Rice production in China’s coastal areas is frequently affected by typhoons, since the associated severe storms, with heavy rain and the strong winds, lead directly to the rice plants becoming flooded or lodged. Long-term flooding and lodging can cause a substantial reduction in rice yield or even destroy the harvest completely. It is therefore urgent to obtain accurate information about paddy rice flooding and lodging as soon as possible after the passing of the storm. This paper proposes a workflow in Google Earth Engine (GEE) for mapping the flooding and lodging area of paddy rice in Wenzhou City, Zhejiang, following super typhoon Maria (Typhoon No.8 in 2018). First, paddy rice in the study area was detected by multi-temporal Sentinel-1 backscatter data combined with Sentinel-2-derived Normalized Difference Vegetation Index (NDVI) using the Random Forests (RFs) algorithm. High classification accuracies were achieved, whereby rice detection accuracy was calculated at 95% (VH+ NDVI-based) and 87% (VV+ NDVI-based). Secondly, Change Detection (CD) based Rice Normalized Difference Flooded Index (RNDFI) and Rice Normalized Difference Lodged Index (RNDLI) were proposed to detect flooding and lodged paddy rice. Both RNDFI and RNDLI were tested based on four different remote sensing data sets, including the Sentinel-1-derived VV and VH backscattering coefficient, Sentinel-2-derived NDVI and Enhanced Vegetation Index (EVI). Overall agreement regarding detected area between the each two different data sets was obtained, with values of 79% to 93% in flood detection and 64% to 88% in lodging detection. The resulting flooded and lodged paddy rice maps have potential to reinforce disaster emergency assessment systems and provide an important resource for disaster reduction and emergency departments.
The impacts of multi-time-scale flows on northward and north-eastward moving tropical cyclones (TCs) near the east coast of China in August and September are investigated using reanalysis data from 1982 to 2012. TCs of interest are under the influence of the subtropical high-pressure system in the western North Pacific (WNP). In August when the subtropical high-pressure system is strong and close to the coast line, most TCs in the region move northward, while more TCs move north-eastward in September when the subtropical high-pressure system retreats to the east.
To investigate the influence from different time-scales, the environmental flow is divided into four components, the synoptic flow, the intraseasonal flow, the interannual flow and the climatological background field. Analysis of steering flows between 25°N and 30°N indicates that the meridional steering vectors from all time-scales point to the north, dominated by the intraseasonal component. The deciding factor on whether a TC moves to the north or north-east between 25°N and 30°N is the zonal steering vector. For the northward moving group, the sum of the zonal steering from all time-scales is very small. On the other hand, the north-east moving group has a net eastward zonal component mainly contributed by the climatological mean flow.
Several individual cases that stood out from the majority of the group are analyzed. For those cases, the intraseasonal flow plays an important role in affecting the movement of the TCs mainly through the wave train, in which a cyclonic circulation is located to the north-west (north) and an anticyclonic circulation to the south-east (east) of TCs. The analysis of the steering vectors indicates the importance of all components with different time-scales to the movement of TCs.
Forest fires, whether caused naturally or by human activity can have disastrous effects on the environment. Turkey, located in the Mediterranean climate zone, experiences hundreds of forest fires every year. Over the past two decades, these fires have destroyed approximately 308000 ha of forest area, threatening the sustainability of its ecosystem. This study analyzes the forest fire that occurred in the Menderes region of Izmir on July 1, 2017, by using pre- and post-fire Sentinel 2 (10m and 20m) and Landsat 8 (30m) satellite images, MODIS and VIIRS fire radiative power (FRP) data (1000m and 375m, respectively), and reference data obtained from a field study. Hence, image processing techniques integrated with the Geographic Information System (GIS) database were applied to a satellite image data set to monitor, analyze, and map the effects of the forest fire. The results show that the land surface temperature (LST) of the burned forest area increased from 1 to 11°C. A high correlation (R= 0.81) between LST and burn severity was also determined. The burned areas were calculated using two different classification methods, and their accuracy was compared with the reference data. According to the accuracy assessment, the Sentinel (10m) image classification gave the best result (96.43% for Maximum Likelihood, and 99.56% for Support Vector Machine). The relationship between topographical/forest parameters, burn severity and disturbance index was evaluated for spatial pattern distribution. According to the results, the areas having canopy closure between 71%–100% and slope above 35% had the highest burn incidence. As a final step, a spatial correlation analysis was performed to evaluate the effectiveness of MODIS and VIIRS FRP data in the post-fire analysis. A high correlation was found between FRP-slope, and FRP-burn severity (0.96 and 0.88, respectively).
Through a cloud-resolving simulation of the rapid intensification (RI) of Typhoon Meranti (2016), the convections, warm core, and heating budget are investigated during the process of RI. By investigating the spatial distributions and temporal evolutions of both convective-stratiform precipitation and shallow-deep convections, we find that the inner-core convections take mode turns, from stratiform-precipitation (SP) dominance to convective-precipitation (CP) prevalence during the transition stages between pre-RI and RI. For the CP, it experiences fewer convections before RI, and the conversion from moderate/moderate-deep convections to moderate-deep/deep convections during RI. There is a clear upper-level warm-core structure during the process of RI. However, the mid-low-level warming begins first, before the RI of Meranti. By calculating the local potential temperature (q) budget of various convections, the link between convections and the warm core (and further to RI via the pressure drop due to the warming core) is established. Also, the transport pathways of heating toward the center of Meranti driven by pressure are illuminated. The total hydrostatic pressure decline is determined by the mid-low-level warm anomaly before RI, mostly caused by SP. The azimuthal-mean diabatic heating is the largest heating source, the mean vertical heat advection controls the vertical downwards transport by adiabatic warming of compensating downdrafts above eye region, and then the radial q advection term radially transports heat toward the center of Meranti in a slantwise direction. Accompanying the onset of RI, the heating efficiency of the upper-level warming core rises swiftly and overruns that of the mid-low-level warm anomaly, dominating the total pressure decrease and being mainly led by moderate-deep and deep convections. Aside from the characteristics in common with SP, for CP, the eddy component of radial advection also plays a positive role in warming the core, which enhances the centripetal transport effect and accelerates the RI of Meranti.
Inclusion of cloud processes is essential for precipitation prediction with a numerical weather prediction model. However, convective parameterization contains numerous parameters whose values are in large uncertainties. In particular, it is still not clear how the parameters of a sub-grid-scale convection scheme can be modified to improve high-resolution precipitation prediction. To address these issues, a micro-genetic (micro-GA) algorithm is coupled to the Kain-Fritsch (KF) convective parameterization scheme (CPS) in the WRF to improve the quantitative precipitation forecast (QPF). The optimization focuses on two parameters in the KF scheme: the convective time scale and the conversion rate. The optimizing process is controlled by the micro-GA using a QPF skill score as the fitness function. Two heavy rainfall events related to typhoons that made landfall over the south-east coastal region of China are selected, and for each case the parameter values are adjusted to achieve the best QPF skill. Significant improvements in QPF are evident with an increase in the average equitable threat score (ETS) by 5.8% for the first case, and by 18.4% for the second case. The results demonstrate that the micro-GA-KF coupling system is effective in optimizing the parameter values, which affect the applicability of CPS in a high-resolution model, and therefore improves the rainfall prediction in both ETS and spatial distribution.
Data from the China Meteorological Administration and ERA-Interim are used to examine the environmental characteristics of landfalling tropical cyclones (TCs) with abrupt intensity change. The results show that, of all 657 landfalling TCs during 1979–2017, 71%, 70% and 65% of all landfalling TDs, TSs and TYs, respectively, intensify. Of all the 16595 samples, 4.0% and 0.2% of typhoons and tropical storms, respectively, experience over-water rapid intensification (RI) process during their life cycle. Meanwhile, 4.5% and 0.6% of typhoons and tropial storms, respectively, undergo over-water rapid decay (RD). These two kinds of cases, i.e., RI and RD, are used to analyze their associated large-scale conditions. Comparisons show that the RI cases are generally on the south side of the strong western Pacific subtropical high (WPSH); warm sea surface temperatures (SSTs) and sufficient water vapor fluxes existing in RI samples is a dominant feature that is conducive to the development of TCs. Also, the moderate low-level relative vorticity is favorable for TC intensification. On the contrary, the RD TCs are located on the west side of the WPSH; significant decreasing SSTs and low-level water vapor transport may synergistically contribute to RD. Simultaneously, low-level relative vorticity seems to be unfavorable for the development of TCs.
Physical models used to forecast the temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. Owing to the existing measuring technology and our knowledge of the physical laws controlling landslide initiation, model uncertainties are due to an inability to accurately quantify the model input parameters and rainfall forcing data. An uncertainty analysis of slope instability prediction provides a rationale for refining the geotechnical models. The Transient Rainfall Infiltration and Grid-based Regional Slope Stability-Probabilistic (TRIGRS-P) model adopts a probabilistic approach to compute the changes in the Factor of Safety (FS) due to rainfall infiltration. Slope Infiltration Distributed Equilibrium (SLIDE) is a simplified physical model for landslide prediction. The new code (SLIDE-P) is also modified by adopting the same probabilistic approach to allow values of the SLIDE model input parameters to be sampled randomly. This study examines the relative importance of rainfall variability and the uncertainty in the other variables that determine slope stability. The precipitation data from weather stations, China Meteorological Administration Land Assimilation System 2.0 (CLDAS2.0), China Meteorological Forcing Data set precipitation (CMFD), and China geological hazard bulletin are used to drive TRIGRS, SLIDE, TRIGRS-P and SLIDE-P models. The TRIGRS-P and SLIDE-P models are used to generate the input samples and to calculate the values of FS. The outputs of several model runs with varied input parameters and rainfall forcings are analyzed statistically. A comparison suggests that there are significant differences in the simulations of the TRIGRS-P and SLIDE-P models. Although different precipitation data sets are used, the simulation results of TRIGRS-P are more concentrated. This study can inform the potential use of numerical models to forecast the spatial and temporal occurrence of regional rainfall-induced shallow landslides.
Relationships between tropical cyclone (TC) precipitation, wind, and storm damage are analyzed for China based on TCs over the period from 1984 to 2013. The analysis shows that the maximum daily areal precipitation from stations with daily precipitation of ≥50 mm and the sum of wind gusts of ≥13.9 m/s can be used to estimate the main damage caused by TCs, and an index combining the precipitation and wind gust of a TC (IPWT) is defined to assess the severity of the combined impact of precipitation and wind. The correlation coefficient between IPWT and the damage index for affecting TCs is 0.80, which is higher than that for only precipitation or wind. All TCs with precipitation and wind affecting China are divided into five categories, Category 0 to Category 4, based on IPWT, where higher categories refer to higher combined impacts of precipitation and wind. The combined impact category is closely related to damage category and it can be used to estimate the potential damage category in operational work. There are 87.7%, 72.9%, 69.8%, and 73.4% of cases that have the same or one category difference between damage category and combined impact category for Categories 1, 2, 3, and 4, respectively. IPWT and its classification can be used to assess the severity of the TC impact and of combined precipitation and wind conveniently and accurately, and the potential damage caused by TCs. The result will be a good supplementary data for TC intensity, precipitation, wind, and damage. In addition, IPWT can be used as an index to judge the reliability of damage data. Further analysis of the annual frequency of combined precipitation-wind impact categories reveals no significant increasing or decreasing trend in impact over China over the past 30 years.
The initial condition accuracy is a major concern for tropical cyclone (TC) numerical forecast. The ensemble-based data assimilation techniques have shown great promise to initialize TC forecast. In addition to initial condition uncertainty, representing model errors (e.g. physics deficiencies) is another important issue in an ensemble forecasting system. To improve TC prediction from both deterministic and probabilistic standpoints, a Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) using an ensemble-based data assimilation scheme and a multi-physics approach based on Weather Research and Forecasting (WRF) model, has been developed in Shanghai Typhoon Institute and running real-time since 2015. Performance of TEDAPS in the prediction of track, intensity and associated disaster has been evaluated for the Western North Pacific TCs in the years of 2015-18, and compared against the NCEP GEFS.
TEDAPS produces markedly better intensity forecast by effectively reducing the weak biases and therefore the degree of underdispersion compared to GEFS. The errors of TEDAPS track forecasts are comparative with (slightly worse than) those of GEFS at longer (shorter) forecast leads. TEDAPS ensemble-mean exhibits advantage over deterministic forecast in track forecasts at long lead times, whereas this superiority is limited to typhoon or weaker TCs in intensity forecasts due to systematical underestimation. Four case-studies for three landfalling cyclones and one recurving cyclone demonstrate the capacities of TEDAPS in predicting some challenging TCs, as well as in capturing the forecast uncertainty and the potential threat from TC-associated hazards.
Reclamation projects are the main method of coastal exploitation, and the hydrodynamic environmental effect, together with the sediment transport response of the reclamation project, is important to the project’s site selection and environmental protection. Herein, a 3D numerical model based on the finite volume community ocean model (FVCOM) is applied to simulate the changes in the Meizhou Bay’s hydrodynamic environment and sediment transport after a reclamation project. The reclamation project greatly alters the shape of the shoreline and narrows the bay, leading to a significant change in its hydrodynamic environment and sediment transport. After the project, the clockwise coastal residual current in the corner above the Meizhou Island gradually disappears. An obvious counter-clockwise coastal residual current emerges around the rectangular corner. The tidal prism decreases by 0.65 × 109 and 0.44 × 109 m3 in the spring and neap tides, respectively. The residence time presents a major increase. These changes lead to the weakening of the water exchange capacity and the reduction of the self-purification capacity of the bay. Currents in the tidal channel weaken, whilst currents in the horizontal channel strengthen. The strength and scope of particle trajectories around the horizontal channel and the Meizhou Island enhance. The suspended sediment concentration (SSC) increases in the majority of the Meizhou Bay but decreases in the lateral bay. The eastern corner of Z2 shows a tendency to erode. The western region of the Meizhou Island, the upper portion of the rectangular corner and the western corner of Z4 show a tendency to deposit. The reclamation project increases the maximum storm surges by 0.06 m and decreases the maximum significant wave heights by 0.09 m.