Wildfires are widespread disasters and are concurrently influenced by global climatic drivers. Due to the widespread and far-reaching influence of climatic drivers, separate regional wildfires may ...have similar climatic cause mechanisms. Determining a suite of global climatic drivers that explain most of the variations in different homogeneous wildfire regions will be of great significance for wildfire management, wildfire prediction, and global wildfire climatology. Therefore, this study first identified spatiotemporally homogeneous regions of burned area worldwide during 2001-2019 using a distinct empirical orthogonal function. Eight patterns with different spatiotemporal characteristics were identified. Then, the relationships between major burned area patterns and sixteen global climatic drivers were quantified based on wavelet analysis. The most significant global climatic drivers that strongly impacted each of the eight major wildfire patterns were identified. The most significant combinations of hotspots and climatic drivers were Atlantic multidecadal Oscillation-East Pacific/North Pacific Oscillation (EP/NP)-Pacific North American Pattern (PNA) with the pattern around Ukraine and Kazakhstan, El Niño/Southern Oscillation-Arctic Oscillation (AO)-East Atlantic/Western Russia Pattern (EA/WR) with the pattern in Australia, and PNA-AO-Polar/Eurasia Pattern-EA/WR with the pattern in Brazil. Overall, these results provide a reference for predicting wildfire and understanding wildfire homogeneity.
This study aimed to evaluate the impact of climate change on flood damage and the effects of mitigation measures and combinations of multiple adaptation measures in reducing flood damage. The ...inundation depth was calculated using a two-dimensional unsteady flow model. The flood damage cost was estimated from the unit evaluation value set for each land use and prefectures and the calculated inundation depth distribution. To estimate the flood damage in the near future and the late twenty-first century, five global climate models were used. These models provided daily precipitation, and the change of the extreme precipitation was calculated. In addition to the assessment of the impacts of climate change, certain adaptation measures (land-use control, piloti building, and improvement of flood control level) were discussed, and their effects on flood damage cost reduction were evaluated. In the case of the representative concentration pathway (RCP) 8.5 scenario, the damage cost in the late twenty-first century will increase to 57% of that in the late twentieth century. However, if mitigation measures were to be undertaken according to RCP2.6 standards, the increase of the flood damage cost will stop, and the increase of the flood damage cost will be 28% of that in the late twentieth century. By implementing adaptation measures in combination rather than individually, it is possible to keep the damage cost in the future period even below that in the late twentieth century. By implementing both mitigation and adaptation measures, it is possible to reduce the flood damage cost in the late twenty-first century to 69% of that in the late twentieth century.
•Multitemporal UAV observations were for the post-fire heavy rain-affected area.•Substantial geomorphological changes were related to landslides and stream shifting.•The change in the vertex normal ...vector effectively captured the shape of the change.•The logSPI was utilized to classify the geological characteristics roughly.•Both logged-burnt and unlogged-burnt areas were highly affected by heavy rain.
Unmanned aerial vehicles (UAVs) are widely utilized for surveying various sediment disasters, and the high-resolution imaging they provide reveals detailed topographic characteristics. However, the availability of multitemporal UAV observations, which reveal geomorphological changes before and after the disaster, is still limited. In this research, we performed multitemporal UAV observations for geomorphological changes before and after extreme postfire heavy rain in the Ozaki Peninsula wildfire area in Japan. Significant geomorphological changes were observed associated with eight landslides and stream shifting, with a spatial resolution of 0.30 cm obtained by structure from motion (SfM) processing. To extract the surface changes based on multitemporal UAV observations, in addition to elevation displacement, vertex normal vectors were calculated for each mesh. We found that the elevation displacement showed comprehensive alterations in the entire area and that the changes in vertex normal vectors effectively captured the shape of these alterations. Deterioration, deposition, and scarping were shown for landslides, and the dynamics of erosion and deposition in the cross-section were for stream shifting. In addition, the detected geomorphological changes were compared with the predisaster topographic characteristics. Landslides occurred with a catchment area of 500 to 2,000 m2 and a slope inclination of 30 to 40°, and stream shifting occurred with a catchment area of 10,000 m2 or more and a slope inclination of less than 20°. In both regions affected by landslides and stream shifting, the logarithmic stream power index (logSPI), a natural logarithmic representation of the conventional stream power index (SPI), could describe the characteristics roughly. A logSPI value of 4 to 5 represented landslides, and 5 and greater represented stream shifting, with the probability of landslides and stream shifting occurring in areas with these values exceeding 99.5% and 99.9%, respectively. Therefore, the logSPI was utilized to roughly classify the terrain and geomorphological condition and the points among the unburnt, unlogged-burnt, and logged-burnt areas were compared. Even when the logSPI was similar, the damage was much more severe in the burnt areas. On slopes with a logSPI of approximately 4, there were signs of erosion in the logged-burnt area, and the increase in fallen trees was significant in the unlogged-burnt area, while there were no substantial changes in the unburnt area. In valleys with a logSPI of 8 or greater, sediment and woody debris were significantly deposited in the burnt areas following the postfire rain, while there were few changes in the unburnt area.
Abstract
Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a ...single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the standardized precipitation index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the global dataset of historical yield for major crops over 1981–2016. Results show that the drought index based on the ensemble precipitation dataset correlates better with the crop yield anomaly than a single dataset. Based on the drought index using ensemble datasets, global crop areas significantly affected by drought during the study period were around 23%, 8%, 30%, and 29% for maize, rice, soybean, and wheat, respectively, induced mainly by medium to longer drought timescale (5–12 months). This study indicates that most crops cultivated in dry regions were affected by droughts worldwide, while rice shows less correlation to drought as it is generally irrigated and cultivated in humid regions with less drought exposure. This study provides a valuable framework for data choices in drought index development and a better knowledge of the drought impact on agriculture using different timescales on a global scale towards understanding crop vulnerability to climate disruptions.
We propose a method for evaluating the risks of slope failure induced by heavy rain over a wide area by combining infiltration analysis, surface flow analysis, and slope stability analysis based on ...three-dimensional limit equilibrium method. The influence of the resolution of terrain data is also examined. To determine rainfall-induced water transportation, we employ an infiltration analysis, using the Green-Ampt model, and a surface flow analysis, using the diffusion wave model. For the slope stability analysis, a three-dimensional arc-slip calculation applying the Hovland method is used. The infiltration depth is obtained from the results of the infiltration analysis, whereas the simulated data are used as input data for the slope stability analysis. A region in Kamaishi City, Iwate Prefecture, Japan, where slope failures were observed during typhoon Hagibis on October 12 and 13, 2019, is selected in this study as the target area and a series of analyses are performed. The topographical survey data by an Unmanned Aerial Vehicle (UAV) observed before and after the typhoon are used to identify the actual collapse locations and compare them with the results of the slope stability analysis. In addition, high-resolution topographic data of the target area using UAV data is created and differences in the factor of safety distribution with and without the UAV data for terrain data are compared and discussed. The obtained results revealed that the proposed method can roughly represent the actual slope failure, which is useful for evaluating rainfall-induced slope failure hazards. The results derived from utilizing detailed terrain data by UAV show the importance of using a high-resolution terrain model for evaluating the stability of microtopography, such as forest roads.
•Rainfall-induced slope failure over a wide area is evaluated by 3D LEM.•Effects of rainfall are considered by infiltration and surface flow analysis.•Performance of the proposed method is discussed by reproducing an actual disaster.•Effect of high-resolution terrain data on the analysis is also investigated.
Most of studies on change-point at a regional or global scale have only examined a single hydrometeorological variable and have been unable to identify any underlying explanations. In this study, we ...identified change-points and long-term trends of six wildfire-related variables and attempted to explain the cause of change-point from atmospheric–oceanic indices. As a result, we discovered that the main change-point dates for the precipitation, temperature, and drought codes, as well as the duff moisture code, fine fuel moisture code, and fire weather index, were 1995–2000 and 2000–2005, respectively. Furthermore, the relationship between the change-point of six variables and atmospheric–oceanic indices was discussed through the correlation coefficient. For example, the Atlantic Multidecadal Oscillation was found to dominate the precipitation in West Africa. In addition, we divided the globe into eight homogenous wildfire weather zones based on the change-point dates and long-term trends of the six variables.
Wildfires can significantly impact regional and global climate, human health, and ecosystems, making it necessary to model their behavior and predict their outcomes. With increasing global ...temperatures and changing precipitation patterns due to climate change, the frequency and intensity of wildfires are expected to increase, heightening the requirement for accurate wildfire simulation models to support wildfire management and mitigation efforts. However, the interactions between the causative variables of wildfires and the wildfire bivariate characteristics have not been explored in wildfire modeling. Therefore, the copula function was applied to solve the complicated and nonlinear relationship of the dependence structure in wildfire statistics and the relationship between wildfire causative variables. Subsequently, we modeled wildfire characteristics globally using six machine learning models and compared the performances of the models. Specifically, the main conclusions were obtained as follows: (1) among the six machine learning models, long short-term memory had the best applicability in simulating wildfire characteristics; (2) when there were 4 predictors, the accuracy of wildfire characteristic simulation reached the average level; and (3) long short-term memory achieved excellent model performance within 56% of the global climate sub-regions. Overall, this analysis provides a reference to better understand wildfire and contributes to wildfire management.
Japan.
In this study, we analyzed the spatial frequency of annual extreme daily precipitation (AEDP) across Japan once every 100 years. A large ensemble analysis was conducted using the database for ...policy decision making for future climate changes (d4PDF) for precipitation data. A non-parametric method was used to calculate the AEDP amounts for each 20-km cell across Japan.
The results indicated that AEDP of RP100-year occurs once approximately every 1.18 years in the historical simulation across Japan. Furthermore, according to the Representative Concentration Pathway delivering 8.5 W/m2 globally (RCP 8.5), the frequency of AEDP of RP100-year increases once every 1.00 years in the +4-K future simulation. The mean of AEDP of RP100-year occurrence area with a return period of 100 years is ~10.38 cells (~4150 km2), with a median of six cells (~2400 km2). With future changes in the mean area of AEDP occurrence with a return period of 100 years predicted in RCP 8.5, there could be a massive increase in the area over which AEDP occurs (by ~2.1 times when mean values are used and with a median predicted increase of ~5.0 times the area). Additionally, we examined extreme precipitation values for various return periods using d4PDF data to consider the relationship between the scale of extreme precipitation and its probability.
•Return area was introduced to compare to return period.•Extreme precipitation of once in 100 years occurs once in 1.18 years in Japan.•The larger the area of extreme precipitation, the higher the increase in the future.•The mean area of extreme precipitation increases by 3.8 times in the future climate.
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