Projection of climate change impacts on precipitation using soft-computing techniques: A case study in Zayandeh-rud Basin, Iran

Kouhestani, Shapour; Eslamian, Seyed Saeid; Abedi‐Koupai, Jahangir; Besalatpour, Ali Asghar

doi:10.1016/j.gloplacha.2016.07.013

Cited by 34 publications

(22 citation statements)

References 37 publications

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“…Surface water resources in this area (by storing in Zayandehrud dam) supply more than 70% of the basin water requirement. MM area has the lowest available water resources including only groundwater resources and there are not any controlled surface water resources (Kouhestani et al, 2016). 53"0'0"E 50"0'0"E 51"0'0"E 52"0'0"E 53"0'0"E 50"0'0"E 51"0'0"E 52"0'0"E 32"0'0"N 33"0'0"N 32"0'0"N Figure 1 shows Total water demand for four different categories that the most important of them is agriculture which is almost 1200 mcm and both of them (Industry and Environment) are lowest Demand which almost are 200 mcm (million cubic meters)…”

Section: The Climatic Zones Of Zayandehrud River Basinmentioning

confidence: 99%

“…Moreover, this method is not sensitive to the sudden failure of non-homogeneous time series (Jaagus, 2006;Mann, 1945). The MK test relations are as follows (Kouhestani et al, 2016):…”

Section: Mann-kendall (Mk) Trend Testmentioning

confidence: 99%

“…In the Equation (1), N: The total number of data and x i and x j are the value of i and j data in time series (j>i). Function sgn(x j -x i ) is defined as follows (Kouhestani et al, 2016):…”

Section: Mann-kendall (Mk) Trend Testmentioning

confidence: 99%

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Spatial-Temporal Analysis of Temperature Trend and its Possible Effects on Water Resources and Water Uses in Climatic Zones of Iran's Zayandehrud River Basin

Hasantabar-Amiri¹,

Ostad‐Ali‐Askari²,

Heidarian³

et al. 2019

American Journal of Engineering and Applied Sciences

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The minimum, moderate and maximum temperature trends at seasonal time scale in 5 climatic zones using 37 meteorological stations in Zayandehrud river basin was studied regionally and the relationship to water resources, water uses of each zone and its possible effects were determined. The results showed that the majority of the climatic zones will have an increasing trend in spring, summer and fall and a decreasing trend in winter. The highest growth rate occurs in overnight temperature in spring and summer and daily temperature in fall. In winter, there is no significant difference between the slopes of temperature variables trend. The Most increasing slopes of the trend are seen in the northern part of the basin and during summer and the least decreasing slopes are in the northern and Northwestern parts of the basin during winter. The temperature rise in fall, spring and summer affect the reduction of water resources and increasing consumption in different areas. Among the various zones, the Eastern part of the basin will be faced with more water stress to meet their needs. The findings of this study can be effective for an identification of sensitive areas to climate change in the basin and planning to control water stress and achieving sustainable river basin management. Cloud coverage and rainfall associated defectively with the subtractive temperature span. Detailing long-term tendencys or determined moves in temperature and precipitation is significant for considerate the present and future alterations in nature. Monthly precipitation alterations may be optimistic or undesirable in various months of the year. While heating can possibly abridge the crop growth period, crop production and water efficiency of all crops are anticipated to reduction due to lower precipitation and higher water prerequisites under higher temperature. The Zayandeh-Rud River basin, Iran, is anticipated to encounter spatiotemporally heterogeneous temperature rise and precipitation decrease that will reduction water supply by mid-century. consequences propose that infrastructural recoveries, strict water request management and ecosystem-based regulatory prioritization, integrated by supply increase can temporarily reduce water stress in a basin.

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Section: The Climatic Zones Of Zayandehrud River Basinmentioning

confidence: 99%

“…Moreover, this method is not sensitive to the sudden failure of non-homogeneous time series (Jaagus, 2006;Mann, 1945). The MK test relations are as follows (Kouhestani et al, 2016):…”

Section: Mann-kendall (Mk) Trend Testmentioning

confidence: 99%

See 1 more Smart Citation

Spatial-Temporal Analysis of Temperature Trend and its Possible Effects on Water Resources and Water Uses in Climatic Zones of Iran's Zayandehrud River Basin

Hasantabar-Amiri¹,

Ostad‐Ali‐Askari²,

Heidarian³

et al. 2019

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The difference might be explained by the measured errors in runoff data, and system errors of SWAT [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79]. After reasonable parameter calibration, in validation process, simulated monthly-runoff was in the range of the observed values ( Figure.3) [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98].…”

Section: Figure5 Observed and Predicted Sediment Yields In Idanak Stmentioning

confidence: 99%

Using SWAT Model to Determine Runoff, Sediment Yield in Maroon-Dam Catchment

2017

International Journal of Research Studies in Agricultural Scien

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“…The General Circulation Model (GCM) projections need to be downscaled before they are applied to force a hydrological model due to their coarse spatial resolution, failure to capture the significant characteristics of climatic variables in the sub-grids of GCMs [11], and inability to generate local climate details to directly gather information for water resources planning and investigations at a local drainage or basin level. Dynamic and statistical downscaling are two commonly used methods in the bias correction of GCM outputs [12]. Statistical downscaling methods aim to establish empirical relationships between the historical large-scale GCM outputs and the local climate characteristics, requiring less computational capacity and technical expertise than dynamic downscaling, and being easily adaptable to local predictive scales, especially if there is sufficient historical data.…”

Section: Introductionmentioning

confidence: 99%

Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Zhu

Yang

Liu

et al. 2019

Water

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Forecasting the potential hydrological response to future climate change is an effective way of assessing the adverse effects of future climate change on water resources. Data-driven models based on machine learning algorithms have great application prospects for hydrological response forecasting as they require less developmental time, minimal input, and are relatively simple compared to dynamic or physical models, especially for data scarce regions. In this study, we employed an ensemble of eight General Circulation Models (GCMs) and two artificial intelligence-based methods (Support Vector Regression, SVR, and Extreme Learning Machine, ELM) to establish the historical streamflow response to climate change and to forecast the future response under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 in a mountainous watershed in northwest China. We found that the artificial-intelligence-based SVR and ELM methods showed very good performances in the projection of future hydrological responses. The ensemble of GCM outputs derived very close historical hydrological hindcasts but had great uncertainty in future hydrological projections. Using the variables of GCM outputs as inputs to SVR can reduce intermediate downscaling links between variables and decrease the cumulative effect of bias in projecting future hydrological responses. Future precipitation in the study area will increase in the future under both scenarios, and this increasing trend is more significant under RCP 8.5 than under scenario 4.5. The results also indicate the streamflow change will be more sensitive to temperature (precipitation) under the RCP 8.5 (4.5) scenario. The findings and approach have important implications for hydrological response studies and the evaluation of impacts on localized regions similar to the mountainous watershed in this study.

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Projection of climate change impacts on precipitation using soft-computing techniques: A case study in Zayandeh-rud Basin, Iran

Cited by 34 publications

References 37 publications

Spatial-Temporal Analysis of Temperature Trend and its Possible Effects on Water Resources and Water Uses in Climatic Zones of Iran's Zayandehrud River Basin

Spatial-Temporal Analysis of Temperature Trend and its Possible Effects on Water Resources and Water Uses in Climatic Zones of Iran's Zayandehrud River Basin

Using SWAT Model to Determine Runoff, Sediment Yield in Maroon-Dam Catchment

Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

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