Coupling SWAT and Bi-LSTM for improving daily-scale hydro-climatic simulation and climate change impact assessment in a tropical river basin.

Global climate change has led to an increase in both the frequency and magnitude of extreme events around the world, the risk of which is especially imminent in tropical regions. Developing hydrological models with better capabilities to simulate streamflow, especially peak flow, is urgently needed to facilitate water resource planning and management as well as climate change mitigation efforts in the tropics. In view of the need, this paper explores the feasibility of improving streamflow simulation performance in the tropical Kelantan River Basin (KRB) of Peninsular Malaysia through coupling a conceptual process-based hydrological model - Soil and Water Assessment Tool (SWAT) with a deep learning model - Bidirectional Long Short-Term Memory (Bi-LSTM) in two ways. All SWAT parameters were set as their default values in one hybrid model (SWAT-D-LSTM), whereas three most sensitive SWAT parameters were calibrated in the other hybrid model (SWAT-T-LSTM). Comparison of daily streamflow simulation results have shown that SWAT-T-LSTM consistently performs better than SWAT-D-LSTM as well as the stand-alone SWAT and Bi-LSTM model throughout the simulation period. Particularly, SWAT-T-LSTM performs considerably better than the other three models in simulating daily peak flow. Based on the latest projection results of five GCMs from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) under three emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), the best-performed SWAT-T-LSTM was run to assess the potential impacts of climate change on streamflow in the KRB. Ensemble assessment results have concluded that both average and extreme streamflow is much likely to increase considerably in the already wet northeast monsoon season from November to January, which has surely raised the alarm for more frequent flood occurrence in the KRB.

Elevated atmospheric CO2 enhances the phytoremediation efficiency of tall fescue (Festuca arundinacea) in Cd-polluted soil.

With the economic development of society, concentrations of atmospheric CO2 and heavy metals in soils have been increasing. The physiological responses of plants to the interaction between soil pollution and climatic change need to be understood. Pot experiments were designed to assess variations in Festuca arundinacea dry weight, leaf type, chlorophyll content, antioxidase activities, and Cd accumulation ability, under different atmospheric CO2 treatments. The results showed that the total dry weights increased with increasing CO2, and Cd concentrations in falling leaf tissues increased with raised atmospheric CO2, before reaching a peak at 600 ppm, above which they remained constant. Compared with the control (400 ppm), 600, 650, and 700 ppm CO2 treatments increased the proportions of the falling tissues by 1.7%, 3.3%, and 4.5%, respectively. Antioxidant enzyme activities in plant leaves increased with increasing atmospheric CO2 levels. The concentration of H2O2 in leaf tissues increased with increasing CO2, reaching a peak at 600 ppm, and then decreased significantly as the CO2 content increased further, to 700 ppm. The results in this study suggest that F. arundinacea could be regarded as a potential candidate for phytoremediation of Cd-polluted soil; especially if senescent and dead leaf tissues could be harvested, and that raised atmospheric CO2 levels could improve its soil remediation efficiency.Novelty statement Extrapolation of results from experiments of environmental impacts in greenhouse to real scale field requires to be considered cautiously. External factors such as water, temperature, humidity, and pollution are variable in real field. Plants will face a lot of beneficial or detrimental conditions which will influence the magnitude of the results. However, the elevation of CO2 is an inevitable phenomenon in future. Therefore, findings from experiments under artificial conditions are sometime a good choice to obtain knowledge about elevated CO2 related impacts on phytoremediation efficiency of a specific plant. The final goal of this work is to find a suitable CO2 fumigation strategy optimized for soil remediation. We report on that elevated atmospheric CO2 can increase the phytoremediation efficiency of Festuca arundinacea for Cd. This is significant because the combined influences of elevated atmospheric CO2 and metal pollution in terms of biomass yield, pollutant uptake, and phytoremediation efficiency would be more complex than the effects of each individual factor.

Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources.

Fast economic development, burgeoning population growth, and rapid urbanization have led to complex pollution sources contributing to water quality deterioration simultaneously in many developing countries including China. This paper explored the use of spatial regression to evaluate the impacts of watershed characteristics on ambient total nitrogen (TN) concentration in a heavily polluted watershed and make predictions across the region. Regression results have confirmed the substantial impact on TN concentration by a variety of point and non-point pollution sources. In addition, spatial regression has yielded better performance than ordinary regression in predicting TN concentrations. Due to its best performance in cross-validation, the river distance based spatial regression model was used to predict TN concentrations across the watershed. The prediction results have revealed a distinct pattern in the spatial distribution of TN concentrations and identified three critical sub-regions in priority for reducing TN loads. Our study results have indicated that spatial regression could potentially serve as an effective tool to facilitate water pollution control in watersheds under diverse physical and socio-economical conditions.

Assessment of the Spatial and Temporal Variations of Water Quality for Agricultural Lands with Crop Rotation in China by Using a HYPE Model.

Many water quality models have been successfully used worldwide to predict nutrient losses from anthropogenically impacted catchments, but hydrological and nutrient simulations with limited data are difficult considering the transfer of model parameters and complication of model calibration and validation. This study aims: (i) to assess the performance capabilities of a new and relatively more advantageous model, namely, Hydrological Predictions for the Environment (HYPE), that simulates stream flow and nutrient load in agricultural areas by using a multi-site and multi-objective parameter calibration method and (ii) to investigate the temporal and spatial variations of total nitrogen (TN) and total phosphorous (TP) concentrations and loads with crop rotation by using the model for the first time. A parameter estimation tool (PEST) was used to calibrate parameters. Results show that the parameters related to the effective soil porosity were highly sensitive to hydrological modeling. N balance was largely controlled by soil denitrification processes. P balance was influenced by the sedimentation rate and production/decay of P in rivers and lakes. The model reproduced the temporal and spatial variations of discharge and TN/TP relatively well in both calibration (2006-2008) and validation (2009-2010) periods. Among the obtained data, the lowest Nash-Suttclife efficiency of discharge, daily TN load, and daily TP load were 0.74, 0.51, and 0.54, respectively. The seasonal variations of daily TN concentrations in the entire simulation period were insufficient, indicated that crop rotation changed the timing and amount of N output. Monthly TN and TP simulation yields revealed that nutrient outputs were abundant in summer in terms of the corresponding discharge. The area-weighted TN and TP load annual yields in five years showed that nutrient loads were extremely high along Hong and Ru rivers, especially in agricultural lands.

[Assessment of regional left ventricular systolic function by VSI in children with Kawasaki disease].

OBJECTIVE: To assess the regional left ventricular systolic function of children with Kawasaki disease before and after treatment by vector myocardial strain and strain rate imaging (VSI) technology. METHODS: The regional left ventricular systolic function was assessed using VSI technology in 32 children with Kawasaki disease before treatment and one month after treatment and in 30 age-matched normal children. RESULTS: Nine segments of the left ventricular in the Kawasaki disease group before treatment had decreased longitudinal peak systolic strain rate (LSRs) compared with the normal control group. After treatment, the LSRs in 9 segments in the Kawasaki disease group increased, but 6 segments had decreased LSRs compared with the normal control group. The radial peak systolic strain rate (RSRs) of 8 segments in the Kawasaki disease group before treatment was lower than that in the control group. After treatment, only one segment had decreased RSRs compared with the control normal group and 5 segments had increased RSRs compared with that before treatment. The circumferential peak systolic velocity (CVs) of 6 segments in the Kawasaki disease group before treatment group was lower than that in the control normal group. After treatment, only one segment had decreased CVs in the Kawasaki disease group compared with the control normal group and 3 segments had increased CVs compared with that before treatment. CONCLUSIONS: The regional left ventricular systolic function in children with Kawasaki disease before and after treatment can be accurately assessed using VSI technology, which shows the clinical significance of this technology in assessment of treatment outcome in children with Kawasaki disease.