Assessing the influence of land use and land cover datasets with different points in time and levels of detail on watershed modeling in the North River Watershed, China

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

A method for detecting the non-stationarity during high flows under global change

The sustainability of existing water resources is influenced by extreme streamflow, and climate variability and human activities are generally the major factors controlling these dynamics. However, most of previously proposed methods to determine the effects of these factors have only been developed under the assumption of stationarity. Therefore, to overcome the existing research gap, an innovative method was proposed in this study to analyze and distinguish the effects of climate variability and human activities on extreme streamflow based on the non-stationarity theory. Accordingly, a rainfall-runoff model was developed using long-term hydrological data in the watersheds of Southeast China, which cover >75,000 km². The model proposed in this study showed an acceptable performance, as indicated by the Nash-Sutcliffe efficiency coefficient (NSE), the Kling-Gupta efficiency (KGE), and percent bias (PBIAS). The NSE, KGE, and |PBIAS| were 0.67-0.75, 0.57-0.74, and 1.22-16.79 during the calibration periods, respectively. And the NSE, KGE, and |PBIAS| were 0.69-0.77, 0.65-0.76, and 0.98-17.51 during the calibration periods, respectively. The trends of the extreme streamflow were analyzed for these watersheds at different time scales. The streamflow extremes at short time scales were found to be more sensitive to changing environment than those at longer time scales. The major factor controlling streamflow extremes at short time scales was human activities and climate change may be the dominant factor influencing streamflow extremes at long time scales. The findings of this study could provide useful insights into water management under global change conditions.

A catchment scale assessment of water balance components: a case study of Chittar catchment in South India

The detailed analyses of the water balance components (WBCs) of the catchment help assess the available water resources, especially in the arid climate regions for their sustainable management and development. This paper mainly used the Soil and Water Assessment Tool (SWAT) model to analyze the variation in the WBCs considering the change in the Land Use and Land Cover (LULC) and meteorological variables. For this purpose, the model used the inputs of LULC and meteorological variables between 2001 and 2020 at 5 years and daily time intervals, respectively, from the Chittar river catchment. The developed models were calibrated using SWAT-CUP split-up procedure (pre-calibration and post-calibration). The model was found to be good in calibration and validation, yielding the coefficient of determination (R²) of 0.94 and 0.81, respectively. Furthermore, WBCs of the catchment were estimated for the near future (2021-2030) at the monthly and annual scales. For this endeavor, LULC was forecasted for the years 2021 and 2026 using Cellular Automata (CA)-Artificial Neural Network (ANN), and for the same period, meteorological variables were also forecasted using the smoothing moving average method from the historical data.

Coupled effects of climate variability and land use pattern on surface water quality: An elasticity perspective and watershed health indicators

Understanding the coupled effects of climate variability and land use on riverine nitrogen is essential for watershed management. The climate-water relationships for ammonium (NH₄-N) and nitrate (NO₃-N) were determined by an elasticity approach and then the watershed health index was estimated using the reliability, resilience, and vulnerability framework. These methods were applied to an in-situ monitoring dataset of N concentrations measured during 2010-2017 from nine sub-watersheds in the Jiulong River Watershed, China. The results showed that temperature and precipitation elasticity of NH₄-N and NO₃-N changed substantially among various land use patterns. The N concentrations were highly sensitive to extreme climate conditions, particularly at urban and agricultural sub-watersheds. The measure of risk indicators revealed that the watershed health index varied from good health to unhealthy status. Linear regression analysis was used to analyze the interactions among watershed characteristics, climate elasticity, and watershed health. Cropland and population had strong positive correlations with climate elasticity of NO₃-N. Forest and elevation had strong negative associations with climate elasticity of NO₃-N. Watershed health significantly declined with increasing proportion of cropland and population density. This study demonstrated that human-impacted watersheds were less healthy to unhealthy and tend to be more sensitive to climate variability than natural watersheds, which is useful for efforts aimed at improving watershed management.

A Coupled Modeling Approach for Water Management in a River-Reservoir System

A coupled model is an effective tool to understand the nutrient fate associated with hydrodynamic and ecosystem processes and thereby developing a water resource management strategy. This paper presents a coupled modeling approach that consists of a watershed model and a hydrodynamic model to evaluate the nutrient fate in a river–reservoir system. The results obtained from the model showed a good agreement with field observations. The results revealed that the Shuikou reservoir (Fuzhou, China)exhibited complicated hydrodynamic characteristics, which may induce the pattern of nutrient export. Reservoirs can greatly lower water quality as a result of decreasing water movement. Three scenarios were analyzed for water management. The NH₃-N (Ammonia Nitrogen) decreased sharply in the outlet of Shuikou reservoir after NH₃-N level in its tributary was reduced. After removing the farming cages, the water quality of the outlet of Shuikou reservoir was improved significantly. The DO (Dissolved Oxygen) had increased by 3%–10%, NH₃-N had reduced by 5%–17%, and TP (Total Phosphorus) had reduced by 6%–21%. This study demonstrates that the proposed coupled modeling approach can effectively characterize waterway risks for water management in such a river–reservoir system.

Comparing the selection and placement of best management practices in improving water quality using a multiobjective optimization and targeting method

Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA) and a watershed model (Soil and Water Assessment Tool—SWAT). For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS), and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.