Bias Correction, Historical Evaluations and Future Projections of Climate Simulations in the Wei River Basin Using CORDEX-EA

The utilization of Regional Climate Methods (RCMs) to predict future regional climate is an important study under the changing environment. The primary objective of the paper is to correct the temperature and precipitation simulations for the period of 1980-2005 and 2026-2098 in the Wei River Basin (WRB), to evaluate the performance of RCMs for the period of 1980-2005, and further, to analyze the future changes of projected temperature and precipitation during 2026-2098. In this paper, the linear scaling method was used to correct the temperature simulations. Quantile mapping, local intensity scaling method and hybrid method were used to correct the precipitation simulations. The future changes of projected temperature and precipitation for the near-term (2026-2050), mid-term (2051-2075) and far-term (2076-2098), relative to the period of 1980-2005, were investigated under RCP 2.6 and RCP 8.5. Results indicate that: (1) The temperature biases were different spatial distributions, and the precipitation wet biases were detected in the WRB. After correction, HadGEM2-ES driven by RegCM4-4 had the best temperature reproducibility, and NCC-NorESM1-M driven by RegCM4-4 had the best precipitation reproducibility. (2) Under RCP 2.6, the projected annual, winter and spring temperature showed decreasing trends. The temperature was higher than that for the period of 1980-2005 except for the spring temperature decreases in the Beiluo River Basin. Under RCP 8.5, the temperature showed significantly increasing trends. The temperature for the near-term was similar to the period of 1980-2005, while the temperature increased significantly for the mid-term and far-term. (3) Under RCP 2.6, the precipitation had decreasing trends. Under RCP 8.5, precipitation trends were also spatially distributed. The relative deviation of winter precipitation was the largest. Relative to the period of 1980-2005, the light and moderate rain days showed little change for the period of 2026-2098, while the extreme rain days showed significantly increasing trends. (4) The results could be beneficial to the future climate projection, which provide references for the water resources management, the future hydrological process changes and attribution analysis in the WRB.

Microplastics in surface waters of the Wei River, China

Microplastics are a new type of persistent organic pollutants, usually on the micron scale. In this study, we investigated the abundance, distribution, and other characterization of microplastics in surface waters of the Wei River Basin in the Shandong peninsula. The results showed that the abundance of microplastics in the surface water of Wei River varied from 0.40 to 1.20 items/L, and the average abundance was 0.81 items/L, which was at a moderate pollution level compared with other rivers. It was found that the abundance of microplastics was higher in densely populated areas, and hydrodynamic conditions such as river inflow and seawater scour also affected the abundance of microplastics. Fiber (83.4%) was the dominant type. Colorless (93.6%) was the dominant color type. The particle size (0.06 - 0.5mm) (47.9%) was the main size of microplastics in the Wei River. This study provides data for the further study of microplastics in rivers and provides a warning for the management and prevention of microplastics in freshwater.

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.