Extensive research of the variability of flows under the impact of climate change has been conducted for the Upper Indus Basin (UIB). However, limited literature is available on the spatial distribution and trends of suspended sediment concentrations (SSC) in the sub-basins of UIB. This study covers the comparative assessment of flows and SSC trends measured at 13 stations in the UIB along with the variability of precipitation and temperatures possibly due to climate change for the past three decades. In the course of this period, the country’s largest reservoir, Tarbela, on the Indus River was depleted rapidly due to heavy sediment influx from the UIB. Sediment management of existing storage and future planned hydraulic structures (to tap 30,000 MW in the region) depends on the correct assessment of SSC, their variation patterns, and trends. In this study, the SSC trends are determined along with trends of discharges, precipitation, and temperatures using the non-parametric Mann–Kendall test and Sen’s slope estimator. The results reveal that the annual flows and SSC are in a balanced state for the Indus River at Besham Qila, whereas the SSC are significantly reduced ranging from 18.56%–28.20% per decade in the rivers of Gilgit at Alam Bridge, Indus at Kachura, and Brandu at Daggar. The SSC significantly increase ranging from 20.08%–40.72% per decade in the winter together with a significant increase of average air temperature. During summers, the SSC are decreased significantly ranging from 18.63%–27.79% per decade along with flows in the Hindukush and Western–Karakorum regions, which is partly due to the Karakorum climate anomaly, and in rainfall-dominated basins due to rainfall reduction. In Himalayan regions, the SSC are generally increased slightly during summers. These findings will be helpful for understanding the sediment trends associated with flow, precipitation, and temperature variations, and may be used for the operational management of current reservoirs and the design of several hydroelectric power plants that are planned for construction in the UIB.

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R² value of 0.85 and 0.74 during the training and testing period, respectively.

In the presented work, design conditions for breakwaters were derived from offshore climate reanalysis data (ERA5), which were downscaled to the nearshore by two numerical approaches, i.e., SwanOne and Delft3D, for different average and extreme wave and weather conditions. Model validation was performed using in situ measurements. The advantages and disadvantages of both numerical approaches were investigated. Both models showed sufficient accuracy according to measurements in the field, where SwanOne offers a simple and fast calculation method, while Delft3D provides a more complete representation, not only of waves but also current dynamics. However, it requires a much broader amount of input parameters and more complex boundary conditions. Then, SwanOne was applicable to calculate nearshore wave characteristics based on the input parameters extracted from the statistical analysis of long-term ERA5 data. Based on this process, design wave heights and periods at the nearshore were determined for 10- to 100-year return periods. For breakwater design on the west coast of the Mekong Delta, maximum wave heights in a range of 1.1 m to 1.3 m at a distance of 100 m to 300 m could be determined for a return period of 20 years, corresponding to water depths of 2.33 m and 2.88 m, respectively.

Fine sediment transport processes and the thermodynamics in reservoirs are key processes governing the water quality of reservoirs. With regard to a sustainable sediment management of reservoirs, the prediction of sediment transport and deposition is becoming increasingly important.The subject of the present work was the 3D numerical simulation of fine sediment transport in a reservoir taking into account stratification and mixing effects which in turn are caused by temperature gradients and wind effects. In order to understand and investigate the driving factors for stratification processes and their impact on fine sediment distribution, the great pre-dam of the Dhünn reservoir in Germany served as case study. The investigations were conducted in sensitivity analyses adopting a 3D sediment transport model with Delft 3D. The impact of various physical and numerical parameters on temperature and fine sediment transport modeling was examined: the number of vertical layers, the input data for the heat model (e.g. relative humidity, air temperature, cloud coverage, solar radiation), the vertical diffusivity and wind effects. The sensitivity studies showed that the input data for the heat model have a minor impact on the temperature and sediment transport modeling within the tested range of parameters. However, the vertical diffusivity and especially the inclusion of wind showed a greater influence on the simulated temperature and suspended sediment concentration gradients. The temperature modeling results by inclusion/exclusion of wind were qualitatively compared with temperature data from literature and with measurement data over a period of one month. Hereby, the simulations showed a good agreement with measurement data by exclusion of wind effects.The results of the studies provide a solid basis for the development of further models in fields where fine sediment transport is affected by stratification processes and can also be very useful in terms of a better understanding of the interactions between temperature, wind and fine sediment transport.