Improving the understanding of rainfall-runoff processes: Temporal dynamic of event runoff response in Loess Plateau, China.

Enhancing the understanding of the rainfall-runoff temporal dynamics in semi-arid and semi-humid regions is crucial for flood disaster mitigation. Loess Plateau is a unique environment within semi-arid and semi-humid regions, characterized by its deep loess soil, prevalent short-duration intense rainfall, and changes in underlying surface conditions. In this research, 25 catchments from the Loess Plateau were chosen to examine the temporal variations in event runoff responses across different time scales.

Study on long short-term memory based on vector direction of flood process for flood forecasting.

Accurate flood forecasting is crucial for flood prevention and mitigation, safeguarding the lives and properties of residents, as well as the rational use of water resources. The study proposes a model of long and short-term memory (LSTM) combined with the vector direction (VD) of the flood process. The Jingle and Lushi basins were selected as the research objects, and the model was trained and validated using 50 and 49 measured flood rainfall-runoff data in a 7:3 division ratio, respectively.

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time series prediction models. However, the LSTM model has issues of underestimating peak flows and poor robustness in flood forecasting applications. Therefore, based on a thorough analysis of complex underlying surface attributes, this study proposes a framework for distinguishing runoff models and integrates a Grid-based Runoff Generation Model (GRGM).

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep Learning (DL) models, namely Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer, to develop a series of RPV-DL flood forecasting models, namely RPV-LSTM, RPV-TCN, and RPV-Transformer models. The models are evaluated using observed flood runoff data from nine typical basins in the middle Yellow River region.

Study on the response analysis of LID hydrological process to rainfall pattern based on framework for dynamic simulation of urban floods.

An in-depth analysis of the urban flood disaster level in response to different rainfall characteristics and Low Impact Development (LID) measures is of significant importance for addressing unfavorable management conditions and implementing effective flood control measures. This study proposes a dynamic urban flood simulation framework based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS) spatial analysis, incorporating an active inundation seed search algorithm. The framework is calibrated and validated using nine historical urban flood events.

BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region.

In recent years, urban flood disasters caused by sudden heavy rains have become increasingly severe, posing a serious threat to urban public infrastructure and the life and property safety of residents. Rapid simulation and prediction of urban rain-flood events can provide timely decision-making reference for urban flood control and disaster reduction. The complex and arduous calibration process of urban rain-flood models has been identified as a major obstacle affecting the efficiency and accuracy of simulation and prediction.

Exploration of cultivable fungal communities in deep coal-bearing sediments from ∼1.3 to 2.5 km below the ocean floor.

Although subseafloor sediments are known to harbour a vast number of microbial cells, the distribution, diversity, and origins of fungal populations remain largely unexplored. In this study, we cultivated fungi from 34 of 47 deep coal-associated sediment samples collected at depths ranging from 1289 to 2457 m below the seafloor (mbsf) off the Shimokita Peninsula, Japan (1118 m water depth). We obtained a total of 69 fungal isolates under strict contamination controls, representing 61 Ascomycota (14 genera, 23 species) and 8 Basidiomycota (4 genera, 4 species).

A strigolactone signal is required for adventitious root formation in rice.

Background And Aims: Strigolactones (SLs) and their derivatives are plant hormones that have recently been identified as regulating root development. This study examines whether SLs play a role in mediating production of adventious roots (ARs) in rice (Oryza sativa), and also investigates possible interactions between SLs and auxin.

Methods: Wild-type (WT), SL-deficient (d10) and SL-insensitive (d3) rice mutants were used to investigate AR development in an auxin-distribution experiment that considered DR5::GUS activity, [(3)H] indole-3-acetic acid (IAA) transport, and associated expression of auxin transporter genes.