Identifying the impact of land use land cover change on streamflow and nitrate load following modeling approach: a case study in the upstream Dong Nai River basin, Vietnam.

Tri An Reservoir is a vital source of water for agriculture, industry, hydropower, and public usage in Southern Vietnam. Due to human activities, water eutrophication has become a serious problem in recent decades. This study investigated for the first time the impact of land use and land cover (LULC) change on streamflow and nitrate load from the upstream Dong Nai River basin, which is the largest watershed of the reservoir. The study utilized several LULC scenarios, including LULC 2000, 2010, and 2020. The SWAT model was applied to model the watershed during the period 1997-2009. Results showed that the hydrological model performed satisfactorily based on the Nash-Sutcliffe efficiency (NSE) coefficient, the root mean square error observations standard deviation ratio (RSR), and the percent bias (PBIAS). The average simulated values of monthly streamflow and nitrate load were 453.7, 450.0, 446.7 m3/s and 17,699.43, 17,869.13, 17,590.81 tonnes for the LULC 2000, 2010, and 2020 scenarios, respectively. There were no significant differences in streamflow and nitrate load at the basin level under the different LULC scenarios. However, when looking at the subbasin level, there were differences in nitrate load among the scenarios. This suggests that the impacts of LULC on nitrate load may be more pronounced at smaller scales. Overall, our finding underscores the importance of modeling techniques in predicting the impacts of LULC change on streamflow and water quality, which can ultimately aid in the sustainable management of water resources.

Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam.

Surface water eutrophication due to excessive nutrients has become a major environmental problem around the world in the past few decades. Among these nutrients, nitrogen and phosphorus are two of the most important harmful cyanobacterial bloom (HCB) drivers. A reliable prediction of these parameters, therefore, is necessary for the management of rivers, lakes, and reservoirs. The aim of this study is to test the suitability of the powerful machine learning (ML) algorithm, random forest (RF), to provide information on water quality parameters for the Tri An Reservoir (TAR). Three species of nitrogen and phosphorus, including nitrite (N-NO2-), nitrate (N-NO3-), and phosphate (P-PO43-), were empirically estimated using the field observation dataset (2009-2014) of six surrogates of total suspended solids (TSS), total dissolved solids (TDS), turbidity, electrical conductivity (EC), chemical oxygen demand (COD), and biochemical oxygen demand (BOD5). Field data measurement showed that water quality in the TAR was eutrophic with an up-trend of N-NO3- and P-PO43- during the study period. The RF regression model was reliable for N-NO2-, N-NO3-, and P-PO43- prediction with a high R2 of 0.812-0.844 for the training phase (2009-2012) and 0.888-0.903 for the validation phase (2013-2014). The results of land use and land cover change (LUCC) revealed that deforestation and shifting agriculture in the upper region of the basin were the major factors increasing nutrient loading in the TAR. Among the meteorological parameters, rainfall pattern was found to be one of the most influential factors in eutrophication, followed by average sunshine hour. Our results are expected to provide an advanced assessment tool for predicting nutrient loading and for giving an early warning of HCB in the TAR.