Sediment has the potential to influence the landscape, economy and way of life. If the estimation is accurate and the distribution characteristics are known, sediment can be used as a resource. In this brief review, we evaluated recent advances in suspended sediment estimation techniques between 2011 and 2022. The three most popular techniques are Sediment Rating Curves, Machine Learning Models and Physical and/or Numerical Models. Their potential and limitations are briefly explored. Our findings indicate that Machine Learning Models perform better, whereas Sediment Rating Curves offer flexibility in field applications. On the other hand, the paucity of necessary high-resolution data for model calibration and validation hinders the performance of Physical and/or Numerical Models. The sampling frequency of input sediment data emerges as a pivotal factor influencing the performance of all methods. To overcome this issue, a semi-automatic surrogate method might be useful. Also, particle size could be included in the rating curves to obtain additional information. This is especially valuable for understanding sediment dynamics and the process of delta formation.

The United Nations has reported that the world is off track to achieve water sustainability and this has been exacerbated by climate change. Studies indicate that water-related sustainable development goals (SDGs) have significant synergies and trade-offs involving various sectors. We measure the synergies and trade-offs between climate actions (mitigation and adaptation) and five water-related SDG targets based on a global numerical simulation driven by climate change and socioeconomic scenarios. We found that synergies outweigh trade-offs in all regions regarding water-related targets, with an approximately five-fold difference in the globally affected population. The main trade-off source is increased water use due to economic development in Africa and Central/South America, and increased irrigation water if bio-energy crop production is enhanced in Africa, suggesting that more coordinated policy-making is needed between climate action and water management.

Volcanic regions have complicated groundwater flow systems because of their geological structures, and the effects of rainfall on their groundwater recharge/discharge are still largely unknown. A field survey and chemical analyses were conducted on springs near an andesitic volcano in Japan to investigate changes in groundwater flow processes associated with heavy rainfall. After heavy rainfall, some springs showed a decrease in tracer values while others at lava or pyroclastic flow termini were stable. This indicates that the rainfall component rapidly discharges or the bedrock groundwater component discharges from deeper subsurface areas depending on the structure, length, and permeability of the volcanic ejecta. Hence, the groundwater mixing process in areas with ejecta can be approximated by the binary mixing model. In contrast, tracer values increased after heavy rainfall in areas near agricultural land, which suggests that nitrate ions were loaded into groundwater. Thus, the changes in various groundwater flow processes in volcanic regions after rainfall can be attributed to not only geological/geographical differences but also the groundwater stored in mountain bodies.

This study proposes a reservoir operation method based on simple dam inflow prediction for the Lampao Dam to inform flood control and water resources management in the Chi River Basin, Northeastern Thailand. With the proposed approach, dam inflows are predicted for the subsequent 7 days by formulating the relationship between 28 days of accumulated rainfall and inflows. The proposed inflow prediction method was able to predict the major floods in 2010 and 2011, and, based on the predictions, allowed reservoir operation to provide 100% of the volume needed for water use in all periods during the calculation period. In terms of flood control, the proposed operation method has enabled the maximum discharge in operation to be reduced compared to actual release discharge, thereby reducing the maximum inundation depth by up to 0.7 m in the lower basin in 2011. The results also indicated new areas that may experience increased maximum inundation, estimated to be up to a depth of 0.2 m; however, this was not expected to cause significant damage to the basin as a whole. A rapid increase in discharge was confirmed due to underestimation of inflow. Thus, better operation of the reservoir is possible using our improved rainfall monitoring method.