Dynamic multivariate analysis for pollution assessment and river habitat conservation in the Vietnamese La Buong watershed.

Analysis of temporal patterns of high-dimensional time-series water quality data is essential for pollution management worldwide. This study has applied dynamic factor analysis (DFA) and cluster analysis (CA) to analyze time-series water quality data monitored at the five stations installed along the La Buong river in Southern Vietnam. Application of the DFA identified two types of temporal patterns, one of the run-off driven parameters (total suspended solid (TSS), turbidity, and iron) and the other of diffuse source pollution. The association of the variables like BOD5 and COD at most stations to the run-off-driven parameters revealed their sharing of drivers. On the contrary, separating variables like phosphate (PO43) at the three upstream stations from the run-off patterns suggested their local point-source origin. The DFA-derived factors were later used in the time-point CA to explore the seasonality of water quality parameters and their pollution intensities compared to regulatory levels. The result suggested intensification in wet season of Fe, TSS, BOD5, and COD concentrations at most sites, which are unobservable in run-off detached parameters like reactive nitrogen, phosphate (PO43-), and E. coli. These findings generated robust insights to support water quality management for river habitat conservation.

Hydrological impacts of future climate and land use/cover changes in the Lower Mekong Basin: a case study of the Srepok River Basin, Vietnam.

This study presents hydrological impacts of future climate change (CC) and land use/cover change (LUCC) for the Srepok River Basin (SRB) in the Vietnam's Central Highlands. The hydrology cycle of this basin was reproduced using Soil and Water Assessment Tool (SWAT) allowing an evaluation of hydrological responses to CC and LUCC. Future climate scenarios of the 2015-2100 period under Representative Concentration Pathways (RCP) 4.5 simulated by five General Circulation Models (GCMs) and LUCC scenario in 2050 were developed. Compared to the reference scenario (1980-2005), future LUCC increases the streamflow (0.25%) and surface runoff (1.2%) and reduces the groundwater discharge (2.1%). Climate change may cause upward trends in streamflow (0.1 to 2.7%), surface runoff (0.4 to 4.3%), and evapotranspiration (0.8 to 3%), and a change in the groundwater discharge (- 1.7 to 0.1%). The combination of CC and LUCC increases the streamflow (0.2 to 2.8%), surface runoff (1.6 to 5.6%), and evapotranspiration (1.0 to 3.1%), and reduces the groundwater discharge (1.5 to 2.7%) with respect to the reference scenario. Moreover, the results noted that the water scarcity may happen in the dry-seasonal months.

The responses of river discharge and sediment load to historical land-use/land-cover change in the Mekong River Basin.

The large river basins throughout the world have undergone land-use/land-cover (LULC)-induced changes in river discharge and sediment load. Evaluating these changes is consequently important for efficient management of soil and water resources. In addition, these changes in the transboundary Mekong River Basin (Mekong RB) are not well-known. The present study aimed to investigate the impacts of LULC changes on river discharge and sediment load in the Mekong RB during the period 1980-2015 using Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated and validated using measured data of daily river discharge and monthly sediment load. Analysis of LULC change showed a 2.35% decrease in forest land and a 2.29% increase in agricultural land during the period of 1997-2010. LULC changes in 1997 and 2010 caused increases in river discharge and sediment load by 0.24 to 0.32% and 1.78 to 2.86%, respectively in the study region. Moreover, the river discharge and sediment load of the Mekong River have significantly positive correlation with agricultural land and negative correlation with forest land. The findings give beneficial insights to implement appropriate strategies of water and soil conservation measures to adapt and mitigate the adverse impacts of LULC in the Mekong RB. Further study will consider the impact of future LULC changes and uncertainties associated with the LULC projections for future management of soil and water conservation in the study region.

The challenge of unprecedented floods and droughts in risk management.

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

The effects of climate variability and land-use change on streamflow and nutrient loadings in the Sesan, Sekong, and Srepok (3S) River Basin of the Lower Mekong Basin.

This paper aimed at examining the climate variability and land-use change effects on streamflow and pollutant loadings, namely total suspended sediment (TSS), total nitrogen (T-N), and total phosphorus (T-P), in the Sesan, Sekong, and Srepok (3S) River Basin in the period 1981-2010. The well-calibrated and validated Soil and Water Assessment Tool (SWAT) was used for this purpose. Compared to the reference period, climate variability was found to be responsible to a 1.00% increase in streamflow, 2.91% increase in TSS loading, 11.35% increase in T-N loading, and 19.12% reduction in T-P loading for the whole basin. With regard to the effect of land-use change (LUC), streamflow, TSS, T-N, and T-P loadings increased by 0.01%, 3.70%, 10.12%, and 10.94%, respectively. Therefore, the combination of climate variability and LUC showed amplified increases in streamflow (1.03%), TSS loading (7.09%), and T-N loading (25.05%), and a net effect of decreased T-P loading (10.35%). Regarding the Sekong and Srepok River Basins, the streamflow, TSS, T-N and T-P showed stronger responses to climate variability compared to LUC. In case of the Sesan River Basin, LUC had an effect on water quantity and quality more strongly than the climate variability. In general, the findings of this work play an essential role in providing scientific information to effectively support decision makers in developing sustainable water resources management strategies in the study area.

Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment.

Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood potential within Trotuș river basin in Romania using six ensemble models created by the combination of Analytical Hierarchy Process (AHP), Certainty Factor (CF) and Weights of Evidence (WOE) on one hand, and Gradient Boosting Trees (GBT) and Multilayer Perceptron (MLP) on the other hand. A number of 12 flood predictors, 172 flood locations and 172 non-flood locations were used. A percentage of 70% of flood and non-flood locations were used as input in models. From the input data, 70% were used as training sample and 30% as validating sample. The highest accuracy was obtained by the MLP-CF model in terms of both training (0.899) and testing (0.889) samples. A percentage between 21.88% and 36.33% of study area is covered with high and very high flood potential. The results validation, performed through the ROC Curve method, highlights that the MLP-CF model provided the most accurate results.