Possibility of using multiscale normalized difference vegetation index data for the assessment of total suspended solids (TSS) concentrations in surface water: A specific case of scale issues in remote sensing

Modelling relationships between land use and water quality using statistical methods: A critical and applied review

Land use/land cover (LULC) can have significant impacts on water quality and the health of aquatic ecosystems. Consequently, understanding and quantifying the nature of these impacts is essential for the development of effective catchment management strategies. This article provides a critical review of the literature in which the use of statistical methods to model the impacts of LULC on water quality is demonstrated. A survey of these publications, which included hundreds of original research and review articles, revealed several common themes and findings. However, there are also several persistent knowledge gaps, areas of methodological uncertainty, and questions of application that require further study and clarification. These relate primarily to appropriate analytical scales, the significance of landscape configuration, the estimation and application of thresholds, as well as the potentially confounding influence of extraneous variables. Moreover, geographical bias in the published literature means that there is a need for further research in ecologically and climatically disparate regions, including in less developed countries of the Global South. The focus of this article is not to provide a technical review of statistical techniques themselves, but to examine important practical and methodological considerations in their application in modelling the impacts of LULC on water quality.

Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms

Rapid urbanization led to significant land-use changes and posed threats to surface water bodies worldwide, especially in the Global South. Hanoi, the capital city of Vietnam, has been facing chronic surface water pollution for more than a decade. Developing a methodology to better track and analyze pollutants using available technologies to manage the problem has been imperative. Advancement of machine learning and earth observation systems offers opportunities for tracking water quality indicators, especially the increasing pollutants in the surface water bodies. This study introduces machine learning with the cubist model (ML-CB), which combines optical and RADAR data, and a machine learning algorithm to estimate surface water pollutants including total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD). The model was trained using optical (Sentinel-2A and Sentinel-1A) and RADAR satellite images. Results were compared with field survey data using regression models. Results show that the predictive estimates of pollutants based on ML-CB provide significant results. The study offers an alternative water quality monitoring method for managers and urban planners, which could be instrumental in protecting and sustaining the use of surface water resources in Hanoi and other cities of the Global South.

Determining the main contributing factors to nutrient concentration in rivers in arid northwest China using partial least squares structural equation modeling

Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.

Optimizing payment for ecosystem services in a drinking water source watershed by quantifying the supply and demand of soil retention service

Payment for ecosystem services (PES) plays a vital role in coordinating the relationship between ecosystem services supply and demand sides in watersheds. The upstream soil retention service brings significant off-site benefits to the downstream stakeholders. To fill gaps in the supply and demand of soil retention services for PES, we developed an approach that combined long-term observation data, hydrological model, and cost-benefit analysis. We applied and demonstrated the approach in a typical drinking water source watershed. By constructing the relationship between water clarity and the demanded trophic state, we identified the demand for soil retention as the suspended sediment concentration ≤4.4 mg L^-1 at a transboundary station. Then, a well-calibrated hydrological model was applied to simulate the downstream sediment reduction under 36 upstream reforestation scenarios. Results showed that cropland reforestation effectively reduced downstream sediment loads by up to 37.8%. However, the efficiency of cropland reforestation for soil retention supply was influenced by its area, slope, and location. The cost-benefit analysis revealed that the feasible sediment reduction was 11,000 t per year, and the market-equilibrium price was 5800 CNY (Chinese Yuan, 7 CNY equaled 1 USD in 2020) per ton. The downstream side should pay 64 million CNY annually for soil retention provided by reforesting at upstream sloping cropland of 8° or above. This study suggested that the approach was helpful for integrating soil retention service supply and demand at a watershed scale to support PES decision-making.