| 1. |
- Haghighi, Ali Torabi, et al.
(author)
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When rain does not run, a fingerprint of uncoordinated water resources development
- 2024
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In: Journal of Hydrology. - 0022-1694. ; 629
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Journal article (peer-reviewed)abstract
- The management of water resources in a basin can be challenging due to the sharing of water resources among multiple stakeholders and conflicting interests. This often results in imbalanced water resource development across the basin. While small and medium-sized projects are efficiently implemented in the headwater and upstream areas, the commissioning of large reservoirs faces delays due to the substantial investment required and regulatory processes involved. Also, the lack of coordination and synchronization in water resource development between upstream and downstream regions can lead to water scarcity and inefficient utilization of resources in the lower part of the basin. In this study, we assess the occurrence of water scarcity by comparing the frequency of hydrological and meteorological droughts before and after the detected abrupt change in river flow. To facilitate this assessment, we introduced two new indices: the positive to negative phases of hydrological and meteorological droughts. These indices highlight the increasing occurrence of hydrological droughts, which may not necessarily be linked to meteorological droughts during the identified periods. Furthermore, we propose a Monotone drought analysis method to examine the correlation between meteorological and hydrological droughts and address the impact of anthropogenic activities. We applied this analysis in the Sefid-Rud basin, the largest sub-basin in the southern Caspian Sea situated in Iran, which hosts several dams and reservoirs while more dams are in the construction and planning phase. The assessment took place in 24 gauges across the basin from 1978 to 2017 to detect and evaluate the alterations in flow regimes. Our analysis revealed that the unbalanced development of upstream water resources, driven by uncoordinated water infrastructure projects implemented by various decision-makers, has resulted in a decline of available water at critical points within the basin, which will be aggravated with the commissioning of new dams.
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| 2. |
- Panahi, Mahdi, et al.
(author)
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Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models
- 2022
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In: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 611, s. 128001-
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Journal article (peer-reviewed)abstract
- Although the growing number of synthetic aperture radar (SAR) satellites has increased their application in flood-extent mapping, predictive models for the analysis of flood dynamics that are independent of sensor characteristics must be developed to fully extract information from SAR images for flood mitigation. This study aimed to develop hybrid machine-learning models for flood mapping in the Ahvaz region, Iran, based on SAR data. Each hybrid model consists of a support vector machine (SVM) algorithm coupled with one of the following metaheuristic optimization procedures: grey wolf optimization (GWO), differential evolution, and the imperialist competitive algorithm. Sentinel-1 acquired SAR images before and during flooding between 20 March and 26 May of 2019. The goodness-of-fit level and predictive capability of each model were scrutinized using overall accuracy, producer accuracy, and user accuracy. The SVM-GWO approach yielded the highest accuracy with overall accuracies of 96.07% and 93.39% in the training and validation steps, respectively. Furthermore, this hybrid model provided the most accurate classification of water-inundation class based on producer accuracy (96.67%) and user accuracy (95.05%). The results highlight that wetland is the last land-use/land-cover type to return to normal conditions due to the many previously dry oxbow lakes that could trap water for a long time. Furthermore, the nine most suitable sites for flood-protection structures (e.g., embankments and levees) were identified based on floodwater distribution analysis. This work describes a robust, data-parsimonious approach that will benefit flood mitigation studies seeking to identify the most suitable locations for embankments based on spatio-temporal flood dynamics.
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| 3. |
- Rahmati, Omid, et al.
(author)
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TET : An automated tool for evaluating suitable check-dam sites based on sediment trapping efficiency
- 2020
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In: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526 .- 1879-1786. ; 266
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Journal article (peer-reviewed)abstract
- Sediment control is important for supplying clean water. Although check dams control sediment yield, site selection for check dams based on the sediment trapping efficiency (TE) is often complex and time-consuming. Currently, a multi-step trial-and-error process is used to find the optimal sediment TE for check dam construction, which limits this approach in practice. To cope with this challenge, we developed a user-friendly, cost- and time-efficient geographic information system (GIS)-based tool, the trap efficiency tool (TET), in the Python programming language. We applied the tool to two watersheds, the Hableh-Rud and the Poldokhtar, in Iran. To identify suitable sites for check dams, four scenarios (S1: TE ≥ 60%, S2: TE ≥ 70%, S3: TE ≥ 80%, S4: TE ≥ 90%) were tested. TET identified 189, 117, 96, and 77 suitable sites for building check dams in S1, S2, S3, and S4, respectively, in the Hableh-Rud watershed, and 346, 204, 156, and 60 sites in S1, S2, S3, and S4, respectively, in the Poldokhtar watershed. Evaluation of 136 existing check dams in the Hableh-Rud watershed indicated that only 10% and 5% were well-located and these were in the TE classes of 80–90% and ≥90%, respectively. In the Poldokhtar watershed, only 11% and 8% of the 207 existing check dams fell into TE classes 80–90% and ≥90%, respectively. Thus, the conventional approach for locating suitable sites at which check dams should be constructed is not effective at reaching suitable sediment control efficiency. Importantly, TET provides valuable insights for site selection of check dams and can help decision makers avoid monetary losses incurred by inefficient check-dam performance.
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