Annual Maximum Precipitation Research Articles

Deciphering the Trend and Interannual Variability of Temperature and Precipitation Extremes over Greenland during 1958–2019

Abstract Greenland experienced multiple extreme weather/climate events in recent decades that led to significant melting of the ice sheet. However, how the intensity of extreme climate events over Greenland varied under recent warming has not been fully examined. Here, we collect 176 in situ observations over Greenland and demonstrate that the observed extreme temperature/precipitation events over Greenland are well captured by the RACMO2.3p2 model, in terms of climatological distribution, interannual variability, and long-term trend. Thus, we then investigate the spatiotemporal features of extreme events over Greenland during 1958–2019, using the daily model outputs at 5.5-km resolution. The simulated annual maximum temperature exhibits a significant increasing trend (∼0.13°C decade−1) during 1958–2019, whereas there is a weakening trend (−0.24°C decade−1) in annual minimum temperature over Greenland, especially after the 1990s (−1.24°C decade−1). For the interannual variability, changes in temperature extremes between warm and cold temperature years share large similarities with the distributions of long-term trends. The extreme precipitation events measured by annual maximum daily precipitation amount show a profound increasing trend (0.52 mm day−1 decade−1) over northeastern Greenland during 1958–2019, with large interannual variability in the ice-free coastal region and southern Greenland. Additionally, the changes in extreme warm and cold events are generally linked with the variation of Greenland blocking in summer and Arctic polar vortex in winter, respectively, in terms of favorable circulation background, and the extreme precipitation events are often associated with the position of the polar jet stream.

Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS

Abstract. This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12 km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth-generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions. First, how well are the 3 and 24 h precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC) 4 km Stage IV and Oregon State University Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data on 3 and 24 h timescales, respectively. Our analysis suggests that the 12 km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3 and 24 h precipitation maximum over the CONUS, in contrast to ERA5, which generally underestimates these quantities mainly over the eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3 and 24 h annual and seasonal maximum precipitation. WRF exhibits seasonally dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics but is susceptible to common regional climate model biases. Consequently, if used as input data for domain-specific models, we suggest moderate bias correction be applied to the dynamically downscaled product.

Relation between storm characteristics and extreme precipitation statistics over CONUS

In this paper we use a statistical framework to analyze the relation between storm properties and the statistics of extreme precipitation. We identify storm events using a 24-hour dry hiatus separation. We investigate the statistics of the hourly maximum intensity for the heaviest storm events at durations of 1 and 24 h. A two-parameter Weibull distribution is used to represent precipitation frequencies at several stations from a quality-controlled hourly precipitation dataset over the contiguous United States, encompassing seven climate zones. The Spearman correlation between the distribution parameters and a selection of storm properties (duration, intensity, decorrelation time, convective-like ratio) and station properties (elevation and latitude) is used to measure the relation of these properties with the statistics of extreme precipitation. Our results indicate that observed annual maximum hourly precipitation over the entire study area are likely samples from the used distribution, implying that a two-parameter Weibull distribution is suitable for modeling hourly and 24-hour precipitation extremes over the contiguous United States. The spatial variability of shape parameters obtained for hourly events showed lighter tails in the west coast when compared to the rest of CONUS, while the central-north displays heavier tails. We identify statistically significant links (at the 95% confidence level) between storm characteristics connected with the underlying processes (e.g., typical storm duration, typical temporal autocorrelation, proportion of convective-like storms) and the parameters of the distribution. Notably, characteristics typical of convective precipitation, e.g., sharp decorrelation time and high hourly intensity, tend to be related to distributions with heavier tails. These results provide a first step towards linking the characteristics of storms with the local statistics of extremes.

Frequency analysis of annual maximum daily precipitation in northeastern Algeria: mapping and implications under climate variability

Frequency analysis of annual maximum daily precipitation in northeastern Algeria: mapping and implications under climate variability

Impacts of increasing greenhouse gas concentrations and deforestation on extreme rainfall events in the Amazon basin: A multi‐model ensemble‐based study

AbstractThe main objective of this study was to evaluate the effects of increasing greenhouse gases (GHGs) and total deforestation of the Amazon on extreme rainfall events in the Amazon basin. In order to quantify these impacts, numerical experiments were performed with the Eta regional climate model forced from initial and boundary conditions from the BESM, HadGEM2‐ES, and MIROC5 earth system models. In the experiment related to the increase in GHGs, numerical simulations were performed according to the IPCC RCP8.5 scenario. The effect of deforestation was quantified via an experiment in which the forest in the Amazon basin was replaced by areas of degraded pasture in the Eta model. For the analyses of the changes in extreme rainfall events, the multi‐model ensemble technique was used. The results were evaluated in terms of anomalies relative to the sensitivity and control experiments. In the results, it was observed that in an RCP8.5‐type GHG emission scenario there is a statistically significant increase in the maximum number of consecutive days without rain, a reduction in the maximum number of consecutive rainy days, reduction in total annual precipitation, and reduction in maximum annual precipitation accumulated over one and 5 days, respectively. The results for the scenario with increased GHGs and large‐scale deforestation in the Amazon basin are similar to the RCP8.5 scenario, but the intensity of changes in climate indices is significantly greater. It was also verified that the changes in the climatic indices are strongly associated with alterations in the energy balances at the surface and, consequently, in the large‐scale circulation. In general, it can be highlighted that the climate in the Amazon region is strongly dependent on the presence of the forest.

Climate-Related Risk Modeling of Banana Xanthomonas Wilt Disease Incidence in the Cropland Area of Rwanda.

Banana Xanthomonas wilt (BXW) is a major threat to banana production in Rwanda, causing up to 100% yield loss. There are no biological or chemical control measures, and little is known about the potential direction and magnitude of its spread; hence, cultural control efforts are reactive rather than proactive. In this study, we assessed BXW risk under current and projected climates to guide early warning and control by applying the maximum entropy (Maxent) model on 1,022 georeferenced BXW datapoints and 20 environmental variables. We evaluated the significance of variables and mapped potential risk under current and future climates to assess spatial dynamics of the disease distribution. BXW occurrence was reliably predicted (mean validation AUC values ranging from 0.79 to 0.85). Precipitation of the coldest quarter, average maximum monthly temperature, annual precipitation, and elevation were the strongest predictors, which were responsible for 22.1, 13, 12.6, and 9.4% of the observed incidence variability, respectively, while mean temperature of the coldest quarter had the highest gain in isolation. Furthermore, the most susceptible regions (western, northern, and southern Rwanda) were characterized by elevation (1,350 to 2,000 m), annual precipitation (900 to 1,700 mm), and average temperature (14 to 20°C), among other variables, suggesting that a consistent, rainy, and warm climate is more favorable for BXW spread. Under the future climate, the risk was predicted to increase and spread to other regions. We conclude that climate change will likely exacerbate BXW-related losses of banana land area and yield under the influence of temperature and moisture. Our findings support evidence-based targeting of extension service delivery to farmers and national early warning for timely action.

Exploring the uncertainty of weather generators’ extreme estimates in different practical available information scenarios

ABSTRACT Stochastic weather generators are powerful tools capable of extending the available precipitation records to the desired length. These, however, rely upon the amount of information available, which often is scarce, especially in arid and semi-arid regions. No studies can be found dealing with the uncertainty associated with these estimates related to the amount of information used in the weather generation calibration process, which is precisely the aim of the present study. A Monte Carlo simulation from a synthetic population was performed, evaluating the uncertainty of the simulated quantiles in different practical available information scenarios. The results showed that incorporating a regional study of annual maximum daily precipitation in the model parameterization clearly reduced the uncertainty of all quantile estimates. In addition, it has been proved that the uncertainty of these estimates increases with the population extremality, thus marking the importance of integrating additional information in regions with extreme precipitation patterns.

The Impact of Environmental and Host Factors on Human Cystic Echinococcosis: A County-Level Modeling Study in Western China.

Human cystic echinococcosis (CE) is a parasitic disease caused by tapeworms from the Echinococcus granulosus genus, potentially affected by the environment and host animals. West China is one of the most endemic areas of human CE nation and worldwide. The current study identifies the crucial environmental and host factors of human CE prevalence in the Qinghai-Tibet Plateau and non-Qinghai-Tibet Plateau regions. An optimal county-level model was used to analyze the association between key factors and human CE prevalence within the Qinghai-Tibet Plateau. Geodetector analysis and multicollinearity tests identify key factors, and an optimal model is developed through generalized additive models. In the Qinghai-Tibet Plateau, four key factors were identified from the 88 variables, such as maximum annual precipitation (Pre), maximum summer normalized difference vegetation index (NDVI), Tibetan population rate (TibetanR), and positive rates of Echinococcus coproantigen in dogs (DogR). Based on the optimal model, a significant positive linear relationship was observed between maximum annual Pre and human CE prevalence. A probable U-shaped curve depicts the non-linear relationship between maximum summer NDVI and the human CE prevalence. Human CE prevalence possesses significant positive non-linear relationships with TibetanR and DogR. Human CE transmission is integrally affected by environmental and host factors. This explains the mechanism of human CE transmission based on the pathogen, host, and transmission framework. Therefore, the current study provides references and innovative ideas for preventing and controlling human CE in western China.

Non-stationary evaluation of runoff peaks in response to climate variability and land use change in Ferson Creek, Illinois, USA.

In this paper, we examine how surface runoff affects public safety and urban infrastructure worldwide and how human activity has significantly altered the frequency and magnitude of these events. We investigate this issue in Ferson Creek, IL, USA. Our study focuses on three specific areas of impact: (1) the primary reasons for a considerable increase in average runoff peaks, using annual maximum runoff discharge and annual maximum precipitation and temperature to evaluate the role of climate variability; (2) the effect of land use change on runoff peaks by coupling dominant land use categories with annual maximum runoff discharge; and (3) the use of return level plots as a reference to explore the watershed's sensitivity to land use change. Our findings indicate that land use change has a greater effect on runoff peak values than climate variability in our region of interest. The agricultural areas of Ferson Creek have been most affected by the rapid transformation of about 20% of their land into developed areas. Although agricultural areas can sometimes intensify runoff peaks, their reduction has led to excessive runoff discharges in Ferson Creek, as they have higher relative infiltration capacity than developed areas. We conclude that each watershed has its own fingerprint in terms of the connection between its land use types and hydrological patterns and that the region is most sensitive to the percentage of forests. These results are essential for improving infrastructure design and risk estimation methods in the region of interest.

Brief communication: On the extremeness of the July 2021 precipitation event in western Germany

Abstract. The weather extremity index (WEI) and the cross-scale WEI (xWEI) are useful for determining the extremeness of precipitation events. Both require the estimation of return periods across different precipitation duration levels. For this purpose, previous studies determined annual precipitation maxima from radar composites in Germany and estimated the parameters of a generalized extreme value (GEV) distribution. Including the year 2021 in the estimation of GEV parameters, the devastating event in July 2021 drops from a rank of first to fourth regarding the WEI compared to all events between 2001 and 2020 but remains the most extreme regarding the xWEI. This emphasizes that it was extreme across multiple spatial and temporal scales and the importance of considering different scales to determine the extremeness of rainfall events.

Regional precipitation frequency analysis for 24-h duration using GPM and L-moments approach in South China

There are little researchers that combined satellite-based products and L-moments methods in frequency analysis. The objective of this study was to explore and evaluate the satellite precipitation data applied in univariate L-moments method. The Shaoguan City of South China was selected as the study area. Annual maximum precipitation (AMP) respectively from observational rainfall and Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) gridded rainfall was used for the calculation of estimates using regional L-moments analysis (RLMA) method. Then, the quantiles at grids were corrected by mean bias correction (MBC) method with the estimates at sites. The results showed that the number of homogeneous regions as well as the spatial distribution of parameters ({C}_{v}, {C}_{s}, and {C}_{k}) for two data sets are different. When the return periods are larger than 40 years, the estimates at sites are also larger than those at grids and the difference will increase as return period increases. After being modified, the quantiles from IMERG are more reasonable and combine the spatial distribution characteristic of estimates from two cases, which indicates that the estimation calculated by satellite products with L-moments approach is applicable and more reasonable. This study can also provide reference for solving the issues regarding the density and coverage of rain gauges that affect the accuracy of frequency analysis, especially in remote areas of China or most parts of the word.

Modeling Extreme Stochastic Variations using the Maximum Order Statistics of Convoluted Distributions

Modeling extreme stochastic phenomena associated with catastrophic temperatures, heat waves, earthquakes and destructive floods is an aspect of proactive mitigation of risk. Hydrologists, reliability engineers, meteorologist and researchers among other stakeholders are faced with the challenges of providing adequate model for fitting real life datasets from the extreme natural hazardous occurrences in our environment. Convoluted distributions (CD) and generalized extreme value (GEV) distribution for r- largest order statistics (r-LOS) have been some of the prominent existing techniques for modeling the extreme events. This study explored the properties of order statistics from the convoluted distribution as alternative procedure for analyzing the extreme maximum with the aim of obtaining superior modeling fit compared to some other existing techniques. The new procedure called MAXOS-G employed the potential properties of the Maximum Order Statistics (MAXOS) and the flexibilities of convoluted distributions where G is taken to beWeibull-Exponential Pareto (WEP) and the New Kumaraswamy-Weibull (NKwei) distributions. The maximum order statistics of the WEP distribution (MAXOS-WEP) and NKwei distribution (MAXOS-NKwei) was derived and applied to three datasets consisting of annual maximum flood discharges, annual maximum precipitation and annual maximum one-day rainfall. Some properties of the MAXOS-WEP was investigated including the moment, mean, variance, skewness, and kurtosis. Characterization of WEP distribution by the L-moment of maximum order statistics was presented and coefficient of L-variation, L-skewness and L-kurtosis were derived. The results from the application to three datasets using R-software justified the importance of this new procedure for modeling the maximum events. The MAXOS-NKwei and MAXOS-WEP models provide superior goodness-of-fit to the datasets than the WEP and NKwei distributions and better than some previously proposed convoluted distributions for modeling the datasets.

Examining Seasonality Based on Probabilistic Properties of Extreme Precipitation Timing in the Eastern United States

Global warming is likely to provoke extreme storms in the eastern United States (eUS), ultimately affecting the probabilistic distribution of the dates of daily maximum precipitation. In this study, probabilistic properties of timing of annual maximum precipitation (AMP) were studied using circular statistics at 583 sites in the eUS (1950–2019). A kernel circular density method was applied to examine distributional modes of timing of AMP. The results of circular median show that seasonality is pronounced across the eUS with many locations having their median date of occurrence in summer, and AMP seasonality is strong in the East North Central region. Similarly, results of circular density method applied to the distribution of AMP timing shows that around 90% of the sites have two or three modes of AMP seasonality in the eUS. Comparison of seasonality between two historical records of equal length (1950–1984 and 1985–2019) shows great spatial variability across the eUS. Temporal changes in seasonal modes for AMP dates revealed four different cases of seasonality changes: (i) weakening of seasonality, (ii) strengthening of seasonality, (iii) strong seasonality for both the old and recent periods, (iv) or uniform or no preferred seasonality for both periods. While a spatial coherence of seasonality changes was not observed, majority of sites showed strong seasonality (case iii) for old and recent periods mainly during summer and fall seasons.

The Relationship between the Built Environment and Climate Change: The Case of Turkish Provinces

The relationship between the built environment and climate change has been discussed from many perspectives. This study examines the effect of the built environment on climate change indicators in Turkish provinces over the last 18 years, contributing to the literature on built environment analyses regarding both urban and rural areas, unlike other studies that have focused mostly on urban areas. The study discusses the changes in climate indicators using maps and analyzes the effects of the built environment on climate change using linear regression. The results indicate that provinces in Türkiye have experienced climate change effects such as increased annual mean temperature, maximum temperature, maximum precipitation, extreme weather events, and drought. These effects differed both in terms of geography and the subperiods over the examined period. The results also demonstrate the increase in the built environment to have a positive correlation with the increases in annual maximum temperature and the annual number of extreme weather events. The built environment in Türkiye increased 63% between 1990 and 2018, and the average number of extreme weather events per province increased from 0.3 to 8 over this same period. At the same time, the average annual mean temperature increased from 12.9 to 15.1 °C, the average maximum temperature went up from 24.6 to 25.8 °C, the average annual maximum precipitation increased from 125.6 to 157.7 mm, and the average number of dry months per year increased from 3.4 to 3.8.

Persistent and nonpersistent regional extreme total, daytime, and night‐time precipitation events over southwest China (1961–2019)

AbstractExtreme persistent precipitation is becoming increasingly common globally, and persistent extreme regional precipitation events (PERPEs) pose a particularly serious threat to humans and environmental systems. This study investigated total precipitation (TP), daytime precipitation (DP), and night‐time precipitation (NP) in southwest China (SWC) during 1961–2019. The occurrence of TP, DP, and NP was analysed under the conditions of persistence over 1–5 days, temporal persistence over >2 days, and temporal nonpersistence over ≤2 days, and spatiotemporal overlap for at least 3 days (i.e., a PERPE) using the objective identification technique for regional extreme events. The results indicated the following. (a) Annual maximum daily precipitation ≥200 (100) mm in terms of TP (NP) was recorded mainly in the Sichuan Basin (SCB) and eastern parts of the Yunnan–Guizhou Plateau (YGP), and it occurred mostly in summer. For DP, annual maximum daily precipitation of ≥100 mm was recorded throughout SWC, and it occurred mainly in winter in the Henduan Mountains, summer in the SCB, and spring in western parts of the YGP. (b) The magnitudes of NP and DP differed for events persisting for 1–5 days. The average amount of precipitation with temporal persistence increased during 1961–2019 for TP, DP, and NP. However, the frequency of occurrence of events with temporal persistence increased more in comparison with that of events with temporal nonpersistence. Overall, the frequency of occurrence of events with temporal persistence or temporal nonpersistence in the western SCB and eastern YGP increased more for TP and NP than for DP. (c) In the study period, 22, 44, and 4 PERPEs were detected for TP, NP, and DP, respectively, occurring mainly in the SCB, indicating that TP was dominated by night‐time PERPEs. These findings improve understanding of PERPEs changes in SWC and provide scientific support for implementation of measures intended to prevent and mitigate extreme flood events and waterlogging.

Assessment of climate change impact on maximum precipitation in Spain

AbstractThe potential effect of climate change on maximum precipitation is studied by considering several temporal intervals for its incorporation into flood risk management in Spain. The methodology used allows for a complete assessment of the effect of climate change on maximum precipitation by accounting for uncertainty, and may be applied to other case studies. Behaviour regarding observations, trends and changes in climate model maximum precipitation series are evaluated. In turn, multi‐model mean (and percentile) relative changes accounting for statistical significance, torrential rate changes, and relative changes in daily annual maximum precipitation accumulated along river network are estimated. Results show a large variability in climate model outcomes. In general, a suitable behaviour is found for the climate model ensemble regarding observations: it properly represents seasonality of daily annual maximum precipitation for all regions, and observation‐based statistics are found to be within range of simulation‐based statistics for most regions. As an exception, daily annual maximum precipitation is underestimated in Mediterranean regions, which may be attributed to known limitations of current climate models in adequately reproducing convective precipitation typical of those areas. Results also show an intensifying increase in trends and changes as precipitation duration decreases, a torrentiality rise, and relative changes related to accumulated maximum precipitation of up to 35%. These findings support the notion that the effect of climate change may be stronger for extreme precipitation associated with short precipitation durations. This may be attributed to a stronger scaling between change in extreme precipitation and mean surface temperature increase for shorter durations. The findings also highlight the importance of analysing statistical significance of relative changes in quantile for allowing its attribution to climate change. Statistical model choice is found to be essential for identifying significant changes.

Using Nonstationary Depth-Frequency Curves to Characterize Local Precipitation Trends

Highlights Design storms should incorporate nonstationarity under changing climate scenarios. Three generalized extreme value distributions were fitted to represent nonstationarity for local precipitation analysis. The nonstationary models proposed in this study perform well at sites with strong precipitation trends. Abstract. As climate change advances, the stationarity assumption that governs traditional precipitation analysis is becoming untenable. Studies that incorporate nonstationarity typically use global circulation model (GCM) projections to determine the magnitude and direction of expected precipitation changes. However, the high computational costs and the coarse spatial resolution of GCMs make this method unsuitable for local precipitation analysis. In this study, nonstationarity is represented by a precipitation probability distribution with time-varying parameters. Three generalized extreme value (GEV) distributions are fitted: (1) the shift model, where the GEV location parameter varies linearly with time, (2) the stretch model, where the GEV location and scale parameters both vary linearly with time, and (3) the stationary model, a time-invariant distribution provided for the purpose of comparison. This procedure is applied to 24-h annual maximum precipitation records for ninety years (1900-1989) at five long-term measuring sites in Pennsylvania. Results varied among the five sites, suggesting that localized climate effects can cause precipitation differences at a small spatial scale. No significant nonstationarity was detected in two of the five locations. In three locations, however, increases in GEV location and scale combined to create a substantial, though not always significant, rise in the frequency of extreme precipitation. These trends were extrapolated forward over 30 years (1990-2019) and compared with an observed distribution for that year. The nonstationary models appeared to perform better at sites with stronger precipitation trends, which suggests a simple procedure for selecting sites where nonstationary analysis is most needed. Keywords: Climate change, Design storm, Generalized extreme value, Nonstationarity.

Mid-century change analysis of temperature and precipitation maxima in the Swat River Basin, Pakistan

This study analyzes trends in historical (1989–2018) and projected (2041–2060) temperature and precipitation maxima in the Swat River Basin, Pakistan. This basin has a history of climate-related disasters that directly affected livelihood and personal safety in local communities and are becoming more intense and more frequent due to changing climate. Major economic sources of this basin are agriculture and tourism, both highly sensitive to extreme climate events. Therefore, it is very important to assess future trends in extremes of temperature and precipitation. Non-parametric tests were employed for currently acquired data, while future projections were assessed using the statistical downscaling model (SDSM) with CanESM2 GCM under three scenarios: representative concentration pathways (RCPs) of 2.6, 4.5, and 8.5. The R2 value between monthly observed and simulated temperatures varied from 0.82 to 0.91 and 0.92 to 0.96 for training and confirmation periods, respectively. For areal precipitation, an R2 value of 0.49 was noted for calibration and 0.35 for validation. Observed temperatures showed a decreasing trend at all stations except Saidu Sharif, but the differences were not significant. Precipitation showed an increasing trend at two stations, Kalam and Malam Jabba, and a decreasing trend at two other stations, Dir and Saidu Sharif. A >2°C rise was noted for the annual projected maximum temperature (2041–2060) at areal and Dir, while Kalam, Malam Jabba, and Saidu Sharif showed a 1°C rise. For precipitation, an approximately 12% increase in annual maximum (areal) and seasonal precipitation (summer and autumn) was seen under all scenarios except RCP 4.5 in which there was a 20% and 32% increase in summer and autumn, respectively. The performance of SDSM in simulating maximum temperature and precipitation was satisfactory.

Spatial and Temporal Analysis of Extreme Precipitation under Climate Change over Gandaki Province, Nepal

This paper presents a research study of expected precipitation extremes across the Gandaki Province, Nepal. The study used five indices to assess extreme precipitation under climate change. Precipitation output of two Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase Six (CMIP6) were used to characterize the future precipitation extremes during the rainfall season from June to September (JJAS) and overall days of the year. To characterize extreme precipitation events, we used daily precipitation under the SSP2–4.5 and SSP5–8.5 scenarios from the Beijing Climate Center and China Meteorological Administration, China; and Meteorological Research Institute (MRI), Japan. Considering large uncertainties with GCM outputs and different downscaling (including bias correction) methods, direct use of GCM outputs were made to find change in precipitation pattern for future climate. For 5-, 10-, 20-, 50-, and 100-year return periods, observed and projected 24 h and 72 h annual maximum time series were used to calculate the return level. The result showed an increase in simple daily intensity index (SDII) in the near future (2021–2040) and far future (2081–2100), with respect to the base-year (1995–2014). Similarly, heavy precipitation days (R50 mm), very heavy precipitation days (R100 mm), annual daily maximum precipitation (RX1day), and annual three-day maximum precipitation (RX3day) indices demonstrated an increase in extreme precipitation toward the end of the 21st century. A comparison of R50 mm and R100 mm values showed an extensive (22.6% and 63.8%) increase in extreme precipitation days in the near future and far future. Excessive precipitation was forecasted over Kaski, Nawalparasi East, Syangja, and the western half of the Tanahun region. The expected increase in extreme precipitation may pose a severe threat to the long-term viability of social infrastructure, as well as environmental health. The findings of these studies will provide an opportunity to better understand the origins of severe events and the ability of CMIP6 model outputs to estimate anticipated changes. More research into the underlying physical factors that modulate the occurrence of extreme incidences expected for relevant policies is suggested.

Greening or browning? The macro variation and drivers of different vegetation types on the Qinghai-Tibetan Plateau from 2000 to 2021.

Vegetation greenness is one of the main indicators to characterize changes in terrestrial ecosystems. China has implemented a few large-scale ecological restoration programs on the Qinghai-Tibetan Plateau (QTP) to reverse the trend of ecosystem degradation. Although the effectiveness of these programs is beginning to show, the mechanisms of vegetation degradation under climate change and human activities are still controversial. Existing studies have mostly focused on changes in overall vegetation change, with less attention on the drivers of change in different vegetation types. In this study, earth satellite observation records were used to robustly map changes in vegetation greenness on the QTP from 2000 to 2021. The random forest (RF) algorithm was further used to detect the drivers of greenness browning on the QTP as a whole and in seven different vegetation types. The results show that an overall trend of greening in all seven vegetation types on the QTP over a 21-year period. The area of greening was 46.54×104 km2, and browning was 5.32×104 km2, representing a quarter and 2.86% of the natural vegetation area, respectively. The results of the browning driver analysis show that areas with high altitude, reduced annual precipitation, high intensity of human activity, average annual maximum and average annual minimum precipitation of approximately 500mm are most susceptible to browning on the QTP. For the seven different vegetation types, their top 6 most important browning drivers and the ranking of drivers differed. DEM and precipitation changes are important drivers of browning for seven vegetation types. These results reflect the latest spatial and temporal dynamics of vegetation on the QTP and highlight the common and characteristic browning drivers of vegetation ecosystems. They provide support for understanding the response of different vegetation to natural and human impacts and for further implementation of site-specific restoration measures.