Moderate Rain Research Articles

Dynamical and thermodynamical aspects of precipitation events over India

AbstractThis study examines different precipitation events (PEs) for 1951–2015 using 0.25°×0.25° India Meteorological Department data and diagnoses their associated dynamical and thermodynamical processes during the summer monsoon season over India using ERA5 reanalysis. Our results reveal a significant decreasing trend of mean precipitation, rain event days, light rain events and moderate rain events (MREs) for the overall period. However, the frequency of heavy rain events (HREs) and very heavy rain events (VhREs) has increased. Pettit's test for all PEs and mean precipitation indicates the changes nearly in the 1970s. In spite of this, the rate of change of LREs, MREs and HREs was increased by 17, 91 and 114% after the observed change point with respect to before change point, while for VhREs, this was reduced by 50%. These decreasing (increasing) trends of LREs (VhREs) are attributed to both thermodynamical and dynamical conditions. LREs are driven mainly by convective precipitation having a contribution of ~79%, whereas large‐scale precipitation contributes ~56% to the VhREs. It is observed that the dynamical (thermodynamical) effect largely drives VhREs (LREs) PEs. During VhREs, stronger moisture‐laden low‐level jet (LLJ) from the surrounding ocean converges over the Indian land mass, facilitating favourable conditions for enhanced precipitation. Simultaneously, weaker convective available potential energy (CAPE) during the VhREs underlines inconstancy in thermodynamics. Stronger LLJ and weaker CAPE during the extremes and vice versa for LREs highlight the dominance of the dynamical (thermodynamical) effect during the VhREs (LREs).

A didactic approach to the machine learning application to weather forecast

We propose a didactic approach to use the Machine Learning protocol in order to perform weather forecast. This study is motivated by the possibility to apply this method to predict weather conditions in proximity of the Etna and Stromboli volcanic areas, located in Sicily (south Italy). Here the complex orography may significantly influence the weather conditions due to Stau and Foehn effects, with possible impact on the air traffic of the nearby Catania and Reggio Calabria airports. We first introduce a simple thermodynamic approach, suited to provide information on temperature and pressure when the Stau and Foehn effects take place. Then, in order to gain information also on the rainfall accumulation, the Machine Learning approach is presented: according to this protocol, the model is able to “learn” from a set of input data constituted by the meteorological conditions (in our case dry, light rain, moderate rain and heavy rain) associated to the rainfall, measured in mm. We observe that, since in the input dataset provided by the Salina weather station the dry condition was the most common, the algorithm is very accurate in predicting it. Further improvements can be obtained by increasing the number of considered weather stations and the time interval.

Relationship between Drought and Precipitation Heterogeneity: An Analysis across Rain-Fed Agricultural Regions in Eastern Gansu, China

Based on daily meteorological data from 55 meteorological stations in eastern Gansu from 1960 to 2017, the characteristics of the drought process and precipitation heterogeneity were analyzed, and the relationship between drought and precipitation heterogeneity was evaluated. Results showed that there were 1–3 drought processes in the study area every year. Drought processes in the eastern and north-central regions were more frequent than those in other regions. Droughts were mainly manifested as intra-seasonal droughts, especially across the spring and summer. PCD (Precipitation Concentration Degree, the concentration degree of the precipitation at a certain time) ranged from 0.2 to 0.7 in the area. PCD increased in spring and autumn but decreased in summer and winter for most regions from 1960 to 2017. PCP (Precipitation Concentration Period, the shortest time which the precipitation was concentrated in) was from late April to early May in spring, mid-to-late July in summer, mid-September in autumn, and late January in winter. In the last 58 years, PCP has remained consistent in most regions, varying by approximately 10 days. In addition to insignificant changes in winter, the days with light and moderate rain presented a declining trend, especially in summer and autumn. The larger the PCD, the fewer the days with light and moderate rain, and the stronger the drought intensity. However, in the east-central region, the larger the PCD in autumn, the weaker was the drought intensity. This difference is related to the PCP and the evapotranspiration. Additionally, the later the PCP, the stronger was the drought intensity, particularly in summer and autumn. When PCD was ≥0.5 in spring and ≥0.4 in summer, the PCP was after May and August in spring and summer, respectively. Droughts appeared in 28–56% of periods when seasonal precipitation was above normal. When PCD was ≥0.5 in autumn and PCP was in early and middle September, droughts appeared in 7% of periods when precipitation was above normal. Our results show that although less precipitation is the leading influencing factor of drought in the dry rain-fed agricultural areas, the influence of precipitation heterogeneity should be also considered for the prediction and diagnosis of seasonal drought.

Performance analysis of free space optical system incorporating circular polarization shift keying and mode division multiplexing

Abstract In this work, a free-space optical (FSO) communication system with the integration of mode division multiplexing and circular polarization shift keying (CpolSK) is proposed at 2 × 40 Gbps using LG00 and LG01 modes. Effects of diverse weather conditions such as clear weather, light rain, moderate rain, heavy rain, thin fog, thick fog, and heavy fog are studied on system performance. Further, a detailed comparison of CpolSK and polarization shift keying (PolSK) is performed at different FSO lengths in terms of log bit error rate. For implementation, analysis, and comparison, Optiwave Optisystem software is used and results show that CpolSK covers 100 km link distance and PolSK limits to 90 km only. Also, LG00 mode performs better than LG01 mode under all weather instabilities in the proposed system.

Adaptive Algebraic Reconstruction Algorithms for GNSS Water Vapor Tomography

Objectives Global navigation satellite system(GNSS) tomography, characterized by reconstructing the three-dimensional distribution of the atmospheric water vapor, has proved its capacity for studying the extreme weather events. The ill-conditioned problem resulting from the GNSS acquisition geometry is the critical issue of the GNSS tomography system. Algebraic reconstruction techniques, with the advantages of simple iteration and fast convergence, has been widely applied to the tropospheric tomography. However, the error allocation principle based on the intersection length of GNSS rays with each ray-voxel affects the accuracy of tomographic results. An improved adaptive algebraic reconstruction techniques are proposed to address this problem. Methods In view of the unreasonable error allocation in the traditional algorithm, we suggest a new error allocation principle based on the variation of water vapor density(WVD) in voxels for the adaptive algorithms, in which the product of the intersection length and WVD is considered as a new principle to redistribute the difference between the GNSS slant water vapor(SWV)and the reconstructed SWV. Besides, the weight matrix model of elevation angles is introduced to the improved algorithm to optimize the tomographic results. Results The new algorithms are tested by measured data from Xuzhou continuously operating reference stations(CORS) network and radiosonde during July2016. Experimental results reveal that the WVD derived from the adaptive algorithms performs better than that of the general algorithms in root mean squared error(RMSE), standard deviation(STD) and mean absolute error(MAE), and the RMSE is decreased by 25.91%, 15.81%, and 24.64% for adaptive algebraic reconstruction technique(AART), adaptive multiplicative algebraic reconstruction technique(AMART)and adaptive simultaneous iterative reconstruction technique(ASIRT) respectively. Under the conditions of light rain, moderate rain and heavy rain, the adaptive algorithms retrieve better water vapor profile than the traditional ones. Conclusions Overall, the water vapor profile derived from the adaptive algorithms agrees with the radiosonde distribution better than the traditional algorithms, which reveals the advantage of the proposed method on optimizing the tomography solutions.

Estimating Rainfall with Multi-Resource Data over East Asia Based on Machine Learning

The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades’ estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories’ distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation.

Influence of precipitation on the daily beryllium-7 (7Be) activity concentration in the atmospheric surface layer

The paper presents the monitoring results of the temporal variability of daily 7Be activity concentration in the atmospheric surface layer at Sevastopol in 2011–2020. The 7Be activity concentration in individual samples of atmospheric aerosols varies from 0.1 to 13.3 mBq m−3 and averages 4.0 ± 2.0 Bq m−3. Higher 7Be activity concentrations have been observed during the period from May to August while lower concentrations have been measured from December to January. Quantitative estimates of the influence of precipitation (amount, duration, intensity) on the temporal variability of 7Be activity concentration have been obtained. It has been found that daily 7Be activity concentration decrease by 2–82% on the first day with precipitation. It has been shown that an increase in precipitation duration and a decrease in its intensity lead to a more significant decrease in the daily 7Be activity concentration. The estimates of the scavenging coefficient have been obtained; the average value is 0.6 ± 1.0 h−1. An increase in the precipitation intensity or amount is accompanied by a decrease in the 7Be scavenging coefficient. Mean 0.5-folding and residence times of 7Be activity concentration in the atmosphere during a moderate rain event are 2.9 ± 2.2 and 15.4 ± 13.6 h, respectively. The recovery of 7Be activity concentration in the atmosphere after precipitation has been investigated. It has been found that this process takes 1–2 days. The mean value of the reload coefficient is 0.94 ± 0.34 d−1. The relationship between the value of the reload coefficient and local meteorological parameters (air temperature, relative humidity, atmospheric pressure, wind speed, surface net solar radiation flux) has been investigated. No statistically significant correlations at a 95% confidence level between the reload coefficient and the considered meteorological parameters have been revealed. Parameterization describing a decrease in the daily 7Be activity concentration in the atmosphere due to precipitation and its recovery during the precipitation-free period has been introduced.

Multistage Impacts of the Heavy Rain Process on the Travel Speeds of Urban Roads

Heavy rain causes the highest drop in travel speeds compared with light and moderate rain because it can easily induce flooding on road surfaces, which can continue to hinder urban transportation even after the rainfall is over. However, very few studies have specialized in researching the multistage impacts of the heavy rain process on urban roads, and the cumulative effects of heavy rain in road networks are often overlooked. In this study, the heavy rain process is divided into three consecutive stages, i.e., prepeak, peak, and postpeak. The impact of heavy rain on a road is represented by a three-dimensional traffic speed change ratio vector. Then, the k-means clustering method is implemented to reveal the distinct patterns of speed change ratio vectors. Finally, the characteristics of the links in each cluster are analyzed. An empirical study of Shenzhen, China suggests that there are three major impact patterns in links. The differences among links associated with the three impact patterns are related to the road category, travel speeds in no rain days, and the number of transportation facilities. The findings in this research can contribute to a more in-depth understanding of the relationship between the heavy rain process and the travel speeds of urban roads and provide valuable information for traffic management and personal travel in heavy rain weather.

Improving numerical forecast of the rainstorms induced by Mongolia cold vortex in North China with the frequency matching method

Mongolia cold vortex is an important synoptic system causing summer convective precipitation in North China and the numerical model shows poor performances for forecasting the convective rainstorms. It is, therefore, urgent to improve the forecast skill of the rainstorms induced by Mongolia cold vortex. Taking the Haihe River Basin as an example and using the observational precipitation data monitored by automatic meteorological stations and the forecast precipitation data from European Centre for Medium-Range Weather Forecasts (ECMWF), this study is the first time to recombine the numerical forecast series that have the greatest similarity to the observed precipitation by using correlation analysis on a sliding time window. We then corrected the recombinant forecast series by using frequency matching method (FMM) and finally established a correction model for improving precipitation forecast accuracy. Results revealed that FMM could improve the precipitation forecast skill for 1-3 d lead-time. Compared with light, moderate, and heavy rain, the improvement effects are more obvious for rainstorm (50–99.9 mm) and heavy rainstorm (≥100 mm). Moreover, the calibrated forecasts have good performances to improve the accuracy for most magnitudes but not spatial distributions. These findings demonstrate that FMM has good performance for correcting precipitation forecasts induced by Mongolia Cold Vortex and thus can be used to improve the forecast skill of summer rainstorms, providing more accurate forcing inputs for hydrologic model to improve flooding forecast skill in North China.

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in the groundwater basin of the Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail. CM (CMORPH) has larger underestimation compared to other satellite-based rainfall assessments. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) has the lowest. CM has a good performance to detect no rain, while IMR (GPM IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM), especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall assessments have large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall than to merge both IMR and TR together.

Less rain and rainy days—lessons from 45 years of rainfall data (1971–2015) in the Kathmandu Valley, Nepal

Understanding spatiotemporal variability in rainfall patterns is crucial for evaluating water balances needed for water resources planning and management. This paper investigates spatiotemporal variability in rainfall and assesses the frequency of daily rainfall observations from seven stations in the Kathmandu Valley, Nepal, from 1971 to 2015. Daily rainfall totals were classified into five classes, namely, A (light rain, daily rainfall < 10 mm in a day), B (moderate rain, between 10 and 50 mm), C (heavy rain, between 50 and 100 mm), D (storm, between 100 and 150 mm), and E (large storm, > 150 mm). An ordinary kriging method was used for spatial interpolation using QGIS. We performed Mann–Kendall (MK) test in conjunction with Theil-Sen’s (TS) slope estimator to detect monotonic trends, their significance, and magnitude. We find that the mountain stations depict a decreasing rainfall trend for all seasons, ranging from − 8.4 mm/year at Sankhu to − 21.8 mm/year at Thankot, whereas a mixed pattern is found on the Valley floor. Since the surrounding mountains are the chief source of surface runoff across the valley, rivers, and rivulets are substantially affected by falling rainfall tendency. Both annual rainfall amount and the number of rainy days decreased in the Kathmandu Valley over the study period. We observe a significant reduction in rainfall after 2000. As springs and shallow groundwater are the primary sources of water supply in the Kathmandu valley, it is apparent that decreasing rainfall will have (and is already having) an adverse impact on domestic, industrial, and agricultural water supplies and the livelihoods of people.

The Reliability of X-Band Multiparameter Radar Rainfall Estimates

Rainfall monitoring is an important activity for the development of a water-related disaster mitigation system. The rain gauge measurement is still regarded as the “ground truth”. To obtain the rainfall that represents an area, a high density of rain gauges network is needed. Besides, the installation of a large numbers of rain gauges is considered inefficient. The utilization of advanced technology is deemed necessary to complement the rainfall monitoring system, one of which is by using radar. Altough the radar rainfall estimates have a better spatial resolution, but the reliability needs to be evaluated. This study was carried out to evaluate the reliability of rainfall data from an X-Band Multiparameter radar by classifying the intensity of heavy rain and moderate rain. Then radar rainfall data was corrected using the Adjustment Factor (AF) method. The results show that the radar rainfall has better accuracy in predicting the moderate rain rather than the heavy rain. Furthermore, the X-Band MP radar has better accuracy when predicting rain in an area that has the elevation below the radar than the higher elevation area. Correction of the radar rainfall data using the AF factor produces a rainfall estimates which is closer to the ARR rain.

Effects of biochar application on soil nitrogen and phosphorous leaching loss and oil peony growth

The control of soil nutrient loss to lighten non-point source pollution to water body and improve soil fertility is a global problem. Biochar application to soil is widely considered as an effective method to improve soil fertility and reduce nutrient loss. However, the effect of biochar application to barren karst yellow soil in global karst regions on the loss of nitrogen (N) and phosphorous (P) and the growth of oil peony (an important woody oil crop) remains unclear. We conducted the indoor leaching experiments, in-site observation of the N and P loss with surface runoff under three types of rains and the comparative experiment of oil peony growth when the different amount of biochar was applied to karst yellow soil. The results indicated that soil pH, total nitrogen (TN), total phosphorous (TP), available N and available P contents in the soil increased with an increase of biochar application under indoor simulation but the loss of leaching solution decreased. 2 kg/m2 biochar application caused the least loss of TP, TN, nitrate nitrogen (NN) and ammonia nitrogen (AN) with the leaching solution compared with 0, 4 and 6 kg/m2 application. The rapid and slow attenuation of P and N concentrations, respectively, were observed in karst yellow soil with biochar application. The N/P change in leaching solution showed a parabolic form. Under natural rainfall, 4 kg/m2 biochar application had the best effect on a reduction of TP, TN and AN loss in surface runoff. Rainstorm caused AN and TP large loss but rainstorm, heavy and moderate rains all easily caused NN large loss. 4 kg/m2 biochar application had the best effect on the growth of plant height and base diameter, leaf SPAD and N and P content in organs in the fertilized area but 6 kg/m2 in the unfertilized area. Biochar application effectively improved the fertility of karst yellow soil, reduced the loss of N and P with vertical leaching and surface runoff, and promoted the growth of oil peony. This study provides new knowledge for biochar application.

Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.

Evaluation and Application of Quantitative Precipitation Forecast Products for Mainland China Based on TIGGE Multimodel Data

AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

Projection on precipitation frequency of different intensities and precipitation amount in the Lancang-Mekong River basin in the 21st century

In the Lancang‒Mekong River basin (LMRB), agriculture, dominating the local economy, faces increasing challenges in water supply under climate change. The projection of future precipitation in this basin is essential for understanding the challenges. In this study, the Weather Research and Forecasting (WRF) model was applied to project the LMRB precipitation. Comparing with the historical period (1986–2005), we analyzed the changes of both the projected precipitation amount and the frequency of rainless (<0.1 mm d−1), light rain (0.1–10 mm d−1), moderate rain (10–25 mm d−1), heavy rain (25–50 mm d−1), rainstorm (50–100 mm d−1), and heavy rainstorm (>100 mm d−1) for three periods, namely the near-term (2016–2035), mid-term (2046–2065), and long-term (2080–2099). The results indicate that the precipitation amount during the wet season (April–October) is expected to increase in most areas of the basin for the three periods. As for the precipitation during the dry season (November–March), an increase is projected in most areas for the near-term, while an increase in the lower reach of the basin and a decrease in the upper and middle reach for the mid-term and long-term. The precipitation reduction is expected to be greatest in Myanmar, Laos, Thailand, and Yunnan province of China for the mid-term. The frequency of precipitation in different intensities has prominent regional and temporal differences. During the wet season, the days of rainless and light rain are expected to decrease in the middle reach, whereas the days of rainstorm and heavy rainstorm increase. This feature is especially strong in southern Thailand, southern Laos and Cambodia in the near-term and in Laos and Thailand for the mid-term and long-term. During the dry season, there are projected increasing rainless days and decreasing days of precipitation for the other intensities in the middle reach, and opposite in the rest area of the basin. These projected precipitation changes have potential various impact in different parts of the basin. The middle reach would likely face increasing flood risks because of more days of rainstorm and heavy rainstorm, as well as more precipitation. Yunnan, Myanmar, Thailand and Laos would probably be the center of drought threatens during the dry season due to the increment of rainless days and the precipitation reduction. Besides, the seawater intrusion during the dry season in the near-term and mid-term would be more serious as a result of the precipitation decrease in southern Vietnam.

Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework

The accuracy of the rain distribution could be enhanced by assimilating the remotely sensed and gauge-based precipitation data. In this study, a new nonparametric general regression (NGR) framework was proposed to assimilate satellite- and gauge-based rainfall data over southeast China (SEC). The assimilated rainfall data in Meiyu and Typhoon seasons, in different months, as well as during rainfall events with various rainfall intensities were evaluated to assess the performance of this proposed framework. In rainy season (Meiyu and Typhoon seasons), the proposed method obtained the estimates with smaller total absolute deviations than those of the other satellite products (i.e., 3B42RT and 3B42V7). In general, the NGR framework outperformed the original satellites generally on root-mean-square error (RMSE) and mean absolute error (MAE), especially on Nash-Sutcliffe coefficient of efficiency (NSE). At monthly scale, the performance of assimilated data by NGR was better than those of satellite-based products in most months, by exhibiting larger correlation coefficients (CC) in 6 months, smaller RMSE and MAE in at least 9 months and larger NSE in 9 months, respectively. Moreover, the estimates from NGR have been proven to perform better than the two satellite-based products with respect to the simulation of the gauge observations under different rainfall scenarios (i.e., light rain, moderate rain and heavy rain).

Cascading Hazards in the Aftermath of Australia's 2019/2020 Black Summer Wildfires

AbstractFollowing an unprecedented drought, Australia's 2019/2020 “Black Summer” fire season caused severe damage, gravely impacting both humans and ecosystems, and increasing susceptibility to other hazards. Heavy precipitation in early 2020 led to flooding and runoff that entrained ash and soil in burned areas, increasing sediment concentration in rivers, and reducing water quality. We exemplify this hazard cascade in a catchment in New South Wales by mapping burn severity, flood, and rainfall recurrence; estimating changes in soil erosion; and comparing them with river turbidity data. We show that following the extreme drought and wildfires, even moderate rain and floods led to undue increases in soil erosion and reductions in water quality. While natural risk analysis and planning commonly focuses on a single hazard, we emphasize the need to consider the entire hazard cascade, and highlight the impacts of ongoing climate change beyond its direct effect on wildfires.

Integrated effects of rainfall regime and canopy structure on interception loss: A comparative modelling analysis for an artificial larch forest

AbstractModel optimization is essential for accurately simulating rainfall interception loss (I) in artificial forests and for quantifying the influence of canopy structure, rainfall regime, and meteorological variables on I. Herein, the revised Gash (addressing physical mechanisms) and Wang (addressing canopy structural impacts) models were used for comparative I simulations using a larch plantation in NW China. We used throughfall (Tf) and stemflow (Sf) measurements from the growing seasons of three consecutive years and found that from the total rainfall measured (1371.6 mm) in 59 selected rain events, Tf, Sf, and I accounted for 80.8%, 1.6%, and 17.6%, respectively. Surprisingly, the two tested models showed similar behaviours in their I simulations, with their performances classified as “very good,” exhibiting 5% and 2.3% cumulative I relative errors for the revised Gash and Wang models, respectively. We noted that, although I was well simulated for light–moderate rain events (&lt;25 mm), absolute errors increased significantly as rainfall amount and duration increased, owing to the constant evaporation rate applied in the models. Leaf area index (LAI) explained 40% of the water detention variation in the canopy, rather than I, which only reflected canopy storage capacity dynamics during the growing season. Collectively, our results revealed that models only addressing the independent impact of wet evaporation or structural dynamics are inadequate for accurately simulating I during heavy rain events (&gt;25 mm). Therefore, future models should account for the potential errors arising from evaporation estimation and consider the effects of LAI.

Research on Drivers’ Behavior Characteristics of Expressway Straight Section under Moderate Rainfall

Understanding the behavior characteristics of highway drivers in rainy weather is of great significance to traffic safety. Based on the driving test in the middle rain environment, this paper uses the method of combining the sports index and the driver’s behavior to study the characteristics of the driver’s behavior in the middle rain environment. A total of 15 drivers were recruited in this test to observe the impact of the moderate rain environment on the drivers, and to analyze the correlation of various indicators to summarize the vehicle operation rules on the straight road section under the moderate rain environment. The results of the study show that the speed on straight road sections is generally higher in moderate rain weather, and the driver’s recognition time for signage is longer than in sunny day. Finally, according to the behavior characteristics of drivers in rainy days, the corresponding traffic safety measures are put forward.