Statistical analysis of long term spatial and temporal trends of temperature parameters over Sutlej river basin, India

Singh, Dharmaveer; Glupta, R D; Jain, Sanjay K.

doi:10.1007/s12040-014-0530-0

Cited by 25 publications

(14 citation statements)

References 49 publications

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“…Recent studies on Himalayan climate displayed significant changes in mean state of temperature during the twentieth century (Singh et al, 2015). The difference in patterns of low to medium intensity events at various stations established in Himalayan region was also reported by Joshi et al, (2014).…”

Section: Introductionmentioning

confidence: 58%

Study of Daily Extreme Temperature Indices over Sutlej Basin, N-W Himalayan Region, India

2015

Global NEST Journal

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<div> <p>A detailed analysis of extreme temperature indices for the period of 1970-2005 is conducted over Sutlej river basin located in North-West Himalayan region. For this purpose, daily records of maximum and minimum temperature data were procured from Bhakara Beas Management Board, India for four stations. These stations are installed at varying height from 518m to 976m under different physiographic and climatic conditions. Fourteen extreme indices for temperature as specified by the World Meteorological Organization (WMO) Commission for Climatology (CCL)/Climate Variability and Predictability (CLIVAR) Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) are derived with the aid of RClimDex software.  Linear Regression (LR) method is employed for detecting annual as well as monthly trends in extreme indices of temperature over the period of thirty six years (1970-2005). Large spatial and inter-annual variability in trends of extreme indices is observed. However, a rise in diurnal temperature range is observed for the basin as a whole. This may be attributed to the decrease observed in maximum T<sub>Min</sub> (TXn) and warm nights (TN90p) coupled with the increase in maximum T<sub>Max</sub> (TXx).</p> </div> <p> </p>

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Section: Introductionmentioning

confidence: 58%

Study of Daily Extreme Temperature Indices over Sutlej Basin, N-W Himalayan Region, India

2015

Global NEST Journal

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“…The basin is highly sensitive to climate change, and this is confirmed by the studies of Jain et al (2009) andSingh et al (2015a). Besides, the variability observed in temperature and precipitation has altered the flow of Sutlej river and resulted to the decrease in mean annual as well as summer stream flow, respectively (Bhutiyani et al 2008;Singh et al 2014).…”

Section: Climate Of Study Areamentioning

confidence: 87%

Assessment of impact of climate change on water resources in a hilly river basin

2015

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The sensitivity of Sutlej river sub-basin (middle catchment) which is located in N-W Himalaya is investigated for its hydrologic response to potential changes in climate variability. The predictors of two global climate models (GCMs) that are found to perform well over Indian subcontinents are downscaled, and future time series of temperature (maximum and minimum) and precipitation is generated using statistical downscaling model (SDSM) under A1B, A2, and B2 emission scenarios. An overall increase in mean annual temperature and precipitation is predicted under both the models for future periods. The predicted increase in temperature is relatively higher for HadCM3 model compares to CGCM3 model whereas it is opposite for precipitation. The model also predicts considerable shift in monthly pattern of temperature and precipitation. Further, soil and water assessment tool (SWAT) is employed to appraise future changes in stream flow and water balance of the sub-basin under projected climate scenarios. The simulation results show that in future, increase in mean annual stream flow are likely to range from 1.3 to 7.8 % for CGCM3 model and 0.3 to 3.4 % for HadCM3 model, respectively. However, decrease in mean monthly stream flow is predicted under scenarios of CGCM3 model (

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“…Therefore, downscaling is used to converting the coarse spatial resolution of the GCMs output into a fine resolution which can involve generating point/station data of a specific area by using the GCM climatic output variables [1][2][3][4][5][6][7][8][9]. Downscaling methods can be divided into two types, which are dynamical downscaling (DD) and statistical downscaling (SD).…”

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confidence: 99%

“…Statistical downscaling is also the method which used to achieve the climate change information at the fine resolution through the development of direct statistical relationships between large scale atmospheric circulation and local variables (such as rainfall and temperature). It can be defined as developing quantitative relationships between large-scale atmospheric variables (predictors) and local surface variables (predictands) [9]. In this study, the freely available software SDSM 4.2 which is a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique has been utilized.…”

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confidence: 99%

Statistical Downscaling of Climate Change Scenarios of Rainfall and Temperature over Indira Sagar Canal Command Area in Madhya Pradesh, India

Rituraj

Shukla

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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General circulation models (GCMs) have been employed by climate agencies to predict future climate change. A challenging issue with GCM output for local relevance is their coarse spatial resolution of the projected variables. Statistical Downscaling Model (SDSM) identifies relationships between large-scale predictors (i.e., GCM-based) and local-scale predictands using multiple linear regression models. In this study (SDSM) was applied to downscale rainfall and temperature from GCMs. The data from single station located in the Indira Sagar canal command area at Madhya Pradesh, India were used as input of the SDSM. The study included calibration and validation with large-scale atmospheric variables encompassing the NCEP reanalysis data, the future estimation due to a climate scenario, which is HadCM3 A2. Results of the downscaling experiment demonstrate that during the calibration and validation stages, the SDSM model can be well acceptable regard its performance in the downscaling of daily rainfall and temperature. For a future period (2010-2099), the SDSM model estimated an increase in total average annual rainfall and annual average temperature for station. This indicates that the area of station considered will be wet and humid in the future. Also, the mean temperature is projected to rise to 1.5 0 C to 2.5 0 C for present study area. However, the model projections show a rise in mean daily precipitation with varying percentage in the months of July (0.59% to 2.09%) and August (0.79% to 1.19) under A2 of HadCM3 model for future periods.

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Statistical analysis of long term spatial and temporal trends of temperature parameters over Sutlej river basin, India

Cited by 25 publications

References 49 publications

Study of Daily Extreme Temperature Indices over Sutlej Basin, N-W Himalayan Region, India

Study of Daily Extreme Temperature Indices over Sutlej Basin, N-W Himalayan Region, India

Assessment of impact of climate change on water resources in a hilly river basin

Statistical Downscaling of Climate Change Scenarios of Rainfall and Temperature over Indira Sagar Canal Command Area in Madhya Pradesh, India

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