Climate Modeling of Jhelum River Basin-A Comparative Study

Akhter, Mehnaza; Ahmad, Ahanger Manzoor

doi:10.4172/2573-458x.1000110

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The climate modelling and the impact of climate changes on Jhelum River flow is also analysed by Akhter et al (2015Akhter et al ( , 2016Akhter et al ( & 2017 by using MLR and ANN techniques, they confirmed that the climatic variations in Jhelum River are drastically changing that is the biggest problem in predicting and forecasting the river flow. As the precipitation is continuously decreasing and temperature is increasing rapidly.…”

Section: Forecasting Through Selected Modelmentioning

confidence: 81%

“…As the precipitation is continuously decreasing and temperature is increasing rapidly. Since they worked on the Jhelum River climatic data from Indian side, they have better results of their models [17][18][19]. Although the ACF and PACF of the MLR forecasting errors shows some spikes at initial lag values above the standard error bars which indicates that some dominating time varying components are remaining to be considered in the model.…”

Section: Forecasting Through Selected Modelmentioning

confidence: 99%

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Impact Assessment of Hydroclimatic and Stochastic Variations on Water Stress of Jhelum River Flow Forecast

Hassan¹,

Nazia²

2019

ijSciences

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The hydrological system of a country significantly influences the ecological, agricultural and consequently the economic structure. Since, the accessibility of water resources has governed the power generation sector and agronomic establishments of the region like Pakistan. Moreover, Pakistan is sensitive in climatic variation and faces gradually higher threats regarding rainfall variability, floods and prolonged droughts. Sometimes, these climate change impact embedded with the high frequency and strength of extreme events caused by global warming. These extreme situations occur mostly in the hydrological phase that occurs because of the unexpected variations in temperature and rainfall. UN-ICC report pronounces that Pakistan is in top four unsafe countries that are undesirably affected by climate variability. The water stress Index established by Mallin Falkenmark conferred that Pakistan is in the list of countries that can face serious scarcity of water as its population is continuously increasing and water resources are shrinking. A perfect assessment of upcoming water resources under fluctuating climatic situations is significant for water resource availability and management. This study analyses the impact of hydroclimatic and stochastic conditions of river flow prediction and forecast of Jhelum River flow at Mangla station (second major reservoir of Pakistan, serve about 6 million hectares of cultivated area and generating 6% (about 1000MW) of the country's electrical power generation). Forecasting of river flow is helpful in understanding the prevailing situation of climate variations. The preliminary analysis of 34 years monthly data (Jan. 1976 to December 2010) of river flow, temperature and precipitation indicates that the precipitation of the seasonal and monsoon rainfall (June to September) is dominant on the yearly cycle. During these months the temperature rise is at its year maximums, so, the melting of snow/glaciers contributes a large amount of water in the natural river flow. The autocorrelation function (ACF) shows a strong seasonal variation of river flow, correlation analysis shows that the strong contribution of monsoon and orographic regular runoff is significant. To include the embedded periodic variation of the river flow, seasonal autoregressive moving average (SARIMA) will be used to predict and forecast the river flow. Additionally, the stepwise linear regression is applied to analysis the regular flow along with climatic variations by multiple linear regression show good results and will be helpful in forecasting of the river flow on monthly and annual basis. However, the reduce prediction and forecast accuracy shows that the inconsistence stochastic and hydroclimatic influenced river flows variation from the last 20 years, may be caused by Global and local impact. The results of this study are hoped to contribute considerably in current and future hydrological researches and studies, particularly in the Jhelum River flow at Mangla station of Pakistan.

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Section: Forecasting Through Selected Modelmentioning

confidence: 81%

Section: Forecasting Through Selected Modelmentioning

confidence: 99%

Impact Assessment of Hydroclimatic and Stochastic Variations on Water Stress of Jhelum River Flow Forecast

Hassan¹,

Nazia²

2019

ijSciences

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“…The impacts of climate change predominantly driven by global warming are highly extensive, complicated, and uncertain. The global surface temperature has increased over the twentieth century by 0.74 C and warming trends have augmented over the last 50 years (Akhter & Ahmad, 2017). There is considerable and indeterminate climate capriciousness in the Himalayan region.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river

Tabbussum

Dar

2020

J Flood Risk Management

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This study aimed to develop novel flood forecasting models using artificial neural network (ANN) algorithms. The models were assessed and validated for the case study of an alluvial river in the Kashmir region of the Indian Himalayas-River Jhelum. In September 2014, a major flood occurred in the Kashmir Valley with peak discharge exceeding 115,000 m 3 /s; brought about by the multifarious interaction among atmospheric disturbances. The present study targeted the development of ANN models for flood prognosis. Thereby five neural network models were developed: Bayesian regularisation neural network, the Levenberg-Marquardt neural network, conjugate gradient neural network, scaled conjugate gradient neural network, and resilient backpropagation neural network. Levenberg-Marquardt neural network model, with the mean squared error of 0.002128 (lowest of all models) and coefficient of determination of 95.839% (highest of all models), proved to be the best model based on the statistical validation parameters. All the simulations were evaluated and curves graphed for actual versus predicted discharges for 20% of the data. These models may be used to predict floods, and take advance precautionary measures by Irrigation and Flood Control Department of the State, rather than waiting for flood hydrograph to shoot (norm as of now).

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Forecasting Groundwater Fluctuation from GRACE Data Using GRNN

Kumar

Bhattacharjya

2020

Advances in Intelligent Systems and Computing

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Climate Modeling of Jhelum River Basin-A Comparative Study

Cited by 4 publications

References 8 publications

Impact Assessment of Hydroclimatic and Stochastic Variations on Water Stress of Jhelum River Flow Forecast

Impact Assessment of Hydroclimatic and Stochastic Variations on Water Stress of Jhelum River Flow Forecast

Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river

Forecasting Groundwater Fluctuation from GRACE Data Using GRNN

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