Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India

Mahmood, Rashid; Babel, Mukand S.

doi:10.1007/s00704-012-0765-0

Cited by 188 publications

(191 citation statements)

References 47 publications

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“…This selection was based on the correlation and partial correlation analysis values as displayed from the correlation runs and the level of significance realised in the SDSM environment (see Table 1). These variables also show consistency with the physical and climatological controls in the study area, the analysis of which has also been analysed and consistent with the findings of [10] [30]. However, during calibration, the correlation method was utilized.…”

Section: Minimum Temperature Analysissupporting

confidence: 80%

The Minimum and Maximum Temperature Forecast Using Statistical Downscaling Techniques for Port-Harcourt Metropolis, Nigeria

Weli¹,

Nwagbara²,

Ozabor³

2017

ACS

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This study centers on applying the statistical downscaling technique to the daily minimum and maximum temperatures of Port Harcourt from the period 1985-2014. To select the period of calibration, the wilby and wigley assumption of 2014 was adopted. However, the Bruckner circle assumption was adopted in selecting the normal under review. Secondary data of minimum and maximum temperatures for Port Harcourt were collected from the archive of Nigerian meteorological agency (NIMET). The grid cell of the HadCM3 corresponding to the Port Harcourt meteorological station was selected from the HadCM3 website to generate the largescale predictors. Data for temperature was there after normalized for the period of calibration. To analyze data, ANOVA and Paired t tests were used. Result showed that, the model was significant at p < 0.05 implying that the largescale predictors of the HadCM3 have performed significantly and that temperature pattern in the area is significantly dependent on them. The Duncan statistics showed that in the A2 scenario Maximum temperature will rise with a mean difference of 3.1˚C from 1960-2080, while for B2 the increase will be 0.18˚C for same period. For minimum temperature, the ANOVA also showed a difference of 0.21˚C and 0.11˚C for A2 and B2 respectively. The paired t test statistics showed that these variations in temperatures for both maximum and minimum at A2 and B2 scenarios are significant at p < 0.05. Reforestation, afforestation, carbon sequestration are strongly advocated.

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Section: Minimum Temperature Analysissupporting

confidence: 80%

The Minimum and Maximum Temperature Forecast Using Statistical Downscaling Techniques for Port-Harcourt Metropolis, Nigeria

Weli¹,

Nwagbara²,

Ozabor³

2017

ACS

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“…Downscaled data of maximum temperature (TX), minimum temperature (TN), and precipitation (PP) for the period of 2011-2099 was taken from [1]. They downscaled TX, TN, and PP with a well-known downscaling model, Statistical Downscaling Model (SDSM), under A2 and B2 scenarios of HadCM3 in the Jhelum River basin.…”

Section: Hadcm3 Downscaled Datamentioning

confidence: 99%

“…They downscaled TX, TN, and PP with a well-known downscaling model, Statistical Downscaling Model (SDSM), under A2 and B2 scenarios of HadCM3 in the Jhelum River basin. They selected HadCM3 for two reasons: (a) it showed better results during the evaluation of various GCMs (CGCM2, CSISRO, CCSR/NIES, and HadCM3); and (b) it has been used by a majority of studies for statistical downscaling of climate variables around the Jhelum basin [1]. They also applied the bias correction technique on the downscaled data to get closer to realistic results.…”

Section: Hadcm3 Downscaled Datamentioning

confidence: 99%

“…This increased GHGs concentration has resulted in global warming and a global energy imbalance [1,2]. According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR5), the global average temperature has increased by 0.85˝C (0.65˝C-1.06˝C) over the period of 1800-2012, relative to 1961-1990 [3], and 0.74˝C˘18˝C has been detected during the last hundred years [4].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to dynamical downscaling, SD methods are faster and computationally inexpensive, and thus offer approaches that have been rapidly adopted by a wider community of scientists [17]. For the present study, downscaled data (maximum temperature, minimum temperature, and precipitation) was obtained from [1]. They used a Statistical Downscaling Model (SDSM), a combination of multiple linear regression and a stochastic weather generator, to downscale temperature and precipitation over the period of 2011-2099 under A2 and B2 scenarios of HadCM3, a global climate model.…”

Section: Introductionmentioning

confidence: 99%

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Potential Impacts of Climate Change on Water Resources in the Kunhar River Basin, Pakistan

Mahmood

Jia

Babel

2016

Water

Self Cite

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Abstract:Pakistan is one of the most highly water-stressed countries in the world and its water resources are greatly vulnerable to changing climatic conditions. The present study investigates the possible impacts of climate change on the water resources of the Kunhar River basin, Pakistan, under A2 and B2 scenarios of HadCM3, a global climate model. After successful development of the hydrological modeling system (HEC-HMS) for the basin, streamflow was simulated for three future periods (2011-2040, 2041-2070, and 2071-2099) and compared with the baseline period to explore the changes in different flow indicators such as mean flow, low flow, median flow, high flow, flow duration curves, temporal shift in peaks, and temporal shifts in center-of-volume dates. From the results obtained, an overall increase in mean annual flow was projected in the basin under both A2 and B2 scenarios. However, while summer and autumn showed a noticeable increase in streamflow, spring and winter showed decreased streamflow. High and median flows were predicted to increase, but low flow was projected to decrease in the future under both scenarios. Flow duration curves showed that the probability of occurrence of flow is likely to be more in the future. It was also noted that peaks were predicted to shift from June to July in the future, and the center-of-volume date-the date at which half of the annual flow passes-will be delayed by about 9-17 days in the basin, under both A2 and B2 scenarios. On the whole, the Kunhar basin will face more floods and droughts in the future due to the projected increase in high flow and decrease in low flow and greater temporal and magnitudinal variations in peak flows. These results highlight how important it is to take cognizance of the impact of climate change on water resources in the basin and to formulate suitable policies for the proper utilization and management of these resources.

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Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

Kartal

2024

Hydrological Processes

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Water is essential for humans as well as for all living organisms to sustain their lives. Therefore, any climate‐driven change in available resources has significant impacts on the environment and life. Global climate models (GCMs) are one of the most practical methods to evaluate climate change. Based on this, this research evaluated the capability of GCMs from the Coupled Model Intercomparison Project 6 (CMIP6) to reproduce the historical flow of climate prediction centre data for the Konya Closed basin and to project the climate of the basin using the selected GCMs. Global climate models based on the CMIP6 under the scenario of common socioeconomic pathways (SSP245 and SSP 585) were used to analyse the climate change effect on streamflow of the study area by Bias Correction of GCM Models using Long Short‐Term Memory (LSTM), Bidirectional LSTM (BiLSTM), AdaBoost, Gradient Boosting, Regression Tree, and Random Forest methods. The coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) were used to assess the performance of the methods. Findings show that the Random Forest Model consistently outperformed other models in both the testing and training phases. A significant downward in the volume of water flowing through the region's rivers and streams in the next decades. It is critical to enhance climate‐resilient water infrastructure financing, establish an early warning system for drought, introduce best management practices, implement integrated water resource management, public awareness, and support water research to alleviate the negative consequences of drought and increase resilience against the effects of climate change on Turkey's water resources.

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Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India

Cited by 188 publications

References 47 publications

The Minimum and Maximum Temperature Forecast Using Statistical Downscaling Techniques for Port-Harcourt Metropolis, Nigeria

The Minimum and Maximum Temperature Forecast Using Statistical Downscaling Techniques for Port-Harcourt Metropolis, Nigeria

Potential Impacts of Climate Change on Water Resources in the Kunhar River Basin, Pakistan

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

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