Tha Chin River Basin Research Articles

Hydrological model parameter regionalization: Runoff estimation using machine learning techniques in the Tha Chin River Basin, Thailand

Understanding hydrological processes necessitates the use of modeling techniques due to the intricate interactions among environmental factors. Estimating model parameters remains a significant challenge in runoff modeling for ungauged catchments. This research evaluates the Soil and Water Assessment Tool's capacity to simulate hydrological behaviors in the Tha Chin River Basin with an emphasis on runoff predictions from the regionalization of hydrological parameters of the gauged basin, Mae Khlong River Basin. Historical data of Mae Khlong River Basin from 1993 to 2017 were utilized for calibration, followed by validation using data from 2018 to 2022.•Calibration results showed the SWAT model's reasonable accuracy, with R² = 0.85, and the validation with R² of 0.64, indicating a satisfactory match between observed and simulated runoff.•Utilizing Machine Learning (ML) techniques for parameter regionalization revealed nuanced differences in model performance. The Random Forest (RF) model exhibited an R² of 0.60 and the Artificial Neural Networks (ANN) model slightly improved upon RF, showing an R² of 0.61 while the Support Vector Machine (SVM) model demonstrated the highest overall performance, with an R² of 0.63.•This study highlights the effectiveness of the SWAT and ML techniques in predicting runoff for ungauged catchments, emphasizing their potential to enhance hydrological modeling accuracy. Future research should focus on integrating these methodologies in various basins and improving data collection for better model performance.

Monitoring of salinity of water on the THA CHIN River basin using portable Vis-NIR spectrometer combined with machine learning algorithms

The goal of this work is to study the alternative practices for monitoring the salinity of water using a combination of portable vis-NIR spectrometer and machine learning approachs. Along 80 km of the Tha Chin River basin from the Gulf of Thailand, the data of salinity and NIR spectrum were collected during winter and summer seasons of Thailand. Salinity of water samples was measured by using a handheld electrical conductivity meter and NIR spectra was recorded with portable FQA-NIR GUN in the wavelength range of 600 to 1100 nm. The 10 machine learning models including partial least square regression (PLS), support vector machine (SVR), decision tree (DT), random forest (RF), adaptive boosting (AB), gradient boosting (GB), bagging meta-estimator (BME), extremely randomized trees (ERT), backpropagation neural networks (BPNN) and hybrid principal component analysis-neural network (PCNN) were applied to train the NIRs models for predicting salinity. All machine learning algorithms showed good prediction results which Rp2 values were higher than 0.84. The models built by tree-based algorithms (DT, RF, AB, GB, BME and ERT) displayed higher performances of calibration set and prediction set than those of PLS, SVM, BPNN and PCNN. Among these, the ERT algorithm showed the best performance Rp2 of 0.97, RMSEP of 0.41 g/L and RPD of 6.00. It was shown that NIR spectroscopy coupled with machine learning could be an alternative simpler way for predicting salinity of water.

Monitoring Land Use/Land Cover Change in Tha Chin River Basin

Land use/ land cover (LULC) changes influence the whole natural system directly or indirectly, so monitoring these changes is essential to balance the natural environment. This research surveyed changes in LULC in the Tha Chin River Basin. Using Google Earth Engine and ArcGIS, Landsat 5 and Landsat 8 Thematic Mapping satellite photos were utilized to identify LULC classes for the years 2000, 2010 and 2020 for the Tha Chin river basin. Fifty training samples for five classes were collected by supervised classification to analyze land cover types in the Tha Chin basin. Among these five classes, the urban area shows a significant change from 2000 to 2020; the second significant change is the forest area. The monitoring of changes in LULC would provide a crucial basis for the region environmental management and integrity in the Tha Chin River Basin.

Human and animal microbial source tracking in a tropical river with multiple land use activities.

The enhancement and restoration of the water quality of deteriorating surface water resources can be challenging, particularly for rivers with multiple usages, such as agriculture, animal husbandry, human residence, and industries. Recently, the performance of DNA-based microbial source tracking (MST) indicators detected by end-point and quantitative PCR assays for identifying sources of fecal pollution from human sewage, swine, and cattle and non-host-specific (universal) fecal pollution in the Tha Chin River basin, Thailand, was evaluated. The present study monitored these validated MST markers and various physicochemical and microbial water quality parameters in samples collected from twelve stations along the Tha Chin River during four sampling events in the wet and dry seasons. No significant difference in precipitation was observed between the wet and dry samplings. Universal markers (both PCR and qPCR) were detected in all 48 samples, indicating persistent and continuing fecal contamination. The sewage- and swine-specific qPCR marker concentrations did not vary among the sampling events, whereas cattle-specific qPCR markers were detected only in the wet season. Animal-specific markers were detected in the lower Tha Chin River section, which is characterized by intensive animal farming. Sewage-specific markers were also found in the lower section and near an upstream residential area. The high agreement (87.5-100%) between the PCR and qPCR results suggested that PCR could serve as a lower-cost MST screening test that requires less technical expertise. A multivariate analysis conducted using the survival analysis procedure to include censored data also emphasized the high pollution in the lower section of the river at all sampling events. Universal and swine-specific markers showed moderate correlations with microbial indicators, including total coliforms, fecal coliforms, E. coli, and enterococci. None of the MST markers or microbial parameters were associated with the measured physicochemical parameters. This study provides the first evaluation of MST markers for monitoring surface freshwater in Thailand, and the findings might aid the pollution surveillance of impaired water bodies and the development of strategies for improving their water quality.

Classification of saline water for irrigated agriculture using near infrared spectroscopy coupled with pattern recognition techniques

This research aimed to create near infrared (NIR) spectroscopy models for the classification of saline water with a pattern recognition technique. A total of 112 water samples were collected from the Tha Chin river basin in Thailand. Water samples with salinity less than 0.2 g/l were identified as suitable for agriculture, while water samples with salinity higher than 0.2 g/l were found to be unsuitable. The NIR spectra of water samples were recorded using a Fourier transform (FT) NIR spectrometer in the wavenumber of 12,500–4,000 cm-1. The salinity of each water sample was analysed by electrical conductivity meter. Identification models were established with 5 supervised pattern recognition techniques including k-nearest neighbour (k-NN), support vector machine (SVM), artificial neural network (ANN), soft independent modelling of class analogies (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The performance of the NIR model was carried out with a split-test method. About 80% of spectra (90 spectra) were randomly selected to develop the classification models. After model development, the NIR spectroscopy models were used to classify the categories of the remaining samples (22 samples). The ANN model showed the highest performance for classifying saline water with precision, recall, F-measure and accuracy of 84.6%, 100.0%, 91.7% and 90.9%, respectively. Other techniques presented satisfactory classification results with accuracy greater than 68.2%. This point indicated that NIR spectroscopy coupled with the pattern recognition technique could be applied to classify saline water for agricultural use according to salinity level in natural resources.

Comparative Study Using the 2-Hydrological Models with the Global Weather in a Small Watershed, a Case Study in the Upper Tha Chin River Basin, Thailand

The hydrological study in the upstream of the Huai Khot Wang Man diversion canal in Huai Khun Kaew watershed of the Upper Tha Chin River Basin in Uthai Thani Province, Thailand was studied. The soil and water assessment tool (SWAT) and the integrated flood analysis systems (IFAS) applied to the analysis of flow at the outlet. The global weather data provided automatically by the models including land use covers and soil types. The climate forecast system reanalysis (CFSR) and the near real-time precipitation (GSMaP_NRT) used in SWAT and IFAS, respectively. The model sensitivity with Nash and Sutcliffe efficiency (NSE), correlation (R2), and root mean square error (RSME) were applied. The monthly calibrated results from SWAT fitted to the observed data in 2007-2010 with 0.77, 0.88, and 9.08 m3/s, and verified in 2011 with 0.25, 0.61, and 14.30 m3/s, respectively. The daily results from IFAS during a flood period in 2011 fitted to the observed data with 0.21, 0.39, and 34.32 m3/s. Both models showed applicable for efficient gate operation of the diversion canal from this watershed to the Nong Mamong District in Chai Nat Province.

Seasonal Variation and Retention of Ammonium in Small Agricultural Streams in Central Thailand

Elevated nutrient concentrations are of concern in many agricultural streams. This study observed seasonal variation of inorganic nitrogen concentrations in small drainage streams that receive discharge predominantly from three different types of agricultural practices, swine farms, aquaculture, and paddy fields, in the Tha Chin River Basin in central Thailand. The drainage streams had high inorganic nitrogen concentrations, low dissolved oxygen concentrations (<3 mg L−1), and ammonium was the dominant form of inorganic nitrogen. The highest ammonium concentrations were observed in the drainage streams through swine farm areas, exceeding 4 mg L−1 throughout the year. In-stream ammonium retention was examined using pulse co-injections of ammonium and chloride in a drainage stream, and the effects of in-channel vegetation on ammonium retention were evaluated. This study showed that a considerable amount of ammonium was retained with the presence of in-channel vegetation. After vegetation removal, both the hydraulic and ammonium uptake length increased by >50 m, and the ammonium retention declined by approximately 50%. These results suggest that small agricultural streams experience a high concentration of inorganic nitrogen, similar or even more than larger streams, and that in-channel vegetation plays important role in nitrogen retention. The management of in-channel vegetation will have considerable effects on nitrogen transport.

Water quality degradation and management strategies for swine and rice farming wastewater in the tha Chin River Basin

Water quality in the Tha Chin River regularly exceeds biological oxygen demand (BOD) standards of Thailand’s Enhancement and Conservation of National Environmental Quality Act. This study quantified the BOD loading from rice cultivation and swine farming to the Tha Chin River using effluent data and procedures from the Pollution Control Department (PCD), geospatial land-use maps from the Land Development Department, and water quality data from the Ministry of Natural Resources and the Environment. It was determined that the BOD loading was 12 tons/day from swine farming in 2015 and 52 tons/day, on average, from rice farming between 2002 and 2011. Technology-specific, community-scale wastewater management strategies were recommended for both industries: feasibility studies revealed 66 potential sites for constructed wetland implementation and 7 subdistricts suitable for biogas network pipelines. It was determined that if these projects are implemented in conjunction, the BOD would be reduced by 6% (0.3 mg/L) in the entire river or 11% (0.5 mg/L) at the three water quality monitoring stations proximate to swine farms. These reductions would have a substantial effect on the water quality of the Tha Chin River, and governmental agencies such as the PCD should strongly consider subsidization and implementation of these projects.

Hydrological Study Using SWAT and Global Weather, a Case Study in the Huai Khun Kaeo Watershed in Thailand

The Huai Khun Kaeo watershed was chosen to study the hydrological productions from 2007 to 2013. There is lack of the hydrological observation data which can be considered as the Prediction in Ungauged Basin (PUB). It locates between the Sakae Krang river basin in Uthai Thani and the Tha Chin river basin in Chai Nat provinces, Thailand. The soil and water assessment tool (SWAT) was applied for the simulation of the hydrological response unit productions based on the Climate Forecast System Reanalysis (CFSR) from global weather data. The results of daily streamflow from the model were compared to the observed data at the hydrological observation station downstream end of the watershed. The results showed the Nash and Sutcliffe efficiency (NSE) of 0.10, correlation (R²) of 0.275, and the root mean square error (RSME) of 23.8 with a mean discharge of 11.0 cubic meters per second, respectively.

Application of SWAT Model for Hydrologic and Water Quality Modeling in Thachin River Basin, Thailand

The Soil and Water Assessment Tool (SWAT) model was examined for its applicability in modeling flow, sediments and water quality parameters in upper Thachin River Basin, Thailand covering an area of 5,693 km2. The calibration of flow, sediments, and water quality parameter have shown satisfactory results with R 2=0.77 and E NS coefficient = 0.78 for monthly flow calibration. The monthly flow validation values for R 2 and E NS were 0.8 and 0.79, respectively. Calibration of sediments and water quality parameter were poor due to the non-availability of time series data. Sensitivity analysis showed that curve number is the most sensitive parameter to surface runoff, evapotranspiration and sediment yield and consequently to total water yield. Soil evaporation compensation factor and average slope length (SLSUBBSN) were most sensitive to evapotranspiration and sediment routing, respectively. Muskingum routing factors (MSK_X, MSK_CO1 and MSK_CO2) were also found sensitive to average monthly flows. The study concluded that SWAT model has huge potential in studying hydrology of watersheds even under data scarcity. An adequately calibrated model can be employed for studying land use change scenarios and probable climate change impacts on basin hydrology depending on availability of data.

Loss of Agricultural Land: A Case Study of Tha Chin River Basin, Influenced by Water Diversion to the West

Loss of Agricultural Land: A Case Study of Tha Chin River Basin, Influenced by Water Diversion to the West

The Emergence and Transformation Processes of Waterfront Community Markets in Tha Chin River Basin

In this study of “The Emergence and Transformation Processes of Tha Chin River’s Waterfront Community Markets,” the markets that originated generally in the reigns of King Rama V through to King Rama VII have been studied. The objective was to investigate whether or not the transportation networks and the spatial centrality had any association with the emergence and transformation processes of the Tha Chin River’s waterfront community markets. The framework of this study includes the research and analysis of transportation networks that affected the spatial centrality of the target markets. To assist the investigation, it seems reasonable to assume that the transportation networks and the spatial centrality had a direct bearing on the process of emergence and transformation of the waterfront community market within the Tha Chin River Basin area. Therefore, the study is separated into three periods using maps from the Royal Thai Survey Department as a means to demarcate the three different time zones namely, the first period (2436 BE – 2484 BE) which is shown by the map of 2455 BE – 2482 BE, the second period (2484 BE – 2505 BE) which is indicated in the map entitled L7017, and last but not least, the last period (2506 BE –2554 BE) which is illustrated by the L7018 map.

Modeling non-point source pollution from rice farming in the Thachin River Basin

With the worldwide intensification of agriculture, non-point source pollution of surface waters has become a pressing issue. Conventional river water quality models consider non-point sources as accumulated entries into the rivers and do not investigate into the processes generating the pollution at its source, thus preventing the determination of effective mitigation measures. The models require extensive data inputs, which is a deficiency in many developing and emerging countries with limited data availability. The current study applies a Material Flow Model as a complementary approach to quantify non-point source pollution from agricultural areas. Rice farming in the Thachin River Basin is presented as a case study, with a focus on nutrients. The total nitrogen and phosphorus flows from rice farming to the river system are quantified, the key parameters influencing these flows are determined and potential mitigation measures are discussed. The results show that rice contributes considerable nutrient loads to the Thachin River Basin. Scenario simulations demonstrate that a significant nutrient load reduction could be achieved by following the official recommendations for fertilizer application, thus confirming the local efforts to introduce best management practice. Our results underline the importance of non-point source pollution control in intensive agricultural areas, particularly of tropical lowland delta areas such as the Central Plains of Thailand. The specific benefit of applying a Material Flow Model in this context is that with limited data availability, one can reach an understanding of the system and gain a first overview over its key pollution problems. This can serve as supportive basis for determining consecutive in-depth research requirements.

Province-based self-remediation efficiency of the Tha Chin river basin, Thailand

The Tha Chin River Basin located in the great central basin of Thailand is used for water supply, aquaculture, transportation, and recreation as well as a sink for wastewater discharges. Because of gradual deterioration of water quality and fishery resources, this study aimed to explain recent status of the river self-remediation efficiency that was influenced by nutrient inputs and outputs from the river system. Field surveys were carried out during May 2007 (early rainy season) and October 2007 (late rainy season) within the Tha Chin River located in 4 provinces; Chainat, Suphan Buri, Nakhon Pathom, and Samut Sakhon. The nutrient budgets in each province section were analyzed. Results indicated that the river was in eutrophic condition all year round. High nitrogen and phosphorus loads from surrounding agricultural land use, agro-industry, and community continuously flew into the river system. Those nutrient concentrations were higher in the early rainy season than the late rainy season. The lowest river zone (in Samut Sakhon province) indicated highest dissolved inorganic nitrogen (DIN) and orthophosphate phosphorus (P) discharges of 145.54 and 36.14 tons/day, respectively. The highest remediation efficiency of the river (ca 60% of the total input) was found in the uppermost area of Suphan Buri province. The lowest remediation efficiency (ca 12%) was found in Samut Sakhon province. From the overall view, long term monitoring of river and estuarine DIN and P should be conducted. To make better condition of aquatic environment and fishery resource in each province-based section, the controls of DIN and P remediation efficiencies (e.g. by effective management of flow speed) at 20 and 50%, respectively, were recommended.

Surface Water Pollution Control by Appropriate Effluent Taxation: The Thachin River Basin Study, Thailand

Surface Water Pollution Control by Appropriate Effluent Taxation: The Thachin River Basin Study, Thailand

Modeling the contribution of pig farming to pollution of the Thachin River

Rapid growth of the livestock production sector in South-East Asia during recent decades has led to a widespread degradation of ground and surface waters. The Thachin River Basin in Central Thailand serves as a case study for investigating the origins and pathways of nutrient loads produced by intensive pig farming. A mathematical material flow analysis is used to identify key nitrogen flows and the main parameters determining them. Scenarios of the potential for reducing these flows and achieving compliance with current discharge regulations are investigated. The results show that liquid waste discharged from large pig farms and directly discharged waste from small farms are the key nitrogen flows to the river system. The key driving forces are not only the treatment coverage and efficiencies but also the rate of reuse and recycling of the treated product.

River basin water quality assessment and management: case study of Tha Chin River Basin, Thailand

The water quality improvement plan for the Tha Chin river basin in central Thailand has been studied by using integrated land-use and water quality modelling. There has been little research on water quality planning in this basin due to the complex and intensive data requirements and also due to modelling difficulties. This research attempts to apply the integrated land-use and water quality modelling to the Tha Chin river basin. Loading reduction scenarios were applied to find out the appropriate loading reduction target and hence the improved water quality in the main stream.

Modeling the contribution of point sources and non-point sources to Thachin River water pollution

Major rivers in developing and emerging countries suffer increasingly of severe degradation of water quality. The current study uses a mathematical Material Flow Analysis (MMFA) as a complementary approach to address the degradation of river water quality due to nutrient pollution in the Thachin River Basin in Central Thailand. This paper gives an overview of the origins and flow paths of the various point- and non-point pollution sources in the Thachin River Basin (in terms of nitrogen and phosphorus) and quantifies their relative importance within the system. The key parameters influencing the main nutrient flows are determined and possible mitigation measures discussed. The results show that aquaculture (as a point source) and rice farming (as a non-point source) are the key nutrient sources in the Thachin River Basin. Other point sources such as pig farms, households and industries, which were previously cited as the most relevant pollution sources in terms of organic pollution, play less significant roles in comparison. This order of importance shifts when considering the model results for the provincial level. Crosschecks with secondary data and field studies confirm the plausibility of our simulations. Specific nutrient loads for the pollution sources are derived; these can be used for a first broad quantification of nutrient pollution in comparable river basins. Based on an identification of the sensitive model parameters, possible mitigation scenarios are determined and their potential to reduce the nutrient load evaluated. A comparison of simulated nutrient loads with measured nutrient concentrations shows that nutrient retention in the river system may be significant. Sedimentation in the slow flowing surface water network as well as nitrogen emission to the air from the warm oxygen deficient waters are certainly partly responsible, but also wetlands along the river banks could play an important role as nutrient sinks.