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Floods are both complex and destructive, and in most parts of the world cause injury, death, loss of agricultural land, and social disruption. Flood susceptibility (FS) maps are used by land‐use managers and land owners to identify areas that are at risk from flooding and to plan accordingly. This study uses machine learning ensembles to produce objective and reliable FS maps for the Haraz watershed in northern Iran. Specifically, we test the ability of the support vector regression (SVR), together with linear kernel (LK), base classifier (BC), and hyper‐parameter optimization (HPO), to identify flood‐prone areas in this watershed. We prepared a map of 201 past floods to predict future floods. Of the 201 flood events, 151 (75%) were used for modeling and 50 (25%) were used for validation. Based on the relevant literature and our field survey of the study area, 10 effective factors were selected and prepared for flood zoning. The results show that three of the 10 factors are most important for predicting flood‐sensitive areas, specifically and in order of importance, slope, distance to the river and river. Additionally, the SVR‐HPO model, with area under the curve values of 0.986 and 0.951 for the training and testing phases, outperformed the other two tested models. [ABSTRACT FROM AUTHOR]

Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) and geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining the multilayer perceptron (MLP) and autoencoder models to produce the susceptibility maps for two study areas located in Iran and India. For two cases, nine, and twelve factors were considered as the predictor variables for flood susceptibility mapping, respectively. The prediction capability of the proposed hybrid model was compared with that of the traditional MLP model through the area under the receiver operating characteristic (AUROC) criterion. The AUROC curve for the MLP and autoencoder‐MLP models were, respectively, 75 and 90, 74 and 93% in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iran and India cases, respectively. The results suggested that the hybrid autoencoder‐MLP model outperformed the MLP model and, therefore, can be used as a powerful model in other studies for flood susceptibility mapping. [ABSTRACT FROM AUTHOR]

The anthropogenic impacts of development and frequent droughts have limited Iran's water availability. This has major implications for Iran's agricultural sector which is responsible for about 90% of water consumption at the national scale. This study investigates if declining water availability impacted agriculture in Iran. Using the Mann‐Kendall and Sen's slope estimator methods, we explored the changes in Iran's agricultural production and area during the 1981–2013 period. Despite decreasing water availability during this period, irrigated agricultural production and area continuously increased. This unsustainable agricultural development, which would have been impossible without the overabstraction of surface and ground water resources, has major long‐term water, food, environmental, and human security implications for Iran. Plain Language Summary: Given the heavy reliance of the agricultural sector on water availability, it is important to examine if Iran's agriculture has been impacted by water availability changes in recent decades. The investigation of the long‐term impacts of natural water availability changes on agricultural activities in the country during the 1981–2013 period revealed that the agricultural sector in Iran continued to expand regardless of decreasing water availability in the country. This expansion was facilitated by the excessive use of nonrenewable water resources which has significant environmental and socioeconomic implications. Key Points: Trends in Iran's agricultural production and area did not follow natural water availability changes due to meteorological variabilityIran's agricultural production continuously increased despite water availability reduction during 1981–2013The unsustainable growth of Iran's agriculture has important water, food, environmental, economic, and human security implications [ABSTRACT FROM AUTHOR]

Water flow direction and velocity affect and controls erosion, transport and deposition of sediment in rivers, reservoirs and different hydraulic structures. One of the main structures affected is pumping stations within the dams wherein the velocity distribution near the station intake is disturbed. The two‐dimensional (2‐D) HEC‐RAS 5.01 model was utilized to study, analyse and evaluate the effects of pumping rates and flow depth on the flow velocity distribution, flow stream power and their effects in the Mosul Dam reservoir. The pumping station was considered as a case study. The station is suffering from sediment accumulation around, and in, its intake and suction pipes. The main inflow sources to the reservoir are the Tigris River and run‐off from the valleys within its basin. The reservoir was divided into two parts for the present study, including the upper part near the pumping station (analysed as a two‐dimensional zone), while the lower part was analysed as a one‐dimensional flow to reduce the simulation period computation time (1986–2011). Different operation plans (i.e. pumping rate and water depth) were considered. The results of the depth‐averaged velocity model indicated that when the pumping station was working at a range from the designed full capacity (100% to 25% of its full capacity), the maximum flow velocity increased from 75 to 4 times the normal velocity when there is no pumping dependent on pumping rate and flow depth. For the same operation plans, the flow stream power varied from around zero values to 400 times at full pumping capacity and low flow depth. For sediment routing along the reservoir, the considered statistical criteria indicated the model performance in estimating the total sediment load deposition and invert bed level is much better than in the case of erosion and deposition areas for different considered bed sections of the reservoir. [ABSTRACT FROM AUTHOR]

Prudent management of Iraqi water resources under climate change conditions requires plans to be based on actual figures of the storage capacity of existing reservoirs. With the absence of sediment flushing measures, the actual storage capacity of Dokan Reservoir (operated since 1959) has been affected by the amount of sediment delivered during its operational life leading to an undetermined reduction in its storage capacity. In consequence, there has not been an update on the dam's operational storage capacity curves. In this research, new operational curves were established for the reservoir based on a recent bathymetric survey undertaken in 2014. The reduction in reservoir capacity during the period between 1959 and 2014 was calculated by the mean of the difference between the designed storage capacity and the storage capacity which was concluded from the 2014 bathymetric survey. Moreover, the rate of sediment transported to the reservoir was calculated based on the overall quantities of accumulated sediment and the water discharge of the Lesser Zab River into the reservoir. The results indicate that the dam capacity is reduced by 25% due to sedimentation of an estimated volume of 367 million cubic metres at water level 480 m.a.s.l. The annual sedimentation rate was about 6.6 million cubic metres, and the sediment yield was estimated to be 701.2 t∙km−3∙year. [ABSTRACT FROM AUTHOR]

The Dokan Reservoir dam is a concrete cylindrical arch with gravity abutments, located on the Lesser Zab River about 60 km from the city of Sulaimani in north-eastern Iraq. A bathymetric survey was conducted in November 2014 for a period of 10 days, using an echo sounder of 200-kHz single beam. The survey results indicated an annual average sediment deposition of 3.8 million m3. Thirty-two sediment samples were collected from the reservoir bed. The ratio of gravel, sand, silt and clay was 15:14:48:23, respectively. The reservoir bed is covered mainly with silt. The sediments are composed of silty clay (77.6%), silty sandy clay (10%), sandy gravely silty clay (1.2%) and gravely sandy silty clay (1%). [ABSTRACT FROM AUTHOR]

Mosul Dam, located on the Tigris River north of Iraq, is experiencing sedimentation problems, especially near the Al-Jazeera pumping station that supplies the irrigation water for the Al-Jazeera project. The sources of the sediment accumulated within the reservoir are from the Tigris River, as well as ten side valleys on both sides of the reservoir. The sediment inflow rate into the reservoir and the released values are considered on the basis of the operation schedule of the dam for the considered period from 1986 to 2011. The sediment loads were estimated on the basis of available measurements and estimated literature values. The HEC- RAS 4.1 model was used for flows and sediments in the main river and reservoir. The model was calibrated for flow simulations (coefficient of determination r2 = 0.87) and sediment routing based on bed level, (with resultant r2 = 0.98 and Mean Absolute Deviation of 0.95). The Ackers-White equation was used in the HEC- RAS model for sediment routing because of the wide range of sediment sizes in the study case. The resultant total accumulated sediment load volume was 1.13 km3, a value that is very close to the measured values (1.143 km3) obtained from a previous bathymetric survey. Furthermore, the model indicated most of the sediment (80.7%) was deposited during the first five of the dam operation. [ABSTRACT FROM AUTHOR]