Trends in the Timing and Magnitude of Flood in the Large Karoun River Basin

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Applying the AOGCM-AR5 models to the assessments of land suitability for walnut cultivation in response to climate change: A case study of Iran.

Due to higher temperatures and lower water availability, climate change is likely to have a major impact on walnut production in the near future. Climate change will alter the land suitability for walnut cultivation around the world, especially in arid and semi-arid regions like Iran. Here, land suitability for the cultivation of walnut (Juglans regia L.) in Iran was determined using the GIS for present and future conditions (2020-2049) with an approach to climate change. Accordingly, data from 375 synoptic stations throughout Iran were gathered for climatic factors including average, minimum and maximum temperatures, relative humidity and chilling requirement. Also, ASTER sensors (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and their data provided this research with cells that make a precision of 150 m (5 s), and the data were used for gauging geological parameters such as altitude and land slope. The electrical conductivity (EC) of soil and water were informed by the data bank of the Iranian Water Resources Management. The results of temperature simulations for the future (2020-2049) were analyzed by 21 AOGCM-AR5 models under the RCP4.5 emission scenario. In the first phase of evaluations, the maps of land suitability were constructed for present conditions by considering a network of the above-mentioned parameters. By combining these layers of information, the final map of land suitability was illustrated for walnut cultivation. In the second phase, the NEX-GDDP was used in order to determine land suitability for the future (2020-2049). The results showed that Iran currently has 582844 km2 of land suitable for walnut cultivation. However, the future will see less suitable lands: the current area will be reduced by 6.19%, from 582844 km2 to 546710 km2. In general, the northern, northwestern and western margins of Iran are currently suitable for walnut cultivation. By approximation, these lands will also be major areas for prospective cultivations of walnut in the future (2020-2049), even though their current stretch will be reduced

Climate change impact on flood frequency and source area in northern Iran under CMIP5 scenarios

This study assessed the impact of climate change on flood frequency and flood source area at basin scale considering Coupled Model Intercomparison Project phase 5 General Circulation Models (CMIP5 GCMs) under two Representative Concentration Pathways (RCP) scenarios (2.6 and 8.5). For this purpose, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated and validated for the Talar River Basin in northern Iran. Four empirical approaches including the Sangal, Fill-Steiner, Fuller, and Slope-based methods were used to estimate the Instantaneous Peak Flow (IPF) on a daily basis. The calibrated SWAT model was run under the two RCP scenarios using a combination of twenty GCMs from CMIP5 for the near future (2020-40). To assess the impact of climate change on flood frequency pattern and to quantify the contribution of each subbasin on the total discharge from the Talar River Basin, Flood Frequency Index (FFI) and Subbasin Flood Source Area Index (SFSAI) were used. Results revealed that the projected climate change will likely lead to an average discharge decrease in January, February, and March for both RCPs and an increase in September and October for RCP 8.5. The maximum and minimum temperature will likely increase for all months in the near future. The annual precipitation could increase by more than 20% in the near future. This is likely to lead to an increase of IPF. The results can help managers and policy makers to better define mitigation and adaptation strategies for basins in similar climates

Social acceptability of flood management strategies under climate change using contingent valuation method (CVM)

Floods are natural hazards with serious impact on many aspects of human life. The Intergovernmental Panel on Climate Change (IPCC) reported that climate change already has significant impact on magnitude and frequency of flood events worldwide. Thus, it is suggested to adopt strategies to manage damage impacts of climate change. For this, involving the local community in the decision-making process, as well as experts and decision-makers, is essential. We focused on assessing the social acceptability of flood management strategies under climate change through a socio-hydrological approach using the Contingent Valuation Method (CVM). For this purpose as well, hydro-climate modelling and the Analytical Network Process (ANP) were used. Among twelve investigated flood management strategies, "river restoration", "agricultural management and planning", and "watershed management" were the publicly most accepted strategies. Assessment of the social acceptability of these three strategies was carried out by use of the CVM and Willingness to Pay (WTP) methodology. Generally, 50%, 38%, and 18% were willing to pay and 44%, 48%, and 52% were willing to contribute flood management strategy in zones 1, 2, and 3, respectively. Overall, peoples' WTP for flood management strategies decreased with increasing distance from the river. Among different investigated dependent variables, household income had the highest influence on WTP

Assessing Climate Change Impact on Water Balance Components Using Integrated Groundwater–Surface Water Models (Case Study: Shazand Plain, Iran)

Assessing the status of water resources is essential for long-term planning related to water and many other needs of a country. According to climate reports, climate change is on the rise in all parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects

Effects of projected climate change on quantity and quality of soybean yield under different emission scenarios

Soybean is one of the most important oilseed crops in the world. Its economic value is based on the concentration of protein and oil produced in the seeds. However, in climate change studies, a crop simulation model like AquaCrop is unable to predict the qualitative yield of crops. Therefore, this study aims to simulate qualitative soybean yield based on regression models between observed dry grain yield (Yd) from 12 treatments as independent variables with their corresponding observed values for oil and protein contents as dependent variables. The P-value (<0.05) and R2 value of the linear regression model showed that oil content was positively regressed with yield, whereas protein content was negatively regressed with yield. On the other hand, predicted values of Yd from the calibrated AquaCrop model over general circulation models based on weighted multi-model ensemble means of five emission scenarios have been used for simulation of soybean oil and protein contents in the future. The results obtained by comparing historical period (1985–2010) to the future period (2020–2039) centred on the 2030s, showed that soybean oil content increased similarly as yield increased in the future period while protein content decreased inversely with yield. Overall, statistical indicators showed that the linear regression model performed well to predict the soybean oil and protein content when AquaCrop model not able to simulate the qualitative yield