Search Results (10)

This study investigates the prevalence of parasitic helminths in free-ranging domestic cats and dogs near the Chi River and natural reservoirs in Maha Sarakham Province, Thailand. Fecal samples from 39 cats and 148 dogs were analyzed using a modified formalin-ether concentration technique (FECT). The overall prevalence of helminth infections was 64.1% in cats and 51.4% in dogs. Common parasites were detected including soil-transmitted species like Ancylostoma sp. (hookworm), Toxocara spp., and Strongyloides sp., as well as foodborne helminths such as Taenia sp., Hymenolepis sp., Spirometra sp., and Opisthorchis sp. Multiple parasitic infections were commonly found in dogs (57.9%) and cats (46.2%). Our findings suggest that domestic cats and dogs act as important reservoirs for zoonotic helminths in the region. Notably, Opisthorchis viverrini-like eggs were found exclusively in cats, with a prevalence of 23.1%. The intron 5 of domain 1 of the taurocyamine kinase gene (TkD1Int5) was used for genotyping O. viverrini-like eggs. All O. viverrini-like egg samples with TkD1Int5 haplotypes (Ov116–Ov123) were uniquely found in cats. Genetic analysis revealed that TkD1Int5 haplotypes were similar to those previously reported for Opisthorchis viverrini in various species of cyprinid fish across opisthorchiasis-endemic regions in Thailand and Lao PDR. Three TkD1Int5 haplogroups (I–III) were classified, with O. viverrini-like eggs from cats distributed across all haplogroups. Notably, one haplotype (Ov118) was genetically distinct from the others and did not cluster into any haplogroup. These findings highlight the crucial role of cats as reservoir hosts and their potential contribution to the transmission of the zoonotic liver fluke O. viverrini, posing a notable public health concern. Full article

In addition to changes in the amount of rain, changes in land use upstream are considered a factor that directly affects the maximum runoff flow in a basin, especially in areas that have experienced floods and flash floods. This research article presents the application of the SWAT model to assess runoff in areas that have experienced flash floods, in order to analyze the proportion of land use change to the maximum runoff. Study areas that experienced flash floods were in three basins in Thailand (Lam Saphung Basin, Phrom River Basin, and Chern River Basin Part 1, which is a sub-basin of the Nam Chi River Basin). This study analyzed two main factors that influenced runoff in the river basin by considering two simulation situations: (1) changes in land use affecting runoff assessed by considering land use maps in 2006, 2008, 2010, 2015, 2017, 2019, and 2021 when using rainfall data only in the year 2021 for all cases and (2) changes in the amount of rainfall influencing runoff by considering the rainfall records in 2006, 2008, 2010, 2015, 2017, 2019, and 2021 when using the land use data only in the year 2021 for all cases. The results of the study found that the SWAT model can be effectively applied to estimate annual runoff in areas that have experienced flash floods under eight parameters with R² values of 0.74, 0.82, and 0.74 for the Lam Saphung River Basin, Phrom River Basin, and Chern River Basin Part 1, respectively. In addition, it was found that the proportion of land use changes that involved changes from forested areas to residential areas was the greatest from 2008 to 2010 in the Phrom River Basin and Chern River Basin Part 1. This resulted in an increase in the maximum runoff amounts of 77.78% and 46.87%, respectively. When land use was constant, it was found that the rainfall in 2010, which was the highest, also had the greatest impact on the runoff in all three areas. Full article

In recent years, climate change has greatly affected agricultural activity, sustainability and production, making it difficult to conduct crop management and food security assessment. As a consequence, significant changes in agricultural land and land cover (LC) have occurred, mostly due to the introduction of new agricultural practices, techniques and crops. Earth Observation (EO) data, cloud-computing platforms and powerful machine learning methods can certainly support analysis within the agricultural context. Therefore, accurate and updated agricultural land and LC maps can be useful to derive valuable information for land change monitoring, trend planning, decision-making and sustainable land management. In this context, this study aims at monitoring temporal and spatial changes between 2001 and 2021 (with a four 5-year periods) within the Chi River Basin (NE–Thailand). Specifically, all available Landsat archives and the random forest (RF) classifier were jointly involved within the Google Earth Engine (GEE) platform in order to: (i) generate five different crop type maps (focusing on rice, cassava, para rubber and sugarcane classes), and (ii) monitoring the agricultural land transitions over time. For each crop map, a confusion matrix and the correspondent accuracy were computed and tested according to a validation dataset. In particular, an overall accuracy > 88% was found in all of the resulting five crop maps (for the years 2001, 2006, 2011, 2016 and 2021). Subsequently the agricultural land transitions were analyzed, and a total of 18,957 km² were found as changed (54.5% of the area) within the 20 years (2001–2021). In particular, an increase in cassava and para rubber areas were found at the disadvantage of rice fields, probably due to two different key drivers taken over time: the agricultural policy and staple price. Finally, it is worth highlighting that such results turn out to be decisive in a challenging agricultural environment such as the Thai one. In particular, the high accuracy of the five derived crop type maps can be useful to provide spatial consistency and reliable information to support local sustainable agriculture land management, decisions of policymakers and many stakeholders. Full article

Poor agricultural practices among small-scale sub-Saharan African farmers can lead to soil erosion and reduce agricultural productivity. However, information on such practices is normally not well documented, making it challenging to design future mitigation strategies. We conducted a fine-scale agricultural survey on 200 farm households within the transboundary Sio Malaba Malakisi River Basin (SMMRB) between Kenya and Uganda to quantify the frequency and type of soil conservation practices (SWCPs) implemented. Information on farm sizes, ownership, crops grown, soil fertility, soil erosion, soil water conservation practices, and the decision-making processes was collected. Descriptive and chi-squared statistics were used to present trends in land use, decision-making processes and the extent of adoption of SWCPs, as well as to analyse the relationship between the SWCPs and the farmers’ perceptions on soil erosion. The region showed highly fragmented farms (mean area: 0.6 ha), primarily practising rain-fed subsistence farming. The principal decision-makers of each farm were mainly (63%) male. Various farmers (28%) lacked soil and water conservation practices (SWCPs). However, most farmers (35%) implemented one type of soil and water conservation practice, while 37% practised a combination of two to five soil and water conservation practices. Extensive soil and water conservation practices such as intercropping were widely practised as they were more affordable than intensive measures. Results on the farmers’ perceptions on soil erosion showed that most farmers in the SMMRB reported soil erosion (60%) and even more (92%) reported to have experienced a loss of soil fertility over the last 5 years. There was a significant positive correlation (X2 (2, n = 198) = 92.8, p = < 0.001) between the perception of soil erosion and the perception of the change in soil fertility, suggesting that reducing soil erosion could result in a reduction in the loss of soil fertility. Thus, there is still a need for strategies and measures to address the soil erosion risks currently faced by Sio Malaba Malakisi River Basin farmers. This study is a baseline study that shows the importance of farmers’ perceptions on the practice of soil and water conservation measures in the Sio Malaba Malakisi River Basin and therefore becomes an important avenue for improving the currently practised soil and water conservation measures as well as developing adoption programs as well as future studies that combine scientific and farmers’ perception/knowledge for sustainable agriculture. Further research into the efficiency of currently adopted SWCPs as well as the extent of the farmers’ knowledge and the accuracy of their perceptions is recommended. Full article

The limited availability of high-resolution monitoring systems for the drought phenomena and water dynamics affected by weather anomalies hinders policy decisions in a multitude of ways. This paper introduces the availability of the high-resolution Water Monitoring System (WMS) developed from a mix of sophisticated multi-spectral satellite imageries, analytic and data sciences, and cloud computing, for monitoring the changes in water levels and vegetation water stress at the local scale. The WMS was tested in the Lower Mekong Region (LMR) case basin, Thailand’s Chi River Basin, in the period from January 2021 to April 2021, the dry season. The overall quality of the VHI, VCI, TCI, and NDVI drought simulation results showed a statistically positive Pearson correlation with the reservoir and dam water volume data (ranged between 0.399 and 0.575) but demonstrated a strong negative correlation with the groundwater level data (between −0.355 and −0.504). Further investigation and more detailed analysis of the influence of different physical environmental conditions related to change in groundwater level should be considered to increase scientific knowledge and understanding about the changing nature of the local system from local perspectives with the alternative use of drought indices in data-poor areas. Our result suggests that the WMS can provide quantitative spatiotemporal variations of localized and contextualized surface water changes as a preliminary analysis. The WMS results can offer guidance for finding a better smaller unit management that suits the local conditions, such as water resource management, disaster risk reduction measures (i.e., drought and flood), irrigation practice, land use planning, and crop management. The existing WMS is geared toward the early warning of water and agricultural development, progress on the SDGs, utilization of digital innovation, and improved abilities of decision-makers to monitor and foresee extreme weather events earlier and with high spatial accuracy. Full article

Based on the Standardized Precipitation Index (SPI) and copula function, this study analyzed the meteorological drought in the upper Minjiang River basin. The Tyson polygon method is used to divide the research area into four regions based on four meteorological stations. The monthly precipitation data of four meteorological stations from 1966 to 2016 were used for the calculation of SPI. The change trend of SPI1, SPI3 and SPI12 showed the historical dry-wet evolution phenomenon of short-term humidification and long-term aridification in the study area. The major drought events in each region are counted based on SPI3. The results show that the drought lasted the longest in Maoxian region, the occurrence of minor drought events was more frequent than the other regions. Nine distribution functions are used to fit the marginal distribution of drought duration (D), severity (S) and peak (P) estimated based on SPI3, the best marginal distribution is obtained by chi-square test. Five copula functions are used to create a bivariate joint probability distribution, the best copula function is selected through AIC, the univariate and bivariate return periods were calculated. The results of this paper will help the study area to assess the drought risk. Full article

The Lower Mekong Basin (LMB) is a key biodiversity hotspot. To facilitate conservation and management, we examine mollusc biodiversity patterns and distribution along LMB’s longitudinal gradients, identify environmental drivers, and discuss the importance of these drivers to management. Cluster analysis, redundancy analysis (RDA), and variation partitioning were conducted using mollusc data collected from 63 sampling sites. Results indicated that species diversity is dominated by gastropods (61%) and bivalves (39%) and feeding trait diversity by scrapers (52%) and filter-collectors (37%). Only 48 species (49%) out of 98 taxa have been assessed by the International Union for Conservation of Nature (IUCN) including a growing number of invasive species. The lack of complete, up-to-date information highlights the need for more research on both native and alien species. Cluster analysis revealed a clear mollusc biodiversity structure along the LMB’s longitudinal segments. Diversity was lowest in upstream tributaries, increased in upstream main channels, and was highest in downstream channels and the Mekong delta, the exception being the observed high gastropod abundance in Chi-Mun river mouth and Luang Prabang areas. The RDA and variation partitioning demonstrated that combined physical–chemical and climatic conditions are the key drivers of biodiversity patterns. Given the potential spread of invasive alien species and increasing anthropogenic impacts, further ecological research, regular monitoring, and adaptive management are needed to sustain mollusc biodiversity and associated ecosystem services, which contribute to food security, nutrition, and livelihoods in the LMB. Full article

Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods—Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance—to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region. Full article

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d⁻¹, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate. Full article

In recent years, the Taiwan government has established a number of flood control facilities such as dikes, pumping stations and drainage systems to effectively reduce downstream flooding. However, with continued development and urbanization of catchment areas, the original designs of most flood control facilities have become outdated. Hillside lands in the upper and middle reaches of river basins have undergone urban development through unsound engineering practices, paving the way for heavy downstream flooding. Therefore, proper river basin management should include both upstream and downstream sides. The main purpose of the paper is to simulate non-urban inundation areas with various degrees of development (0%, 10%, 20%, 40% and 60%), over two different return periods of 25 years and 200 years, for intensive rainfall events in the Shi-Chi District, Taiwan. Through hydrological analysis and numerical simulations of inundation, quantitative data on inundation potential have been established based on the land development conditions along the hillsides on the upper and middle reaches of the Keelung River Basin. The simulated results show that the increase in the extent of land development in the upper reaches causes an increase in the area and depth of inundation, resulting in an increased risk of flooding in downstream areas. If the land-use policy makers in the upper reaches of the river basin’s hillsides do not properly manage the land development, the risk of flooding in downstream areas will increase. In such an event, the policy makers should first review the situation to understand the problem with the consideration of this study. Thus, proper development and flood mitigation in hillsides can be established. Full article