Statistical Downscaling of River Runoff in a Semi Arid Catchment

Phillips, R. C.; Carbone, Gregory J.; Mahdavi, Mohammad; Sharifi, Forood; Bihamta, M. R.

doi:10.1007/s11269-012-0170-6

Cited by 35 publications

(14 citation statements)

References 19 publications

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“…Regions with arid and semi-arid climates are more sensitive even to insignificant changes in climatic characteristics (Lins et al 1990;Mehrotra and Mehrotra 1995;Samadi et al 2012). Therefore, such changes in climatic variables will have a significant impact on local hydrological regimes in the arid and semi-arid regions (Samadi et al 2013). Understanding and modelling the potential impacts of climate in an arid region under the current and future climate is therefore very essential (Huth et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…The literature review revealed that only a few studies have been conducted for downscaling rainfall in the arid regions such as, the studies by Şen et al (2012) in the Riyadh City of Saudi Arabia, Goyal et al (2011), Anandhi et al (2008) in the upstream of Malaprabha reservoir in India, Souvignet and Heinrich (2011) in Coquimbo Region of Chile, Samadi et al (2013) in western Iran, and Liu et al (2011) in Tarim River basin in China. However, there is still a need to model and assess the impacts of climate change in arid regions located in other parts of the world.…”

Section: Introductionmentioning

confidence: 99%

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Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan

et al. 2015

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Downscaling rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. A multilayer perceptron (MLP) neural network has been proposed in the present study for the downscaling of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961-1990 and 1991-2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R 2)and Nash-Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and downscaled rainfall showed good agreement during both calibration and validation periods, while the downscaling model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and downscaled rainfall during both calibration and validation p e r i o d si nm o s to ft h es t a t i o n s .

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan

et al. 2015

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“…Recent research has begun to examine the relative roles of human activities and climate change on river runoff (Masih et al, 2009;Samadi et al, 2012). A number of studies have defined climate change scenarios based on the effects of human activities on land use change and combined the climate change scenarios with hydrological simulation to analyse the variability of river runoff quantitatively (Ficklin et al, 2009;Montenegro and Ragab, 2010).…”

Section: Introductionmentioning

confidence: 99%

Relative effects of human activities and climate change on the river runoff in an arid basin in northwest China

et al. 2013

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Understanding the mechanisms of river runoff variation is important for the effective management of water resources in arid and semi‐arid regions. This study uses long‐term observational data as a basis for examining the effects of human activities and climate change on the runoff variation of Jinghe River Basin, a typical arid inland basin in northwest China. A distributed hydrological model called the Soil and Water Assessment Tool, combined with a sequential cluster method and a separation approach, was used to quantify and distinguish the effects of human activities and climate change on runoff. The hydrological sequence before 1981 can be considered natural. However, human activities have significantly affected runoff since 1981. The runoff reduction caused by human activities between 1981 and 2008 accounted for 85.7% of the total reduction in the downstream of Jinghe River, whereas that caused by climatic variation was only 14.3%. This observation suggests that human activities are the major driver of runoff variation in the basin. Although the role of climate change in driving runoff variation has been identified to be prevalent and dominant in arid regions, this study highlights the importance of human activities. Copyright © 2013 John Wiley & Sons, Ltd.

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“…This assumption can be partially validated through a cross validation process, by which the model is judged by its ability to accurately 'predict' existing 20 th century data from a wide range of climate regimes, and by the projection of a period in the observation record. Downscaled climate model results have been used as input to hydrologic models for various parts of the world (Reaney and Fowler, 2008;Hageman et al, 2011;Crosbie et al, 2011;Samadi et al, 2012), using various GCMs and downscaling methods. The use of multiple models can improve projections of regional climate, especially when applied to the problem of future hydrology (Hagemann et al, 2011;Crosbie et al, 2011), so we use five global models in our training and forecasting process.…”

Section: Experimental Methodsmentioning

confidence: 99%

THE APPLICATION OF A STATISTICAL DOWNSCALING PROCESS TO DERIVE 21{sup ST} CENTURY RIVER FLOW PREDICTIONS USING A GLOBAL CLIMATE SIMULATION

Werth¹,

Chen²

2013

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Executive SummaryThe ability of water managers to maintain adequate supplies in coming decades depends, in part, on future weather conditions, as climate change has the potential to alter river flows from their current values, possibly rendering them unable to meet demand. Reliable climate projections are therefore critical to predicting the future water supply for the United States. These projections cannot be provided solely by global climate models (GCMs), however, as their resolution is too coarse to resolve the small-scale climate changes that can affect hydrology, and hence water supply, at regional to local scales. A process is needed to 'downscale' the GCM results to the smaller scales and feed this into a surface hydrology model to help determine the ability of rivers to provide adequate flow to meet future needs.We apply a statistical downscaling to GCM projections of precipitation and temperature through the use of a scaling method. This technique involves the correction of the cumulative distribution functions (CDFs) of the GCM-derived temperature and precipitation results for the 20 th century, and the application of the same correction to 21 st century GCM projections. This is done for three meteorological stations located within the Coosa River basin in northern Georgia, and is used to calculate future river flow statistics for the upper Coosa River. Results are compared to the historical Coosa River flow upstream from Georgia Power Company's Hammond coal-fired power plant and to flows calculated with the original, unscaled GCM results to determine the impact of potential changes in meteorology on future flows.

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Statistical Downscaling of River Runoff in a Semi Arid Catchment

Cited by 35 publications

References 19 publications

Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan

Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan

Relative effects of human activities and climate change on the river runoff in an arid basin in northwest China

THE APPLICATION OF A STATISTICAL DOWNSCALING PROCESS TO DERIVE 21{sup ST} CENTURY RIVER FLOW PREDICTIONS USING A GLOBAL CLIMATE SIMULATION

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