Typhoon Rainfall Research Articles

Tropical cyclone-induced rainfall variability and its implications for drought in Taiwan: Insights from 1981 to 2022

This study analyzes the interplay between tropical cyclones (TCs) and drought in Taiwan over the period from 1981 to 2022, leveraging data from CHIRPS, FLDAS, IBTrACS, and Sentinel and Landsat satellite imagery. Our findings reveal a significant decline in TC-induced rainfall over the last decade, with a turning point in 2017. We establish a low to moderate positive correlation between TC-induced rainfall and subsequent spring soil moisture levels, with April exhibiting the strongest connection. This relationship strengthens in transitions from agricultural to forested areas. Additionally, our research identifies a robust correlation (r = 0.77) between TC-induced rainfall and reservoir water levels, underscoring the consistent link between TCs and hydrological drought. However, intriguing disparities emerge in water availability patterns. In 2007 and 2020, reservoirs maintained normal levels despite reduced typhoon rainfall in prior years. Conversely, in 2019, water shortages occurred despite typical TC-induced rainfall. These cases suggest that water availability is influenced by factors beyond TCs, including water demand, infrastructure, monsoon and spring rainfall, air temperature, and water management strategies. Furthermore, we uncover a symmetrical relationship between TCs and monsoon-induced rainfall, indicating their potential to balance reservoir water supply. Nevertheless, both TCs and the monsoon exhibit declining trends in the past decade, heightening the risk of spring season water scarcity in Taiwan. Effective water resource management demands consideration of natural and anthropogenic factors, encompassing land use changes, deforestation, production activities, water pollution, and policy strategies. These measures are essential to mitigate water scarcity and promote sustainability in Taiwan.

Separation and spatial variations of typhoon and non‐typhoon rainfall at different timescales in typical region of southeast China

AbstractRainfall in East Asia is affected by two rain‐bearing systems: tropical cyclones and monsoon‐related frontal systems. Distinguishing typhoon rainfall (TR) and non‐typhoon rainfall (non‐TR) helps to understand the evolution process of regional rainfall at different timescales. Taking Fujian Province in the southeast coast of China as an example, based on the fixed box approach of separating TR, the method of determining the size of fixed box is explored. TR and non‐TR are separated in Fujian Province, and the spatial variations of TR and non‐TR at different timescales (annual, monthly, day of annual‐maximum‐rain) are analysed. The results showed that (1) according to the relationship between the sizes of fixed box and the rate of change of TR, the size of fixed box could be reasonably determined; thus, the expended size of fixed box suitable for separating TR in Fujian was 3.5°, namely the range of 20°–31.8°N and 112.3°–124.2°E. (2) The spatial variations of TR at different timescales in Fujian were similar: TR decreased from the coast to the inland, and the northeast coast of Fujian was the high‐value region. Due to the difference of water vapour sources, non‐TR in March–June increased from the coast to the inland, but the high value of non‐TR in July–September was distributed in the eastern coast. (3) The contribution rates of average TR to the total rainfall of the year, July–September and 1 day were 12.8%, 34.6% and 35.7%, respectively. In eastern coast, TR in July–September accounted for 1/3–1/2 of total rainfall, and the rainfall in a day was mainly affected by TR; while in western inland, rainfall was mainly non‐TR and the influence of TR was less than 1/4.

Correction to: Effects of Long-term Climate Change on Typhoon Rainfall Associated with Southwesterly Monsoon Flow near Taiwan: Mindulle (2004) and Morakot (2009)

Correction to: Effects of Long-term Climate Change on Typhoon Rainfall Associated with Southwesterly Monsoon Flow near Taiwan: Mindulle (2004) and Morakot (2009)

Climatic and Environmental Impacts on the Sedimentation of the SW Taiwan Margin Since the Last Deglaciation: Geochemical and Mineralogical Investigations

AbstractMany scientific studies have been conducted to constrain present and past source‐to‐sink processes and their controlling factors. The role of typhoon and monsoon rainfall on chemical weathering and soils erosion in east Asia is still not well established. Clay minerals and major elements, combined with Nd and Sr isotopic compositions were analyzed on sediments from Core MD18‐3569 located on the Taiwan margin in the northeastern South China Sea. The aim was to reconstruct the weathering history of small river basins of southwest Taiwan and to establish the impact of East Asian summer monsoon and typhoon rainfall on source to sink processes since the last glacial period. 87Sr/86Sr and ɛNd values and predominant illite‐chlorite indicate that the rivers of southwest Taiwan is the sole source of sediments to Core MD18‐3569 since last deglaciation. Variations in clay mineral assemblages and major elements allowed us to evaluate the intensity of past weathering in the rivers of southwest Taiwan. We demonstrated that long‐term changes of chemical weathering intensity record from Taiwan are driven by changes in the summer monsoon rainfall. The deglaciation is characterized by a progressive increase in the intensity of chemical weathering, which peaked at the beginning of the Holocene. The chemical weathering degree of sediments from Taiwan decreases during the Holocene concurrently with a weakening of the summer monsoon, increase in typhoon activity, and changes in the vegetation cover. All these processes induce soil erosion, regressive pedogenesis, due to shorter residence time of minerals in the soils of southwest Taiwan.

A New Post-Processing Method for Improving Track and Rainfall Ensemble Forecasts for Typhoons over Eastern China

This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated.

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960–2018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model’s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0–200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.

Effects of rainfall pattern classification methods on the probability estimation of typhoon-induced debris-flow occurrence

ABSTRACT The frequent occurrence of typhoons causes geological disasters, such as debris flow and landslide, by bringing extreme rainfall events. Due to the lack of data collection on extreme rainfall events caused by typhoons, the relationship between rainfall patterns and debris flow has not been deeply studied. Therefore, based on hourly rainfall data during typhoons in Wenzhou from 1980 to 2017, this study used a variety of methods to classify the rainfall events and analyze the characteristics of typhoon-induced rainfall events and their impacts on the probability of debris-flow occurrence. Three classification techniques, including dynamic time warping, K-Means cluster, and self-organizing maps, are applied with two ways to normalize rainfall records, including dimensionless rainfall density curves and dimensionless rainfall cumulation curves, for extracting rainfall patterns from recorded 1 h rainfall data. The rainfall patterns are then used for the estimation of typhoon-induced debris-flow occurrence probability. Results show that different methods present different rainfall patterns. The probability of debris flows varies with different patterns of rainfall events. The research results help deepen the understanding of typhoon rainfall events and debris-flow disaster prevention in the region and contribute to regional flood control and disaster reduction.

Scenario mapping for critical infrastructure failure under typhoon rainfall: A dependency and causality approach

The critical infrastructure system exhibits a high degree of instability because of the frequent occurrence of extreme natural disasters. This study proposes a method for exploring the cascading failures of critical infrastructure systems under such impacts, based on dependency relationships for importance analysis and probability assessment. With this method, a cascading failure scenario graph of critical infrastructure is established and empirical research is conducted using typhoon rainfall events as a case study. The main contributions of this study are summarized as follows: it proposes a method for the causal identification of the interrelationships among critical infrastructures; it sorts and evaluates the importance and cascading failure probability of critical infrastructure by using the Interpretative Structural Modeling (ISM) and Rank-order Centroid method (ROC); and it generates scenario graphs of critical infrastructure cascading failures to visualize event states, environmental characteristics, and evolutionary paths. This study provides a new method and tool for disaster risk management of complex systems involved in critical infrastructure.

Effects of Long-term Climate Change on Typhoon Rainfall Associated with Southwesterly Monsoon Flow near Taiwan: Mindulle (2004) and Morakot (2009)

Effects of Long-term Climate Change on Typhoon Rainfall Associated with Southwesterly Monsoon Flow near Taiwan: Mindulle (2004) and Morakot (2009)

Effect of land cover pattern on rainfall during a landfalling typhoon: A simulation of Typhoon Hato

The impact of urbanization on weather events has received increasing attention. Taking Typhoon Hato as an example, the impact of anthropogenic land cover change on typhoon landfall and sequential rainfall is quantified in this study. A numerical model is first built based on the Weather Research and Forecast model. Then, the model is validated, the performance of parameters for the different meteorological and microphysical processes is assessed and a set of optimal parameters are selected. After that, the responses of typhoon and the corresponding rainfall to land cover patterns under three conditions of no urbanization, current urbanization and future urbanization in Pearl River Delta are simulated and compared. Verification experiments show that it is necessary to consider the microphysical process to enhance the accuracy of typhoon simulation. The dramatic change of land cover has limited influence on typhoon tracks. However, the urbanized land surface promotes typhoon rainfall significantly. The hourly rainfall rate under the urbanization condition is about double that under the no urbanization condition. With the intensifying urbanization in the future, the maximum hourly rainfall rate will increase by 21%. The urban heat island effect almost disappears as Typhoon Hato approaches the inland region, and the urban dynamic effect plays an important role in typhoon rainfall. The intensified rainfall is related to the retention of typhoon caused by rough surface of the city. The wind speed under the condition of no urbanization is in general 2 m/s greater than that under the urbanization condition. The increase in surface roughness reduces the typhoon movement speed and more rainfall is generated. With the development of urbanization, intensified rainfall at typhoon landfall needs to be paid more attention to.

The Influence of Typhoon Events on the Design Storm for the Shanghai Metropolitan Area in the Yangtze River Delta, China

Given the fact that the high frequency of extreme weather events globally, in particular typhoons, has more of an influence on flood forecasting, there is a great need to further understand the impact of typhoon events on design storms. The main objectives of this paper are to examine the magnitude, occurrence, and mechanism of typhoon events in southeast coastal China and their contribution to the design storm study. We take Shanghai, which is a typical metropolitan region in the Yangtze River Delta, China, as an example. The impact of typhoons on the rainfall frequency analysis is quantitatively evaluated using stochastic storm transposition (SST)-based intensity–duration–frequency (IDF) estimates with various temporal and spatial structures under different return periods. The results show that there is significant variability in the storm magnitude within the transposition domain across different durations, highlighting the spatiotemporal heterogeneity over the coastal area. Moreover, the probability of random storm transposition exhibits an uneven distribution. The frequency of typhoon rainfall events within the transposition domain is notably high, and there is considerable variability in the structure of rainfall. Typhoon rainfall amplifies the intensity of design storms, and its contribution increases with return periods. The variability in design storms increases accordingly. Based on the advantages of SST, which retains the spatiotemporal structure of the rainfall in the generated scenarios, the overall framework provides an effective way to examine the impact of diverse characteristics of typhoon rainfall on frequency analysis and facilitate a deeper exploration of the direct impact of various types of extreme storms on the intensity, spatial, and temporal distributions of design storms amidst evolving environmental conditions over this metropolitan region.

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from −0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

Flood disasters caused by typhoon rainfall seriously threaten regional social and economic development. Accurately assessing the risk of typhoons and their secondary disasters is a great challenge in disaster prevention and reduction. To address this, the city of Fuzhou, Fujian Province, which was severely affected by Typhoon “Lupit” (2109), was selected as a case study. A typhoon rainfall flood disaster system including four components (the disaster-causing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention and reduction capacity) was constructed. A typhoon-rainfall process comprehensive intensity index (TPCI) based on different time scales within the typhoon process was developed to accurately evaluate the flood risk. The TPCI represented the disaster-causing factors of rainfall intensity, duration, and concentration features. Geographical similarity and random forest (RF) were applied to screen 23 typical indices for an urban flood disaster risk assessment model. The results indicated that the TPCI based on a 6 h precipitation simulation at a 24 h time scale was highly effective in highlighting the role of short-term precipitation in the typhoon process. A total of 66.5% of the floodplain area had a medium-grade or higher TPCI value, while 32.5% of the area had a low-grade TPCI. Only 1% of the flooded areas were not identified, which indicated that the TPCI could accurately capture the risk of typhoon rainfall. The urban flood disaster risk assessment model comprehensively considered socioeconomic and natural environment conditions. High-risk areas were identified as regions with extreme precipitation and dense populations. The dynamic evaluation results accurately described the spatiotemporal differences in the flood disaster risk. A period of extreme precipitation lagged the landfall time of Typhoon “Lupit”, causing the proportion of areas above the medium–high-risk threshold of flood disasters to rapidly increase from 8.29% before the landfall of the typhoon to 23.57% before its demise. The high-risk areas of flood disasters were mainly distributed in the towns of Shangjie, Nanyu, and Gaishan, which was consistent with the observed disasters. These study findings could contribute to the development of effective measures for disaster prevention and reduction, and improve the resilience of urban areas to typhoon disasters.

Variations of stable isotope in Typhoon Lupit (2021) rainfall and its response to upstream convective processes

AbstractTyphoons produce a large amount of rainfall with a highly depleted isotopic composition. However, the mechanisms of isotope fractionation during typhoons remain poorly understood. Here, we present the hourly resolution isotope composition of rainfall during Typhoon Lupit (2021) in Fuzhou, on the southeast coast of China, to better understand these processes. Our results showed that the rainfall of Typhoon Lupit had significant fluctuations in δ18O values, ranging from −20.1‰ to −6.3‰, and exhibited a three‐stage variation with distinctly negative δ18O values in the second stage. In the first stage of Lupit, water vapour originated primarily from the nearby sea area, which did not undergo sustained convective activity, resulting in relatively enriched δ18O values. In the second stage, the δ18O values were extremely negative mainly because of the accumulated convective activity and continuous precipitation as they travelled towards the centre of the typhoon. In the third stage, the mixing of more localized and nearby sea water vapour caused an increase in the δ18O values. In summary, the three‐stage characteristics of Typhoon Lupit were largely caused by the integration of accumulated convective activity and moisture sources. Our findings reveal how upstream rainout affects the isotopic behaviour of rainfall and may have implications for understanding the dynamics of typhoon rainfall isotopes.

Impact assessment of climate change on compound flooding in a coastal city

Flood disasters in coastal cities are generally caused by the combined effect of typhoon rainfall, storm surge and other factors. Coastal cities will face more severe compound flooding under climate change. Although some studies have analyzed compound flooding under climate change, there is a lack of research considering the combined effect of sea level rise (SLR) and future rainfall changes, especially lack of the jacking effect of SLR on the volume and duration of compound flooding. This paper investigates the impact of future rainfall changes and SLR on compound flood risk, via a case study of a coastal city, Haikou, China. Future rainfall in Haikou is obtained by bias correction of rainfall from five global climate models (GCMs) in CMIP5. The SLR in 2050 and 2090 is predicted according to IPCC. Then, a 1D/2D hydrological-hydrodynamic model for dynamic simulation of surface runoff is applied to simulate compound flooding. Results show that both future rainfall changes and SLR will lead to the increase of flood extent, flood depth and flood duration. But future rainfall changes will have a greater impact on flood, and the combined effect of future rainfall changes and SLR on flood is greater than the sum of their individual effects. The individual effects of SLR and future rainfall changes would increase flood depth in 10.6 % and 32.1 % of the study area, respectively, while their combined effect would increase this proportion to 34.9 %. Also, the jacking effect of SLR on compound flooding will lead to an increase in flood volume, but it presents a nonlinear relationship that gradually slows down with the increase of SLR. A SLR of 0.3 m and 0.63 m will lead to a 2.34 % and 3.22 % increase in flood volume, respectively. And the jacking effect of SLR is more obvious when future rainfall changes are considered, which will lead to a sharp increase in the duration of compound flooding. Moreover, water storage facilities and tidal gates are effective ways to reduce the impact of climate change on compound flooding.

Short-term forecasting of typhoon rainfall with a deep-learning-based disaster monitoring model

Short-term forecasting of typhoon rainfall with a deep-learning-based disaster monitoring model

TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data

Currently, most deep learning (DL)-based models for precipitation forecasting face two conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect of forecasting precipitation intensity. Therefore, this study proposes “time series residual convolution (TSRC)”, a DL-based convolutional neural network for precipitation nowcasting over China with a lead time of 3 h. The core idea of TSRC is it compensates the current local cues with previous local cues during convolution processes, so more contextual information and less uncertain features would remain in deep networks. We use four years’ radar echo reflectivity data from 2017 to 2020 for model training and one year’s data from 2021 for model testing and compare it with two commonly used nowcasting models: optical flow model (OF) and UNet. Results show that TSRC obtains better forecasting performances than OF and UNet with a relatively high probability of detection (POD), low false alarm rate (FAR), small mean absolute error (MAE) and high structural similarity index (SSIM), especially at longer lead times. Meanwhile, the results of two case studies suggest that TSRC still introduces smoothing effects and slightly outperforms UNet at longer lead times. The most considerable result is that our model can forecast high-intensity radar echoes even for typhoon rainfall systems, suggesting that the degenerate effect of forecasting precipitation intensity can be improved by our model. Future works will focus on the combination of multi-source data and the design of the model’s architecture to gain further improvements in precipitation short-term forecasting.

An urban DEM reconstruction method based on multisource data fusion for urban pluvial flooding simulation

A digital elevation model (DEM) is crucial for hydraulic/hydrodynamic simulation of urban pluvial flooding. To reduce the simulation error caused by the low resolution and poor vertical accuracy of DEM data, a novel urban terrain reconstruction method is proposed based on multisource data fusion (MDF). A 1D-2D coupled pluvial flood model under the typhoon rainfall scenario is built to verify the superiority of the proposed method. The results show that the systematic elevation error of terrain is reduced with MDF-kriging and MDF-IDW, and the performances of simulations are improved by 5% and 23% in terms of peak water-level error compared to using conventional kriging and IDW methods, and by 0.13m and 0.11m in terms of manhole water level accuracy compared to using raw DEM. Moreover, the temporospatial patterns of the flooding simulation results are more rational with the scattered flooding eliminated, and the proportion of flood area with ground elevation lower than 8 m increased from 37.9% to 92.34% and 89.33%, respectively. This method is proven to be a notably effective and low-cost DEM reconstruction method for improving the accuracy of urban pluvial flooding simulation.

Developmental characteristics of rainfall-induced landslides from 1999 to 2016 in Wenzhou City of China

Many landslides are triggered by excess precipitation. In the eastern part of China, landslides caused by extreme rainfall from typhoons in the monsoon season are the main geomorphological process with catastrophic impacts on society and the environment. In this study, Wenzhou City in eastern China was taken as the study area, and we compiled a detailed inventory of rainfall-triggered landslides between 1999 and 2016. The developmental characteristics of these landslides were determined with an emphasis on temporo-spatial distribution. The results showed that most of the landslides were located in the mountainous area of the western part of Wenzhou City. Landslides triggered by typhoon rainfall were commonly concentrated in a short period from July to September, when more than 70% of the landslides occurred. The landslides in this region were mainly of the debris-flow type, most of which were on a small scale, but had severe effects because of large elevation differences and long runout distances. Because the precipitation in typhoon events was commonly extreme, the initiation area of most landslides coincided with the region of highest hourly precipitation. Our results can provide reference data and guidelines for developing an early warning system for landslides and risk reduction in the study area.

Spatial patterns of typhoon rainfall and associated flood characteristics over a mountainous watershed of a tropical island

The spatial distribution of rainfall in a watershed has an important impact on flood warning and forecasting. In this study, we used the K-means++ clustering method to classify the spatial characteristics of typhoon rainfall events in the Songtao reservoir watershed in the central region of Hainan Island, based on 63 typhoon rainfall event data from 1990 to 2020. Then, the relationship between the synoptic fields, spatial patterns of rainfall, and flood characteristics was explained based on tropical cyclone best-track datasets, atmospheric reanalysis data, and inflow flood series. The results showed that there are three types of cumulative rainfall patterns in the Songtao Reservoir watershed. The heavy rainfall region of pattern 1 is concentrated east of the watershed, whereas the heavy rainfall regions of pattern 2 are mainly concentrated southwest and east of the watersheds of many mountains. The heavy rainfall region of pattern 3, with a southwest-northeast orientation, is located on the west side of the watershed. The spatial coefficient of variation and index of rainfall location clearly distinguished among these three patterns. The composite analysis of typhoon tracks, wind fields, and moisture flux fields can be used to interpret the formation mechanism of these spatial patterns of rainfall events in the watershed. The spatial patterns of cumulative rainfall for each type were dominated by one or two types of hourly spatial rainfall patterns. Flood events resulting from cumulative rainfall patterns 1 and 2 have similar flood behavior metrics. Large floods in the watershed show an obvious seasonal variation, and the corresponding rainfall events mostly show the spatial characteristics of the cumulative rainfall pattern 3. These results can enhance the understanding of the linkage between typhoons, mountainous watershed rainfall, and flooding processes.