The Impact of Climate Change on Climate Variables and Meteorological Drought Using the climate change Toolkit (CCT) in the Karkheh River Basin, Iran

Drought appears as an environmentally integral part of climate change. This study was conducted to investigate the impact of climate change on climate variables, meteorological drought and pattern recognition for severe weather conditions in the Karkheh River Basin in the near future (2043-2071) and the distant future (2072-2100). The outputs of GFDL-ESM2, HadGEM2-ES, IPSL-CM5A-LR, MIROC and NoerESM1-M models were downscaled under the RCP 2.6 and RCP8.5 scenarios using the Climate Change Toolkit (CCT) at 17 meteorological stations. Then the SPEI index was calculated for the base and future periods and compared with each other. The results showed that the basin annual precipitation will likely increase in both future periods, especially in the near future. The annual maximum and minimum temperatures may also increase especially in the distant future. The rise in the maximum temperature will be possibly greater than the minimum temperature. Seasonal changes in maximum and minimum temperatures and precipitation indicate that the greatest increase in temperature and decrease in precipitation may occur in summer. Hence meteorological drought was also found to increase in the distant future. The application of the CCT model in the region showed that at least once a wet period similar to the flood conditions of 2019 will be observed for the near future. There will also be at least one similar drought in 2014 for the distant future in the region. However, in previous climate studies, future events have not been calculated based on identifying the pattern of those events in the past.

Effects of the Karkheh Dam construction on haze generation due to geomorphological changes (in the Khuzestan Province, Southwest of Iran)

An important factor for occurrence of dust storms is the construction of the Karkheh Dam in the Khuzestan province of Iran. It has reduced the annual mean of flow discharge in the Karkheh River from 120 to 50 m3/s and dried lands around river. The area of dried lands is 90.17 km2 around river and 333.45 km2 in the Hawr-al-Azim wetland. The Rosgen method, Fluvial-12 software, Shulits equation showed instability of the plan, cross sections of river and longitudinal slope of river, respectively, around Pay-e-pol hydrometric station (the upstream of river). After dam construction, extreme erosion occurred in this part of river. The type of sediment is clay and silt with D50 = 8 μm. The eroded sediment settles in downstream of river (around Hamidiyeh hydrometric station) and the Hawr-al-Azim wetland. The wind can easily lift these particles especially from May to July. Because of size of these particles, the haze concentration increased from 25% to 45% in dust storms. After construction dam, the dust storm days increased to 90 days in 2008. By increasing the stability of the river, the dust storms reduced from 2011. The annual volume of generated haze by geomorphological characteristic changes is almost 3107 m3.

Bias correction capabilities of quantile mapping methods for rainfall and temperature variables

This study aims to conduct a thorough investigation to compare the abilities of QM techniques as a bias correction method for the raw outputs from GCM/RCM combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the CORDEX dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.

Probable evidence of a Middle Palaeolithic site in the northern parts of the Susiana Plain, Khuzestan, Iran

There is a considerable body of studies regarding the activities of the Pleistocene human population in the Zagros and Alborz regions of Iran, as well as significant progress in the Palaeolithic studies in other regions, such as the foothills, plains and deserts’ margins. However, some of these peripheral regions and foothills are still neglected, and the information about the Palaeolithic period in these areas is limited. Khuzestan province, especially its northern regions, is one of these unstudied regions, yet the limited information about this region seems very interesting. Khervali, located on the western foothills of the Zagros Mountains and on the northern heights of Susa, nearby the western bank of the Karkheh River, is one of the few Palaeolithic sites identified in recent years. The site was identified in 2012 and was systemically surveyed. Due to the extension of the site and the distribution of the artefacts, sampling all the site was not feasible, therefore, four sections of the site were chosen for taking the samples and a total of 330 stone artefacts were collected. The results of the techno-typology analyses, as well as the frequency of the flakes, the Levallois samples and different types of scrapers, revealed that the artefacts date to the middle Palaeolithic period, with considerable access to the local raw materials.

Probable evidence of a Middle Palaeolithic site in the northern parts of the Susiana Plain, Khuzestan, Iran

There is a considerable body of studies regarding the activities of the Pleistocene human population in the Zagros and Alborz regions of Iran, as well as significant progress in the Palaeolithic studies in other regions, such as the foothills, plains and deserts’ margins. However, some of these peripheral regions and foothills are still neglected, and the information about the Palaeolithic period in these areas is limited. Khuzestan province, especially its northern regions, is one of these unstudied regions, yet the limited information about this region seems very interesting. Khervali, located on the western foothills of the Zagros Mountains and on the northern heights of Susa, nearby the western bank of the Karkheh River, is one of the few Palaeolithic sites identified in recent years. The site was identified in 2012 and was systemically surveyed. Due to the extension of the site and the distribution of the artefacts, sampling all the site was not feasible, therefore, four sections of the site were chosen for taking the samples and a total of 330 stone artefacts were collected. The results of the techno-typology analyses, as well as the frequency of the flakes, the Levallois samples and different types of scrapers, revealed that the artefacts date to the middle Palaeolithic period, with considerable access to the local raw materials.

Diagnosis of Erodible Locations in River Bends Using a Combined Method (GIS, RS and the CCHE2D Model) (Case Study: The Karkheh River in Iran)

In this research, a combined method was developed to determine the erodibility of bends in the Karkheh River. For this purpose, a 40 km reach of the Karkheh River downstream of the Karkheh Dam was considered. The value of the shear stress was the calculated using the CCHE2D model. The results from the model show that in 1996 (before construction of the Karkheh dam), the length of the erodible reach was 1314 m; in 2011 (after construction of the Karkheh dam), this length was reduced to 840 m. Furthermore, the model illustrates that the location of the maximum shear stress is a function of the relative curvature (R/W) in the bends. For small values of the R/W (less than 1.5), the maximum shear stress occurs on the convex bank of a river bend. By increasing the R/W, the location of the maximum shear stress transfers to the concave bank of the river bend. Also, this location is displaced towards downstream by increasing the R/W.

Rainfall Prediction with AMSR–E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran

Accurate estimates of daily rainfall are essential for understanding and modeling the physical processes involved in the interaction between the land surface and the atmosphere. In this study, daily satellite soil moisture observations from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR–E) generated by implementing the standard National Aeronautics and Space Administration (NASA) algorithm are employed for estimating rainfall, firstly, through the use of recently developed approach, SM2RAIN and, secondly, the nonlinear autoregressive network with exogenous inputs (NARX) neural modelling at five climate stations in the Karkheh river basin (KRB), located in south-west Iran. In the SM2RAIN method, the period 1 January 2003 to 31 December 2005 is used for the calibration of algorithm and the remaining 9 months from 1 January 2006 to 30 September 2006 is used for the validation of the rainfall estimates. In the NARX model, the full study period is split into training (1 January 2003 to 31 September 2005) and testing (1 September 2005 to 30 September 2006) stages. For the prediction of the rainfall as the desired target (output), relative soil moisture changes from AMSR–E and measured air temperature time series are chosen as exogenous (external) inputs in NARX. The quality of the estimated rainfall data is evaluated by comparing it with observed rainfall data at the five rain gauges in terms of the coefficient of determination R2, the RMSE and the statistical bias. For the SM2RAIN method, R2 ranges between 0.32 and 0.79 for all stations, whereas for the NARX- model the values are generally slightly lower. Moreover, the values of the bias for each station indicate that although SM2RAIN is likely to underestimate large rainfall intensities, due to the known effect of soil moisture saturation, its biases are somewhat lower than those of NARX. Moreover, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN–CDR) is employed to evaluate its potential for predicting the ground-based observed station rainfall, but it is found to work poorly. In conclusion, the results of the present study show that with the use of AMSR–E soil moisture products in the physically based SM2RAIN algorithm as well as in the NARX neural network, rainfall for poorly gauged regions can be predicted satisfactorily.