  
Error Correction Model ◽  
Series Data ◽  
Integration Model ◽  
Correction Model ◽  
Linear Trend Analysis ◽  
Model Approach

This study addressed yam production in response to climate change in Cross River State using a co-integration model approach. The specific objectives of this paper are to analyze the trend in yam production, annual precipitation, and annual temperature, and to analyze the impact of climate variables on yam production. Time-series data from 1996 to 2017 was used. Based on the analysis, which constituted a linear trend analysis, co-integration test, and error correction model, the study came up with robust findings. The linear trend analysis for yam production revealed a steady increase in output between the years 2005 and 2016. The result of the rainfall trend analysis showed the presence of rainfall variability and irregularity. The trend line for temperature showed an overall downward trend for the period under study. However, the Error Correction Model result showed that temperature was statistically significant and negatively impacted yam production. The study recommends that policymakers should take appropriate steps to encourage the development of pest- and disease-tolerant yam varieties because an increase in temperature leads to the proliferation of insects, pests, and diseases.

scholarly journals Bayesian Modelling of Extreme Rainfall Data of Some Selected Locations in Nigeria

2020 ◽  
pp. 16-26
Author(s):  
Olawale Basheer Akanbi
Keyword(s):  
Climate Change ◽  
Bayesian Methods ◽  
Fossil Fuels ◽  
Extreme Rainfall ◽  
Rainfall Data ◽  
Bayesian Modelling ◽  
Data Set ◽  
Maximum Rainfall ◽  
Block Maxima ◽  
Informative Approach

Climate change occurs when there is rise in average surface temperature on earth, which is mostly due to the burning of fossil fuels usually by human activities. It has been known to contribute greatly to the occurrence of extreme storms and rainfall, this trend continues as the effect of climate change becomes more pronounced. Therefore, this study modelled the extreme rainfall data of three locations (Calabar, Ikeja, Edo) in Nigeria. The block maxima method was used to pick out the maximum rainfall data in each year to form annual maxima data set. The parameters [location, scale, shape] were estimated using both the Classical and Bayesian methods. The result shows that the Bayesian Informative approach is a very good procedure in modelling the Nigerian Extreme Rainfall data.

scholarly journals Development of Intensity Duration Frequency (IDF) Curves for Rainfall Prediction within the Middle Niger River Basin

2020 ◽  
Vol 4 (2) ◽  
pp. 382-397
Author(s):  
J. O. Ehiorobo ◽  
O.C. Izinyon ◽  
R. I. Ilaboya
Keyword(s):  
River Basin ◽  
Rainfall Intensity ◽  
Rainfall Data ◽  
Annual Maximum ◽  
Return Periods ◽  
Maximum Rainfall ◽  
Niger River ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Best Fit

Rainfall Intensity-Duration-Frequency (IDF) relationship remains one of the mostly used tools in hydrology and water resources engineering, especially for planning, design and operations of water resource projects. IDF relationship can provide adequate information about the intensity of rainfall at different duration for various return periods. The focus of this research was to develop IDF curves for the prediction of rainfall intensity within the middle Niger River Basin (Lokoja and Ilorin) using annual maximum daily rainfall data. Forty (40) year’s annual maximum rainfall data ranging from 1974 to 2013 was employed for the study. To ascertain the data quality, selected preliminary analysis technique including; descriptive statistics, test of homogeneity and outlier detection test were employed. To compute the three hours rainfall intensity, the ratio of rainfall amount and duration was used while the popular Gumbel probability distribution model was employed to calculate the rainfall frequency factor. To assess the best fit model that can be employed to predict rainfall intensity for various return periods at ungauged locations, four empirical IDF equations, namely; Talbot, Bernard, Kimijima and Sherman equations were employed. The model with the least calculated sum of minimized root mean square error (RMSE) was adopted as the best fit empirical model. Results obtained revealed that the Talbot model was the best fit model for Ilorin and Lokoja with calculated sum of minimized error of 1.32170E-07 and 8.953636E-08. This model was thereafter employed to predict the rainfall intensity for different durations at 2, 5, 10, 25, 50 and 100yrs return periods respectively.

scholarly journals DYNAMICS PATTERNS OF INFLOW IN THE RESERVOIR THAT OPERATED FOR TWO DECADES

2021 ◽  
Vol 56 (4) ◽  
pp. 92-103
Author(s):  
Heriantono Waluyadi ◽  
Pitojo Tri Juwono ◽  
Widandi Soetopo ◽  
Rispiningtati ◽  
Lily Montarcih Limantara ◽  
...  
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Rainfall Data ◽  
Operation Pattern ◽  
The Pacific ◽  
Ocean Region ◽  
Reservoir Watershed ◽  
June July

Climate change in the past 20 years brings significant alteration in the earth surface. It affects extremely anomaly temperature, such as the ENSO, IOD, and SOI phenomena. The Pacific Ocean Region, the Indian Ocean Region, and the Darwin – Tahiti Region undergo an increase and a decrease in the sea surface temperatures (SST); thus, it can lead to seasonal change in Indonesia. Due to ENSO, IOD, and SOI, climate change also highly affects the operation pattern of reservoirs, food production, and other commodities. This research used SST data (Nino 1.2, Nino 3, Nino 3.4, Nino 4, IOD West, IOD East, and SOI) from National Oceanic and Atmospheric Administration (NOAA) and rainfall data from 1998 to 2018 of nine stations at Wonogiri Reservoir watershed. Trend analysis of the SST index indicated an increase in trend SST index. Trend analysis of monthly rainfall average at Wonogiri Watershed area indicated a decrease in January, March, April, May, June, July, August, and October, while it increased in February, September, November, and December. Multiple linear regression analysis with the stepwise regression method indicated that during the rainy season, the rainfall at Wonogiri Watershed and Inflow at Wonogiri reservoir were influenced by the SST index (Nino 1.2, Nino 3, Nino 3.4, Nino 4). Meanwhile, during the dry season, the rainfall at Wonogiri Watershed and the Inflow at Wonogiri reservoir were influenced by the SST index (IOD West, IOD East, and SOI). With monthly correlations between SST and rainfall data that have a dynamic characteristic, it can be used to calculate the inflow probability distribution in optimizing reservoir operation patterns.

scholarly journals A data portal for regional climatic trend analysis in a Peruvian High Andes region

2011 ◽  
Vol 6 (1) ◽  
pp. 219-226 ◽  
Author(s):  
M. Schwarb ◽  
D. Acuña ◽  
Th. Konzelmann ◽  
M. Rohrer ◽  
N. Salzmann ◽  
...  
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Climate Change Adaptation ◽  
Air Temperature ◽  
Data Series ◽  
Adaptation Measures ◽  
Climatic Trend ◽  
Temperature And Precipitation ◽  
Temperature Trends ◽  
Data Portal

Abstract. In the frame of a Swiss-Peruvian climate change adaptation initiative (PACC), operational and historical data series of more than 100 stations of the Peruvian Meteorological and Hydrological Service (SENAMHI) are now accessible in a dedicated data portal. The data portal allows for example the comparison of data series or the interpolation of spatial fields as well as download of data in various data formats. It is thus a valuable tool supporting the process of data homogenisation and generation of a regional baseline climatology for a sound development of adequate climate change adaptation measures. The procedure to homogenize air-temperature and precipitation data series near Cusco city is outlined and followed by an exemplary trend analysis. Local air temperature trends are found to be in line with global mean trends.

scholarly journals Rainfall Trend Analysis in Tehsils of Palghar District, Maharashtra State, India

2021 ◽  
Vol 9 (11) ◽  
pp. 238-253
Author(s):  
Dr. Sumit M. Dhak
Keyword(s):  
Trend Analysis ◽  
Linear Trend ◽  
Daily Rainfall ◽  
Kendall Test ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Rainfall Trend ◽  
Department Of Agriculture ◽  
Increasing Trend ◽  
Slope Estimator

Abstract: A detailed trend analysis of monthly and annual rainfall for Tehsils of Palghar district were carried out using 22 years (1998-2019) daily rainfall data taken from Department of Agriculture, Maharashtra State. In this study, to analyse the trend, the non-parametric test (Mann-Kendall test) and Sen’s slope estimator were used. For developing a functional relationship between variables, a linear trend of rainfall data for the studied area evaluated using the linear regression. The results showed that the trend analysis of monthly rainfall has a varied trend of rainfall in the rainy months in tehsil of Palghar District. The month of July significant increasing trend was observed at Jawhar (42.91 mm/year), Vikramgad (29.90 mm/year), Wada (24.06 mm/year), Talasari (31.36 mm/year), Palghar (25.299 mm/year), Mokhada (29.96 mm/year) and Dahanu (38.14 mm/year), whereas non-significant increasing trend 2.76 mm/year was observed at Vasai tehsil of Palghar District during 1998-2019. The month of June, August, September and October rainfall did not show any significant trend in tehsil of Palghar District and non significant decreasing as well as non significant increasing trend was observed in tehsil of Palghar District during 1998 – 2019. The result concluded that annual rainfall trend was increased in Jawhar, Vikramgad, Wada, Talasari, Palghar, Mokhada and Dahanu; whereas Vasai tehsil rainfall trend was decreased in tehsil of Palghar District during 1998 -2019. Keywords: Rainfall, Trend Analysis, Mann Kendall’s Test, Sen Slopes, Regression

scholarly journals To what extent does the detection of climate change in Hungary depend on the choice of statistical methods?

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Beatrix Izsák ◽  
Tamás Szentimrey
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Linear Trend ◽  
Meteorological Data ◽  
Warming Trend ◽  
Data Series ◽  
Data Sets ◽  
Raw Data ◽  
Temperature Trends ◽  
Trend Data

AbstractThe trend analysis of meteorological time series has gained prominence in recent decades, the most common method being the so-called ‘linear analytical trend analysis’. Until the mid-1990s, trend analysis was commonly performed on non-homogenized data sets, which frequently led to erroneous conclusions. Nowadays, only homogenized data sets are examined, so it really is possible to detect climate change in long meteorological data sets. In this paper, the methodology of linear trend analysis is summarized, the way in which the model can be validated is demonstrated, and there is a discussion of the results obtained if unjustified discontinuities caused by changing measurement conditions, such as the relocation of stations, changes in measurement time, or instrument change occur. On the basis of an examination of records for the preceding 118 years, it is possible to state that both annual and seasonal mean temperature trends display a significant warming trend. In the case of homogenized data series, the change is significant over the entire territory of Hungary; in the case of raw data series, however, the change is not significant everywhere. The validity of the linear model is tested using the F-test, a task as yet carried out on the entire Hungarian data series, series comprising records for over 100 years. Furthermore, neither has a comparison been made of the trend data for raw data series and the homogenized data series with the help of information on station history to explore the causes of inhomogeneity.

scholarly journals Rainfall Analysis for Hyderabad and Nawabshah, Sindh, Pakistan

2020 ◽  
Vol 10 (6) ◽  
pp. 6597-6602
Author(s):  
A. A. Mahessar ◽  
A. L. Qureshi ◽  
B. Sadiqui ◽  
S. M. Kori ◽  
K. C. Mukwana ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Rainfall Data ◽  
Precipitation Pattern ◽  
Rainfall Time Series ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Rainfall Analysis ◽  
The Mean ◽  
The Difference ◽  
Annual Maximum Rainfall

The climatic change has a visible impact in recent abnormal weather events, such as Pakistan’s intensification of the hydrological cycle with changing precipitation pattern, water availability periods, and weather-induced natural disasters. The rainfall flush flood of 2010 alone displaced millionσ of people and damaged properties in just one stroke. The next year, the shocking rainfall flood of 2011 in Sindh, only underscored the enormity of the challenge posed by climate change. The current paper presents the analysis carried out for one-day annual maximum rainfall for Hyderabad and Nawabshah cities, Sindh, Pakistan for the period from 1961 to 2011 using STATISTICA Software for interpolating and forecasting the rainfall time series. The maximum values of observed rainfall were 250.70mm and 256.30mm, while the minimum values were 3.0mm and 0.0mm for Hyderabad and Nawabshah respectively, while the mean of fifty-one (51) years of rainfall data is 51.96mm and 45.3 mm and the computed standard deviations were 42.693mm and 43.896mm respectively. The difference between the mean and standard deviation of one-day maximum rainfall is small, which showed the consistency of the data. The polynomial trend curved lines exhibited fluctuations in the rainfall data, which indicates a continual change in rainfall behavior. Hence, the rainfall data are subjected to a moving mean smoothing with a duration shorter than 3 years. Through these trends, the future one-day annual maximum rainfall can be predicted. The correlation of one-day annual maximum rainfall between Hyderabad and Nawabshah cities had R2 of 0.973. The computed results of return periods of 3, 5, and 10 years for one-day annual maximum rainfall for both cities revealed that the rainfall values for Hyderabad are higher.

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