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China’s complex terrain and diverse rainfall patterns contribute to uneven and distinctiverainfall distributions. Over 70% of disasters and accidents on conventional non-highspeed railways in China are influenced by rainfall. This paper analyses the spatiotemporal variations in rainfall warning risk on conventional non-highspeed railways in China. The study reveals the following findings: (1) Both the annual total rainfall and the risk hours of railway rainfall warnings exhibit east–west and south–north spatial distributions, with more rainfall in East China and South China and less rainfall in West China and North China. Southern China experiences the highest rainfall levels and the most intense rainfall, whereas the northern and northeastern regions have the highest risk hours for railway inspections. Sichuan and Yunnan have the highest occurrence rates of railway speed restrictions and closures. (2) The peak locations and periods of railway rainfall warning risk hours in various regions are closely related to the main monsoon rain belt and typhoon activities in China. Influenced by the East Asian summer monsoon, the Southeast Region experiences the earliest peak (June) in railway inspection and speed restriction rainfall warning risk hours. As the main rain belt of the monsoon moves northwards and the subtropical high extends westwards, July becomes the peak month for warning of railway rainfall risk hours in the Northeast, Central North, and Southwest Regions. In August, the impact of typhoon-induced heavy rainfall leads to a peak in railway closure rainfall warning risk hours in Southeast China. (3) In comparison, the complex terrain of the Southwest region results in a significantly higher comprehensive risk index for railway rainfall than other regions do, making it the area with the greatest railway rainfall warning pressure. The Northeast Region, with lower annual total rainfall, presents the highest frequency index for railway rainfall warning risk, implying a relatively strict preventive approach. The Southeast Region, with the highest annual total rainfall, has a comprehensive risk index second only to the Southwest Region, indicating slightly lower prevention pressure. (4) Over the past decade, the Southwest region experiences a significant increase in conventional non-highspeed railway rainfall volume, frequency, and comprehensive risk index during the main rainy season. Considering the complex terrain and frequent seismic activity in this region, it is likely to be a critical focus for future railway rainfall warning efforts.

Weather is the atmospheric condition in a certain region with limited geographical coverage and occurs within a relatively short period of time, related to the state of the air on Earth. In certain conditions, weather forecasts may be inaccurate or inconsistent with actual conditions. This is because rainfall does not always follow a consistent pattern. To address this issue, this study aims to apply fuzzy inference system (FIS) models of the Mamdani and Takagi-Sugeno-Kang (TSK) types to predict rainfall levels in Mataram City and compare their accuracy. The data used are daily weather records from the Meteorology, Climatology, and Geophysics Agency (BMKG) for the period of January to March 2025, including temperature, humidity, sunlight duration, wind speed, and rainfall on the previous day. The results show that the TSK FIS model outperforms the Mamdani FIS model in both modeling and testing datasets. In the modeling data, the RMSE values for the TSK and Mamdani FIS models are 16.61 and 18.40, respectively, while in the testing data, the RMSE values are 20.28 and 22.60, respectively. Moreover, the linguistic accuracy of the TSK FIS model is also superior to that of the Mamdani FIS model. Therefore, the TSK FIS model is considered more accurate in modeling rainfall prediction in Mataram City.

Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

Basin-scale runoff forecasting requires controlling absolute relative errors within 0.2 for accurate predictions. This study examines the sensitivity of simulated flood deviations in frequently flooded humid regions to discrepancies in precipitation driving the Weather Research and Forecasting (WRF)-Hydro model. Key parameters of various WRF-Hydro modules are calibrated from 23 flood events between 2003 and 2017. Two experiments were designed with minimized uncertainty in both model and parameterization to explore the sensitivity of streamflow changes to precipitation misestimations, both in total and graded precipitation. The sensitivity of runoff relative errors is more pronounced than that of precipitation relative errors, influenced by the magnitude, variability, and duration of rainfall. During processes involving heavy rainstorm, precipitation absolute relative errors increased by 50%, while runoff absolute relative errors increased by more than 60%. This sensitivity trend is linked to variations in components generated by misestimated precipitation. Runoff relative errors are higher when precipitation is misestimated at high rainfall levels compared to the same misestimation at low rainfall levels. For effective flood simulation and prediction, it is crucial to emphasize the accuracy of precipitation, especially during high rainfall events. Future research will incorporate more realistic precipitation forcing mechanisms and additional metrics for evaluating precipitation forecast.

Climate change, driven by human and natural processes, has increased flood frequency, impacting infrastructure, and resources. This study explores the relationship between land use/land cover (LULC) changes, rainfall patterns, and floods in Borno Metropolis, Nigeria, during the 2024 floods. Using Google Earth Engine (GEE), Landsat images from 1987 to 1990, 2013 to 2014, and 2024 were analyzed to calculate environmental indices, including the soil adjusted vegetation index (SAVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI). Sentinel-1 Synthetic Aperture Radar (SAR) images identified flood-affected areas in 2024. Rainfall data from CHIRPS (1987–2024) were analyzed using Mann–Kendall and Sen’s slope tests. Rule-based classification identified environmental changes, and statistical tests such as Pearson, Spearman, Kendall, and point–biserial were applied to assess relationships between climatic and environmental factors and floods. Python was used for all analyses. The findings revealed that 330 km 2 (12.6%) of the total area experienced flooding in 2024. Vegetation cover decreased by 16.1 km 2 (0.61%) in 2024 compared to 1987–1990, and non-vegetated areas increased significantly, reaching 19.5 km 2 in 2024. Built-up/bareland areas expanded by 59.4 km 2 (2.39%) from 2013–2014 to 2024. Spearman analysis effectively highlighted non-linear relationships between indices and floods. Point–biserial tests confirmed correlations between rainfall and flooding ( r pb = 0.15 , p = < 0.001 ) , indicating that higher rainfall levels increase flood likelihood. The heavy rainfall of 863 mm in 2024 was a key factor in increasing runoff and intensifying floods. This study highlights critical flood-affected areas, providing valuable insights for flood management planning to help governments and local communities reduce risks.

Streamflow recession, shaped by hydrological processes, runoff dynamics, and catchment storage, is heavily influenced by landscape structure and rainstorm characteristics. However, our understanding of how recession relates to landscape structure and rainstorm characteristics remains inconsistent, with limited research examining their combined impact. This study examines this interplay in shaping recession responses upon 291 sets of recession parameters obtained through the decorrelation process. The data originate from 19 subtropical mountainous rivers and cover events with a wide spectrum of rainfall amounts. Key findings indicate that the recession coefficient (a) increases while the exponent (b) decreases with the L/G ratio (the median of ratios between flow-path length and gradient), suggesting that longer and gentler hillslopes facilitate flow accumulation and aquifer connectivity, ultimately reducing nonlinearity. Additionally, in large catchments, the exponent (b) increases with increasing rainfall due to greater landscape heterogeneity. Conversely, in small catchments, it declines with rainfall, indicating that these catchments have less landscape heterogeneity and thus reduced runoff heterogeneity. Our findings underscore the necessity for further validation of how L/G and drainage area regulate recession responses to varying rainfall levels across diverse regions.

Green roofs are becoming a popular ecological alternative in urban areas worldwide. In this study, we constructed two modular green roofs (commercial substrate green roof and biochar substrate green roof) and analyzed the effects that the green roof substrate amended with biochar on the runoff retention capacity, water quality, pollutants releasing characteristic, and pollution load by simulating rainfall experiment (rainfall levels 10~80 mm). Results showed that the mean retention ratio was no significant differences between the commercial substrate (72.54%) and the biochar substrate (72.08%). Both the two kinds of substrates showed that the concentrations of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and iron (Fe) decreased gradually with the extension of rainfall time. Electrical conductivity (EC) and pH, as well as mean concentrations of TN, COD, TP, total suspended solids (TSS), and Fe, showed no differences between the green roof runoff of two kind of substrate. However, the neutralizing capacity of biochar substrate for the pH of green roof runoff was stronger than the commercial substrate, and the mean concentration of the TN and COD in the commercial substrate (16.14 mg/L and 171.79 mg/L, respectively) was about two times higher than the biochar substrate (9.85 mg/L and 97.31 mg/L, respectively). Similarly, the pollution load of TN and COD in the commercial substrate was significantly higher than that in the biochar substrate. Therefore, the biochar substrate could effectively reduce the pollution load of TN and COD in the runoff of green roof. Consequently, we suggest that the biochar could be applied to green roof substrates in order to reduce the impact of city non-point pollution on receiving water bodies.

Tunnel invert damage will lead to track misalignment and consequently seriously affect the operation safety of the railway line. In this paper, the rainfall intensity, geological conditions, and damage manifestations were studied by reviewing cases with tunnel invert damage induced by heavy rainfall (HR-TID) in China over the last two decades. Based on the Malazhai Tunnel of the Shanghai-Kunming Railway, the effects of rainfall levels and drainage system blockage ratio on the water pressure outside the tunnel and its structure deformation were investigated through scaled-down modeling tests. The evolution mechanism of HR-TID was demonstrated based on the results of field studies and model tests, which in turn bring out sound proposals of treatment measures for HR-TID. It is manifest that heavy rainfall events, catchment topography, highly permeable ground, and the development of karst fissures are key factors leading to tunnel invert damage. With the increase of rainfall and blockage ratio of the drainage system, the water pressure in the proximity of the tunnel bottom increases rapidly, leading to the tunnel invert uplift and making a worse working condition at the tunnel invert. To ensure operation safety, emergency treatment should be put into effect as soon as possible to resume tunnel drainage operation, following the long-term treatment of blocking the infiltration channels and paths. The effectiveness of treatment for HR-TID presented in the case study can provide as a promising reference for similar tunnel projects.

Accurate water vapor information is crucial for improving the quality of numerical weather forecasting. Previous studies have incorporated tropospheric water vapor data obtained from a global navigation satellite system (GNSS) into numerical weather models to enhance the accuracy and reliability of rainfall forecasts. However, research on evaluating forecast accuracy for different rainfall levels and the development of corresponding forecasting platforms is lacking. This study develops and establishes a rainfall forecasting platform supported by the GNSS-assisted weather research and forecasting (WRF) model, quantitatively assessing the effect of GNSS precipitable water vapor (PWV) on the accuracy of WRF model forecasts for light rain (LR), moderate rain (MR), heavy rain (HR), and torrential rain (TR). Three schemes are designed and tested using data from seven ground meteorological stations in Xi’an City, China, in 2021. The results show that assimilating GNSS PWV significantly improves the forecast accuracy of the WRF model for different rainfall levels, with the root mean square error (RMSE) improvement rates of 8%, 15%, 19%, and 25% for LR, MR, HR, and TR, respectively. Additionally, the RMSE of rainfall forecasts demonstrates a decreasing trend with increasing magnitudes of assimilated PWV, particularly effective in the range of [50, 55) mm where the lowest RMSE is 3.58 mm. Moreover, GNSS-assisted numerical weather model shows improvements in statistical forecasting indexes such as probability of detection (POD), false alarm rate (FAR), threat score (TS), and equitable threat score (ETS) across all rainfall intensities, with notable improvements in the forecasts of HR and TR. These results confirm the high precision, visualization capabilities, and robustness of the developed rainfall forecasting platform.

Prediction of response values is a primary goal in many applications. The standard approach to this problem is kriging which is essentially a linear prediction using optimal least squares interpolation of the random field. However, the optimal predictor is not necessarily a linear one unless the geostatistical data support the Gaussian model. As data often exhibit non-normality, some of the most effective spatial processes are reviewed in the current study. The usefulness of the presented models is demonstrated based on the prediction of rainfall levels in Fars Province, Iran. The measurements were taken from 100 stations. To assess the predictive performance of the evaluated models, 15 stations were randomly withheld. Subsequently, the predicted values at these locations were evaluated against the measured ones. The results of the study indicated that, comparing to some well-known models, the skew Gaussian model introduced in this article demonstrated a better performance in the prediction.