Huijuan Bo

Flood seasonality strongly influences flood risk and its management worldwide. Anthropogenic climate change and land-surface human activities are widely recognized as the two major drivers of its evolution, yet their relative contributions remain poorly quantified. In this study, the factorial experiment design of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) was used to disentangle the individual and combined effects of these two drivers on global flood seasonality. Using observation-driven ISIMIP2a simulations, the ability of the ISIMIP framework to reproduce two key seasonality metrics—mean flood timing and flood timing concentration—was first evaluated. Historical and future changes in flood seasonality under RCP6.0 and RCP8.5 were then assessed at multiple spatial scales, including global grid cells, 32 major river basins, and Köppen–Geiger climate zones. Quantitative results show that north of 40° N, under the influence of land-surface human activities, the proportion of grid cells exhibiting earlier mean flood timing under RCP6.0 increases from 54.43% (climate-only) to 65.76%, with the largest change observed in the cold climate zone (median: −0.0708). In contrast, the tropical zone shows a delayed trend (median: +0.0051). Spatially, a pronounced polarization pattern emerges: high-latitude cold regions experience widespread earlier and more dispersed flood timing, while low-latitude tropical regions exhibit later and more concentrated flood timing. At the basin scale, under RCP8.5, the Pearl River Basin shows the strongest delay (ΔF = +0.1356), whereas the St. Lawrence River Basin shows the strongest advance (ΔF = −0.07445). Land-surface human activities amplify or even reverse the direction of changes in mean flood timing. When human activities are included, 31 out of 32 major basins exhibit earlier flood timing, with seven basins reversing from delay to advance. The anthropogenic climate change signal emerges over most of the globe during the first half of the 21st century, and the timing of emergence is closely linked to the warming threshold. This study reveals the modulating role of land-surface human activities in the evolution of flood seasonality and quantifies the emergence time and warming conditions associated with the anthropogenic signal, providing a scientific basis for basin-scale flood-risk management and climate adaptation strategies.

Based on the Weibull random distribution of the micro-unit strength of fiber reinforced concrete, the parameters are introduced to describe the micro-unit strength of concrete reasonably, and it is used to establish a damage-softening constitutive model that can reflect the whole process of fiber reinforced concrete damage with the Drucker-Prager yield criterion. On this basis, according to the stress-strain curves of concrete with different palm fiber content and cement content, the functional relationship is discussed among the parameters with palm fiber content and cement content of concrete damage-softening model based on Weibull distribution, then a more realistic concrete damage- softening constitutive model is established with the modified model parameters. To consider the dynamic characteristics of micro-elements in palm fiber concrete during loading-unloading, a modified linear regression model parameter solving method is proposed. By comparing the experimental results with the calculated values, the rationality of the constitutive model and the parameter solution method is verified. The constitutive model proposed in this paper can well reflect the stress-strain relationship and damage evolution process of plam fiber reinforced concrete under different mix ratios, which can provide reference for the engineering application of brown fiber concrete.

Natural hydrological processes have been changed under the combined influences of climate change and intensive human activities in the Huangbaihe River Basin, where large-scale phosphate mining has been taking place. Therefore, evaluating the impact of climate change and intensive human activities on runoff variation and detecting the main driving factor leading to the variation are important for more efficient water resource management and more sustainable development of the regional economy. Despite numerous studies having been performed on this topic, little research focused on the impact of mining on runoff variation. The non-parametric Mann-Kendall (MK) trend test and accumulative anomaly methods were applied to identifying basic trends and change points of the hydro-meteorological elements over the period from 1978 to 2016. Then, the Soil Water and Assessment Tool (SWAT) and the Slope Changing Ratio of Accumulative Quantity (SCRAQ) were both used to quantify the contributions of climate change and anthropogenic activities on runoff variation. In this step, the runoff data were restored to their natural state before the construction of Xuanmiaoguan (XMG) dam. Due to the lack of locally observed evapotranspiration data, Global Land Evaporation Amsterdam Model and an empirical equation applied to obtain the evapotranspiration data. The results revealed that the change points are in 1985 and 2006. Therefore, the total period was divided into three periods, that is, the baseline period Ta (1978–1984), change period Tb (1985–2005) and change period Tc (2006–2016). Compared with the baseline period Ta, climate change dominates the runoff variation in the period Tb and is responsible for 60.5 and 74.4% of runoff variation, while human activities contribute the most to runoff variation for the period Tc (79.3 and 86.1%). Furthermore, an analysis of the underlying mechanism of underground phosphate mining indicates that mining can affect overland flow and baseflow simultaneously. This study can provide some information in determining the contributions of climate change and human activities in intensive phosphate mined basins and areas lack of evapotranspiration data.