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. Subsequently, the impact of rainfall patterns on urban inundation under LID measures is analyzed based on the dynamic urban flood simulation framework. The results show that the urban flood simulation framework exhibits good applicability, with Nash-Sutcliffe Efficiency (NSE) values of 0.825 and 0.763 during the calibration and validation periods, respectively. The extent of inundation shows little variation for rainfall events with a return period greater than 20 years, and the location of flooding is minimally affected by rainfall patterns. LID measures have a decreasing effect on urban inundation control as the return period of rainfall increases, and there are variations in hydrological responses to different rainfall patterns under the same return period. For single-peak rainfall events with the same return period, the control rates of inundation volume, flow, and infiltration decrease as the rainfall peak coefficient increases, indicating a weakening effect of LID measures on flood control with increasing rainfall peak coefficient. Under the same return period conditions, LID measures exhibit the best runoff control effect for uniform rainfall, while their effectiveness is lower for double-peak rainfall events and single-peak rainfall events with an r = 0.75 coefficient. The findings of this study provide a theoretical basis for urban flood warning and management of Low Impact Development measures.

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. This study proposes a multi-scale urban rain-flood model rapid construction method framework, BK-SWMM, focusing on urban rain-flood model parameters and based on the basic architecture of Storm Water Management Model (SWMM). The framework comprises two main components: 1) constructing a SWMM uncertainty parameter sample crowdsourcing dataset and coupling Bayesian Information Criterion (BIC) and K-means clustering machine learning algorithm to discover clustering patterns of SWMM model uncertainty parameters in urban functional areas; 2) coupling BIC and K-means with SWMM model to form BK-SWMM flood simulation framework. The applicability of the proposed framework is validated by modeling three different spatial scales in the study regions based on observed rainfall-runoff data. The research findings indicate that the distribution pattern of uncertainty parameters, such as depression storage, surface Manning coefficient, infiltration rate, and attenuation coefficient. The distribution patterns of these seven parameters in urban functional zones indicate that the values are highest in the Industrial and Commercial Areas (ICA), followed by Residential Areas (RA), and lowest in Public Areas (PA). All three spatial scales' REQ, NSEQ, and RD2 indices were superior to the SWMM and less than 10%, greater than 0.80, and greater than 0.85, respectively. However, when the study area's geographical scale expands, the simulation's accuracy will decline. Further research is required on the scale dependency of urban storm flood models.

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). Penicillium and Aspergillus relatives were the most dominant genera within the Ascomycetes, followed by the members of genera Cladosporium, Hamigera, Chaetomium, Eutypella, Acremonium, Aureobasidium, Candida, Eurotium, Exophiala, Nigrospora, Bionectria and Pseudocercosporella. Four Basidiomycota species were identified as genera Schizophyllum, Irpex, Bjerkandera and Termitomyces. Among these isolates, Cladosporium sphaerospermum and Aspergillus sydowii relatives were isolated from a thin lignite coal-sandstone formation at 2457 mbsf. Our results indicate that these cultivable fungal populations are indigenous, originating from past terrigenous environments, which have persisted, possibly as spores, through ∼20 million years of depositional history.

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. The effects of exogenous application of GR24 (a synthetic SL analogue), NAA (α-naphthylacetic acid, exogenous auxin) and NPA (N-1-naphthylphalamic acid, a polar auxin transport inhibitor) on rice AR development in seedlings were investigated. KEY RESULTS: The rice d mutants with impaired SL biosynthesis and signalling exhibited reduced AR production compared with the WT. Application of GR24 increased the number of ARs and average AR number per tiller in d10, but not in d3. These results indicate that rice AR production is positively regulated by SLs. Higher endogenous IAA concentration, stronger expression of DR5::GUS and higher [(3)H] IAA activity were found in the d mutants. Exogenous GR24 application decreased the expression of DR5::GUS, probably indicating that SLs modulate AR formation by inhibiting polar auxin transport. The WT and the d10 and d3 mutants had similar expression of DR5::GUS regardless of exogenous application of NAA or NPA; however, AR number was greater in the WT than in the d mutants. CONCLUSIONS: The results suggest that AR formation is positively regulated by SLs via the D3 response pathway. The positive effect of NAA application and the opposite effect of NPA application on AR number of WT plants also suggests the importance of auxin for AR formation, but the interaction between auxin and SLs is complex.