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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches
Bushra Praveen1, Swapan Talukdar2, Shahfahad3
1School of Humanities and Social Sciences, Indian Institute of Technology Indore, Simrol, Indore, 453552, India.
Scientific Reports
|June 27, 2020
Summary
India
Area of Science:
- Climatology
- Hydrology
- Environmental Science
Background:
- Long-term rainfall analysis is crucial for understanding climate variability in India.
- Spatiotemporal rainfall patterns are essential for water resource management and agricultural planning.
Purpose of the Study:
- To analyze and forecast long-term spatiotemporal rainfall changes across India.
- To identify trends and change points in rainfall data from 1901 to 2015.
- To project future rainfall patterns for the next 15 years.
Main Methods:
- Pettitt test for abrupt change point detection.
- Mann-Kendall (MK) test and Sen's Innovative trend analysis for trend analysis.
- Artificial Neural Network-Multilayer Perceptron (ANN-MLP) for rainfall forecasting.
- Kriging geo-statistical technique for mapping rainfall trends.
Main Results:
- Most meteorological divisions show a significant negative rainfall trend annually and seasonally.
- 11 out of 17 divisions exhibit a declining monsoon rainfall trend at a 0.05% significance level.
- A significant negative trend of -8.5 was recorded for overall annual rainfall.
- Rainfall increased from 1901-1950 and declined significantly after 1951.
- Future 15-year rainfall forecasts indicate a significant decline across all divisions.
Conclusions:
- India is experiencing a significant long-term decline in rainfall, particularly after 1951.
- The study highlights potential impacts on water resources and future water demand.
- Climate factors like precipitation convective rate and cloud cover may influence observed rainfall changes.
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