Homogeneity Analysis of Precipitation Series in North Iraq

Agha, Omar M. A. Mahmood; Bağçaci, Soner Çağatay; Şarlak, Nermin

doi:10.9790/0990-0503025763

Cited by 40 publications

(24 citation statements)

References 14 publications

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“…Therefore, the reliability of the recorded climatic data is highly important. Thus many Page 3 of 11 57 researches check the homogeneity of the climatic data series before it is used for any potential trend analysis (Alexandersson 1986; Kang and Yusof 2012;Alghazali and Alawadi 2014;Al-lami et al 2015;Agha et al 2017). Different tests are used to test the homogeneity of data series used for climatic studies.…”

Section: Homogeneity Tests For Rainfall Data Seriesmentioning

confidence: 99%

Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka

Perera¹,

Rathnayake

2019

Appl Water Sci

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Impact of climate change or climate variability on water resources is an exceedingly concerned issue. Hydropower development is one of the most affected industries due to the climatic variability. Therefore, this paper presents the promising results from a study of the impact of climate variability on hydropower generation of Erathna run-of-the-river (ROR) hydropower plant located in Rathnapura district, Sri Lanka. This study was based on surrounded rain gauges outside the catchment as Erathna catchment area is an un-gauged catchment. 30-year rainfall trend analysis from 1988 to 2017 was done using Mann-Kendall and Sen's slope estimator tests to predict the available trends. Pearson's correlation coefficient was used to investigate the relationship between rainfall and Erathna power generation. Results show negative trends for annual rainfalls in several rain gauges, while seasonal trend analyses support that observation. July is the most critical month for most of the rain gauges around the catchment. The results also show a good correlation between the rainfalls and power generation. Therefore, the results conclude the importance of rainfall trend analysis in un-gauged catchments and its forecasting capacity of water resources usage in hydropower development.

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Section: Homogeneity Tests For Rainfall Data Seriesmentioning

confidence: 99%

Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka

Perera¹,

Rathnayake

2019

Appl Water Sci

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“…More advanced extraction techniques, such as Object-Based Image Analysis, were used to extract similar features of the impact and the environmental factors. These features were then integrated into a multi-temporal change Geodatabase as point GIS [13,10,11,15]. A new temporal layer of the database was derived from each satellite image, representing a different state in the process of impact changes.…”

Section: Describe the Analysis Of Environmental Changes For Gismentioning

confidence: 99%

“…The overall progress of E&M changes was then factored by noting the presence or absence of the main features at different points in time. For more detailed analysis, the datasets were disaggregated by geographic boundaries, allowing disparity in the change levels to be assessed [11]. The progress of the effect was further evaluated by normalising the numbers of features (soil, water and vegetation) in each region to baseline statistical data of the satellite images, which were constructed using the pre-disaster image and the objectives of each evaluation-analysis project.…”

Section: Describe the Analysis Of Environmental Changes For Gismentioning

confidence: 99%

“…Additionally, environmental changes, ecological environment evaluations and crop yield assessment in the area. The available data has been used, such as temperature, remote sensing data and a worldclass scale of other data sets used [11].…”

Section: Recycling Environmental Climate Change Data Based On the Remmentioning

confidence: 99%

“…These assumptions and errors were minimised in the environmental change knowledge data before the image analysis was begun by identifying likely forms of miss-interpretation and impact changes. In addition to the physical factor analysis, economic, social levels were successfully used to infer information about the evaluation and monitoring strategy [16,11,10]. A detailed discussion of these sectors and factors was not possible with remote sensing alone, but the information from satellite imagery was used to complement results acquired with ground surveys and modern techniques of the digital processing images with GIS [12].…”

Section: Use Of the Statistics-analyticalmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating and monitoring of changes in the environmental elements by using remote sensing techniques in Iraq

Al-Tameemi

2018

MATEC Web Conf.

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The primary aim of project highlight was to E&M a universally-applicable suite of elements and techniques that allow remote sensing techniques to the E&M-environmental changes and human interventions and the impact of disaster management systems for the period (2010-2015). The framework allows evaluators to monitor changes in a manner that is rapid, independent and reliable. Such a pattern is required to promote contribution and transparency towards a more systematic understanding of the process of E&M. The factors are based on the use of Very High-Resolution Optical Satellite Imagery and have been developed to fit existing change approaches, such as the In-Disaster Needs Assessment-IDNA, and the Affected Cluster Framework. The elements encompass a range of environmental, climate, economic, health, physical and social factors, which can be analysed to provide a holistic representation of the E&M changes process. The paper begins by addressing the need for a E&M framework and by reviewing current guidelines and systems. Which are continuously developed in this case study. Recommendations are made on: 1- How to use disaster management systems; 2- Which elements should be monitored; and 3- How they can be evaluated, drawing on the experience of environmental scientific researcher, and feedback by the evaluators’ academics.

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Application of artificial neural networks for time series rainfall forecasting in the Loktak lift irrigation command area of Manipur, India

Yumkhaibam,

Kusre

2023

Irrigation and Drainage

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The primary source of water for irrigation and other agricultural activities is rainfall. It has an immediate effect on crop growth and productivity. Forecasting this rainfall in advance allows farmers to effectively plan their cropping pattern. In recent years, forecasting rainfall has become very popular due to the availability of the latest computation techniques. Artificial neural networks (ANNs) are one such technique widely used for rainfall prediction by a number of researchers. These models are more reliable as they make better predictions because of their nonlinear data learning method. In the present study, an ANN model was developed to predict the annual, monsoon and postmonsoon season rainfall. The model was developed using 34 years of data from 1985 to 2018 in the command area of the Loktak Lift Irrigation Project in Manipur, India. The ANN model was trained using the rectified linear unit (ReLU) activation function. The 3‐year input model excelled in all seasons, with the best model achieving a 0.36 coefficient of determination (R2), 75.7 root mean square error, 0.60 correlation coefficient and 62.5 mean absolute error. These performance indicators were comparable with studies performed by other researchers. Thus, the model can be adopted for the study area.

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Homogeneity Analysis of Precipitation Series in North Iraq

Abstract: Reliability

Cited by 40 publications

References 14 publications

Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka

Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka

Evaluating and monitoring of changes in the environmental elements by using remote sensing techniques in Iraq

Application of artificial neural networks for time series rainfall forecasting in the Loktak lift irrigation command area of Manipur, India

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