Stem embolism vulnerability curve depends on methods used: is there a fifth mechanism of cavitation?

A long-established ecological paradigm predicts a functional relationship determining vulnerability to cavitation: vulnerability increases with vessel hydraulic efficiency and vessel diameter. Even within a species, big vessels cavitate before small ones. Some centrifuge methods for measuring vulnerability are prone to artifacts due to nano-particles seeding early embolism, as the particles are drawn into vessels during measurements. Both the Sperry and Cochard rotors are prone to early cavitation due to nano-particles drawn into long and wide vessels in Robinia pseudoacacia and Quercus acutissima, whereas extraction centrifuge methods produce vulnerability curves more resistant to cavitation. Sufficient nano-particles pass through the stems to seed early embolism in all rotor designs. For several years, people have thought that early embolism is induced by nano-particles present in laboratory water. One new hypothesis is that the origin of nano-particles is from cut-open living cells but a much bigger study including many species is required to confirm this idea. This paper confirms the hypothesis in comparisons between short-vesselled Acer, and long-vesselled Robinia, and Quercus. Our new results and a review of old results justifies bigger study. Hypothetical nano-particles might explain why different methods for measuring vulnerability curves cause different T50 = tensions causing 50% loss of hydraulic conductivity. Hence the hypothesis for future research should be that the open-vessel artifact is consistent with 'long' vessels surrounded by cut open living cells.

Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions.

Establishment and characteristics analysis of a crop–drought vulnerability curve: a case study of European winter wheat

As an essential component of drought risk, crop–drought vulnerability refers to the degree of the adverse response of a crop to a drought event. Different drought intensities and environments can cause significant differences in crop yield losses. Therefore, quantifying drought vulnerability and then identifying its spatial characteristics will help understand vulnerability and develop risk-reduction strategies. We select the European winter wheat growing area as the study area and 0.5∘ × 0.5∘ grids as the basic assessment units. Winter wheat drought vulnerability curves are established based on the erosion–productivity impact calculator model simulation. Their loss change and loss extent characteristics are quantitatively analysed by the key points and cumulative loss rate, respectively, and are then synthetically identified via K-means clustering. The results show the following. (1) The regional yield loss rate starts to rapidly increase from 0.13 when the drought index reaches 0.18 and then converts to a relatively stable stage with the value of 0.74 when the drought index reaches 0.66. (2) In contrast to the Pod Plain, the stage transitions of the vulnerability curve lags behind in the southern mountain area, indicating a stronger tolerance to drought. (3) According to the loss characteristics during the initial, development, and attenuation stages, the vulnerability curves can be divided into five clusters, namely low-low-low, low-low-medium, medium-medium-medium, high-high-high, and low-medium-high loss types, corresponding to the spatial distribution from low latitude to high latitude and from mountain to plain. The paper provides ideas for the study of the impact of environment on vulnerability and for the possible application of vulnerability curve in the context of climate change.

Quantifying Key Points of Hydraulic Vulnerability Curves From Drought-Rewatering Experiment Using Differential Method

Precise and accurate estimation of key hydraulic points of plants is conducive to mastering the hydraulic status of plants under drought stress. This is crucial to grasping the hydraulic status before the dieback period to predict and prevent forest mortality. We tested three key points and compared the experimental results to the calculated results by applying two methods. Saplings (n = 180) of Robinia pseudoacacia L. were separated into nine treatments according to the duration of the drought and rewatering. We established the hydraulic vulnerability curve and measured the stem water potential and loss of conductivity to determine the key points. We then compared the differences between the calculated [differential method (DM) and traditional method (TM)] and experimental results to identify the validity of the calculation method. From the drought-rewatering experiment, the calculated results from the DM can be an accurate estimation of the experimental results, whereas the TM overestimated them. Our results defined the hydraulic status of each period of plants. By combining the experimental and calculated results, we divided the hydraulic vulnerability curve into four parts. This will generate more comprehensive and accurate methods for future research.

The 2017 Rigopiano Avalanche—Dynamics Inferred from Field Observations

Data on the disastrous snow avalanche that occurred on 18 January 2017 at the spa hotel Rigopiano, municipality of Farindola in the Abruzzo region of central Italy, are analyzed in different ways. The main results are the following. (i) The 2017 Rigopiano avalanche went beyond the run-out point predicted by the topographic-statistical α-β model with standard Norwegian calibration, while avalanches in neighboring paths appear to have run no farther than the β-point of their respective paths during the same period. (ii) The curvature and super-elevation of the trimline between 1500 and 1300 m a.s.l. indicate that the velocity of the front was around 40 m s−1. In contrast, the tail velocity of the avalanche can hardly have exceeded 25 m s−1 in the same segment. (iii) The deposits observed along all of the lower track and in the run-out zone suggest that the avalanche eroded essentially the entire snow cover, but fully entrained only a moderate amount of snow (and debris). The entrainment appears to have had a considerable decelerating effect on the flow front. (iv) Estimates of the degree to which different parts of the building were damaged is combined with information about the location of the persons in the building and their fates. This allows to refine a preliminary vulnerability curve for persons in buildings obtained from the 2015 Longyearbyen avalanche, Svalbard.

Vulnerability Analysis to Drought Based on Remote Sensing Indexes

A vulnerability curve is an important tool for the rapid assessment of drought losses, and it can provide a scientific basis for drought risk prevention and post-disaster relief. Those populations with difficulty in accessing drinking water because of drought (hereon “drought at risk populations”, abbreviated as DRP) were selected as the target of the analysis, which examined factors contributing to their risk status. Here, after the standardization of disaster data from the middle and lower reaches of the Yangtze River in 2013, the parameter estimation method was used to determine the probability distribution of drought perturbations data. The results showed that, at the significant level of α = 0.05, the DRP followed the Weibull distribution, whose parameters were optimal. According to the statistical characteristics of the probability density function and cumulative distribution function, the bulk of the standardized DRP is concentrated in the range of 0 to 0.2, with a cumulative probability of about 75%, of which 17% is the cumulative probability from 0.2 to 0.4, and that greater than 0.4 amounts to only 8%. From the perspective of the vulnerability curve, when the variance ratio of the normalized vegetation index (NDVI) is between 0.65 and 0.85, the DRP will increase at a faster rate; when it is greater than 0.85, the growth rate of DRP will be relatively slow, and the disaster losses will stabilize. When the variance ratio of the enhanced vegetation index (EVI) is between 0.5 and 0.85, the growth rate of DRP accelerates, but when it is greater than 0.85, the disaster losses tend to stabilize. By comparing the coefficient of determination (R2) values fitted for the vulnerability curve, in the same situation, EVI is more suitable to indicate drought vulnerability than NDVI for estimating the DRP.

Assessment of the physical vulnerability of buildings affected by slow-moving landslides

Physical vulnerability is a challenging and fundamental issue in landslide risk assessment. Previous studies mostly focus on generalized vulnerability assessment from landslides or other types of slope failures, such as debris flow and rockfall, while the long-term damage induced by slow-moving landslides is usually ignored. In this study, a method was proposed to construct physical vulnerability curves for masonry buildings by taking the Manjiapo landslide as an example. The landslide's force acting on the buildings' foundation is calculated by applying the landslide residual-thrust calculation method. Considering four rainfall scenarios, the buildings' physical responses to the thrust are simulated in terms of potential inclination by using Timoshenko's deep-beam theory. By assuming the landslide safety factor to be landslide intensity and inclination ratio to be vulnerability, a physical vulnerability curve is fitted and the relative function is constructed by applying a Weibull distribution function. To investigate the effects of buildings' parameters that influence vulnerabilities, the length, width, height, and foundation depth and Young's modulus of the foundation are analysed. The validation results on the case building show that the physical vulnerability function can give a good result in accordance with the investigation in the field. The results demonstrate that the building length, width, and foundation depth are the three most critical factors that affect the physical vulnerability value. Also, the result shows that the higher the ratio of length to width of the building, the more serious the damage to the building. Similarly, the shallower the foundation depth is, the more serious the damage will be. We hope that the established physical vulnerability curves can serve as tools for the quantitative risk assessment of slow-moving landslides.

Improved Framework for Assessing Vulnerability to Different Types of Urban Floods

Vulnerability assessment is an essential tool in mitigating the impact of urban flooding. To date, most flood vulnerability research has focused on one type of flood, such as a pluvial or fluvial flood. However, cities can suffer from urban flooding for several reasons, such as precipitation and river levee overtopping. Therefore, a vulnerability assessment considering different types of floods (pluvial floods, fluvial floods, and compound flooding induced by both rainfall and river overtopping) was conducted in this study. First, a coupled urban flood model, considering both overland and sewer network flow, was developed using the storm water management model (SWMM) and LISFLOOD-FP model to simulate the different types of flood and applied to Lishui, China. Then, the results of the flood modeling were combined with a vulnerability curve to obtain the potential impact of flooding on different land-use classes. The results indicated that different types of floods could have different influence areas and result in various degrees of flood vulnerability for different land-use classes. The results also suggest that urban flood vulnerability can be underestimated due to a lack of consideration of the full flood-induced factors.

SurEau.c : a mechanistic model of plant water relations under extreme drought

SummaryWe describe the operating principle of the detailed version of the soil-plant-atmosphere model SurEau that allows, among other things, to predict the risk of hydraulic failure under extreme drought. It is based on the formalization of key physiological processes of plant response to water stress. The hydraulic functioning of the plant is at the core of this model, which focuses on both water flows and water pools using variable hydraulic conductances. The model considers the elementary flow of water from the soil to the atmosphere through different plant organs (roots, trunk branches, leaves and buds) that are described by their symplasm and their apoplasm compartments. Within each organ the flow of water between the apoplasm and the symplasm is also represented; as well as the flow outside the system, from the symplasm of each organ to the atmosphere, through the cuticular conductance. For each organ, the symplasm is described by a pressure volume curves and the apoplasm by the vulnerability curve to cavitation of the xylem. The model can thus compute the loss of conductance caused by cavitation, a leading mechanisms of plant desiccation and drought-induced mortality. Some example simulations are shown to illustrate how the model works.