A study on the runoff prediction mechanism of "water-soil-heat" in cold alpine regions with complex spatial distribution.

To address the challenge of runoff prediction in cold alpine regions with complex spatial distributions, this study proposes an integrated "Water-Soil-Hseat" framework for runoff modeling. This framework incorporates key factors such as precipitation, glacier meltwater, soil spatial distribution, and temperature-induced melt processes, providing a more comprehensive understanding of runoff generation mechanisms. Precipitation and glacier meltwater serve as the primary hydrological variables, while soil spatial distribution acts as an impact factor, and temperature-induced melt processes drive the runoff.

Infrared and Harsh Light Visible Image Fusion Using an Environmental Light Perception Network.

The complementary combination of emphasizing target objects in infrared images and rich texture details in visible images can effectively enhance the information entropy of fused images, thereby providing substantial assistance for downstream composite high-level vision tasks, such as nighttime vehicle intelligent driving. However, mainstream fusion algorithms lack specific research on the contradiction between the low information entropy and high pixel intensity of visible images under harsh light nighttime road environments. As a result, fusion algorithms that perform well in normal conditions can only produce low information entropy fusion images similar to the information distribution of visible images under harsh light interference.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting.