Khojiakbar Khasanov

Reservoir sedimentation is commonly regarded as a gradual technical problem of storage-capacity loss and declining operational efficiency. Under accelerating climate change, hydrological non-stationarity, and increasing water scarcity, however, sedimentation increasingly undermines reservoir safety by progressively eroding operational buffers, including flood attenuation volume, emergency drawdown capacity, and overall system controllability. This critical review synthesizes global evidence on sedimentation processes, monitoring advances, and management strategies, with particular emphasis on how climate variability amplifies sediment-related risks. A combined bibliometric and thematic assessment reveals a structural imbalance in the literature: while significant progress has been achieved in sediment quantification, bathymetric surveying, and geospatial modeling, comparatively limited attention has been devoted to translating these diagnostics into safety-relevant indicators, probabilistic risk metrics, and governance-oriented decision thresholds. Using Uzbekistan as a representative water-scarce case, the review demonstrates that sedimentation-driven degradation is non-linear, with risk escalating once dead storage is depleted and flood-buffer capacity contracts. Severe storage losses in small and flood-control reservoirs illustrate how degraded buffers may coexist with continued operation, masking latent safety vulnerabilities. The paper advances a risk-oriented interpretive framework that links sediment diagnostics to operational buffer condition and adaptive governance triggers. Reframing sedimentation as a climate-amplified safety issue supports earlier detection of emerging risks and strengthens reservoir resilience under non-stationary hydroclimatic conditions. These findings are reinforced by regression evidence identifying design capacity as a significant driver of sedimentation, unsupervised classification validating the risk thresholds, and a structurally-grounded composite risk index that surfaces under-recognized vulnerable reservoirs.

This study examines the optimisation of Continuously Operating Reference Station (CORS) networks through a combined bibliometric and spatial analysis approach, with particular reference to Uzbekistan as an illustrative case. A total of 522 publications were reviewed, of which 53 were identified as most relevant for detailed thematic analysis, as they provided insights into global research trends, methodological approaches, and applications in geodetic network design. To demonstrate how these approaches can be applied in practice, a simplified GIS-based spatial analysis was conducted in ArcGIS Pro to evaluate the distribution of existing CORS stations in Uzbekistan. A 40 km geodetic buffer was used as a representative distance derived from commonly reported baseline ranges in the literature (30-70 km). It should be emphasised that such a buffer-based approach only reflects spatial coverage and does not account for positioning accuracy, network geometry, or GNSS error sources. The results indicate uneven spatial coverage, with relatively dense station distribution in central and eastern regions, while western areas remain less represented. This finding is presented as a preliminary spatial observation rather than a validated assessment of network performance. Overall, the study does not aim to define optimal station spacing, or to provide a full technical evaluation, but rather to synthesise existing knowledge and demonstrate how simplified spatial methods can support initial planning. Future work should focus on integrating these insights with empirical GNSS data and advanced optimisation techniques for more robust network design.

This study explores the construction of?small reservoirs (SRs) as?a?strategic solution to?address water scarcity in?Kashkadarya Province, Uzbekistan, where agricultural productivity is?heavily dependent on?irrigation. By?utilising geographic i...

This study investigates the impact of sedimentation on the storage capacity of the Tupalang Reservoir, located in Surkhandarya, Uzbekistan, over a period of more than 30 years. Sedimentation poses a significant challenge by gradually reducing reservoir capacity, affecting water availability for irrigation, hydropower, and drinking supply. In the study, sedimentation was evaluated using GIS-based geostatistical methods using USV data in the reservoir. For the bathymetric data processing that was collected in 2023, four interpolation techniques—IDW, RBF, OK, and EBK —were applied, with RBF demonstrating the highest predictive accuracy. Results indicate a capacity loss of 28.05 million cubic meters (Mm³), or 5.65% of the total volume, primarily in the dead storage zone between 830 m and 890 m above sea level. Using bathymetric surveys conducted in 2003, 2007, 2010, and 2023, this research assesses changes in reservoir volume and identifies sedimentation patterns. The findings highlight a decline in sedimentation rates from 1.51 Mm³ per year in the early years to 0.3 Mm³ per year after 2010, attributed to effective management practices such as hydraulic washing. The study underscores the importance of proactive sediment management strategies, including dredging and sediment traps, to sustain reservoir functionality and recommends ongoing monitoring using advanced geospatial techniques

Mudflow reservoirs play a crucial role in mitigating flood risks triggered by natural events like heavy rains and snowmelt, safeguarding surrounding areas from potential inundation. However, sedimentation poses a significant challenge by reducing the capacity and effectiveness of these mudflow reservoirs over time. This study focused on estimating the capacity of the Kalkama mudflow reservoir, constructed in 1987, using a geostatistical approach. Bathymetric survey data were analyzed using various interpolation methods. Kriging (Ordinary Kriging) provided the best performance with the lowest RMSE (0.28) and a high R² (0.99), indicating it is the most accurate method for this dataset. Based on this method, a spatial model of the mudflow reservoir was developed to assess its current capacity. Findings indicate a capacity loss of 2.33 million m³ (23.6%) over 36 years, alongside a 22% reduction in surface area at Full Storage Level, and the dead volume was completely filled with sediment.

The article is devoted to the analysis of the dynamics of the morphometric parameters of the river channel and the hydraulic parameters of the flow under constrained conditions. The Kipchak hydraulic track located in the lower reaches of the Amu Darya River, constrained by the pontoon bridge, is characterized by intensive channel processes. The dynamics of the hydraulic resistance of the channel depending on the flow during the periods of observation in a confined section of the river has a wide range of 0.045 to 0.068, with a change in the roughness of the channel within 0.0195 to 0.065. The calculated values of the flow rates, taking into account the inhibitory effect of the river banks, gave a satisfactory agreement with the measured ones, the hydraulic resistance of the channel, depending on the value of this parameter, varied within 0.045 to 0.068 with the dynamics of the Chezy coefficient from 27 to 67 m0.5 / s

This article presents the method for calculating the area and volume of the water reservoir using Remote Sensing and Geoinformation Technologies. All calculations were performed for the Tashtepa water reservoir which recommended for construction in the Tashkent region. The territory of Tashtepa water reservoir was explored using the Google Earth program, was identified the alignment for the construction of dams, and the file was saved as a KML (Keyhole Markup Language) format. The alignment and the digital elevation model of the reservoir area from the online database were uploaded to the Global Mapper program which one of the geographic information system programs. Contours were formed in the digital elevation model, there was defined the longitudinal profile of the alignment along dams, surface and volume of the water reservoir were obtained for each circuit and based on the results were obtained curve lines between contour and area F=f(H), contour and volume W=f(H).