Evaluation of global land use/land cover products for hydrologic simulation in the Upper Yom River Basin, Thailand

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.

Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau

Land use/land cover (LULC) change and climate change are interconnected factors that affect the ecological environment. However, there is a lack of quantification of the impacts of LULC change and climate change on landscape ecological risk under different shared socioeconomic pathways and representative concentration pathways (SSP-RCP) on the Mongolian Plateau (MP). To fill this knowledge gap and understand the current and future challenges facing the MP's land ecological system, we conducted an evaluation and prediction of the effects of LULC change and climate change on landscape ecological risk using the landscape loss index model and random forest method, considering eight SSP-RCP coupling scenarios. Firstly, we selected MCD12Q1 as the optimal LULC product for studying landscape changes on the MP, comparing it with four other LULC products. We analyzed the diverging patterns of LULC change over the past two decades and observed significant differences between Mongolia and Inner Mongolia. The latter experienced more intense and extensive LULC change during this period, despite similar climate changes. Secondly, we assessed changes in landscape ecological risk and identified the main drivers of these changes over the past two decades using a landscape index model and random forest method. The highest-risk zone has gradually expanded, with a 30% increase compared to 2001. Lastly, we investigated different characteristics of LULC change under different scenarios by examining future LULC products simulated by the FLUS model. We also simulated the dynamics of landscape ecological risks under these scenarios and proposed an adaptive development strategy to promote sustainable development in the MP. In terms of the impact of climate change on landscape ecological risk, we found that under the same SSP scenario, increasing RCP emission concentrations significantly increased the areas with high landscape ecological risk while decreasing areas with low risk. By integrating quantitative assessments and scenario-based modeling, our study provides valuable insights for informing sustainable land management and policy decisions in the region.

Assessing the future climate change, land use change, and abstraction impacts on groundwater resources in the Tak Special Economic Zone, Thailand

Groundwater is an important source of water supply in the Tak Special Economic Zone of Thailand. However, groundwater is under stress from climate change, land use change, and an increase in abstraction, affecting the groundwater level and its sustainability. Therefore, this study analyses the impact of these combined stresses on groundwater resources in the near, mid, and far future. Three Global Climate Models are used to project the future climate under SSP2-4.5 and SSP5-8.5 scenarios. According to the results, both maximum and minimum temperatures are likely to show similar increasing trends for both scenarios, with a rise of approximately 1 (1.5), 2 (3), and 3 (5) °C expected for SSP2-4.5 (SSP5-8.5) in each consecutive period. Annual rainfall is expected to continually increase in the future, with around 1500-1600 mm in rainfall (11ꟷ5.43% higher). Land use change is predicted for two scenarios: business as usual (BU) and rapid urbanisation (RU). The forest area is expected to increase to 30% (35%) coverage in 2090 for BU (RU) while agriculture is likely to reduce to 60% (50%) with the urban area increasing to 2.4% (7%). Water demand is predicted to increase in all future scenarios. The SWAT model is used to project recharge, which is likely to increase by 10-20% over time. The highest increase is predicted in the far future under SSP2 and RU scenarios. MODFLOW was used to project future groundwater resources, but due to the lack of consistent data, the time scale is reduced to yearly simulation. The results reveal that the groundwater level is expected to increase in the central part (urban area) of the study area and decrease along the boundary (agricultural area) of the aquifer. This research can aid policymakers and decision-makers in understanding the impact of multiple stressors and formulating adaptation strategies to manage groundwater resources in special economic zones.

Impacts of climate and land use change on groundwater recharge under shared socioeconomic pathways: A case of Siem Reap, Cambodia

The rapid pace of urbanization blended with climate change has significantly altered surface and groundwater flows. In the context of tourism-driven economic potential areas, these drivers have greater effects, including threatening groundwater availability. This study assessed the combined impacts of climate and land use changes on the groundwater recharge (GWR) in Siem Reap, Cambodia utilizing Phase Six of the Coupled Model Intercomparison Project (CMIP6) global climate models (GCMs), DynaCLUE land-use model, and Soil Water Assessment Tool (SWAT). Three climate models CanESM5, EC_Earth3, and MIROC6, out of seven, best captured the observed data after performance evaluation through the entropy method, were bias-corrected linearly for two shared socioeconomic pathways (SSPs) - SSP2-4.5 and SSP5-8.5. The results indicate a general increase in precipitation under both SSPs, while the average annual maximum temperature is likely to increase by 0.024 °C/year and 0.049 °C/year under SSP2-4.5 and SSP5-8.5, respectively. A similar trend but relatively higher increase is expected for the minimum temperature. Furthermore, the historical land use change showed the expansion of urban settlement by 373% between 2004 and 2019 at the expense of forest and shrubland. Future land use projections from the DynaCLUE model show that the urban settlements in the study area are likely to expand, from their 2019 condition, by 55% in 2030, 209% in 2060, and 369% in 2090 under SSP2 and at double of these rates under SSP5 scenario. The GWR is expected to rise by 39-53% during the wet season and decrease by 13-29% during the dry season under both scenarios. Meanwhile, under constant land use, the GWR is likely to increase more compared to other scenarios, highlighting the importance of land use planning to policymakers and planners. Additionally, the study shall also be important to practitioners and researchers in understanding, planning, and evaluating the performance of multiple climate models in groundwater assessment.

Study of Non-Point Pollution in the Ashe River Basin Based on SWAT Model with Different Land Use

The Ashe River Basin (ARB), long known as the “Golden Waterway” in Manchu, has become one of China’s most polluted rivers. The basin area of the Ashe River is 3545 km2 and the total length of the river is 257 km. There have not been specific studies on land use change and non-point pollution in the ARB region. This paper uses the ARB watershed as the study area, simulates the watershed using the SWAT (Soil and Water Assessment Tool) model, and analyzes the hydrological processes and the temporal and spatial changes of total nitrogen and total phosphorus in the watershed with hydrology and water quality as the objectives under different periods of land use to reduce pollution in the watershed and protect the environment. The results show that the simulation of runoff, and even the R2 and NS (both the coefficient of determination and the Nash–Sutcliffe efficiency coefficient are simulated by SWAT-CUP, which is generally used to validate the simulation results of the hydrological model, where the closer the result is to 1, the better the effect) of total nitrogen and total phosphorus in the watershed, are also all above 0.75 and have good applicability during regular and validation periods. Since 2000, the simulated monthly average total nitrogen and total phosphorus levels have progressively grown. The most polluted areas are concentrated in the middle and lower reaches of the watershed near the main streams owing to the rise in load per unit area caused by the collection of pollutants from the upper watershed to the watershed outlet, and even an increase in fertilizer application due to the larger area of cultivated land.

A Methodology to Generate Integrated Land Cover Data for Land Surface Model by Improving Dempster-Shafer Theory

Land cover type is a key parameter for simulating surface processes in many land surface models (LSMs). Currently, the widely used global remote sensing land cover products cannot meet the requirements of LSMs for classification systems, physical definition, data accuracy, and space-time resolution. Here, a new fusion method was proposed to generate land cover data for LSMs by fusing multi-source remote sensing land cover data, which was based on improving Dempster-Shafer evidence theory with mathematical models and knowledge rules optimization. The new method has the ability to deal with seriously disagreement information, thereby improving the robustness of the theory. The results showed the new method can reduce the disagreement between input data and realized the conversion of multiple land cover classification systems to into a single land cover classification system. China Fusion Land Cover data (CFLC) in 2015 generated by the new method maintained the classification accuracy of the China land use map (CNLULC), which is based on visual image interpretation and further enriched land cover classes of input data. Compared with Geo-Wiki observations in 2015, the overall accuracy for CFLC is higher than other two global land cover data. Compared with the observations, the 0–10 cm soil moisture simulated by the CFLC in Noah–MP LSM during the growing season in 2014 had better performance than that simulated by initial land cover data and MODIS land cover data. Our new method is highly portable and generalizable to generate higher quality land cover data with a specific land cover classification system for LSMs by fusing multiple land cover data, providing a new approach to land cover mapping for LSMs.

Multiple drivers of hydrological alteration in the transboundary Srepok River Basin of the Lower Mekong Region

Human-induced changes in land and water resources adversely affect global hydrological regimes. Hydrological alteration of the natural flow regime is considered to have a significant damaging and widespread impact on river ecosystems and livelihoods. Therefore, understanding the hydrological alteration of rivers and the potential driving factors affecting such alterations are crucial to effective water resources management. This study analyses the impact of changes in land use, climate, and hydropower development on the hydrological regime of the Srepok River Basin in the Lower Mekong Region. The Lower Mekong Basin (LMB) in Southeast Asia is known for its agriculture, forests, fisheries, wildlife, and diverse natural ecosystems. Historical land use and climate change are quantified (utilising European Space Agency land cover and observed meteorological data) and correlated with the hydrological indicators using the Indicators of Hydrologic Alteration (IHA) software. Moreover, pre and post impacts on the hydrological regime by hydropower development are quantified using the Range of Variability Approach (RAV) in IHA software. The results reveal that land use, rainfall, and temperature affect different aspects of the hydrological regime, with corroborating evidence to support variation among the most correlated IHA and environmental flow component (EFC) parameters with the three drivers. The highest and lowest correlations among the IHA and EFC parameters under each driver are against land use (0.85, -0.83), rainfall (0.78, -0.54), and minimum and max temperatures (0.42, -0.47). Among the parameters, the fall rate has the most significant effect on hydrological alteration of all drivers. Hydropower development in the basin mostly affects the fall rate and reversal. Identifying the connection between these multiple drivers and hydrological alteration could help decision-makers to design more efficient and sustainable water management policies.