This study aims at evaluating the geographical influences of rice-based protection dykes on floodwater regimes along the main rivers, namely the Mekong and the Bassac, in the Vietnamese Mekong Delta (VMD). Specifically, numerous low dykes and high dykes have been constructed particularly in the upper delta’s floodplains to protect the double and triple rice cropping against the annual flooding. For the whole deltaic domain, a 1D-quasi-2D hydrodynamic model setup was used to simulate seventy-two (72) scenarios of dyke construction development in the context of low, medium, and high floods that occurred in the VMD to examine the effects of different flood magnitudes on a certain dyke construction area. Based on the model simulation results, we established an evaluation indicator, the so-called Geographical Impact Factor (GIF), to evaluate the impacts of zone-based dyke compartments on the floodwater regimes along the main rivers for different kinds of floods. Our findings revealed different rates of influences on the floodwater levels along the Mekong and Bassac Rivers under different scenarios of zone-based high-dyke developments. GIF is a useful index for scientists and decision-makers in land use planning, especially in rice intensification, in conjunction with flood management for the VMD and for similar deltas worldwide.

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.

In many developing countries, despite of much national and international effort to improve the water supply situation, the technical and ecological as well as economical solutions are still insufficient. The situation in karst regions is even more severe due to the extreme climatic and hydrogeological conditions. In karst areas, a large percentage of the precipitation rapidly infiltrates from the surface into the karst rock. The water resources are therefore mainly stored in the karst aquifers as well as in underground river systems. Because of lacking storage possibilities on the surface, bad accessibility of the underground resources and often long running dry seasons, serious water shortages occur. Against this backdrop, an interdisciplinary research group of the Karlsruhe Institute of Technology (KIT), together with industry partners, concentrates intensively on the development and implementation of concepts and technologies for an adapted water resources management in karst regions of Southeast Asia. This paper focuses on the Indonesian-German joint project for the development of an underground hydropower pilot plant in a karst region on Java, Indonesia, which is, from German side, supported by the German Federal Ministry of Education and Research (BMBF). After years of research and construction works facing several setbacks due to earthquakes and flood events, since mid of 2011 the plant is operated continuously under the responsibility of an Indonesian operational team.

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.

Water scarcity can be defined as a lack of sufficient water resources or as the limited or even missing access to a safe water supply. Latter can be classified as ‘economic water scarcity’ which among others can commonly be met in tropical and subtropical karst regions of emerging and developing countries. Karst aquifers, mostly consisting of limestone and carbonate rock, show high infiltration rates which leads to a lack of above ground storage possibilities. Thus, the water will drain rapidly into the underground and evolve vast river networks. Considering the lack of appropriate infrastructure and limited human capacities in the affected areas, these underground water resources cannot be exploited adequately. Against this, background innovative and adapted technologies are required to utilize hard-to-access water resources in a sustainable way. In this context, the German–Indonesian joint R&D project “Integrated Water Resources Management (IWRM) Indonesia” dealt with the development of highly adaptable water technologies and management strategies. Under the aegis of the Karlsruhe Institute of Technology (KIT) and funded by the German Ministry of Education and Research (BMBF), these innovative technical concepts were exemplarily implemented to remedy this deficiency in the model region Gunung Sewu, a karst area situated on the southern coast of Java Island, Indonesia. The experiences gained through the interdisciplinary joint R&D activities clearly showed that even in the case of availability of appropriate technologies, a comprising transfer of knowhow and the buildup of capabilities (Capacity Development) is inevitable to sustainably implement and disseminate new methods. In this context, an adapted water supply facility was developed by KIT which hereafter shall serve for demonstration, teaching, and research purposes. The plant’s functionality, its teaching and research concept, as well as the design process, which was accomplished in collaboration with the University Gadjah Mada (UGM), Yogyakarta, Indonesia, is the content of this present paper.