Precipitation Duration Research Articles

Spatiotemporal Variation Characteristics of Extreme Precipitation in the Mid–Lower Reaches of the Yangtze River Basin Based on Precipitation Events

In addition to greater precipitation on extreme days of precipitation, preceding and succeeding precipitation (PSP) is often an objective component of flooding in the mid–lower reaches of the Yangtze River Basin (MLRYRB). In this study, focused on the temporal distribution pattern of precipitation, the concept of an extreme precipitation event (EPE), defined as a consecutive precipitation event having at least one daily precipitation extreme, is proposed to consider PSP in an extreme event. We analyzed the spatiotemporal variation of four types of EPEs based on daily data obtained from 130 monitoring stations covering 1960–2019. Extreme precipitation increased significantly over the last 60 years (p < 0.01). The frequency and precipitation amount of single-day EPEs accounted for only 13% and 21%, respectively, while multi-day continuous EPE types that are associated with PSP accounted for 87% and 79%, respectively, confirming the connotations of EPEs. The front and late EPEs under the 100-year return level reached 250 mm and 230 mm, respectively. Furthermore, climate warming could lead to significant increases in the frequency of single-day and late EPEs, particularly in the southern region. The EPE concept may be helpful in exploring disaster-causing processes under extreme weather, and it provides a theoretical basis for deriving the precipitation hazard chain, which is more applicable to basins with long precipitation durations.

Effect of acid type on biomineralization of soil using crude soybean urease solution

The one-phase-low-pH method is a simple, efficient, and user-friendly biogrouting technique that can effectively improve the biomineralization of enzyme-induced carbonate precipitation (EICP) using free urease enzyme. One of the most significant advantages of this method is its capacity to effectively delay calcium carbonate (CaCO3) precipitation by reducing the pH of the solution through the addition of acid. This prevents bioclogging during the biogrouting process and improves the biomineralization effect. However, the biomineralization of the one-phase-low-pH based EICP method may be influenced by the specific acid used. To investigate the impact of acid type on the one-phase-low-pH EICP method using crude soybean urease solution (CSUS), four types of acids, including hydrochloric acid (HCl), nitric acid (HNO3), acetic acid (CH3COOH), and lactic acid (C3H6O3), were used to adjust the pH of CSUS. A series of macroscopic and microscopic experiments were conducted to evaluate the effect of acid type on the one-phase-low-pH EICP method. The results indicate that the acid has an inhibition on the urease activity (UA) of CSUS. Among the acids tested, HNO3 exhibits the most pronounced inhibitory effect on the UA of CSUS, followed by HCl, and the least pronounced inhibitory effect for CH3COOH and C3H6O3 under the same pH conditions. Meanwhile, CH3COOH and C3H6O3 could provide a longer delay duration of CaCO3 precipitation than HNO3 and HCl. Therefore, the one-phase-low-pH EICP method based on CH3COOH and C3H6O3 can significantly improve the effective biocementation depth compared to that based on HNO3 and HCl. Nevertheless, the different types of acids appear to have no obvious effect on the polymorph and crystalline of the precipitated CaCO3 crystals.

Hourly Downscaling of Daily Climate Projection Data for the Nakdong River Basin using the Nearest Neighbor Search Method

Hydrological and water quality models have been continuously improved for assessing the impacts of climate change. Accordingly, meteorological data with high spatial and temporal resolutions are required. Current climate change scenario-based projections provide meteorological data on monthly and daily scales, which are too coarse for regional climate change assessments. In this study, we propose a temporal downscaling method based on the nearest neighbor search methodology for temporal downscaling from daily time-step meteorological data to hourly time-step data. To verify the temporal downscaling method, historical meteorological data from weather stations located in the Nakdong River basin were used, and the normalized root mean square error (NRMSE) between observational and simulated data was calculated. Consequently, the simulation errors were approximately 2–4% for temperature and precipitation, and approximately 7–16% for wind speed, relative humidity, and solar radiation. Next, using the temporal downscaling method, hourly future climate projection data were derived from the daily SSP5-8.5 climate scenario projections retrieved from weather stations in the target area. According to the downscaled hourly climate projection results, under the SSP5-8.5 climate change scenario, the future annual maximum temperature is projected to increase by up to 7.0 ℃ and 5.6 ℃ at Daegu and Busan weather stations, compared to the reference period maximum temperatures of 36.2 ℃ and 33 ℃, respectively. The annual precipitation duration is projected to decrease by up to 103.3 h (21.25%) and 157.2 h (27.57%), compared to the reference period durations of 486.2 h and 570.2 h, respectively. Annual precipitation intensity is projected to increase by up to 0.71 mm/h (33.5%) and 0.83 mm/h (31.2%), compared to the reference period precipitation intensities of 2.12 mm/h and 2.66 mm/h, respectively. The temporal downscaling method presented in this study is expected to contribute to the preparation of meteorological input data to effectively simulate detailed hourly rainfall runoff and the behavior of water quality factors, thereby improving the assessment of climate change impacts.

Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3

Abstract. A historic rainstorm occurred over Henan, China, in July 2021 (“7.20” extreme precipitation event), resulting in significant human casualties and socioeconomic losses. A global variable-resolution model (MPAS-Atmosphere v7.3) was employed to simulate this extreme precipitation event. A series of simulations have been done at both quasi-uniform (60 and 15 km) and variable-resolution (60–15 and 60–3 km) meshes from hydrostatic to nonhydrostatic scale with two parameterization scheme suites. For the 48 h peak precipitation duration (20–22 July), the 60–3 km variable-resolution simulation coupled with the scale-aware convection-permitting parameterization scheme suite stands out predominantly among other simulation experiments as it reproduces this extreme precipitation event most accurately. At 15 km resolution, the 60–15 km variable-resolution simulation achieves comparable forecasting skills to the 15 km quasi-uniform simulation but at a much reduced computing cost. In addition, we found that the default mesoscale suite generally outperforms the convection-permitting suite at 15 km resolution as simulations coupled with the convection-permitting suite missed the third peak of this extreme precipitation event, while the mesoscale suite did not. Furthermore, it is found that the large-scale circulation plays a critical role in the peak precipitation simulations at 15 km resolution, via influencing the simulated low-level wind. During the second peak precipitation period, simulations with the convection-permitting parameterization scheme suite at 15 km resolution generate a prominent low-level easterly wind component bias, which is largely attributed to the excessively evaporative cooling in the lower troposphere. This study further reveals that at 15 km resolution the diabatic heating from the grid-scale precipitation accounts more for the low-level wind bias than the convective-scale precipitation. Given that two different cloud microphysics schemes, namely Thompson and WSM6 schemes, are used in the convection-permitting and default mesoscale parameterization scheme suites, respectively, these microphysics schemes are found to be the primary contributor to the low-level wind simulation bias.

Extreme weather has variable effects on reproductive success of grassland songbirds at the northern extent of their range

Abstract Grassland songbirds breeding in Canada and the United States have experienced significant population declines likely because of habitat loss and degradation. Many climate change models predict an increase in the frequency, intensity, and duration of extreme precipitation and temperature events that could place further pressures on declining species. We monitored the fate of 1,868 individual nesting attempts of 7 grassland songbird species in response to various precipitation and temperature measures over a 10-yr period (1997 to 2002 and 2004 to 2008) in Saskatchewan, Canada. Daily nest survival rates of 5 species, including 3 at-risk species, were negatively influenced by high levels of precipitation, although the amount of precipitation where declines in daily nest survival occurred varied. Daily nest survival rates of 2 species were negatively correlated with high temperatures. We failed to detect any relationship between precipitation or temperature and the number of fledglings produced from successful nests. Extreme weather events could add additional stressors to declining populations of grassland birds in Canada. Increases in the frequency and intensity of extreme weather, specifically extreme precipitation events and short-term high temperatures, will likely lead to lower reproductive success for several species compared to current levels. This may be especially problematic for management of Anthus spragueii (Sprague’s Pipit) and Centronyx bairdii (Baird’s Sparrow), where a large proportion (> 75%) of the breeding population occurs near the northern edge of the Great Plains. The continuing loss and degradation of northern grasslands may limit the ability of these species to disperse and find favorable climate conditions.

The impact of monsoon on the landfalling tropical cyclone persistent precipitation in South China

Interactions between landfalling tropical cyclones (TCs) and monsoons in South China significantly influence precipitation duration, leading to severe disasters. Previous studies have primarily been individual cases, lacking systematic large-scale statistical analysis of the monsoon and landfalling tropical cyclone persistent precipitation (LTCPP) relationship. This study quantitatively investigated the relationship between monsoonal wind intensity before TCs landfall and post-landfall persistent precipitation induced by TCs in South China, employing the ERA5 reanalysis data and the best track data of 147 TCs from 1979 to 2018. The LTCPP was characterized by the frequency of persistent precipitation events during 0–72 h after TC landfall within a 500 km radius from the TC center. TCs were subdivided into weak and strong LTCPP groups based on the category-specific median of Frequency of 24 h Landfalling Tropical Cyclone Persistent Precipitation (FLTCPP24): 2705 h for TS, 6007 h for STS, and 6419 h for TY. A South China Tropical Cyclone Precipitation Monsoon Index (SCTCPM) was proposed to quantify monsoonal wind intensity derived from zonal winds at 850 hPa over two regions located in the Indian Ocean and Northwestern Pacific Ocean, within 5 d before TC landfall. The results reveal that SCTCPM < 9 m s−1 yields a 72% probability of weak LTCPP occurrence, which increases to 77% when SCTCPM < 6 m s−1. Conversely, SCTCPM > 18 m s−1 corresponds to an 80% probability of strong LTCPP. SCTCPM is an effective indicator for monsoonal wind that impacts LTCPP. Enhanced monsoonal winds, quantified by higher SCTCPM, result in post-landfall changes in horizontal wind speed, moisture transport, convective activity and upward motion, ultimately increasing LTCPP. This study deepens our understanding of the monsoon-TC relationship, emphasizing the crucial role of monsoonal wind in LTCPP in South China and offering valuable insights for disaster preparedness and risk mitigation.

The role of regional water vapor dynamics in creating precipitation extremes

While sub-daily precipitation extremes cause flash flooding and pose risk to life, longer precipitation extremes threaten infrastructure such as water supply dams. Frequent storm or floods events replenish water supplies, ensuring the health of our ecosystems, while rarer larger storms or floods cause damage to property and life. These differing impacts depend on both storm rarity and duration and are largely dependent on coincident atmospheric water vapour. Using a novel metric that quantifies the extent of concurrency that exists between precipitation and total water vapour extremes, large regional variations are identified across the globe. Tropical regions such as Northeast Africa and South/East Asia consistently exhibit greater concurrency across all precipitation durations. In contrast, areas of the extra-tropics, such as the Mediterranean and Northwest Americas, show a rapid decline in concurrency with increasing duration. However, for rare events of long duration, non-tropical regions maintain high concurrency. With the link between climate change and increasing total water vapour well established, these results suggest that flood events will increase globally, with increases most apparent for longer and rarer events. This work underscores the need for tailored regional strategies in managing extreme precipitation and flood events in the future.

Runoff from an extensive green roof during extreme events: Insights from 15 years of observations

AbstractWhile green roofs have gained widespread popularity as a measure to detain and retain runoff in urban areas, their performance during extreme events is not well studied. In this study 15 years of runoff and precipitation observations from a small extensive green roof in Norway are analysed. GEV‐distributions were fitted to the annual max values for precipitation and runoff in order to develop intensity‐duration‐frequency (IDF) and runoff‐duration‐frequency (RDF) data. Using the IDF and RDF data a total of 31 extreme events were identified (containing precipitation or runoff values with return period greater than 2 years for one or more durations). While nearly all extreme runoff events were caused by extreme precipitation, only 69% of the extreme precipitation events resulted in extreme runoff. The assumption of 1:1 equivalency of return periods did not hold true, and deviations were mainly explained by variations in substrate water content prior to the extreme event. Moreover, in 50% of the events, the runoff duration with the greatest return period was shorter than the precipitation duration with the greatest return period. Hence, the results indicate that the use of design storms to predict runoff from green roofs may be inappropriate. The potential of having IDF and RDF data available was demonstrated by the development of simple empirical equations, which ensure conservations of both return period and duration. To generate reliable green roof RDF data, future research should prioritize evaluating various continuous models with the aim of accurately describing extreme events.

On the persistence and related mechanisms for day–night compound humid heat extremes in the Northern Hemisphere

Hot extremes pose adverse impacts on human health and ecosystem, leading to aggravated damage when they combine high-humidity and occur in the both daytime and nighttime. Although considerable studies have focused on hot extremes, understandings about day–night Compound humid heat (quantified by Moist Enthalpy) Extremes (CMEEs) are still lacking. This work investigates their frequency, linear trends and temporal persistence in the Northern Hemisphere, and two typical vulnerable regions are selected as Central Europe (CE) and the Arabian Peninsula (AP), both exhibiting high frequency and positive trends, but with contrasting persistence, which is quantified by the bivariate Dynamical System method. Results reveal their regional dependence and physical processes: the dual importance of sensible and latent heat in CE is attributed to the combination of an anomalous anticyclone and evaporation, whereas the dominance of latent heat in AP is largely owing to the convective precipitation. CMEEs in AP can be further divided into two groups with distinct persistence, and this disparity actually depends on the preceding precipitation duration and its associated water vapor supply.

Connecting flow duration curve and precipitation duration curve based on the relationship deduced from machine learning in the watersheds of northern China

Obtaining reliable hydrological data for ungauged watersheds has always been a significant challenge in the water resources management. The flow duration curve (FDC) and precipitation duration curve (PDC) are classical methods to address this challenge. However, traditional FDC and PDC methods mostly rely on pre-assumed probability distribution function form, which may result in sub-optimal model parameters and consequently sub-optimal simulation results of hydrological data, and the common function forms are not applicable to deal with the data including zero-values, which often occurs in the discharge data of arid region and daily rainfall data. To address the issues in FDC and PDC , this study firstly determines the most suitable function form to describe FDC and PDC from various distribution functions, i.e., Log Normal, Generalized Pareto Distribution and H2018 function, from 1960 to 2010 in 7 watersheds in the north of China. The multiple methods including Random Forest, polynomial, Multilayer Perceptron and K-Nearest Neighbor are employed to simulate the parameters of the functions, aiming to deduce the optimal parameters of FDC functions. The results show that, H2018 function form has the best performance in simulating the PDC and FDC and in general, parameter a and b of H2018 function indicate an opposite trend, and parameter k declines in mostly condition at the annual scale from 1960 to 2010. The results of different methods indicate that the Random Forest method is the most efficient and accurate approach to deduce FDC parameters from measured precipitation and climate factors, compared with polynomial, Multilayer Perceptron and K-Nearest Neighbor. Based on the relationship between PDC parameters and FDC parameters, the FDC of ungauged basins can be deduced from the observed precipitation data. The research findings provide a new method to estimate FDC for the water resources management in the ungauged basins.

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

AbstractClimate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns.

Investigating the intensity of urban heat island and the impacts of local climate using verified WRF data: A case study of Rasht, Northern Iran

Due to the complicated and uncertain dynamics of the urban heat island (UHI) in humid subtropical areas, the diurnal, seasonal and long-term variations of the UHI were investigated in Rasht, Iran. Besides, the study utilizes the heat and moisture transfer concepts to analyze the impacts of daytime rainfall duration on UHI. Meteorological data at five virtual stations was acquired by verified Weather Research and Forecasting (WRF) model. Overall, the nighttime UHI peaks in summer (3.4 °C) and is lowest in fall (1.4 °C) over 2017–2019. Moreover, the maximum and minimum daytime UHI occurred in winter (1.2 °C) and summer (0.6 °C), respectively. In hot seasons, an increase in daytime precipitation duration initially leads to an increase in daytime UHI, followed by a subsequent decrease. Conversely, in cold seasons, daytime UHI shows a decreasing trend regardless of rain conditions. Additionally, extended daytime precipitation attenuates nocturnal UHI in hot seasons, while no significant influence is observed in cold seasons. An increase in midday cloud cover during hot seasons reduces the nighttime UHI. The nighttime UHI is inversely proportional to the third root of wind speed, in low cloudiness conditions of hot seasons. Furthermore, the Ladsat-5/7/8 imageries revealed an increasing trend in the surface UHI (SUHI) during hot seasons over the past two decades, while SUHI in cold seasons remains insignificant. The article reports the most influential parameters on daytime, nighttime and seasonal UHI and the best mitigation policies for the humid subtropical climate, particularly in hot seasons.

Using the exponential distribution as a synthetic curve volume of runoff

The paper analyzes the problem of determining the volume of runoff from the basin by means of runoff simulation methods using a one-parameter exponential distribution. Using the data of the hydrometeorological service on the maximum daily amount of precipitation of various types for some weather stations located throughout the territory of Ukraine, the dependencies of the probability distribution of daily precipitation layers were obtained, in the form of the total depth of precipitation and the total duration of precipitation. As a result of the approximation of the probability distribution of daily precipitation layers, the values of the frequency of events and the average value of the depth of daily precipitation layers for different regions of Ukraine and correlation coefficients were obtained. The use of the cumulative exponential distribution makes it possible to obtain a synthetic curve of the volume of runoff to determine the volume of annual surface runoff that is subject to treatment at treatment facilities.

Temporal Scaling Characteristics of Sub‐Daily Precipitation in Qinghai‐Tibet Plateau

AbstractThe Qinghai‐Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and related natural disasters. However, the lack of sub‐daily precipitation observations in this region has hindered our understanding of rainstorm‐related hazards and their societal impacts. To address this data gap, a new approach is devised to estimate sub‐daily precipitation in QTP using daily precipitation data and geographical information. The approach involves establishing a statistical relationship between daily and sub‐daily precipitation based on data from 102 observation sites. This process results in a set of functions with six associated parameters. These parameters are then modeled using local geographical and climatic information through a machine learning algorithm called support vector regression. The results indicated that the temporal scaling characteristics of sub‐daily precipitation can be accurately described using a logarithmic function. The uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be closely linked to geographical conditions, enabling the creation of a 1‐km parameters data set. This data set can be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set holds significant potential for various applications, including risk analysis, forecasting, and early warning for rainstorm‐related natural disasters in QTP. The innovative method developed in this study proves to be an effective approach for estimating sub‐daily precipitation and assessing its uncertainty in ungauged regions.

A 131-year evidence of more extreme and higher total amount of hourly precipitation in Hong Kong

Based on the observations of hourly precipitation for 131 years from Hong Kong Observatory Headquarters, this study examined the long-term changes in the characteristics of hourly precipitation extremes in terms of intensity, total precipitation amount, duration, and frequency. Results show that the hourly precipitation extremes have significantly intensified by 29%–38% from 1885 to 2022. The 131-year observations evidence that the more extreme the hourly precipitation is (i.e. higher percentiles), the faster the increasing rate it has. Specifically, the magnitudes of hourly precipitation with the 95th, 97.5th, 99th, and 99.9th percentiles increased by rates of 0.03 mm, 0.05 mm, 0.07 mm, and 0.12 mm per year, respectively. Through the secular trend analysis, we found that only the maximum intensity of extreme precipitation events (i.e. events with maximum intensity exceeding the 95th percentiles) shows a significant increasing trend during 1885–2022, while the trends in the total precipitation amount, duration, and mean intensity are not significant. However, by comparing the percentile bin values between three sub-periods of the 131-year record, we found a significant rise over time in the total precipitation amount, mean intensity, and maximum intensity of extreme precipitation events with different intensities (i.e. 95th, 96th, 97th, 98th, and 99th percentiles), while the change in the duration is not significant. The analysis of the frequency of precipitation events shows significant increases in the proportion of extreme precipitation events during 1885–2022. The observations of 53 stations across Hong Kong from 1986 to 2022 show significant intensification and increasing frequency in the hourly precipitation extremes in most areas of Hong Kong. Meanwhile, the precipitation duration shows a decreasing tendency, which may explain the insignificant changes in the total precipitation amount. These findings provide important insights into the longer-term variations in the characteristics of hourly precipitation extremes.

Spatiotemporal Variation in Extreme Climate in the Yellow River Basin and its Impacts on Vegetation Coverage

Global warming and extreme climate events (ECEs) have grown more frequent, and it is essential to investigate the influences of ECEs on vegetation in the Yellow River Basin (YRB) and other environmentally fragile areas. This study was based on data from 86 meteorological stations in the YRB for the period 2000–2020. Twenty-five extreme climate indices (ECIs) were chosen, encompassing four dimensions: extreme value, intensity, duration, and frequency. The trend analysis approach was used to examine the spatiotemporal characteristics of extreme climate conditions. Additionally, geographical detectors and Pearson correlation analysis methods were employed to quantitatively assess the influence of ECEs on the Normalized Difference Vegetation Index (NDVI). The Multiscale Geographically Weighted Regression (MGWR) method was adopted to analyze the regression of twenty-five ECIs. The findings revealed the following: (1) Over the last 21 years, there has been a distinct rise in both the extreme precipitation indices (EPIs) and the extreme temperature indices (ETIs). (2) The spatial distribution of the NDVI throughout the year displayed the characteristic of being high in the south and low in the north. The annual NDVI demonstrated a noteworthy increase at a rate of 0.055/decade, with the enhancement encompassing an extensive area of 87.33%. (3) The investigation revealed that EPIs, including PRCPTOT, R10mm, CWD, R95p, and CDD, had explanatory values surpassing 0.4. This implied that the intensity, frequency, and duration of extreme precipitation played pivotal roles in steering vegetation alterations in the YRB. (4) The correlation between the EPIs and vegetation was greater than the ETIs. Grassland meadows exhibited greater sensitivity to precipitation than woody plants. The EPIs (excluding CDD and SDII) and the ETIs (TXn) displayed a substantial positive correlation with the NDVI in regions hosting grasslands, broadleaf forests, and shrubs. Desert vegetation and cultivated plants were less affected by ECEs. This study underscores the importance of the interplay between extreme climate and vegetation in the YRB. Additionally, it provides a scientific basis for formulating environmental safeguarding strategies.

Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution

AbstractThe flow duration curve (FDC) is the cumulative distribution function, which represents the relationship between the frequency and magnitude of streamflow, and the precipitation duration curves (PDC) follows the same principle. Nowadays, the correlation between the shape of PDC and FDCs, their respective physical control factors including human activities, and their fitting conditions in unmeasured catchments across China have not been fully understood. In this paper, daily precipitation and streamflow data with 30 years records from 224 hydrological stations in the middle and lower Yangtze River basin were chosen to fit PDC and FDCs through gamma distribution. Framework was proposed for modelling FDCs to analyse the relationship, similarity, regional patterns and response mechanism of fitting parameters between PDC and FDCs, dividing the streamflow time series into fast and base flow and stations into three categories in consideration of human activities to attribute the shapes of PDC and FDCs to catchment meteorological and geographical characteristics and physical processes under natural conditions. Results indicate that the parameters of PDC and certain FDCs (TFDC, FFDC, SFDC) share similar spatial patterns but vary for the different duration and interactions of the processes. The climate and catchment characteristics such as extreme properties of precipitation, base flow index (BFI), Pmax*αp and concentration ratio index (CIM) will influence the shape of PDC and FDCs. The relationship between BFI and SFDC/TFDC can be better reflected in “Regulated watersheds”, while CIM/Pmax*αp and PDC/FFDC in “watersheds in the mainstream” are more related. This paper provides a deeper understanding and more accurate way of the application of these parameters in describing and predicting hydrological processes and estimation of PDC and FDCs in unmeasured catchments, and can be applied to more future research about processes based on catchment rainfall‐runoff responses and physical controls.

Analysis model of flood spatiotemporal change characteristics based on k-means clustering algorithm

&lt;p&gt;In order to analyze the spatiotemporal change characteristics of regional flood disaster, analyze the main change characteristics and hydrological response of precipitation distribution in Guangxi, and improve the comprehensive utilization efficiency of water resources, this paper introduces the k-means clustering algorithm to design an analysis model of spatiotemporal change characteristics of flood disaster. First of all, obtain the flood data in Guangxi and the observation data of the national meteorological station within 5 km, complete the collection of the basic data of mountain flood disasters. Based on the collected data, an analysis was conducted on the spatial and temporal distribution of flash floods in the Guangxi region. Secondly, the Kriging spatial interpolation method was used to analyze the spatial distribution of precipitation data in Guangxi. The Mann-Kendall trend test was then employed to examine the trend of precipitation-related statistical parameters over time. Additionally, wavelet theory was applied to analyze the time series of annual precipitation and precipitation with different durations in Guangxi. Subsequently, the k-means clustering algorithm was introduced to construct a model for analyzing the spatiotemporal characteristics of flood changes, determining the concentration and duration of precipitation in different years in the region. Finally, analyze the spatiotemporal change characteristics of flood events in different seasons under various indicators, and realize the analysis of flood spatiotemporal change characteristics. The research results indicate that the Frank Copula function fits the best correlation between annual precipitation and temperature, and can better characterize the correlation between the two. The Frank Copula function has the best fitting effect on the correlation between precipitation and temperature in autumn in Guilin, summer in Nanning, and summer and winter in Beihai. In Guangxi Zhuang Autonomous Region, the annual precipitation shows a gradually decreasing trend, especially at R and P stations. In summary, the Frank Copula function can effectively characterize the correlation and trend of precipitation and temperature in different seasons and regions of Guangxi.&lt;/p&gt;&#x0D;

Spatial and Temporal Variability Characteristics and Driving Factors of Extreme Precipitation in the Wei River Basin

As global climate change intensifies, the global atmospheric circulation process is undergoing significant changes, and the local water vapor pattern has also changed. This study takes the Wei River Basin as the research area. Firstly, an evaluation index system for extreme precipitation was established, and the time-series characteristics of the magnitude, frequency, and duration of extreme precipitation were analyzed. Statistical methods were used to analyze the non-consistency in time-series changes in extreme precipitation indicators. Using spatial heterogeneity analysis methods, the spatial variation differences in extreme precipitation in the Wei River Basin were identified. This study selected the El Niño-Southern Oscillation (ENSO) index, global land-ocean temperature index (LOTI), and land surface temperature (LST) index to quantitatively evaluate the impact of climate change on regional extreme precipitation and analyzed the correlation between temperature and extreme precipitation, identifying the key driving factors of extreme precipitation changes. The conclusions of this study are as follows: (1) The southern region of the Wei River Basin experiences more frequent and intense precipitation events, while the northern region experiences relatively few. (2) From 1981 to 2021, the intensity, frequency, and duration of precipitation events in the Wei River Basin gradually increased, with the most significant increase in extreme precipitation in the Guanzhong Plain. (3) Global climate change has an important impact on precipitation events in the Wei River Basin. The increase in the ENSO, LOTI, and LST indices may indicate an increase in the probability of drought and flood events in the Wei River Basin. The relationships between extreme precipitation and temperature present a peak structure. This conclusion is helpful to better understand the impact of climate change on extreme precipitation in the Wei River Basin and provides some support for the response to extreme meteorological events under the background of future climate change.

Синоптические условия повышения уровня рек на юге Иркутской области и Забайкалья в июле 2023 года

The article discusses natural and anthropogenic factors of rising river levels and associated environmental consequences, existing probabilistic estimates of the amount of floods using various hydrological and climate models. Orographic, climatic and circulation features of the occurrence of floods in the Irkutsk region are identified. A study was carried out of the circulation and weather factors of increasing the intensity and duration of precipitation in early July 2023, which was accompanied by an increase in river levels in the south of the Irkutsk region and the Baikal region. The dynamics of atmospheric processes near the Earth's surface and at the levels of isobaric surfaces AT850 hPa, AT-700 hPa, AT-500 hPa and AT-300 hPa, reflecting the conditions of surface and highaltitude cyclo- and frontogenesis, are analyzed. The contribution of advective flows of heat and moisture, mesojets in the middle troposphere and upper tropospheric jet currents in increasing the amount and duration of atmospheric precipitation is highlighted. A significant contribution to the increase in precipitation and the rise in river levels was made by moist air masses moving in the lower troposphere from the water area of the lake. Baikal, in the middle troposphere from the Yellow Sea. The time of passage of the flood and the height of the rise in the level of individual watercourses as a result of the rains were estimated.