The HKO （Hong Kong Observatory） has been carrying out an inter-comparison of automatic raingauges since 2011 for identifying raingauges that can meet the ~ 5% accuracy requirement of the WMO （World Meteorological O...The HKO （Hong Kong Observatory） has been carrying out an inter-comparison of automatic raingauges since 2011 for identifying raingauges that can meet the ~ 5% accuracy requirement of the WMO （World Meteorological Organization） in measuring rainfall amount. The inter-comparison was conducted at HKO＇s meteorological stations at King＇s Park and Hong Kong International Airport in Hong Kong. Two 0.1-mm resolution Pluvio-OTT weighing gauges were introduced in 2013. This type of raingauges has outperformed others in the WMO＇s field inter-comparison held between October 2007 and April 2009. The performances of 14 raingauges, comprising five different measurement methods, viz. drop-counting, weighing, tipping bucket with software correction, tipping bucket with extra pulse correction and tipping bucket without correction, were evaluated. The focus was to study their performances in rainfall intensity measurement, especially during heavy rain situations. Different high rainfall intensity episodes were selected for analysis. Among these episodes, the maximum 1-minute rainfall intensity as high as around 130 mm/hr was recorded by the Pluvio-OTT raingauges. This paper serves to conclude the 3-year （2011-2013） inter-comparison exercise for rainfall amount measurements and to provide preliminary 1-year （2013） comparison results on rainfall intensity measurements.展开更多
This study investigates the intraseasonal variability (ISV) of rainfall in Tanzania during the March-April-May (MAM) season, specifically identifying the dominant peaks of ISV in rainfall for that period. The 5-day ru...This study investigates the intraseasonal variability (ISV) of rainfall in Tanzania during the March-April-May (MAM) season, specifically identifying the dominant peaks of ISV in rainfall for that period. The 5-day running mean during the MAM season reveals that Tanzania experienced an irregular pattern of wet and dry days in the year 2022, indicating the presence of ISV that led to fluctuations in weather patterns. Moreover, the study identifies the dominant peak date, where a significant peak was observed in the 10 - 25-day range, showing that ISV exhibits a quasi-biweekly oscillation around 17 days, with composite evolution from day −8 to day +8 after filtering, and day 0 marking peak rainfall. Furthermore, composite atmospheric circulation analysis reveals critical interactions with ISV. Geopotential height wind patterns at 850 hPa indicate that negative/positive geopotential height anomalies over the Western Indian Ocean and Mozambique Channel enhance low-level convergence/divergence of moisture, resulting in wet/dry phase, meanwhile strong positive geopotential height anomalies at 200 hPa are associated with the upper-level divergence that supports peak rainfall (day 0). During Lag −4 to Lag 0, the results revealed dominant negative OLR anomalies (−18 to −20 W/m2) indicating peak dates of ISV of rainfall while the transition to positive OLR anomalies after Lag +2 showed the starting point of a dry phase of ISV. Also, at the initial phase (Lag −8 to Lag −6), weak positive and limited moisture flux anomalies were observed over the region, while in the peak phase (Lag 0), strong positive anomalies dominated, reflecting intense moisture convergence from both the South West Indian Ocean (SWIO) and the Congo Basin, associated with maximum ISV of rainfall activity. After lag 0, transition into the dry phase (Lag +6 to Lag +8), negative anomalies developed as moisture transport diminishes and winds shift, suppressing convergence over Tanzania, leading to the dry phase. The results highlight the significance of integrating ISV patterns into weather forecasting and disaster preparedness to reduce the risks associated with extreme rainfall events like floods and droughts. Additionally, the findings offer valuable insights for managing water resources, planning agriculture, and enhancing climate resilience in areas of Tanzania that depend on rainfall.展开更多
Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These su...Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These surge events typically occur for days in the early-summer season(from April to June)and can lead to heavy rains in South China.This study categorizes surge events into three types of flow patterns and examines their multiscale variations and impacts on rainfall.The first type occurs mainly in April,with the southeasterlies enhanced by a deepening trough in South China and the western Pacific subtropical high established over the SCS.The second type of surge events mostly appears in June,featuring the prevailing southwesterlies of summer monsoon from the Indian Ocean during the active phases of intraseasonal oscillations.Most surge events exhibit semi-diurnal variations with morning and afternoon peaks of northward moisture fluxes.Specifically,the first type features a dominant afternoon peak,while the second type shows a dominant early-morning peak,which is induced by thermal contrast between the Indochina Peninsula and the SCS.In general,the surge events enhance moisture convergence and increase rainfall downstream in South China,but they show some regional differences.The second type strengthens moisture convergence and rainfall in coastal regions with a morning peak.In contrast,the first type enhances inland rainfall with a morning peak,while moisture divergence dominates coastal regions.The third type of surge events denotes transitional conditions between the first two types,in terms of atmospheric circulations,diurnal cycles,and rainfall patterns.These results highlight a diversity of regional moisture surges and related rainfall ranging from diurnal to sub-seasonal scales.展开更多
Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learnin...Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.展开更多
This study evaluates the efficacy of sustainable erosion control using slag-based alkali-activated cement crusts under varying rainfall and wind conditions. The rainfall intensities ranged from 30 mm/h to 120 mm/h, wi...This study evaluates the efficacy of sustainable erosion control using slag-based alkali-activated cement crusts under varying rainfall and wind conditions. The rainfall intensities ranged from 30 mm/h to 120 mm/h, with durations ranging from 15 min to 90 min, and crust slopes of ∼2° (gentle) and 30° (steep). Wind tunnel experiments were conducted at wind velocities of 14 m/s, 21 m/s, and 28 m/s to investigate post-rainfall wind erodibility, along with changes in crust strength and microstructure analysis. The findings show the development of hydrated cementitious phases in alkali-activated material, which form around and between the particles during the alkaline activation process. Alkali-activated cement crusts significantly reduced erosion caused by rainfall and subsequent wind by several orders of magnitude. At the highest rainfall intensity of 120 mm/h, rainfall erosion was measured to be 1654.81 kg/m2 for untreated samples and 0.89 kg/m2 for treated samples, demonstrating a substantial 99.95% reduction in erosion due to the treatment. Similarly, at the highest wind speed tested, wind erosion was 122.75 kg/m2 for untreated samples and 0.095 kg/m2 for treated samples, indicating a significant 99.92% reduction in erosion due to the formation of an alkali-activated cement crust on the soil surface. However, exposure of the samples to 120 mm/h rainfall for 90 min resulted in a 5.2-fold increase in wind erosion compared to pre-rainfall conditions. Similarly, penetrometer results indicated a 37%–54% reduction in post-rainfall surface strength.展开更多
In this paper,the rainfall features in southwestern China are studied using daily rainfall station data.The rainfall features are distinct along the eastern and western edges of the Hengduan Mountains and over the mou...In this paper,the rainfall features in southwestern China are studied using daily rainfall station data.The rainfall features are distinct along the eastern and western edges of the Hengduan Mountains and over the mountains,especially in terms of rainfall frequency.The rainfall amounts and frequencies are much higher along the eastern and western edges than over the mountains,particularly during spring,which is partly contributed by the number and duration of rainfall events.The differences are more obvious in the nocturnal rainfall than in the daytime rainfall.The rainfall differences over the three regions could be affected by the large-scale environment.By analyzing reanalysis data,the large-scale circulations linked to the different rainfall features over southwestern China,and the interactions of these circulations with the topography are also discussed.展开更多
Background:Soil seed banks may offer great potential for maintaining and restoring desert ecosystems that have been degraded by climate change and anthropogenic disturbance.However,few studies have explored the year‑t...Background:Soil seed banks may offer great potential for maintaining and restoring desert ecosystems that have been degraded by climate change and anthropogenic disturbance.However,few studies have explored the year‑to‑year dynamics in the species composition(richness and abundance)of these desert soil seed banks.Thus,we conducted a 4‑year study to assess the effects of environmental factors(meteorology and microtopography)and aboveground vegetation on the soil seed bank of the Tengger Desert,China.Results:We found the seed bank was dominated by annual herb species both in species richness and abundance.More rainfall in the growing season increased the number of seeds in the soil seed bank,and quadrat micro‑elevation had a negative effect on soil seed bank size.The species composition in the seed bank had significantly larger between‑year similarity than that in the aboveground vegetation due to the dominance of annual herb species.For different life forms,the species composition of annual herbs showed distinctly larger temporal similarity between the aboveground vegetation and the seed bank compared with perennial herbs and shrubs.Conclusions:Our findings highlight that the combined effects of environmental factors and plant life forms deter‑mine the species composition(especially the abundance)of soil seed banks in deserts.However,if degraded desert ecosystems are left to regenerate naturally,the lack of shrub and perennial herb seeds could crucially limit their restoration.Human intervention and management may have to be applied to enhance the seed abundance of perennial lifeforms in degraded deserts.展开更多
Spring consecutive rainfall events(CREs) are key triggers of geological hazards in the Three Gorges Reservoir area(TGR), China. However, previous projections of CREs based on the direct outputs of global climate model...Spring consecutive rainfall events(CREs) are key triggers of geological hazards in the Three Gorges Reservoir area(TGR), China. However, previous projections of CREs based on the direct outputs of global climate models(GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF(Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6(Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6,indicating larger uncertainties in the CREs projected by MIROC6.展开更多
Intense precipitation infiltration and intricate excavation processes are crucial factors that impact the stability and security of towering and steep rock slopes within mining sites.The primary aim of this research w...Intense precipitation infiltration and intricate excavation processes are crucial factors that impact the stability and security of towering and steep rock slopes within mining sites.The primary aim of this research was to investigate the progression of cumulative failure within a cracked rock formation,considering the combined effects of precipitation and excavation activities.The study was conducted in the Huangniuqian eastern mining area of the Dexing Copper Mine in Jiangxi Province,China.An engineering geological investigation was conducted,a physical model experiment was performed,numerical calculations and theoretical analysis were conducted using the matrix discrete element method(Mat-DEM),and the deformation characteristics and the effect of the slope angle of a fractured rock mass under different scenarios were examined.The failure and instability mechanisms of the fractured rock mass under three slope angle models were analyzed.The experimental results indicate that as the slope angle increases,the combined effect of rainfall infiltration and excavation unloading is reduced.A novel approach to simulating unsaturated seepage in a rock mass,based on the van Genuchten model(VGM),has been developed.Compared to the vertical displacement observed in a similar physical experiment,the average relative errors associated with the slope angles of 45,50,and 55were 2.094%,1.916%,and 2.328%,respectively.Accordingly,the combined effect of rainfall and excavation was determined using the proposed method.Moreover,the accuracy of the numerical simulation was validated.The findings contribute to the seepage field in a meaningful way,offering insight that can inform and enhance existing methods and theories for research on the underlying mechanism of ultra-high and steep rock slope instability,which can inform the development of more effective risk management strategies.展开更多
Systematically determining the discriminatory power of various rainfall properties and their combinations in identifying debris flow occurrence is crucial for early warning systems.In this study,we evaluated the discr...Systematically determining the discriminatory power of various rainfall properties and their combinations in identifying debris flow occurrence is crucial for early warning systems.In this study,we evaluated the discriminatory power of different univariate and multivariate rainfall threshold models in identifying triggering conditions of debris flow in the Jiangjia Gully,Yunnan Province,China.The univariate models used single rainfall properties as indicators,including total rainfall(R_(tot)),rainfall duration(D),mean intensity(I_(mean)),absolute energy(Eabs),storm kinetic energy(E_(s)),antecedent rainfall(R_(a)),and maximum rainfall intensity over various durations(I_(max_dur)).The evaluation reveals that the I_(max_dur)and Eabs models have the best performance,followed by the E_(s),R_(tot),and I_(mean)models,while the D and R_(a)models have poor performances.Specifically,the I_(max_dur)model has the highest performance metrics at a 40-min duration.We used logistic regression to combine at least two rainfall properties to establish multivariate threshold models.The results show that adding D or R_(a)to the models dominated by Eabs,E_(s),R_(tot),or I_(mean)generally improve their performances,specifically when D is combined with I_(mean)or when R_(a)is combined with Eabs or E_(s).Including R_(a)in the I_(max_dur)model,it performs better than the univariate I_(max_dur)model.A power-law relationship between I_(max_dur)and R_(a)or between Eabs and R_(a)has better performance than the traditional I_(mean)–D model,while the performance of the E_(s)–R_(a)model is moderate.Our evaluation reemphasizes the important role of the maximum intensity over short durations in debris flow occurrence.It also highlights the importance of systematically investigating the role of R_(a)in establishing rainfall thresholds for triggering debris flow.Given the regional variations in rainfall patterns worldwide,it is necessary to evaluate the findings of this study across diverse watersheds.展开更多
Based on hourly rain gauge data during May–September of 2016–20,we analyze the spatiotemporal distributions of total rainfall(TR)and short-duration heavy rainfall(SDHR;hourly rainfall≥20 mm)and their diurnal variat...Based on hourly rain gauge data during May–September of 2016–20,we analyze the spatiotemporal distributions of total rainfall(TR)and short-duration heavy rainfall(SDHR;hourly rainfall≥20 mm)and their diurnal variations over the middle reaches of the Yangtze River basin.For all three types of terrain(i.e.,mountain,foothill,and plain),the amount of TR and SDHR both maximize in June/July,and the contribution of SDHR to TR(CST)peaks in August(amount:23%;frequency:1.74%).Foothill rainfall is characterized by a high TR amount and a high CST(in amount);mountain rainfall is characterized by a high TR frequency but a small CST(in amount);and plain rainfall shows a low TR amount and frequency,but a high CST(in amount).Overall,stations with high TR(amount and frequency)are mainly located over the mountains and in the foothills,while those with high SDHR(amount and frequency)are mainly concentrated in the foothills and plains close to mountainous areas.For all three types of terrain,the diurnal variations of both TR and SDHR exhibit a double peak(weak early morning and strong late afternoon)and a phase shift from the early-morning peak to the late-afternoon peak from May to August.Around the late-afternoon peak,the amount of TR and SDHR in the foothills is larger than over the mountains and plains.The TR intensity in the foothills increases significantly from midnight to afternoon,suggesting that thermal instability may play an important role in this process.展开更多
To have effective water resource management,the distributed hydrological models are commonly applied for supporting the decision-making processes.Among different inputs,the spatial distributed rainfall plays significa...To have effective water resource management,the distributed hydrological models are commonly applied for supporting the decision-making processes.Among different inputs,the spatial distributed rainfall plays significant role in those model simulations.Many interpolation methods have been developed for generating distributed rainfall based on measurement samples.However,depending on the catchment characteristics and data availability,the suitable interpolation algorithm is case-dependent.This paper presents one operational approach for determining the resonable interpolation algorithm in a complex large catchment(Var catchment,France).Based on the daily rainfall data(2008–2014)collected from 16 stations in the Var catchment,six different interpolation approaches including:inverse distance weight(IDW),spline,kriging with linear and spherical semi-variogram models and geographically weighted regression considering elevation effects and the combined impacts of elevation and distance to the sea were tested.Integrated the results of statistical and modeling assessments,the 400m resolution distributed rainfall generated by IDW algorithm shows high preference in generating distributed rainfall in the Var catchment.Moreover,the strategy described in the article also shows promising acceptability for other catchments.展开更多
Seasonal rainfall plays a vital role in both environmental dynamics and decision-making for rainfed agriculture in Ethiopia, a country often impacted by extreme climate events such as drought and flooding. Predicting ...Seasonal rainfall plays a vital role in both environmental dynamics and decision-making for rainfed agriculture in Ethiopia, a country often impacted by extreme climate events such as drought and flooding. Predicting the onset of the rainy season and providing localized rainfall forecasts for Ethiopia is challenging due to the changing spatiotemporal patterns and the country's rugged topography. The Climate Hazards Group Infra Red Precipitation with Station Data(CHIRPS), ERA5-Land total precipitation and temperature data are used from 1981–2022 to predict spatial rainfall by applying an artificial neural network(ANN). The recurrent neural network(RNN) is a nonlinear autoregressive network with exogenous input(NARX), which includes feed-forward connections and multiple network layers, employing the Levenberg Marquart algorithm. This method is applied to downscale data from the European Centre for Medium-range Weather Forecasts fifth-generation seasonal forecast system(ECMWF-SEAS5) and the Euro-Mediterranean Centre for Climate Change(CMCC) to the specific locations of rainfall stations in Ethiopia for the period 1980–2020. Across the stations, the results of NARX exhibit strong associations and reduced errors. The statistical results indicate that, except for the southwestern Ethiopian highlands, the downscaled monthly precipitation data exhibits high skill scores compared to the station records, demonstrating the effectiveness of the NARX approach for predicting local seasonal rainfall in Ethiopia's complex terrain. In addition to this spatial ANN of the summer season precipitation, temperature, as well as the combination of these two variables, show promising results.展开更多
An extreme rainfall event occurred over Hangzhou,China,during the afternoon hours on 24 June 2013.This event occurred under suitable synoptic conditions and the maximum 4-h cumulative rainfall amount was over 150 mm.T...An extreme rainfall event occurred over Hangzhou,China,during the afternoon hours on 24 June 2013.This event occurred under suitable synoptic conditions and the maximum 4-h cumulative rainfall amount was over 150 mm.This rainfall event had two major rainbands.One was caused by a quasi-stationary convective line,and the other by a backbuilding convective line related to the interaction of the outflow boundary from the first rainband and an existing low-level mesoscale convergence line associated with a mei-yu frontal system.The rainfall event lasted 4 h,while the back-building process occurred in 2 h when the extreme rainfall center formed.So far,few studies have examined the back-building processes in the mei-yu season that are caused by the interaction of a mesoscale convergence line and a convective cold pool.The two rainbands are successfully reproduced by the Weather Research and Forecasting(WRF)model with fourlevel,two-way interactive nesting.In the model,new cells repeatedly occur at the west side of older cells,and the backbuilding process occurs in an environment with large CAPE,a low LFC,and plenty of water vapor.Outflows from older cells enhance the low-level convergence that forces new cells.High precipitation efficiency of the back-building training cells leads to accumulated precipitation of over 150 mm.Sensitivity experiments without evaporation of rainwater show that the convective cold pool plays an important role in the organization of the back-building process in the current extreme precipitation case.展开更多
Available water for communities is insufficient in the central part of Myanmar due to limited rainfall and surface water resources. Over the last two decades, afforestation and reforestation projects have been impleme...Available water for communities is insufficient in the central part of Myanmar due to limited rainfall and surface water resources. Over the last two decades, afforestation and reforestation projects have been implemented in this region to provide sufficient water to local communities, expecting forested areas to store more rainwater than other land uses. However, there has been no research and very limited information on rainfall partitioning into throughfall(TF) and stemflow(SF), particularly concerning tree characters. Gross rainfall, TF under different canopy types, and SF of different tree types were measured in 2019. TF and SF were frequently observed even without rain but under foggy conditions. Therefore, both were partitioned into TF and SF from rainfall and fog individually. Sparser canopies resulted in larger TF from rainfall than denser canopies. However, a denser canopy delivered larger TF from fog than a sparser one. TF rates from rainfall in sparser and denser canopies were 54.5% and 51.5%, respectively, while those from fog were 15.2% and 27.2%, respectively. As a result, total TF rate in the denser canopy(70.7%) was significantly larger than that from the sparser one(64.3%). Short trees with small crown projection area and smooth bark(Type Ⅰ) resulted in larger SF from rainfall than taller trees with large crown projection area and rough bark(Type Ⅱ). However, Type Ⅱ trees resulted in larger SF from fog. SF rates by rainfall from Type Ⅰ and Ⅱ trees were 17.5% and 12.2%, respectively, while those by fog were 22.2% and 39.5%, respectively. No significant total SF rates were found for Type Ⅰ(22.5%) and Ⅱ trees(20.1%). A denser canopy results in larger TF, and Type Ⅰ trees result in larger SF. In an area where foggy conditions occur frequently and for a lengthy period, however, Type Ⅱ trees will result in larger SF. These three tree characters(dense canopies, short trees with small crown projection area and smooth bark, and tall trees with large crown projection area and rough bark) should be considered for afforestation and reforestation projects in the Popa Mountain Park to enhance net water input by forests.展开更多
This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity(ETS) method to improve precipitation forecasts during heavy rainfall events in South China and th...This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity(ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying the sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations improve the prediction of jet streams, strong winds, and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transport. In the Sichuan Basin, adaptive observations refine water vapor transport and adjust vortex dynamics. This research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction.展开更多
The spatial distribution and temporal process of rainfall are major problems in small mountainous catchments.This study investigated the spatiotemporal characteristics of rainfalls using data obtained from a dense mon...The spatial distribution and temporal process of rainfall are major problems in small mountainous catchments.This study investigated the spatiotemporal characteristics of rainfalls using data obtained from a dense monitoring network(10 gauges)in a small catchment of 48.6 km2.The rainfall process is determined by empirical relations(rainfall-elevation and rainfall-duration relations)with a random fluctuation.The rainfall-elevation relation of event-scale is R=21.7h+4.6,the rainfall-area relations are suggested as a Gaussian distribution,and the rainfall-duration relation is a power law function.The process was investigated at 1-min resolution and characterized by index including the onset time,duration,and center time of the rainfall peak period.Analysis revealed that the errors were random and followed a normal distribution.Consequently,a method that incorporates the relations of rainfall amount and the stochastic errors is proposed to simulate the rainfall process.Comparing with the monitored data,both the errors of simulated rainfall amount and peak intensity were in the range of[−30%,30%].Additionally,the relations of the simulated and monitored rainfall amount/intensity with the area are both very close.This study marks an attempt to establish a framework for simulation of rainfall at a high temporal resolution(1-min)which is significant to the hydrological hazards forecasting,and realizing the spatial distribution of rainfall within a small catchment.展开更多
Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machin...Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.展开更多
This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula...This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.展开更多
The critical rainfall of runoff-initiated debris flows is utmost importance for local early hazard forecasting.This paper presents research on the critical rainfall of runoff-initiated debris flows through comparisons...The critical rainfall of runoff-initiated debris flows is utmost importance for local early hazard forecasting.This paper presents research on the critical rainfall of runoff-initiated debris flows through comparisons between slope gradients and three key factors,including topographic contributing area,dimensionless discharge,and Shields stress.The rainfall amount was estimated by utilizing in-situ rainfall records and a slope-dependent Shields stress model was created.The created model can predict critical Shields stress more accurately than the other two models.Furthermore,a new dimensionless discharge equation was proposed based on the corresponding discharge-gradient datasets.The new equation,along with factors such as contributing area above bed failure sites,channel width,and mean diameter of debris flow deposits,predicts a smaller rainfall amount than the in-situ measured records.Although the slope-dependent Shields stress model performs well and the estimated rainfall amount is lower than the in-situ records,the sediment initiation in the experiments falls within sheet flow regime due to a large Shields stress.Therefore,further sediment initiation experiments at a steeper slope range are expected in the future to ensure that the sediment transport belongs to mass failure regime characterized by a low level of Shields stress.Finally,a more accurate hazard forecast on the runoff-initiated debris flow holds promise when the corresponding critical slope-dependent dimensionless discharge of no motion,fluvial sediment transport,mass flow regime,and sheet flow regime are considered.展开更多
文摘The HKO （Hong Kong Observatory） has been carrying out an inter-comparison of automatic raingauges since 2011 for identifying raingauges that can meet the ~ 5% accuracy requirement of the WMO （World Meteorological Organization） in measuring rainfall amount. The inter-comparison was conducted at HKO＇s meteorological stations at King＇s Park and Hong Kong International Airport in Hong Kong. Two 0.1-mm resolution Pluvio-OTT weighing gauges were introduced in 2013. This type of raingauges has outperformed others in the WMO＇s field inter-comparison held between October 2007 and April 2009. The performances of 14 raingauges, comprising five different measurement methods, viz. drop-counting, weighing, tipping bucket with software correction, tipping bucket with extra pulse correction and tipping bucket without correction, were evaluated. The focus was to study their performances in rainfall intensity measurement, especially during heavy rain situations. Different high rainfall intensity episodes were selected for analysis. Among these episodes, the maximum 1-minute rainfall intensity as high as around 130 mm/hr was recorded by the Pluvio-OTT raingauges. This paper serves to conclude the 3-year （2011-2013） inter-comparison exercise for rainfall amount measurements and to provide preliminary 1-year （2013） comparison results on rainfall intensity measurements.
文摘This study investigates the intraseasonal variability (ISV) of rainfall in Tanzania during the March-April-May (MAM) season, specifically identifying the dominant peaks of ISV in rainfall for that period. The 5-day running mean during the MAM season reveals that Tanzania experienced an irregular pattern of wet and dry days in the year 2022, indicating the presence of ISV that led to fluctuations in weather patterns. Moreover, the study identifies the dominant peak date, where a significant peak was observed in the 10 - 25-day range, showing that ISV exhibits a quasi-biweekly oscillation around 17 days, with composite evolution from day −8 to day +8 after filtering, and day 0 marking peak rainfall. Furthermore, composite atmospheric circulation analysis reveals critical interactions with ISV. Geopotential height wind patterns at 850 hPa indicate that negative/positive geopotential height anomalies over the Western Indian Ocean and Mozambique Channel enhance low-level convergence/divergence of moisture, resulting in wet/dry phase, meanwhile strong positive geopotential height anomalies at 200 hPa are associated with the upper-level divergence that supports peak rainfall (day 0). During Lag −4 to Lag 0, the results revealed dominant negative OLR anomalies (−18 to −20 W/m2) indicating peak dates of ISV of rainfall while the transition to positive OLR anomalies after Lag +2 showed the starting point of a dry phase of ISV. Also, at the initial phase (Lag −8 to Lag −6), weak positive and limited moisture flux anomalies were observed over the region, while in the peak phase (Lag 0), strong positive anomalies dominated, reflecting intense moisture convergence from both the South West Indian Ocean (SWIO) and the Congo Basin, associated with maximum ISV of rainfall activity. After lag 0, transition into the dry phase (Lag +6 to Lag +8), negative anomalies developed as moisture transport diminishes and winds shift, suppressing convergence over Tanzania, leading to the dry phase. The results highlight the significance of integrating ISV patterns into weather forecasting and disaster preparedness to reduce the risks associated with extreme rainfall events like floods and droughts. Additionally, the findings offer valuable insights for managing water resources, planning agriculture, and enhancing climate resilience in areas of Tanzania that depend on rainfall.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)National Natural Science Foundation of China(42475003)Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2023SP209)。
文摘Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These surge events typically occur for days in the early-summer season(from April to June)and can lead to heavy rains in South China.This study categorizes surge events into three types of flow patterns and examines their multiscale variations and impacts on rainfall.The first type occurs mainly in April,with the southeasterlies enhanced by a deepening trough in South China and the western Pacific subtropical high established over the SCS.The second type of surge events mostly appears in June,featuring the prevailing southwesterlies of summer monsoon from the Indian Ocean during the active phases of intraseasonal oscillations.Most surge events exhibit semi-diurnal variations with morning and afternoon peaks of northward moisture fluxes.Specifically,the first type features a dominant afternoon peak,while the second type shows a dominant early-morning peak,which is induced by thermal contrast between the Indochina Peninsula and the SCS.In general,the surge events enhance moisture convergence and increase rainfall downstream in South China,but they show some regional differences.The second type strengthens moisture convergence and rainfall in coastal regions with a morning peak.In contrast,the first type enhances inland rainfall with a morning peak,while moisture divergence dominates coastal regions.The third type of surge events denotes transitional conditions between the first two types,in terms of atmospheric circulations,diurnal cycles,and rainfall patterns.These results highlight a diversity of regional moisture surges and related rainfall ranging from diurnal to sub-seasonal scales.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Postdoctoral Fellowship Program of CPSF(GZC20232598)+1 种基金China Postdoctoral Science Foundation(2024M753168)National Key Scientific and Technological Infrastructure Project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.
文摘This study evaluates the efficacy of sustainable erosion control using slag-based alkali-activated cement crusts under varying rainfall and wind conditions. The rainfall intensities ranged from 30 mm/h to 120 mm/h, with durations ranging from 15 min to 90 min, and crust slopes of ∼2° (gentle) and 30° (steep). Wind tunnel experiments were conducted at wind velocities of 14 m/s, 21 m/s, and 28 m/s to investigate post-rainfall wind erodibility, along with changes in crust strength and microstructure analysis. The findings show the development of hydrated cementitious phases in alkali-activated material, which form around and between the particles during the alkaline activation process. Alkali-activated cement crusts significantly reduced erosion caused by rainfall and subsequent wind by several orders of magnitude. At the highest rainfall intensity of 120 mm/h, rainfall erosion was measured to be 1654.81 kg/m2 for untreated samples and 0.89 kg/m2 for treated samples, demonstrating a substantial 99.95% reduction in erosion due to the treatment. Similarly, at the highest wind speed tested, wind erosion was 122.75 kg/m2 for untreated samples and 0.095 kg/m2 for treated samples, indicating a significant 99.92% reduction in erosion due to the formation of an alkali-activated cement crust on the soil surface. However, exposure of the samples to 120 mm/h rainfall for 90 min resulted in a 5.2-fold increase in wind erosion compared to pre-rainfall conditions. Similarly, penetrometer results indicated a 37%–54% reduction in post-rainfall surface strength.
基金jointly supported by the National Key R&D Program of China [grant number 2018YFC1507603]the National Natural Science Foundation of China [grant number41875112 and 41675075]
文摘In this paper,the rainfall features in southwestern China are studied using daily rainfall station data.The rainfall features are distinct along the eastern and western edges of the Hengduan Mountains and over the mountains,especially in terms of rainfall frequency.The rainfall amounts and frequencies are much higher along the eastern and western edges than over the mountains,particularly during spring,which is partly contributed by the number and duration of rainfall events.The differences are more obvious in the nocturnal rainfall than in the daytime rainfall.The rainfall differences over the three regions could be affected by the large-scale environment.By analyzing reanalysis data,the large-scale circulations linked to the different rainfall features over southwestern China,and the interactions of these circulations with the topography are also discussed.
基金supported by the National Natural Science Foundation of China(Grant No.3197529)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23060200)。
文摘Background:Soil seed banks may offer great potential for maintaining and restoring desert ecosystems that have been degraded by climate change and anthropogenic disturbance.However,few studies have explored the year‑to‑year dynamics in the species composition(richness and abundance)of these desert soil seed banks.Thus,we conducted a 4‑year study to assess the effects of environmental factors(meteorology and microtopography)and aboveground vegetation on the soil seed bank of the Tengger Desert,China.Results:We found the seed bank was dominated by annual herb species both in species richness and abundance.More rainfall in the growing season increased the number of seeds in the soil seed bank,and quadrat micro‑elevation had a negative effect on soil seed bank size.The species composition in the seed bank had significantly larger between‑year similarity than that in the aboveground vegetation due to the dominance of annual herb species.For different life forms,the species composition of annual herbs showed distinctly larger temporal similarity between the aboveground vegetation and the seed bank compared with perennial herbs and shrubs.Conclusions:Our findings highlight that the combined effects of environmental factors and plant life forms deter‑mine the species composition(especially the abundance)of soil seed banks in deserts.However,if degraded desert ecosystems are left to regenerate naturally,the lack of shrub and perennial herb seeds could crucially limit their restoration.Human intervention and management may have to be applied to enhance the seed abundance of perennial lifeforms in degraded deserts.
基金funding from the NFR COMBINED (Grant No.328935)The BCPU hosted YZ visit to University of Bergen (Trond Mohn Foundation Grant No.BFS2018TMT01)+2 种基金supported by the National Key Research and Development Program of China (Grant No.2023YFA0805101)the National Natural Science Foundation of China (Grant Nos.42376250 and 41731177)a China Scholarship Council fellowship and the UTFORSK Partnership Program (CONNECTED UTF-2016-long-term/10030)。
文摘Spring consecutive rainfall events(CREs) are key triggers of geological hazards in the Three Gorges Reservoir area(TGR), China. However, previous projections of CREs based on the direct outputs of global climate models(GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF(Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6(Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6,indicating larger uncertainties in the CREs projected by MIROC6.
基金the Research Fund of National Natural Science Foundation of China(NSFC)(Grant Nos.42477142 and 42277154)the Project of Slope Safety Control and Disaster Prevention Technology Innovation team of“Youth Innovation Talent Introduction and Education Plan”of Shandong Colleges and Universities(Grant No.Lu Jiao Ke Han[2021]No.51)。
文摘Intense precipitation infiltration and intricate excavation processes are crucial factors that impact the stability and security of towering and steep rock slopes within mining sites.The primary aim of this research was to investigate the progression of cumulative failure within a cracked rock formation,considering the combined effects of precipitation and excavation activities.The study was conducted in the Huangniuqian eastern mining area of the Dexing Copper Mine in Jiangxi Province,China.An engineering geological investigation was conducted,a physical model experiment was performed,numerical calculations and theoretical analysis were conducted using the matrix discrete element method(Mat-DEM),and the deformation characteristics and the effect of the slope angle of a fractured rock mass under different scenarios were examined.The failure and instability mechanisms of the fractured rock mass under three slope angle models were analyzed.The experimental results indicate that as the slope angle increases,the combined effect of rainfall infiltration and excavation unloading is reduced.A novel approach to simulating unsaturated seepage in a rock mass,based on the van Genuchten model(VGM),has been developed.Compared to the vertical displacement observed in a similar physical experiment,the average relative errors associated with the slope angles of 45,50,and 55were 2.094%,1.916%,and 2.328%,respectively.Accordingly,the combined effect of rainfall and excavation was determined using the proposed method.Moreover,the accuracy of the numerical simulation was validated.The findings contribute to the seepage field in a meaningful way,offering insight that can inform and enhance existing methods and theories for research on the underlying mechanism of ultra-high and steep rock slope instability,which can inform the development of more effective risk management strategies.
基金supported by the National Key R&D Program of China(No.2023YFC3007205)the National Natural Science Foundation of China(Nos.42271013,42077440)Project of the Department of Science and Technology of Sichuan Province(No.2023ZHCG0012).
文摘Systematically determining the discriminatory power of various rainfall properties and their combinations in identifying debris flow occurrence is crucial for early warning systems.In this study,we evaluated the discriminatory power of different univariate and multivariate rainfall threshold models in identifying triggering conditions of debris flow in the Jiangjia Gully,Yunnan Province,China.The univariate models used single rainfall properties as indicators,including total rainfall(R_(tot)),rainfall duration(D),mean intensity(I_(mean)),absolute energy(Eabs),storm kinetic energy(E_(s)),antecedent rainfall(R_(a)),and maximum rainfall intensity over various durations(I_(max_dur)).The evaluation reveals that the I_(max_dur)and Eabs models have the best performance,followed by the E_(s),R_(tot),and I_(mean)models,while the D and R_(a)models have poor performances.Specifically,the I_(max_dur)model has the highest performance metrics at a 40-min duration.We used logistic regression to combine at least two rainfall properties to establish multivariate threshold models.The results show that adding D or R_(a)to the models dominated by Eabs,E_(s),R_(tot),or I_(mean)generally improve their performances,specifically when D is combined with I_(mean)or when R_(a)is combined with Eabs or E_(s).Including R_(a)in the I_(max_dur)model,it performs better than the univariate I_(max_dur)model.A power-law relationship between I_(max_dur)and R_(a)or between Eabs and R_(a)has better performance than the traditional I_(mean)–D model,while the performance of the E_(s)–R_(a)model is moderate.Our evaluation reemphasizes the important role of the maximum intensity over short durations in debris flow occurrence.It also highlights the importance of systematically investigating the role of R_(a)in establishing rainfall thresholds for triggering debris flow.Given the regional variations in rainfall patterns worldwide,it is necessary to evaluate the findings of this study across diverse watersheds.
基金supported by the National Natural Science Foundation of China(Grant Nos. U2142202, 41975056, 42230612, and 41975058)Youth Innovation Promotion Association,Chinese Academy of Sciencesthe National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility”(EarthLab)
文摘Based on hourly rain gauge data during May–September of 2016–20,we analyze the spatiotemporal distributions of total rainfall(TR)and short-duration heavy rainfall(SDHR;hourly rainfall≥20 mm)and their diurnal variations over the middle reaches of the Yangtze River basin.For all three types of terrain(i.e.,mountain,foothill,and plain),the amount of TR and SDHR both maximize in June/July,and the contribution of SDHR to TR(CST)peaks in August(amount:23%;frequency:1.74%).Foothill rainfall is characterized by a high TR amount and a high CST(in amount);mountain rainfall is characterized by a high TR frequency but a small CST(in amount);and plain rainfall shows a low TR amount and frequency,but a high CST(in amount).Overall,stations with high TR(amount and frequency)are mainly located over the mountains and in the foothills,while those with high SDHR(amount and frequency)are mainly concentrated in the foothills and plains close to mountainous areas.For all three types of terrain,the diurnal variations of both TR and SDHR exhibit a double peak(weak early morning and strong late afternoon)and a phase shift from the early-morning peak to the late-afternoon peak from May to August.Around the late-afternoon peak,the amount of TR and SDHR in the foothills is larger than over the mountains and plains.The TR intensity in the foothills increases significantly from midnight to afternoon,suggesting that thermal instability may play an important role in this process.
基金National Key Research and Development Program of China,Grant/Award Number:2023YFC3006702。
文摘To have effective water resource management,the distributed hydrological models are commonly applied for supporting the decision-making processes.Among different inputs,the spatial distributed rainfall plays significant role in those model simulations.Many interpolation methods have been developed for generating distributed rainfall based on measurement samples.However,depending on the catchment characteristics and data availability,the suitable interpolation algorithm is case-dependent.This paper presents one operational approach for determining the resonable interpolation algorithm in a complex large catchment(Var catchment,France).Based on the daily rainfall data(2008–2014)collected from 16 stations in the Var catchment,six different interpolation approaches including:inverse distance weight(IDW),spline,kriging with linear and spherical semi-variogram models and geographically weighted regression considering elevation effects and the combined impacts of elevation and distance to the sea were tested.Integrated the results of statistical and modeling assessments,the 400m resolution distributed rainfall generated by IDW algorithm shows high preference in generating distributed rainfall in the Var catchment.Moreover,the strategy described in the article also shows promising acceptability for other catchments.
基金the funding provided by the “German–Ethiopian SDG Graduate School: Climate Change Effects on Food Security (CLIFOOD)”, established by the Food Security Center of the University of Hohenheim (Germany) and Hawassa University (Ethiopia)provided by the German Academic Exchange Service (DAAD) through funds from the Federal Ministry for Economic Cooperation and Development (BMZ)。
文摘Seasonal rainfall plays a vital role in both environmental dynamics and decision-making for rainfed agriculture in Ethiopia, a country often impacted by extreme climate events such as drought and flooding. Predicting the onset of the rainy season and providing localized rainfall forecasts for Ethiopia is challenging due to the changing spatiotemporal patterns and the country's rugged topography. The Climate Hazards Group Infra Red Precipitation with Station Data(CHIRPS), ERA5-Land total precipitation and temperature data are used from 1981–2022 to predict spatial rainfall by applying an artificial neural network(ANN). The recurrent neural network(RNN) is a nonlinear autoregressive network with exogenous input(NARX), which includes feed-forward connections and multiple network layers, employing the Levenberg Marquart algorithm. This method is applied to downscale data from the European Centre for Medium-range Weather Forecasts fifth-generation seasonal forecast system(ECMWF-SEAS5) and the Euro-Mediterranean Centre for Climate Change(CMCC) to the specific locations of rainfall stations in Ethiopia for the period 1980–2020. Across the stations, the results of NARX exhibit strong associations and reduced errors. The statistical results indicate that, except for the southwestern Ethiopian highlands, the downscaled monthly precipitation data exhibits high skill scores compared to the station records, demonstrating the effectiveness of the NARX approach for predicting local seasonal rainfall in Ethiopia's complex terrain. In addition to this spatial ANN of the summer season precipitation, temperature, as well as the combination of these two variables, show promising results.
基金supported by the National Natural Science Foundation of China (Grant Nos.41730965, U2242204, and 41175047)the National Key Basic Research and Development Project of China (Grant No.2013CB430104)+2 种基金the Key Project of the Joint Funds of the Natural Science Foundation of Zhejiang Province (Grant No.LZJMZ23D050003financial support from the China Scholarship Council for her visit to CAPSUniversity of Oklahoma
文摘An extreme rainfall event occurred over Hangzhou,China,during the afternoon hours on 24 June 2013.This event occurred under suitable synoptic conditions and the maximum 4-h cumulative rainfall amount was over 150 mm.This rainfall event had two major rainbands.One was caused by a quasi-stationary convective line,and the other by a backbuilding convective line related to the interaction of the outflow boundary from the first rainband and an existing low-level mesoscale convergence line associated with a mei-yu frontal system.The rainfall event lasted 4 h,while the back-building process occurred in 2 h when the extreme rainfall center formed.So far,few studies have examined the back-building processes in the mei-yu season that are caused by the interaction of a mesoscale convergence line and a convective cold pool.The two rainbands are successfully reproduced by the Weather Research and Forecasting(WRF)model with fourlevel,two-way interactive nesting.In the model,new cells repeatedly occur at the west side of older cells,and the backbuilding process occurs in an environment with large CAPE,a low LFC,and plenty of water vapor.Outflows from older cells enhance the low-level convergence that forces new cells.High precipitation efficiency of the back-building training cells leads to accumulated precipitation of over 150 mm.Sensitivity experiments without evaporation of rainwater show that the convective cold pool plays an important role in the organization of the back-building process in the current extreme precipitation case.
文摘Available water for communities is insufficient in the central part of Myanmar due to limited rainfall and surface water resources. Over the last two decades, afforestation and reforestation projects have been implemented in this region to provide sufficient water to local communities, expecting forested areas to store more rainwater than other land uses. However, there has been no research and very limited information on rainfall partitioning into throughfall(TF) and stemflow(SF), particularly concerning tree characters. Gross rainfall, TF under different canopy types, and SF of different tree types were measured in 2019. TF and SF were frequently observed even without rain but under foggy conditions. Therefore, both were partitioned into TF and SF from rainfall and fog individually. Sparser canopies resulted in larger TF from rainfall than denser canopies. However, a denser canopy delivered larger TF from fog than a sparser one. TF rates from rainfall in sparser and denser canopies were 54.5% and 51.5%, respectively, while those from fog were 15.2% and 27.2%, respectively. As a result, total TF rate in the denser canopy(70.7%) was significantly larger than that from the sparser one(64.3%). Short trees with small crown projection area and smooth bark(Type Ⅰ) resulted in larger SF from rainfall than taller trees with large crown projection area and rough bark(Type Ⅱ). However, Type Ⅱ trees resulted in larger SF from fog. SF rates by rainfall from Type Ⅰ and Ⅱ trees were 17.5% and 12.2%, respectively, while those by fog were 22.2% and 39.5%, respectively. No significant total SF rates were found for Type Ⅰ(22.5%) and Ⅱ trees(20.1%). A denser canopy results in larger TF, and Type Ⅰ trees result in larger SF. In an area where foggy conditions occur frequently and for a lengthy period, however, Type Ⅱ trees will result in larger SF. These three tree characters(dense canopies, short trees with small crown projection area and smooth bark, and tall trees with large crown projection area and rough bark) should be considered for afforestation and reforestation projects in the Popa Mountain Park to enhance net water input by forests.
基金jointly supported by the Guangdong Province University Student Innovation and Entrepreneurship Project (580520049)the Guangdong Ocean University Scientific Research Startup Fund (R20021)the Key Laboratory of Plateau and Basin Rainstorm and Drought Disasters in Sichuan Province Open Research Fund (SZKT201902)。
文摘This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity(ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying the sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations improve the prediction of jet streams, strong winds, and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transport. In the Sichuan Basin, adaptive observations refine water vapor transport and adjust vortex dynamics. This research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction.
基金supported by the Second Scientific Expedition on the Qinghai-Tibet Plateau(2019QZKK0903-02)NSFC(42322703 and U21A2008)+1 种基金Sichuan S&T project(2022JDJQ0008)Western Light of Young Scholars,CAS.
文摘The spatial distribution and temporal process of rainfall are major problems in small mountainous catchments.This study investigated the spatiotemporal characteristics of rainfalls using data obtained from a dense monitoring network(10 gauges)in a small catchment of 48.6 km2.The rainfall process is determined by empirical relations(rainfall-elevation and rainfall-duration relations)with a random fluctuation.The rainfall-elevation relation of event-scale is R=21.7h+4.6,the rainfall-area relations are suggested as a Gaussian distribution,and the rainfall-duration relation is a power law function.The process was investigated at 1-min resolution and characterized by index including the onset time,duration,and center time of the rainfall peak period.Analysis revealed that the errors were random and followed a normal distribution.Consequently,a method that incorporates the relations of rainfall amount and the stochastic errors is proposed to simulate the rainfall process.Comparing with the monitored data,both the errors of simulated rainfall amount and peak intensity were in the range of[−30%,30%].Additionally,the relations of the simulated and monitored rainfall amount/intensity with the area are both very close.This study marks an attempt to establish a framework for simulation of rainfall at a high temporal resolution(1-min)which is significant to the hydrological hazards forecasting,and realizing the spatial distribution of rainfall within a small catchment.
基金supported by the State Administration of Science,Technology and Industry for National Defence,PRC(KJSP2020020303)the National Institute of Natural Hazards,Ministry of Emergency Management of China(ZDJ2021-12)。
文摘Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.
基金supported by the Korea Meteorological Administration Research and Development Program “Developing Application Technology for Atmospheric Research Aircraft” (Grant No. KMA2018-00222)
文摘This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.
基金supported by the by the Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019QZKK0902)Beijing Municipal Science and Technology Project (Z191100001419015)
文摘The critical rainfall of runoff-initiated debris flows is utmost importance for local early hazard forecasting.This paper presents research on the critical rainfall of runoff-initiated debris flows through comparisons between slope gradients and three key factors,including topographic contributing area,dimensionless discharge,and Shields stress.The rainfall amount was estimated by utilizing in-situ rainfall records and a slope-dependent Shields stress model was created.The created model can predict critical Shields stress more accurately than the other two models.Furthermore,a new dimensionless discharge equation was proposed based on the corresponding discharge-gradient datasets.The new equation,along with factors such as contributing area above bed failure sites,channel width,and mean diameter of debris flow deposits,predicts a smaller rainfall amount than the in-situ measured records.Although the slope-dependent Shields stress model performs well and the estimated rainfall amount is lower than the in-situ records,the sediment initiation in the experiments falls within sheet flow regime due to a large Shields stress.Therefore,further sediment initiation experiments at a steeper slope range are expected in the future to ensure that the sediment transport belongs to mass failure regime characterized by a low level of Shields stress.Finally,a more accurate hazard forecast on the runoff-initiated debris flow holds promise when the corresponding critical slope-dependent dimensionless discharge of no motion,fluvial sediment transport,mass flow regime,and sheet flow regime are considered.
