Department of Water

Cnidarian medusae and ctenophores were long considered trophic dead ends due to their low nutritional value and they are often overlooked in the digestive tracts of predators. Traditional and emerging methods of analysing diets, including gut content analysis, stable isotopes, and metabarcoding, have limitations in identifying jellyfish consumption. This review explores the potential of using endoparasitic trematodes as novel trophic tracers of jellyfish predation. Trematodes, which require multiple hosts to complete their life cycles, can serve as natural dietary tags, with their presence in definitive hosts reflecting consumption of infected intermediate hosts, including jellyfish. First, we systematically compiled published records of trematode metacercariae in jellyfish and supplemented these with new, unpublished records from off eastern Australia. To identify potential jellyfish predators, we then examined the published literature for reports of definitive hosts of trematode species known to infect jellyfish as metacercariae. We identified 230 definitive host-parasite associations involving 64 putative trematode species. Molecular and morphological analyses linked these parasites to 140 fish species across 48 families, confirming some known predators of jellyfish (e.g. some carangids) and identifying some previously unrecognised potential jellyfish predators (e.g. some scombrids). While host specificity of digeneans in jellyfish and fish hosts varies, some trematodes show strong potential as indicators of jellyfish predation. However, the accurate identification of metacercariae (to the level of individual species) remains a key limitation, underscoring the need for integrative taxonomic approaches that incorporate molecular data. This review highlights the promise of trematodes as powerful tools for elucidating jellyfish predation and improving our understanding of marine food webs.

In response to the severe flood control challenges in the Taihu Lake Basin, this study focuses on the coordinated operation of newly constructed projectssuch as the Xinmeng River and Xingou Riverand the existing water conservancy system. Addressing the drainage difficulties in the upstream Huxi Area and the WuchengXixiYuxi Area, the research adopts a technical framework of scenario-driven analysisscheme simulationmulti-objective decision-making. Through simulation and optimization based on mathematical models and an intelligent decision-support system, an operationally feasible joint operation scheme is proposed. The results demonstrate that optimized joint operation can significantly unleash the flood control potential of water conservancy projects, providing valuable methodological insights and a practical reference for coordinated operation and intelligent decision-making in complex river network regions.

Cell lysis is a critical step in the analysis of cellular components, influencing the efficiency, reproducibility, and integrity of biomolecular extractions. Conventional lysis techniques, including mechanical and chemical methods, often present trade-offs between throughput, scalability, and sample integrity. Mechanical approaches such as bead milling and sonication can efficiently disrupt cells but suffer from issues such as back-mixing and biomolecule degradation. Chemical lysis techniques, while effective at preserving nucleic acids, require additional purification steps to remove residual chemicals that may interfere with downstream analyses. Electrochemical lysis (ECL) has emerged as a promising alternative, leveraging the electrochemical lysis of phosphate buffered saline (PBS) to induce membrane disruption at low potentials (2 to 5 V). ECL minimizes sample perturbation and aligns with green chemistry principles by reducing solvent and reagent consumption. This study evaluates ECL alongside conventional cell lysis methods using microbiological, electrochemical, and mass spectrometry (MS) analyses, using the Liquid Microjunction Surface Sampling Probe (LMJ SSP) for MS data acquisition. Cell lysates prepared using ECL were more reproducible samples abundant in diverse biomolecules compared to conventional methods. The integration of ECL with the LMJ SSP- MS has enabled rapid biochemical characterization with minimal sample preparation, direct sample introduction, and improved workflow efficiency.

This research intends to assess the existing performance index of the weir that is integrated with the variability of crop productivity as a strategic effort in supporting the building of a weir performance index model scientifically which so far, the assessment now is based on the consensus result. The methodology consists of a field survey, inventory of weir physics, analysis of performance weight, and then determining the performance of each component of irrigation. The research result shows that of 28 weirs as the samples of research, only 1 (one) weir (3.60%) is in good condition, 18 (eighteen) weirs (64.3%) are in moderate condition, and 9 (nine) weirs (32.10%) are in bad condition. The dominant of weir’s condition is on the moderate and bad conditions, it shows that there is a need to increase the effective and planned operation and maintenance activity. The performance index model of weirs and the relation between weir performance and variability of crop productivity can be used as the technical monitoring tool for detecting the decreasing of weir function and to support the formulation of technical effort in the activity of operation and maintenance as well as rehabilitation in order to increase the agricultural productivity and food resilience.

In dam operations, sudden discharges during extreme rainfall events can pose severe flood risks to downstream communities. This study developed a dam discharge-based river water level forecasting model using a data-driven deep learning approach, long short-term memory (LSTM). To enhance predictive performance, physics-based HEC-RAS simulation outputs, including extreme events, were incorporated as additional inputs. The Seomjin River Basin in South Korea, which recently experienced severe flooding, was selected as the study area. Hydrological data from 2010 to 2023 were utilized, with 2023 reserved for model testing. Forecasts were generated for four lead times (3, 6, 12, and 24 h), consistent with the operational flood forecasting system of the Ministry of Environment, South Korea. Using only observed data, the model achieved high accuracy at upstream sites, such as Imsil-gun (Iljung-ri, R2 = 0.92, RMSE = 0.27 m) and Gokseong (Geumgok Bridge, R2 = 0.91, RMSE = 0.35 m), for a 6-h lead time. However, performance was lower at Gurye-gun (Songjeong-ri, R2 = 0.72, RMSE = 1.48 m) due to the complex influence of two dams. Incorporating enhanced inputs significantly improved predictions at Gurye-gun (R2 = 0.91, RMSE = 1.17 m at 3 h). Overall, models using only observed data performed better at upstream sites, while enhanced inputs were more effective in downstream or multi-dam regions. The 6-h lead time yielded the highest overall accuracy, highlighting the potential of this approach to improve real-time dam operations and flood risk management.

Water is a fundamental resource for human survival, agriculture, industry, and the environment.However, with the growing global population, climate change, and unsustainable water management practices, the world is facing increasing pressure on freshwater resources.The future of water resources management relies heavily on the integration of futuristic and sustainable engineering and technology solutions.The field of water resources engineering is undergoing a transformative shift, guided by cutting-edge technologies and sustainable practices, aiming to enhance water availability, improve water quality, and adapt to the challenges posed by climate change.Various fields associated with innovation includes but not limited to assessing the effects of climate and land-use change on flood, design of water distribution networks, coastal engineering, flood forecasting and warning system, flood hazard analysis multi-criteria decision-making (MCDM) techniques, impact of projected climate variability on agricultural water demand, India's flood risk assessment and mapping, weir and spillways designing, ensemble modeling of ice glacier, experimental study on effect of deficit irrigation, utilizing statistical and MCDM techniques in indexing morphometric parameters, mitigating the after-effect of climate-induced disaster through energy dissipation, assessment of irrigation water management, trend analysis of precipitation and drought characteristics, experimental investigation of the efficiency of stone revetment, evapotranspiration study for cropping system, rainfall-runoff modeling, etc.One of the most promising technological advancements in water resource management is the development of management system assesses the combined effects of climate and land-use change on flood recurrence.Extreme weather events such as floods and droughts have become more frequent and intense, which has negatively impacted agricultural productivity and other livelihoods that depend on the river.In addition, the changes in temperature and precipitation patterns have led to changes in the hydrological cycle, which has affected the availability and quality of water in the river.The changes in the hydrological cycle have also led to the increased occurrence of landslides, which have further impacted the ecological health of the river (Shams et al. 2024).Not only that, but a water distribution network (WDN) is also one of the costliest components of any water supply project, therefore, the cost optimization of a WDN design is required.The simplest WDN optimization problem consists of selecting the most appropriate diameter for each pipe of the network from the set of available discrete pipe-diameters to minimize the network cost.Gangwani et al. (2024) proposed a fast, efficient, and parameter-less Rao-II algorithm has been used earlier with penalty-based approaches for the optimal design of WDNs.In this study, the application of a Rao-II algorithm is further explored with three self-adaptive penalty approaches to compare the convergence characteristics.Basu et al. (2024) suggested that development of coastal roads and bridges is important to overcome traffic congestion in densely populated coastal cities like Mumbai.In the study, they emphasized the importance of assessment of wave conditions required for the extension of jetties to facilitate the transportation of bridge components to build the sea link bridge during the non-monsoon season so that zero downtime for operability at jetties is ascertained.Flooding poses a severe threat to communities and infrastructure worldwide, which requires advanced flood forecasting warning systems.In this research paper, Patel et al. (2024a) proposed a real-time flood forecasting and warning system for the Dharoi Dam in the state of Gujarat, India.The novel system combines ensemble techniques and hydrological modeling simulations to enhance flood prediction accuracy and provides a timely warning.The study focuses on critical gaps in the current flood forecasting capabilities, recognizes the need for improved flood management in the region, and builds upon the existing research conducted globally and in India.Flood risk assessment remains a crucial element, particularly within locations highly susceptible to repeated flood occurrences.Solanki et al. (2024) seek to conduct an elaborate flood risk analysis for Mumbai, India based on an integrated method of geographic information systems and analytic hierarchy process (AHP).In this study, land use/land cover, average annual rainfall, elevation, drainage density, Normalized Difference Vegetation Index, distance from rivers as well as distance from roads are identified and considered.One area may be likely to be affected by flood and another with drought due to large land area of India and its climatic condition.Water scarcity predominantly affects agriculture production.