Dam Water Research Articles

The Development of a Hydrological Method for Computing Extreme Hydrographs in Engineering Dam Projects

Engineering dam projects benefit society, including hydropower, water supply, agriculture, and flood control. During the planning stage, it is crucial to calculate extreme hydrographs associated with different return periods for spillways and diversion structures (such as tunnels, conduits, temporary diversions, multiple-stage diversions, and cofferdams). In many countries, spillways have return periods ranging from 1000 to 10,000 years, while diversion structures are designed with shorter return periods. This study introduces a hydrological method based on data from large rivers which can be used to compute extreme hydrographs for different return periods in engineering dam projects. The proposed model relies solely on frequency analysis data of peak flow, base flow, and water volume for various return periods, along with recorded maximum hydrographs, to compute design hydrographs associated with different return periods. The proposed method is applied to the El Quimbo Hydropower Plant in Colombia, which has a drainage area of 6832 km2. The results demonstrate that this method effectively captures peak flows and evaluates hydrograph volumes and base flows associated with different return periods, as a Root Mean Square Error of 11.9% of the maximum volume for various return periods was achieved during the validation stage of the proposed model. A comprehensive comparison with the rainfall–runoff method is also provided to evaluate the relative magnitudes of the various variables analysed, ensuring a thorough and reliable assessment of the proposed method.

A comparative analysis of deep-learning models for dam discharge forecasting for disaster management

ABSTRACT This paper represents a comprehensive study of dam water discharge prediction using various deep-learning models. The main aim of this paper is to provide a data-driven solution for effective water resource management and thereby controlling the effects of floods and droughts. The Malampuzha dam, which has a spillway, a right bank canal (RBC), and a left bank canal (LBC), is considered for the study. While the spillway helps to control the water level in the reservoir by allowing extra water to flow downstream during times of severe rainfall or high inflow, hence reducing the risk of floods, the LBC and RBC are intended to divert water for agriculture. Altogether, 10 years of consecutive meteorological and dam-related data are utilized for training and testing the models in forecasting water discharge through LBC, RBC, and the spillway. Different deep-learning models, namely, long short-term memory, Bi-directional long-short term memory, recurrent neural network, gated recurrent unit, one-dimensional convolutional neural network (1D-CNN), deep neural network, autoencoder, and residual networks are explored in predicting the accuracy of dam water discharge. After testing eight deep-learning models, it was discovered that 1D-CNN performed well in predicting the discharge of water.

Employing deep learning in crisis management and decision making through prediction using time series data in Mosul Dam Northern Iraq

Specifically, Iraq is threatened in its second-largest northern city, Mosul, by the collapse of the Mosul Dam due to problems at the root of the dam, causing a wave of floods that will cause massive loss of life, resources, and public property. The objective of this study is to effectively monitor the level of dam water by predicting the level of water held by the dam In anticipation of achieving flood stage and breaking the dam, and supporting its behavior through formation 14-day time series data to predict seven days later. Used six deep learning models (deep neural network (DNN), convolutional neural network (CNN), convolutional neural network long short-term memory (CNN-LSTM), CNN-LSTM-Skip and CNN-LSTM Skip Attention) that models were trained to predict the water level in the dam; these levels of being under surveillance and prepared In case of danger, alert people to potential flood threats depending on the dam’s water level. These time series were created from the actual data sets of the dam; it’s a fundamental historical reading for 13 years (1993–2006) of the water level stored in the Mosul dam and was adopted in coordination with the Iraqi Ministry of Water Resources and the Centre for Research on Dams and Water Resources at Mosul University. The methodology applied in this study shows the model’s performance efficiency and the prediction results’ low error rate. It also compares those practical results to determine and adopt the performance-efficient model and a lower error rate. The comparison of these results proved the accuracy of its results, and superior to the CNN-LSTM model, it has the highest ability to perform through high accuracy with MAE = 0.087153 and time steps = 0 s 196 ms/step and loss = 0.00067. The current study demonstrated the ability to predict the water level in Mosul Dam, which suffers from foundation problems and may collapse in the future. Therefore, the water level in the dam must be monitored accurately. It also aims to test the effectiveness of the six models proposed in this study after evaluating their performance and applying the prediction process within a scenario to obtain predictive values after 14 days. The results showed the practical effectiveness of the hybrid CNN-LSTM model in correctly and accurately obtaining predictive values within the integrated framework of the required scenario. The study concluded that it is possible to enhance the ability to monitor and identify the potential risk of Mosul Dam at an early stage, and it also allows for proactive crisis management and sound decision-making, thus mitigating the adverse effects of crises on public safety and infrastructure and reducing human losses in areas along the Tigris River.

Flood Detection and Monitoring using IOT

One of the most destroying characteristic pf natures that can happen any place in the globe is flooding. In consideration to keep an eye on the surge circumstance particularly in a low line range, a framework was put and developed to track the Dam water level, Door opening status and precipitation level in real-time. A application-based data source for the open, reacting to their require for data on water conditions and flooding, and a Status page for data approximately flooding between the included specialists and specialists to move forward their obligations and participation are the two essential objectives of the made framework. A sensor organizes, a processing/transmission unit, and a database/application server make up the made framework. With the offer assistance of a remote sensor arrange that communicates with the application server over versatile Common Parcel Radio Benefit (GPRS), it is conceivable to remotely screen this real-time information of water condition. To encourage communication between the application server a microcontroller will be utilized. Ultrasonic sensors will be utilized to bring the correct level of the water in the dam. The entryway opening status will be picked up through a few sensors like water sensors and rain sensors. The versatile application which is made and associated with a few IOT Sensors will be having two major segments i.e. Flood Status and Emergency services. A few crisis administrations like healing centres, transport will be included in this application which will be valuable in the overwhelmed circumstances and post overflowed circumstances.

Water Volume and 3D Topographic Analysis of Hub Dam, Karachi, Using Remote Sensing and Global Bathymetry Data

Background: Karachi one of the largest and fastest-growing cities in the world, the city faces severe water scarcity and aggravated by climate change, excessive groundwater extraction in dense urban areas and inefficient water distribution systems. The Hub Dam, along with the Keenjhar Lake, is the source of water for Karachi's population as well as for the industrial and urban agricultural sectors. It is therefore important to have current information on the dam's storage capacity, its topographic features and the hydrological dynamics of its catchment area, which remains poorly understood to date. Objectives: This study's primary objectives are (1) to assess the hydrography of Hub Dam using global bathymetric and remote sensing data and (2) to create a detailed 3D model of the dam to facilitate analysis of its hydrological features. (3) Also generate data-driven insights to guide improvements in Karachi’s urban water management policies. Methods: The methodology integrates global bathymetric data with remote sensing and Google Earth Engine (GEE) for analysing Hub Dam’s hydrological and topographic characteristics. Data processing and visualization were conducted in Google Colab by python codes. This process enabling an efficient workflow for handling and analysing large datasets. Results: Hub Dam 3D model is developed. The approaches is enhancing visualization of the dam's water volume and terrain features to support detailed hydrological analysis. 3D volumetric analysis estimates with total water volume at approximately 79.954 · 106 m3. It was found that currently, the Hub Dam reservoir has enough water to fulfil Karachi needs for 503 days, a critical reserve for urban planning, resource management. Using a topographic analysis that includes surrounding slopes and watershed features, key elements affecting dam performance were identified. This result helps to adjust the filling of the dam with water. The Hub Dam's ability to store water was confirmed through a preliminary volume assessment. Conclusion: The study successfully achieved its objectives. The results obtained highlight the importance of adaptive well informed strategies for sustaining well managed water resources in response to environmental pressures. The current study further advance rational water management by integrating remote sensing and data-driven methodologies into this area of knowledge. That is, the effectiveness of Using Remote Sensing and Global Bathymetry Data has been repeatedly proven by other studies in different areas of knowledge. However, in the current study, this scientific approach has successfully demonstrated the efficient and rapid acquisition of accurate, up-to-date information on reservoir capacity and hydrological characteristics, which opens up new opportunities for efficient water management.

Stone instance segmentation of rubble masonry based on laser scanning point clouds

Laser scanning allows for objective area-based analysis of water dam structures to enable targeted interventions in case of displacements, requiring comparison of the same areas over different epochs. This comparison improves if identical areas, e.g. stones, from multiple epochs can be automatically derived in advance. We present an instance segmentation algorithm based on a 3D point cloud with reflected intensity information to identify individual stones in rubble masonry dams that can be used within deformation monitoring. The algorithm uses initial k-means classification followed by image-based processing steps, resulting in a segmented 3D point cloud with individual stones. It is evaluated against a manually labeled reference and analyzed on terrestrial laser scanning (TLS) and UAV-based data sets. Correctly identified stones are 89.92% for TLS and 90.82% for UAV data. Additionally, stone centroids and shapes were evaluated without significant deviation. A first outlook on deformation analysis was provided using ICP matching of stones from two simulated epochs.

Implementation and performance monitoring of the treatment system to remove arsenic from dam water in a full-scale urban water treatment plant

Nowadays, health and environmental problems caused by elevated arsenic concentration in water sources have become a major concern for researchers and environmental experts. Therefore, it is necessary to study processes that significantly reduce the arsenic content in aqueous solutions, which is of great interest to environmental experts. This work aimed to evaluate the effect of the pre-oxidation/coagulation process with commercially available coagulants (FeCl3, Al2 (SO4)3·18H2O, and PACl) on the efficiency of arsenic removal (with initial concentration of 73 ± 2.5 μg/L) from Talvar Dam entering the Sheikhi Jan Hamadan water treatment plant. This was demonstrated on a laboratory and operational scale at the water treatment plant under different scenarios (pre-oxidant agents, type of coagulant, solution pH, and different coagulant dosages). It should be noted that, as part of an emergency project, the possibility of arsenic removal at the Sheikhi Jan water treatment plant, which receives Talvar Dam water at a flow rate of 1800 L/s, was investigated. The priority of the researchers and city officials in the present study was to use the potential of the water treatment plant and the existing equipment so that they could remove arsenic quickly and without additional costs. This study was conducted in three stages: laboratory scale, implementation of the process in the water treatment plant, and monitoring of the treatment plant's performance, which continues to this day. The results of this project showed that the pre-oxidation/coagulation process can effectively reduce the concentration of arsenic at the inlet of the treatment plant from to less than national and international recommended values.

ANALISIS INTENSITAS CURAH HUJAN DAN KURVA IDF (INTENSITY-DURATION-FREQUENCY) METODE MONONOBE DI KOTA SALATIGA

Extreme rainfall in Indonesia has led to natural disasters in many areas including Salatiga City. Many human activities depend on the amount of rainfall that falls on the earth's surface. In the field of civil engineering, research on rainfall is very common because many activities related to civil engineering use rainfall data such as for water resource management, drainage development, dam construction and other building construction. This study aims to determine the amount of rainfall intensity and IDF curves in Salatiga City for return periods of 2 years, 5 years, 10 years, 25 years, 50 years and 100 years. The data used in this study is the maximum daily rainfall data collected from Salatiga Central Bureau of Statistics (BPS). The type of distribution selected in this study is the Log Pearson Type III probability distribution. The results of the analysis show that the highest rainfall intensity occurs at a short duration (5 minutes) in the 100-year return period of 106.66 mm/hour and the results of the IDF curve show that the shorter the rainfall time, the higher the rainfall intensity while the longer the rainfall time, the smaller the rainfall intensity at each return period T.

Heavy Metal Rejection Performance and Mechanical Performance of Cellulose-Nanofibril-Reinforced Cellulose Acetate Membranes.

In this research, cellulose acetate (CA) and CA nanocomposite membranes, reinforced with mass fractions of cellulose nanofibrils (CNF), are prepared using the phase separation technique. The membranes are extensively characterized using several techniques: Fourier Transform Infrared (FTIR) spectroscopy confirms the chemical structures, while Scanning Electron Microscopy (SEM) reveals their surface morphology. Mechanical characterization is conducted to explore the mechanical behavior of the membranes under wet and dry conditions through tensile testing. The mechanical properties of CA and CA-CNF membranes are also estimated using the Mori-Tanaka mean-field homogenization method and compared to experimental findings. The flux performance for pure and dam water, assessed at 3 bar, demonstrates that CNF reinforcement notably enhances the CA membrane's performance, particularly in flux rate and fouling resistance. The CA membrane shows high efficiency in removing Fe2+, Ba2+, and Al3+ from dam water, while CA-CNF membranes exhibit a varied range of removal efficiencies for the same ions, with the 0.5 wt % CNF variant showing superior resistance to surface fouling. Additionally, while CNF increases tensile strength and stiffness, it leads to earlier failure under smaller deformations, especially at higher concentrations. This research provides a detailed assessment of CA and CA-CNF membranes, examining their chemical, structural, and mechanical properties alongside their effectiveness in water treatment applications.

Spatiotemporal evolution of the physicochemical parameters of the waters of the Oum Errabiaa watershed dams (Morocco)

This study was conducted to determine the characteristics of the physicochemical parameters of the water of the dams of the Oum Errabiaa watershed during flood and low water periods. Data were collected by water sampling and measurements performed in the various dams from 2016 to 2021. The results showed that the water temperature varied from 11.77 °C to 30.20 °C, depending mainly on the sunshine. The water was alkaline with an average pH of 8.23 and had a weak to high electrical conductivity (143-3180µS/cm). The water was relatively well oxygenated at the surface and oxygen depleted in depth (1.44 – 15.04mg/L). The average concentration of suspended solids (SS) was 19.76mg/L, indicating that the dam water is relatively unpolluted. The average values of nutrients, NH4+, NTK and NO3- of 0.18mg/L, 0.53mg/L and 3.51mg/L, respectively, were in compliance with the WHO guidelines for drinking water production. The average concentration of SO42- was 90.60mg/L, which is considered high, exceeding the World Health Organization (WHO) water quality standard of 50mg/l. The average chlorophyll a was 5.51µg/L, which is considered to be low to moderate in dams. Overall, the Oum Errabiaa’s water dams of watershed have good physicochemical quality, except for the high concentration of sulfates. This pollution is likely due to agricultural and industrial activities in the watershed. The use of multivariate techniques (ACPN) enabled us to define the origin of nutrients through surface inputs, on the one hand, and through the photosynthetic activity of cyanobacteria, on the other hand.

Towards the development of a ‘land-river-lake’ two-stage deep learning model for water quality prediction and its application in a large plateau lake

Accurate prediction of water quality that is essential for effective lake management requires a comprehensive understanding of influential factors in the ’land-river-lake’ continuum. We develop a novel deep learning model employing a Long Short-Term Memory (LSTM) model for predicting water quality, and apply it for a large plateau lake, Lake Dianchi. The model comprises two stages: the first establishes a correlation between land and rivers by predicting nutrient loads, incorporating socioeconomic and meteorological features. The second establishes a correlation between feeding rivers and the lake by predicting nutrient concentrations, taking water quality, meteorological conditions, riverine import, and agricultural Non-Point Source (NPS) loads into consideration. Compared to single-stage deep learning models developed using classical methods, the two-stage model reduces Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), indicating enhanced prediction accuracy. Shapley Additive Explanation (SHAP) analysis is further employed to identify key contributing features. Precipitation emerges as the primary feature in the first stage, while in the second stage, disparities in the most contributing features for two zones of the lake are revealed due to dam isolation and water diversion projects. In the main zone, the primary predicting features are temperature and agricultural NPS features, while in the dam-isolated zone, Secchi depth is the key predictor, highlighting spatial–temporal variations in driving features. Our model contributes a novel deep learning framework and valuable insights for water quality management in large lakes, emphasizing the importance of considering the dynamic interplay of features or factors in the ’land-river-lake’ system.

The Changing Hydrology of an Irrigated and Dammed Yangtze River: Streamflow, Extremes, and Lake Hydrodynamics

AbstractUnderstanding the role of anthropogenic activities in the hydrological cycle is critical to support sustainable water management for the Yangtze River Basin (YRB), which experiences extensive dam operation, irrigation and water withdrawal. However, this remains challenging due to insufficient accuracies of existing process‐based models for fully depicting anthropogenic activities as part of the hydrological cycle. To this end, this study enhances a national‐scale coupled land surface‐hydrologic‐hydrodynamic model (CLHMS) with a dynamic irrigation scheme for distinct crops, an extended reservoir operation scheme incorporating both water storage anomalies and water demand anomalies, and a cost‐function‐based approach to link water demands with reservoir operation. The enhanced model is extensively validated against historical streamflow, water storage of 90 reservoirs, and irrigation water withdrawal in the YRB, and the water level and storage of the Poyang Lake (PYL). By setting up controlled experiments in the YRB, we show that the streamflow decreases by 2%–6% due to irrigation and water withdrawal, and manifests an attenuated seasonality due to reservoir operation. At the basin scale, the increasing trend of extreme flood peaks exhibits a reversal under human activities, with the flood mitigation effect of irrigation and water withdrawal accounting for up to 50% of that of reservoir operation. The hydrodynamics of the PYL also exhibits considerable human‐induced alterations, with a 1.79 m‐decrease in the water level at the end of flood season. Our study sheds light on quantifying anthropogenic hydrologic impacts at basin scales, with important implications for understanding the co‐evolution between anthropogenic activities and the hydrological cycle.

Suspended Load Estimation in Data Scarce Rivers

AbstractSediment rating curves (SRCs) are tools of satisfactory reliability in the attempt to describe the sediment regime in catchments with limited or poor-quality records. The study valorised the most suitable SRC development method for the estimation of the coarse suspended sediment load at the outlet of nine Mediterranean sub-watersheds. Four established grouping techniques were assessed, to minimize the uncertainty of the results, namely simple rating curve, different ratings for the dry and wet season of the year, hydrographic classification, and broken line interpolation, at three major Greek rivers (Aliakmon, Acheloos – upper route, Arachthos). The methods’ performance was benchmarked against sediment discharge field records, utilizing statistical measures and graphical analyses. The necessary observations were conducted by the Greek Public Power Corporation. The results were site/station dependent, and no methodology emerged as universally accepted. The analysis designated that the simple rating curve performs best at the cross-sections Moni Ilarion, Moni Prodromou, and Arta bridge, the different ratings for the dry and wet season of the year at Grevena bridge and Gogo bridge, the hydrographic classification at Velventos and Plaka bridge, and the broken line interpolation at Avlaki dam and Tsimovo bridge. In this regard, the study advocates the use of multiple SRC methods. Despite its limitations, the method merits a rather simple and cost-effective generation of a (continuous, detailed, sufficiently accurate) synthetic suspended sediment discharge timeseries, with high interpolating, extrapolating and reproducibility potential. The success of the application could benefit, among others, water quality restoration and dam management operations.

Potential of selected microalgae in the bioremediation of wastewater impacted dam water: A case of Goreangab Dam in Windhoek, Namibia

The study was aimed at determining the efficacy of freshwater microalgae in the treatment of polluted dam water with respect to the reduction of pathogen loads, as well as heavy metals and nutrients concentrations. Microalgae were collected from the Klein Windhoek River, as well as the Avis and Goreangab Dams in Windhoek, Namibia. Identification of the microalgae species revealed the presence of the Microcystis species in the Goreangab Dam (control), Anabaena species in the Klein Windhoek River (treatment 1) and the Scenesdesmus species as well as the Anabaena species in the Avis Dam (treatment 2). During treatment, the water samples were assessed on day 0, day 6 and day 12 for faecal and total coliform bacteria, heavy metals (Pb and Cd) as well as nutrients (phosphorous and nitrates). A one-way analysis of variance (ANOVA) indicated that there were no statistical differences between the mean values of Pb (p = 0.346) and Cd (p = 0.940) concentrations after treatment with microalgae. The Kruskal Wallis tests indicated statistical differences between the mean values of the nitrate levels (H(3) = 8.597, p = 0.035) before and after treatment with microalgae. However, the ANOVA indicated that there were no significant differences between the mean values of total phosphate uptake (p = 0.052) after treatment with microalgae. The results for pathogen loads in the wastewater samples indicated statistically significant differences between the mean values of total coliforms ((H(3) = 10.352, p = 0.015) as well as faecal coliforms (H(3) = 10.421, p = 0.015) before and after treatment with microalgae. We conclude that while our microalgae species were not effective in the removal of heavy metals from the dam water, they were nonetheless effective in the removal of microbial contaminants as well as excessive nutrients from wastewater.

Statistical studies of the characteristics and key influencing factors of sediment changes in the upper Yangtze River over the past 60 years

Sediments in the upper Yangtze River have significantly changed over the past 60 years. The characteristics and causes of these sediment changes must be determined in the upcoming phases of the comprehensive management of the Yangtze River Basin. Based on sediment observation data from 1960 to 2020, this study examined the trends and sudden changes in the sediments in the mainstream and its significant tributaries in the upper Yangtze River. The relative contributions of precipitation change and various human activities to sediment load changes are quantified. The relationships between sediment variations and the construction and large dam projects, soil and water conservation measures, and earthquake disasters are established. The results indicate that there was a significant decline in sediment load with main changes occurring around the 1985s and 2010s. Multiple double mass curves are used to quantify the relative contributions of precipitation change and human activities to sediment load changes. The relative contribution of dams to the decrease in sediment load ranges from 53 % to 74 %. Precipitation change serves a significant role in the observed changes, accounting for a range of 10 % to 27 %. Soil conservation measures take some time to show their impact on sediment reduction. These measures work by stabilizing the soil and improving hydrological conditions. On average, they contribute between 7 % and 20 % towards reducing sediment. In recent times, there has been a noticeable rise in sediment load in rivers like the Jialing and Min Rivers. This unexpected increase could potentially be linked to a combination of factors including earthquakes, heavy precipitation, and localized sediment scouring. The results of this study offer valuable insights into the variations in river sediment load, aiding in the improved management of water resources and sediment in large dams. These findings provide helpful insights for dam management.

Entamoeba histolytica in Different Water Sources of Niğde Province of Turkey

SummaryPurposeThis study was carried out to determine the presence of Entamoeba histolytica in water sources of Niğde province in Turkey, between June and November 2021.MethodsA total of 90 water samples were taken from 15 different water sources (drinking water, well water, spring water, wastewater and dam water) every month and the presence of E. histolytica antigens in the samples was examined by ELISA.ResultsThe positivity for E. histolytica was determined in 7 (7.7%) of 90 samples. While no antigens were found in any of the samples in June and September, E. histolytica was positive for three samples (20%) in July, one sample (6.6%) in August and October and two samples in November (13.3%). One of 24 dam samples (4.1%), 1 of 12 wastewater samples (8.3%), 1 of 12 well samples (8.3%), and 4 of 24 fountain samples (16.6%) that examined by ELISA were found positive. On the other hand, none of the examined 18 spring samples were positive. In addition, 4 (8.8%) of 45 samples that examined in summer and 3 (6.6%) of 45 samples that examined in autumn were detected positive by using ELISA. Entamoeba histolytica positivity in samples was statistically insignificant in terms of months, water resources and seasons (P > 0.05).ConclusionAs a result, the presence of E. histolytica, which is an important public health problem in water sources, was determined for the first time in Niğde province of Türkiye with this study.

COMPARATIVE ANALYSIS FOR VOLUME ESTIMATION IN SMALL DAMS USING CONVENTIONAL BATHYMETRY AND REMOTE SENSING

The efficient management of water resources is a fundamental prerogative to guarantee sustainable development, especially in regions dependent on small dams to retain and dispose of water. These structures play an essential role in various activities, from providing drinking water to supporting agriculture. Within the scope of water management, the precise determination of the volume of water retained in dams is a critical aspect, directly influencing decisions related to the sustainable use of these resources. This study aimed to carry out a comparative analysis between conventional bathymetry and remote sensing, aiming to evaluate their accuracy in determining the accumulated volume of water in small dams. It was observed that both remote sensing methods II and III presented an overestimation of the volume accumulated in dams compared to conventional bathymetry. Method II demonstrated superior precision, with an error varying between 3.22% and 30.22%, which decreased as the area increased. On the other hand, method III presented an error ranging between 20.68% and 43.12%, following a similar pattern to that of method II in relation to the increase in area. It is concluded that remote sensing presents itself as a valid approach for estimating the water surface and accumulated volume, especially in small dams, being useful for preliminary and environmental studies in areas with low sediment deposition and no vegetation invasion, since conventional bathymetry often proves to be costly and logistical, especially in situations where access and size are challenging.

Anticipating responses to climate change and planning for resilience in California’s freshwater ecosystems

As human-caused climate changes accelerate, California will experience hydrologic and temperature conditions different than any encountered in recorded history. How will these changes affect the state’s freshwater ecosystems? Rivers, lakes, and wetlands are managed as a water resource, but they also support a complex web of life, ranging from bacteria, fungi, and algae to macrophytes, woody plants, invertebrates, fish, amphibians, reptiles, birds, and mammals. In much of the state, native freshwater organisms already struggle to survive massive water diversions and dams, deteriorating water quality, extensive land cover modification for agriculture and urban development, and invasions of exotic species. In the face of climate change, we need to expand efforts to recover degraded ecosystems and to protect the resilience, health, and viability of existing ecosystems. For this, more process-based understanding of river, lake, and wetlands ecosystems is needed to forecast how systems will respond to future climate change and to our interventions. This will require 1) expanding our ability to model mechanistically how freshwater biota and ecosystems respond to environmental change; 2) hypothesis-driven monitoring and field studies; 3) education and training to build research, practitioner, stewardship, and policy capabilities; and 4) developing tools and policies for building resilient ecosystems. A goals-driven, hypothesis-informed collaboration among tribes, state (and federal) agencies, nongovernmental organizations, academicians, and consultants is needed to accomplish these goals and to advance the skills and knowledge of the future workforce of practitioners, regulators, and researchers who must live with the climate changes that are already upon us and will intensify.

PRELIMINARY MONITORING OF WATER QUALITY AT BANTURUNG VILLAGE, BUKIT BATU DISTRICT, PALANGKA RAYA CITY

Water is a vital necessity for human and living things in general. It is undeniable that the decline in water quality today is the result of human activities that overexploit the environment. People's lifestyles that pay less attention to environmental aspects such as throwing garbage out of place, disposing of hazardous waste, and changing the function of forest areas that can increase the potential for erosion and often cause sedimentation at the bottom of the waters have a negative impact both directly and indirectly on the natural environment, especially water sources. High rate of forest degradation and deforestation has a significant impact on changes and decreases in water quality. In this study, water quality monitoring was conducted using several parameters as an initial step to understand the condition at the Dam and Clean Water Facility in Banturung Village, Bukit Batu District, Palangka Raya City. The results obtained from the analyzed parameters include odorless, pH 5.9, turbidity 1.0 NTU, temperature 28.1 °C, color 3.63 TCU, total dissolved solids (TDS) 10.2 mg/L, nitrate (NO3-) 0.608 mg/L, nitrite (NO2-) 0.002 mg/L, iron (Fe) 0.038 mg/L, and manganese (Mn) 0.006 mg/L.

Dispersive micro Solid-Phase extraction with Bimetallic Ni, Zn-MOF sorbent and gas chromatography method for Organophosphorus pesticide determination in environmental water samples

A dispersive micro solid-phase extraction coupled with the GC-FID method was developed to determine three Organophosphorus pesticides: Fenitrothion, Chlorpyrifos, and Diazinon. Various sorbents, including Fe3O4 NPs, Zn-MOF, Ni-MOF, Bimetallic Ni, Zn-MOF, and Fe3O4 NPs@SiO2@ Bimetallic Ni, Zn-MOF, were synthesized and evaluated for their extraction efficiency of OPPs. Fe3O4 NPs@SiO2@ Bimetallic Ni, Zn-MOF was identified as the optimal sorbent based on its performance. The affective factors in the procedure were optimized using one factor of the time and experimental design methods. The significant factors in this method included sorbent amount, extraction time, and desorption solvent volume with an optimum value of 37 mg, 8.3 min, and 231 µL, respectively. Under optimized conditions, the method exhibited distinct linear ranges for Fenitrothion, Chlorpyrifos, and Diazinon, ranging from 0.24 to 259, 0.17–234, and 0.25–247 ng mL−1, respectively, with high corresponding R2 values (0.9947, 0.9929, and 0.9943). The limits of detection (LOD) were determined based on three times the standard deviation of the blank sample divided by the slope of the calibration curve for each OPP to be 0.07, 0.05, and 0.07 ng mL−1 for Fenitrothion, Chlorpyrifos, and Diazinon, respectively, while the limits of quantification (LOQ) were 0.24, 0.17, and 0.25 ng mL−1. Precision was assessed by calculating intra-day and inter-day relative standard deviation (RSD%) values in one day and three consecutive days with three replicates of each analysis for three concentrations of 10, 25, and 50 ng mL−1, which were found to be below 4.27 %, 4.35 %, and 4.32 % for Fenitrothion, Chlorpyrifos, and Diazinon, respectively, for intra-day analysis, and below 5.43 %, 5.11 %, and 5.29 % for inter-day analysis, respectively. Additionally, the enrichment factor (EF) values for Fenitrothion, Chlorpyrifos, and Diazinon with a concentration of 20 ng mL−1 were determined to be 78.6, 82.4, and 75.7, respectively. Analysis of spiked real samples in two concentrations of 5.0 or 25.0 ng mL−1, such as tap water, river water, dam water, and well water, revealed recovery percentages in the range of 91.2 % to 97.7 %, with relative standard deviations ranging from 3.79 % to 4.98 %, further validating the method’s reliability for practical applications. Overall, the method provides wide linearity, low LODs and LOQs, responsible RSDs, high EFs, and proper recovery percentages for determining OPPs.