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Accueil du site → Doctorat → Inde → 2016 → Hydrological modelling of climate change impacts of a semi arid basin Vulnerability on water resources farmers perception and adaptation strategies

Anna University (2016)

Hydrological modelling of climate change impacts of a semi arid basin Vulnerability on water resources farmers perception and adaptation strategies

Shimola, K.

Titre : Hydrological modelling of climate change impacts of a semi arid basin Vulnerability on water resources farmers perception and adaptation strategies

Auteur : Shimola, K.

Université de soutenance : Anna University

Grade : Doctor of Philosophy (PhD) 2016

Description partielle
Climatic change creates shifts in the timing and magnitude of climate. The present study examined the trend and change point detection of climate variables to assess the climate variablilty and change. The Statistical Downscaling Model (SDSM) was applied using three sets of data such as observed daily climate data (maximum temperature, minimum temperature and precipitation) for the period of 1970–2001. NCEP re-analysis data composed of 26 daily atmospheric variables for the same period which were selected at grid box covering each of the stations were considered. SDSM also used HadCM3 GCM SRES A2a and B2a emission scenarios predictor data for the generation of future scenario climate variables. The observed data for the period 1970–1985 were used for calibration and those of period 1986– 2001 for validation. The calibrated model was run with the model’s parameter and the model validation for the period 1986–2001 was done by generating 100 series of daily climate variables. The outputs were statistically analysed. The statistics of observed data and the model predicted data for the same period were compared to evaluate the model’s performance. The performance of the model was explained by standard error, explained variance (E), coefficient of regression for climate data such as maximum temperature, minimum temperature and precipitation. The performance evaluation was also carried out by other statistical parameters such as standard deviation, maximum, dry-spell length, wet-spell length of observed and simulated precipitation datasets. Thus, these climate variables were downscaled for future time slices such as 2020s (2010–2039), 2050s (2040–2069) and 2080s (2070–2099). Three future periods 2020s, 2050s and 2080s were compared to the observed climate variables.

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