scholarly journals Statistical modeling of monthly rainfall variability in Soummam watershed of Algeria, between 1967 and 2018

2020 ◽  
Vol 33 (4) ◽  
Author(s):  
Amir Aieb ◽  
Khalef Lefsih ◽  
Marco Scarpa ◽  
Brunella Bonaccorso ◽  
Nicola Cicero ◽  
...  
Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1863 ◽  
Author(s):  
Teresita Canchala ◽  
Wilfredo Alfonso-Morales ◽  
Wilmar Loaiza Cerón ◽  
Yesid Carvajal-Escobar ◽  
Eduardo Caicedo-Bravo

Given that the analysis of past monthly rainfall variability is highly relevant for the adequate management of water resources, the relationship between the climate-oceanographic indices, and the variability of monthly rainfall in Southwestern Colombia at different time scales was chosen as the research topic. It should also be noted that little-to-no research has been carried out on this topic before. For the purpose of conducting this research, we identified homogeneous rainfall regions while using Non-Linear Principal Component Analysis (NLPCA) and Self-Organizing Maps (SOM). The rainfall variability modes were obtained from the NLPCA, while their teleconnection in relation to the climate indices was obtained from Pearson’s Correlations and Wavelet Transform. The regionalization process clarified that Nariño has two regions: the Andean Region (AR) and the Pacific Region (PR). The NLPCA showed two modes for the AR, and one for the PR, with an explained variance of 75% and 48%, respectively. The correlation analyses between the first nonlinear components of AR and PR regarding climate indices showed AR high significant positive correlations with Southern Oscillation Index (SOI) index and negative correlations with El Niño/Southern Oscillation (ENSO) indices. PR showed positive ones with Niño1 + 2, and Niño3, and negative correlations with Niño3.4 and Niño4, although their synchronous relationships were not statistically significant. The Wavelet Coherence analysis showed that the variability of the AR rainfall was influenced principally by the Niño3.4 index on the 3–7-year inter-annual scale, while PR rainfall were influenced by the Niño3 index on the 1.5–3-year inter-annual scale. The El Niño (EN) events lead to a decrease and increase in the monthly rainfall on AR and PR, respectively, while, in the La Niña (LN) events, the opposite occurred. These results that are not documented in previous studies are useful for the forecasting of monthly rainfall and the planning of water resources in the area of study.


2002 ◽  
Vol 8 (1) ◽  
pp. 21-25 ◽  
Author(s):  
Takeo Maruyama ◽  
Toshihiko Kawachi ◽  
Shigeya Maeda

2010 ◽  
Vol 49 (5) ◽  
pp. 1032-1043 ◽  
Author(s):  
Daniel Vila ◽  
Ralph Ferraro ◽  
Hilawe Semunegus

Abstract Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.


1984 ◽  
Vol 14 (1-2) ◽  
pp. 159-174 ◽  
Author(s):  
Maria de Nazaré Góes Ribeiro ◽  
Joachim Adis

Rainfall data registered betwe en 1910 and 1979 at Manaus confirm the existence of a dry season between June and November (monthly rainfall: 42-162mm) and a rainy season from December until May (monthly rainfall: 211-300mm). Annual precipitation amounted to 2105mm with about 75% of the rainfall recorded during the rainy season. Rainfall data collected over 12 months at eigth stations in the vicinity of and at Manaus are compared. Annual precipitation was lower in Inundation Regions (1150-2150mm) compared with Dryland Regions (2400-2550mm). Considerable differences are found in rainfall patterns (intensity, frequency and time of rainfall). This is also truefor neighbouring stations, even if data of a 11-year record period are compared. Thus, it is highly recommended that preciptation data for bioecological studies be collected at the study site.


2012 ◽  
Vol 25 (24) ◽  
pp. 8422-8443 ◽  
Author(s):  
G. Mengistu Tsidu

Abstract Recent heightened concern regarding possible consequences of anthropogenically induced global warming has spurred analyses of data aimed at detection of climate change and more thorough characterization of the natural climate variability. However, there is greater concern regarding the extent and especially quality of the historical climate data. In this paper, rainfall records of 233 gauge stations over Ethiopia for the 1978–2007 period are employed in an analysis that involves homogenization, reconstruction, and gridding onto a regular 0.5° × 0.5° resolution grid. Inhomogeneity is detected and adjusted based on quantile matching. The regularized expectation-maximization and multichannel singular spectrum analysis algorithms are then utilized for imputation of missing values, and the latter has been determined to have a marginal advantage. Ordinary kriging is used to create a gridded monthly rainfall dataset. The spatial and temporal coherence of this dataset are assessed using harmonic analysis, self-organizing maps, and intercomparison with global datasets. The self-organizing map delineates Ethiopia into nine homogeneous rainfall regimes, which is consistent with seasonal and interannual rainfall variations. The harmonic analysis of the dataset reveals that the annual mode accounts for 55%–85% of the seasonal rainfall variability over western Ethiopia while the semiannual mode accounts for up to 40% over southern Ethiopia. The dataset is also intercompared with Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), Climatic Research Unit time series version 3 (CRUTS3.0), Tropical Rainfall Measuring Mission (TRMM), and interim ECMWF Re-Analysis (ERA-Interim) rainfall. The correlation of the dataset with global datasets ranges from 0.52 to 0.95 over sparse to dense rain gauge regions. The GPCP rainfall has a small bias and good correlation with the new dataset whereas TRMM and ERA-Interim have relatively large dry and wet biases, respectively.


2018 ◽  
Vol 14 (3) ◽  
pp. 413-440 ◽  
Author(s):  
Conor Murphy ◽  
Ciaran Broderick ◽  
Timothy P. Burt ◽  
Mary Curley ◽  
Catriona Duffy ◽  
...  

Abstract. A continuous 305-year (1711–2016) monthly rainfall series (IoI_1711) is created for the Island of Ireland. The post 1850 series draws on an existing quality assured rainfall network for Ireland, while pre-1850 values come from instrumental and documentary series compiled, but not published by the UK Met Office. The series is evaluated by comparison with independent long-term observations and reconstructions of precipitation, temperature and circulation indices from across the British–Irish Isles. Strong decadal consistency of IoI_1711 with other long-term observations is evident throughout the annual, boreal spring and autumn series. Annually, the most recent decade (2006–2015) is found to be the wettest in over 300 years. The winter series is probably too dry between the 1740s and 1780s, but strong consistency with other long-term observations strengthens confidence from 1790 onwards. The IoI_1711 series has remarkably wet winters during the 1730s, concurrent with a period of strong westerly airflow, glacial advance throughout Scandinavia and near unprecedented warmth in the Central England Temperature record – all consistent with a strongly positive phase of the North Atlantic Oscillation. Unusually wet summers occurred in the 1750s, consistent with proxy (tree-ring) reconstructions of summer precipitation in the region. Our analysis shows that inter-decadal variability of precipitation is much larger than previously thought, while relationships with key modes of climate variability are time-variant. The IoI_1711 series reveals statistically significant multi-centennial trends in winter (increasing) and summer (decreasing) seasonal precipitation. However, given uncertainties in the early winter record, the former finding should be regarded as tentative. The derived record, one of the longest continuous series in Europe, offers valuable insights for understanding multi-decadal and centennial rainfall variability in Ireland, and provides a firm basis for benchmarking other long-term records and reconstructions of past climate. Correlation of Irish rainfall with other parts of Europe increases the utility of the series for understanding historical climate in further regions.


2009 ◽  
Vol 6 (4) ◽  
pp. 5471-5503 ◽  
Author(s):  
C. L. Wong ◽  
R. Venneker ◽  
S. Uhlenbrook ◽  
A. B. M. Jamil ◽  
Y. Zhou

Abstract. This study analyzed and quantified the spatial patterns and time-variability of rainfall in Peninsular Malaysia on monthly, yearly and monsoon temporal scales. We first obtained an overview of rainfall patterns through the analysis of 16 point data sources. The results led to choosing three distinct regions, i.e.~the east coast, inland and west coast regions. For detailed analysis, Shepard's interpolation scheme was applied to the station data to produce daily rainfall fields on a 0.05 degree resolution grids for the period 1971–2006. The rainfall characteristics in time and space derived from a frequency analysis were found to be distinctly different in these three regions. In the east coast region, monthly rainfall shows a significant periodicity dominated by an annual cycle, followed by a half-year cycle. The inland and west coast regions show that the dominant periodic fluctuations in the monthly rainfall are dominated by a half-year cycle, followed by an annual cycle. The long-term rainfall variability analysis shows that the dry and wet conditions in Peninsular Malaysia are not primarily governed by the ENSO events. The results from the individual regions suggest that although the relative variability is influenced by ENSO, local and regional conditions have an effect on the interannual rainfall variability, which is superimposed on the large-scale weather conditions. A significant increasing trends in annual rainfall (9.3 mm/year) and northeast monsoon rainfall (6.2 mm/monsoon) were only detected in the west coast region. No trend was found in the monthly rainfall, except for November in the west coast region. The spatial variation analysis shows that the east coast region, which received substantially higher amounts of rainfall during the northeast monsoon, has lower spatial rainfall variability and a more uniform rainfall distribution than other regions. A larger range for the monthly spatial variation was observed in the west coast region.


Author(s):  
Ernest Othieno Odwori

Increased wet season rainfall is associated with improved water supply at point water sources and improved river flows and water reservoir levels. For piped water supply schemes with surface water intakes, this is supposed to enhance operations since there is adequate raw water unlike in the dry season where operations are interrupted due to insufficient flows. However, this is not the case in Nzoia River Basin as established by this study. As rainfall increases, drinking water production in treatment plants at Moi’s Bridge, Lumakanda and Busia water supplies decrease and vice versa. Nzoia River Basin is one of the regions that is highly vulnerable to climate variability in Kenya, hence understanding rainfall variability and trends is important for better water resources management and especially drinking water supply. This study aimed at assessing rainfall variability and trends for 3 rainfall stations in Nzoia River Basin; Leissa Farm Kitale, Webuye Agricultural Office and Bunyala Irrigation Scheme and its impact on drinking water production at Moi’s Bridge, Lumakanda and Busia water supplies treatment plants. The rainfall data used in this study covers 31 years period from 1970 to 2001 and was obtained from the Kenya Meteorological Department (KMD), Nairobi, Kenya. Monthly water supply production data for Moi’s Bridge, Lumakanda and Busia water supplies covering 15 years period from 2000 to 2014 was obtained from the County governments of Uasin Gishu, Kakamega and Busia. Rainfall variability and trend was analysed using the parametric test of Linear regression analysis and the non-parametric Mann Kendall statistical test. Monthly rainfall and monthly drinking water production was analysed using Pearson moment correlation to establish the relationship between monthly rainfall and monthly drinking water supply production at Mois Bridge, Lumakanda and Busia Water supplies treatment plants. The results of variability and trend in annual rainfall shows Webuye Agricultural Office recording declining rainfall at -0.8994 mm/31 years (-0.029 mm/ year); whereas Leissa Farm Kitale shows increasing rainfall at 1.0325 mm/31 years (0.033 mm/ year) and Bunyala Irrigation Scheme’s rainfall is increasing at 0.5245 mm/31 years (0.017 mm/ year). Drinking water supply production at Moi’s Bridge, Lumakanda and Busia water supplies has been increasing with time between 2000 and 2014. The results of Pearson moment correlation coefficient shows a strong negative relationship between monthly rainfall and monthly drinking water supply production at 0.05 significance level for Moi’s Bridge, Lumakanda and Busia water supplies. This shows that as rainfall increases, drinking water supply production in treatment plants at Moi’s Bridge, Lumakanda and Busia water supplies decreases. During the rainy season, the cost of water treatment goes up as a result of increased turbidity. Increased rainfall in Nzoia River Basin presents water treatment challenges to the existing water supply treatment plants resulting into reduced production.Water supply managers should improve the capacity of the existing water supply treatment plants to cope with the increased rainfall variability under the changing climatic conditions.


Author(s):  
Lamek Nahayo ◽  
Lanhai Li ◽  
Christophe Mupenzi

Climate change causes loss on lives and livelihoods while regular update strengthens resilience. This study aimed to analyze the rainfall variability impact on livelihoods in Northern Rwanda. The data on community losses due to rainfall variability were considered from 2013 to 2019. The GIS and SPSS helped the data analysis process. The results showed high mean monthly rainfall (119.345 and 90.05 mm) in 2013 and 2017, respectively. This caused landslide, flood, rainstorms, windstorms, lightning, and hailstorms occurrence, which killed/injured people, damaged houses and cropland, livestock loss, and destruction of infrastructures. The correlation analysis indicated a statistically significant p-value of 0.0151 lower than 0.05 and approved that rainfall variability negatively impacts livelihoods. This study can enable policymakers to better understand how changes in rainfall impact livelihoods and strategic measures to adopt for climate variability and climate change adaptation.


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