Simple Daily Intensity Index Research Articles

Does ERA5-Land Effectively Capture Extreme Precipitation in the Yellow River Basin?

ERA5-Land is a valuable reanalysis data resource that provides near-real-time, high-resolution, multivariable data for various applications. Using daily precipitation data from 301 meteorological stations in the Yellow River Basin from 2001 to 2013 as benchmark data, this study aims to evaluate ERA5-Land’s capability of monitoring extreme precipitation. The evaluation study is conducted from three perspectives: precipitation amount, extreme precipitation indices, and characteristics of extreme precipitation events. The results show that ERA5-Land can effectively capture the spatial distribution patterns and temporal trends in precipitation and extreme precipitation; however, it also exhibits significant overestimation and underestimation errors. ERA5-Land significantly overestimates total precipitation and indices for heavy precipitation and extreme precipitation (R95pTOT and R99pTOT), with errors reaching up to 89%, but underestimates the Simple Daily Intensity Index (SDII). ERA5-Land tends to overestimate the duration of extreme precipitation events but slightly underestimates the total and average precipitation of these events. These findings provide a scientific reference for optimizing the ERA5-Land algorithm and for users in selecting data.

Rainfall characteristics over the Congo Air Boundary Region in southern Africa: A comparison of station and gridded rainfall products

Strong meridional rainfall gradients exist between the tropics and subtropics in southwestern Africa, bounded to the north by the moist Congo basin and to the south by the Kalahari Desert. This domain received relatively little scientific attention compared to the rest of southern Africa. In this study, the limited available station data are assessed against six gridded rainfall products (CHIRPS, PERSIANN-CDR, ERA5, GPCC and CPC) for various rainfall characteristics. The nearest neighbour approach was used to match the closest rainfall dataset pixel to each station location, with the assumption that each rain gauge represents observation of various pixels of products, irrespective of product resolution. Results reveal that ERA5, CHIRPS and PERSIANN-CDR tend to represent the monthly rainfall totals and number of rainy days well for most stations although magnitudes and monthly peaks differ. CPC and GPCC tend to perform poorly for magnitudes of rainfall, rainy days and monthly cycles especially for Angolan stations. These products also fail to adequately capture spatial distributions of rainfall, with poor representation of the strong gradients found in the region.Correlations between various gridded rainfall products mostly show good agreement in rainfall totals and rainy days. For early summer (October–November-December) moderate rainy days, ERA5 and CHIRPS products tend to have more days than the stations while CPC and GPCC products perform poorly over Angola and in the south. ERA5 generally overestimates rainfall in mountainous regions, while other products tend to underestimate it. Based on the Simple Daily Intensity Index, it was found that for most of the gridded rainfall products tend to overestimate rainfall during rainy days in the northern and wetter part of the domain. Furthermore, for heavy rainfall, CPC and GPCC tend to compare fairly well with stations in the southern and eastern parts of the domain but poorly with those in the western parts. PERSIANN-CDR tends to underestimate heavy rainy days for most stations in early and late summer (January–February-March-April). However, CHIRPS compares well at several stations, while ERA5 performs well for stations located in the south. This study helps provide useful guidance in choosing suitable rainfall gridded datasets for assessing long term rainfall cycles, daily rainfall characteristics as well as extremes over southwestern Africa.

The Dynamics of Vegetation Evapotranspiration and Its Response to Surface Meteorological Factors in the Altay Mountains, Northwest China

The Altay Mountains’ forests are vital to Xinjiang’s terrestrial ecosystem, especially water regulation and conservation. This study evaluates vegetation evapotranspiration (ET) from 2000 to 2017 using temperature, precipitation, and ET data from the China Meteorological Data Sharing Service. The dataset underwent quality control and was interpolated using the inverse distance weighted (IDW) method. Correlation analysis and climate trend methodologies were applied to assess the impacts of temperature, precipitation, drought, and extreme weather events on ET. The results indicate that air temperature had a minimal effect on ET, with 68.34% of the region showing weak correlations (coefficients between −0.2 and 0.2). Conversely, precipitation exhibited a strong positive correlation with ET across 98.91% of the area. Drought analysis, using the standardized precipitation evapotranspiration index (SPEI) and the Temperature Vegetation Dryness Index (TVDI), showed that ET was significantly correlated with the SPEI in 96.47% of the region, while the TVDI displayed both positive and negative correlations. Extreme weather events also significantly influenced ET, with reductions in the Simple Daily Intensity Index (SDII), heavy precipitation days (R95p, R10), and increases in indicators like growing season length (GSL) and warm spell duration index (WSDI) leading to variations in ET. Based on the correlation coefficients and their significance, it was confirmed that the SII (precipitation intensity) and R95p (heavy precipitation) are the main factors causing vegetation ET increases. These findings offer crucial insights into the interactions between meteorological variables and ET, essential information for sustainable forest management, by highlighting the importance of optimizing water regulation strategies, such as adjusting species composition and forest density to enhance resilience against drought and extreme weather, thereby ensuring long-term forest health and productivity in response to climate change.

Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data.

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980-2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985-2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025-2049, F2: 2050-2074, F3: 2075-2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future.

Trends in extreme rainfall and their relationship to flooding episodes in Vhembe district, South Africa

Extreme weather events, such as heavy rainfall, are being increased by climate change in various regions, and such events often cause floods. This study examined the trends and variability of extreme rainfall indices using daily rainfall data (1981–2023) from three study sites at different socio-economic development spectra in Vhembe District, Limpopo Province, South Africa. The analyses focus on indices such as the annual total rainfall from wet days (PRCPTOT), the maximum number of consecutive dry days (CDD) and wet days (CWD), annual maximum 1-day and 5-day rainfall (RX1 day and RX5 day), the Simple Daily Intensity Index (SDII), the number of days exceeding varying amounts of precipitation (R10, R20, R40) and the annual number of wet days with rainfall greater than the 95th and 99th percentile (R95p and R99p) of the 1981–2023 daily rainfall. We discuss the observed trends in extreme rainfall indices in light of the actual flood occurrences to establish linkages. Several statistically significant and marginal changes in extreme rainfall trends were identified and provided key insights into reported flooding events in the district—flooding episodes were mainly attributed to the significant increases in total precipitation (PRCPTOT) and rainfall exceeding the 99th percentile of daily rainfall (R99p). Other significant contributors were declining CDD and increasing RX1day at Duthuni, increasing R40 at Musina as well as increasing R1 and declining CDD at Sane. However, the low altitude, urbanization, poor waste management and inadequate drainage systems were among the key non-climatic drivers of flood risk across the study sites, but these warrant further investigation. The complex interplay between climatic and non-climatic drivers of flood risk underscores the importance of localized climate studies and the need for adaptive strategies to minimize loss and damage. Overall, this research provides valuable insights into localized extreme rainfall trends, which are essential for developing site-specific flood mitigation and adaptation strategies. However, such initiatives require placing vulnerable communities at the centre in order to develop solutions that are locally led and relevant.

Climate Change Impact on Rain-Fed Maize Yield Cultivated with Small-Scale Landowners in Wolaita Zone, Ethiopia

Ethiopia is a country that heavily relies on rainfall-aided cultivation which is carried out by small-scale landowners, leaving it very vulnerable to climate change and fluctuation. The primary goal of this research is to investigate how climate change affects maize yield in Wolaita zone of Ethiopia. The authors were employed a linear regression method to evaluate the relationship between climate parameters and maize yield. Sen's slope magnitude estimator and the Mann-Kendal trend test were used to assess the significance of climate change. The outcome demonstrated that the temperature extreme indices of warm days and the length of warm days were considerably higher by 37.5% and 3.7% of days per year, however, cold days and cold spells were significantly decreased. Over the 1981-2021 periods, there was a significant upward pattern in TXx and TNn at an average of 0.033°C and 0.034°C. There was a considerable decline of 2.3% in the simple daily precipitation intensity index and 33% decreased in extremely heavy precipitation, respectively. The correlation analysis's findings indicated that growing period precipitation and maize outputs were positively correlated, but negatively correlated with maximum and minimum temperatures. Extreme temperature and precipitation were more explained a maize yield than average climate patterns. 12.4%, 14.76%, 13.08%, and 7.95% of maize output variability was attributed by the growing season mean climate conditions, which include precipitation, mean, minimum, and maximum temperature. The variability of maize output was explained by combined impact of precipitation and temperature extremes were 67.7% and 45.0%, respectively. Therefore, livelihood diversification and relevant policy formulation are suggested to adapt inevitable climate change by implementing irrigation and resistant varieties to improve maize yield production.

Spatiotemporal variability identification and analysis for non-stationary climatic trends for a tropical river basin of India

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety.

Extreme climate events and future population exposure under climate change in the Huaihe River basin of China based on CMIP6 multimodel ensembles projections

AbstractThe Huaihe River basin (HRB) of China located in the climate transition zone between warm temperate and subtropical areas is highly sensitive to climatic change. However, the changes in future climate extreme events under anthropogenic warming and the population exposure to these climate extremes in HRB remain unexplored. Here, using the eight commonly used extreme climate indices and based on the bias‐corrections of 16 global climate models (GCMs) in CMIP6, we present a projection and uncertainty analysis of extreme events and investigate the corresponding population exposure risk in HRB under three shared socioeconomic pathways (SSP1‐2.6, SSP2‐4.5, SSP5‐8.5). The 16‐GCM ensemble mean projects an evident warming trend under all three scenarios with a total increase of 25.6–68.0 days in summer days (>25°C) by the end of the century in HRB. Larger increases (decreases) in maximum and minimum temperatures (frost days) are projected in the western HRB. Very heavy rain days (R20mm), maximum 5‐day precipitation (RX5day) and simple daily intensity index (SDII) will experience intensification across most of HRB (especially in southern and western HRB). The consecutive dry days is projected to decrease in northwestern HRB and increase in southern HRB. However, there is a large spatial variability in GCM uncertainty with a higher SSP scenario generally having higher uncertainty. Increases in summer days and R20mm exacerbate population exposure in HRB in near future (2030–2059), but in far future (2070–2099) although summer days (R20mm) continues to rise, population exposure is expected to decrease due to the rapid decline in population density.

Evaluation and projection of extreme precipitation using CMIP6 model simulations in the Yellow River Basin

Abstract The capabilities of 23 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 were evaluated for six extreme precipitation indices from 1961 to 2010 using interannual variability and Taylor skill scores in the Yellow River Basin and its eight subregions. The temporal variations and spatial distributions of extreme precipitation indices were projected from 2021 to 2050 under the shared socioeconomic pathway scenarios (SSP2–4.5 and SSP5–8.5). The results show that most GCMs perform well in simulating extreme values (1-day maximum precipitation (RX1day) and 5-day maximum precipitation (RX5day)), duration (consecutive dry days), and intensity index (simple daily intensity index (SDII)), and perform poor in simulating the threshold indices (precipitation on very wet days (R95p) and number of heavy precipitation days (R10mm)). The projected changes in extreme precipitation indicate that under the SSP2-4.5 scenario, future extreme precipitation will increase by 15.7% (RX1day), 15.8% (RX5day), 30.3% (R95p), 1d (R10mm), and 6.6% (SDII), respectively, decrease by 2.1d (CDD). The aforementioned changes are further enhanced under the SSP5-8.5 scenario. Extreme precipitation changes widely in Hekou Town to Longmen, in the northeastern part of the region from Longmen to Sanmenxia, below Huayuankou, and in the interflow basin.

Spatiotemporal variations of extreme weather events and climate drivers in the Three Gorges Reservoir Area and its surrounding regions from 1960 to 2020

The Three Gorges Reservoir Area and its Surrounding Region (TGRASR) play a crucial role in the Yangtze River Basin. Over the past years, there has been growing concern about the effects of extreme weather events in this area, mainly attributed to climate change. To effectively address the implications of climate change, a thorough understanding of the temporal and spatial variability of these events is necessary. However, some of the previous studies have been limited to analyzing trend changes in events and have not sufficiently considered the connection with atmospheric circulation indices. In this study, we analyzed the spatio-temporal variations of extreme weather events using the Mann-Kendall and Sen's slope trend methods. Then, the correlation between extreme weather events and climate factors is explored using the Mantel test, Geo-Detector method (GDM), and Partial wavelet coherency (PWC) in terms of linear, hierarchical heterogeneity, and nonlinear, respectively. Finally, we investigated the potential climate factors that affect the temporal and spatial variability of extreme weather occurrences. The results showed that(1) among the 12 extreme indices analyzed in the TGRASR region from 1960 to 2020, there was a decreasing trend observed in Consecutive dry days (CDD), Consecutive wet days (CWD), and Annual count of days when precipitation is ≥10 mm (R10), while the remaining indices exhibited an increasing trend. Subsequent analysis employing the M-K mutation revealed that the mutation point for CWD occurred around 1975, resulting in a significant decrease in its trend. Additionally, the Simple daily intensity index (SDII), Annual count of days when the daily maximum temperature (TX) is >25 °C (SU25), and R10 displayed a decreasing trend. Mutations in the trends SU25, Annual count of days when the daily minimum temperature (TN) is >20 °C (TR20), and Annual count of days with at least 6 consecutive days when TX > 90th percentile (WSDI) were observed in 1997, 1999, 2003, and 2005, respectively. Following these mutations, all four indices displayed a substantial increase. (2) CDD, CWD, and R10 exhibit a decreasing trend across most stations, whereas the remaining indices demonstrate an increasing trend. Regarding spatial distribution, indices characterizing precipitation frequency and intensity exhibit higher mean values in the southeastern part of the TGRASR. (3) Mantel test: North Pacific pattern (NP), North Tropical Atlantic (NTA), and Global Mean Land/Ocean Temperature (GMLOT) have significant linear correlations with extreme precipitation and extreme temperature events; GDM: GMLOT and NTA are the main contributors to extreme precipitation and extreme temperature; PWC: 12 extreme indices have some nonlinear associations with all five climate factors. The study's findings can assist decision-makers in formulating sustainable water resource management strategies and measures for mitigating drought and flood risks. Additionally, the study contributes to uncovering climatic factors that influence changes in extreme weather events.

Comprehensive analysis of spatiotemporal variability of rainfall-based extremes and their implications on agriculture in the Upper Ganga Command Area.

The agricultural sector in India primarily relieson monsoon rains, making it necessary for agricultural water managers to study variations in water availability by analyzing rainfall patterns. This information is critical for deciding cropping patterns and irrigation approaches, managing water resources, and improving crop water productivity. The long-term spatiotemporal variability over the command area, India Meteorological Department (IMD) based selection of thresholds for extremes indices, and thorough analysis of the consequences of extreme on agriculture make the study unique. These aspects collectively enhance the perception of the climate-agriculture relationship, which is essential for promoting agriculture resilience under changing rainfall patterns. This type of research is very limited in the literature. Therefore, the present study focuses on comprehensively analyzing the spatiotemporal variability of rainfall and extreme precipitation indices and their implications on agriculture. The study is carried out in the UpperGanga Command Area (UGCA), India's oldest irrigation canal system, by using long-term (1901-2021) high-resolution (0.25 × 0.25°) daily rainfall gridded data obtained from IMD, Pune. Eight different extreme rainfall indices are used to assess spatiotemporal variations. The Mann-Whitney-Pettitt test identified 1970 as a significant change point. Rainfall trends and extreme indices for pre- and post-1970 periods were examined using the non-parametric modified Mann-Kendall test. The results show significant increases in rainfall trends for the annual, monsoon, and kharif seasons from 1901 to 1970, but significant decreases were observed during 1971-2021. This difference resulted in a noticeable decrease in average precipitation in the latter period, approximately 62mm less than in 1901-1970. Similarly, extreme precipitation indices, including the number of rainy days (NxRainy), consecutive dry days (CWD), simple daily intensity index (SDII), and total precipitation (PRCPTOT), exhibited increasing trends in 1901-1970, but they showed significant decreasing trends in 1971-2021. In addition, there is a strong positive correlation between these indices and total precipitation. The study also found that geographical factors influence these trends, with all indices, except SDII, strongly correlated with latitude and elevation, but not with longitude. The implications of these negative rainfall and extreme rainfall trends were further analyzed, and the results indicate a significant impact on the cropping patterns in the study area. The present research findings will be beneficial for regional water resource managers and policymakers in better understanding the existing trends in rainfall distribution over the UGCA.

Trend Analysis for Temperature and Precipitation Extreme Events over the Headwater of Upper Blue Nile, Ethiopia

Purpose: Increased trends in extreme climate events such as prolonged hot or cold days and intensively rainy days or consecutive dry days have greater negative impacts on society. While studies are conducted on climate variability and impacts most of them focus on the mean climatologies. Extrem events of Tempertaure and preciptation however have profound effect on human health and the environment. The present study aims to examine the spatial and temporal characteristics of means and extremes of temperature and precipitation events in the Tana-Beles Sub-basin for the 1980-2015 periods. Methodology: Daily temperature and preciptaion time series was collected from the Ethiopian meteorological institute covering the 1980-2015. From the daily climate variables 22 daily extreme climate indices are calculated based on the Expert Team on Sector-specific Climate Indices (ET-SCI) definitions. Findings: The Monthly, seasonal and annual temperatures show mostly increasing trends. The mean annual maximum and minimum temperatures increased slightly, respectively, by 0.047 and 0.014 oC/yr. The frequency of cold days/nights has reduced by 33/38 days/nights annually; whereas, the frequency of warm days/nights has increased by 91/83 days/nights. The mean monthly, seasonal and annual precipitations also show mostly increasing trends.Among the selected precipitation indices the number of very heavy rain days (R20mm), Simple Daily Intensity Index (SDII), very wet days exceeding the 95th percentile (R95p), and annual total wet days precipitation (RCPTOT) shows increasing trend; whereas, only the maximum 1-day precipitation (Rx1day), show an insignificant decreasing trend. Precipitation during the study period shows high variability spatially and temporally. Unique Contribution to Theory, Policy and Practice: The study reveals increasing in number of hot days and vraible prciptation events, hence measures must be taken to reduce risks of extreme tempertures such as direct effect and indirect malaria distributions to new areas that have been cold before, and agricultural advised forming is also suggested to cope with the variabilities of prciptation. Keywords: Climate trends; extremes climate events; Temperature and rainfall; and Tana-Beles Sub basins.

Temporal and Spatial Variations of Extreme Climate Events in Northwestern China from 1960 to 2020

In the context of global warming, the frequency and intensity of extreme weather and climate events have been increasing. Characterized by scarce water resources and fragile ecosystems, Northwest China has experienced a climate shift since the 1980s. In this study, spatial and temporal patterns of changes in the indices of climate extremes, based on daily maximum and minimum temperature and precipitation at 172 meteorological stations in Northwest China, were analyzed for the period 1960–2020. A total of 26 indices divided into two categories, 16 extreme temperature indices and 10 extreme precipitation indices, were used. Analysis of these indices revealed a general warming trend in the region, which consistent with global warming. The regional occurrence of summer days, tropical nights, growing season length, warm nights, warm days, and warm spell duration index increased by 0.22, 0.14, 0.29, 0.73, 0.46, and 0.11 days/decade, respectively. Over the same period, the occurrence of frost days, icing days, cool nights, cool days, and cold spell duration index decreased by −0.38, −0.21, −0.93, −0.44, and −0.13 days/decade, respectively. The decreasing trends in cold extremes were greater than the increasing trends in warm extremes. Additionally, many regions have experienced increasing trends in several precipitation indices. The annual total wet-day precipitation increased by 5.3 mm/decade. Increasing trends were also evident in simple daily intensity index, heavy precipitation days, very heavy precipitation days, very wet days, and extremely wet days. Consecutive dry days decreased by −1.5 days/decade, while no significant change was observed in consecutive wet days. In contrast to the remarkable spatial consistency of temperature extremes, precipitation extremes exhibited large and expected spatial variability. Most precipitation indices showed increasing trends in the western region of Northwest China and decreasing trends in the eastern part of Northwest China. These results indicate a transition from cold–dry to warm–wet in Northwestern China. Our findings suggest that Northwest China is experiencing more extreme climate events, which could consequently impact hydrological processes, ecological processes, and human health. These observations increase our understanding of the interactions between climate change and regional climate variability, which is conducive to improving disaster prevention.

Suspended sediment yield under alternating dry/wet cycles in a Mediterranean river catchment: the case of the Ofanto River, southern Italy

ABSTRACT This paper investigates how hydrological drought affected suspended sediment yield (SSY) in the Mediterranean Ofanto River basin, southern Italy, and in its five sub-basins from 1951 to 1989. The Standardized Precipitation Evaporation Index over a scale of 12 months (SPEI12) has been used to compute the hydrological drought in the investigated span of time. SPEI12, mean and maximum monthly discharge (Qmean and Qmax), monthly rainfall erosivity, monthly simple daily intensity index and monthly SSY were used to assess the relationships between dry/wet cycles, streamflow and SSY through the Pearson correlation matrix. Qmean and Qmax are significantly correlated with SSY, while SPEI12 and rainfall intensity do not show good correlation with SSY. Furthermore, from the overall analyses it emerges that sediment yield estimations were higher mainly during the wet period following a period of drought or during a drought period.

The Dichotomy of Wet and Dry Trends Over India by Aerosol Indirect Effects in CMIP5 Models

AbstractAerosol‐cloud interactions, also known as aerosol indirect effect (AIE), substantially impact rainfall frequency and intensity. Here, we analyze NEX‐GDDP, a multimodel ensemble of high‐resolution (0.25°) historical simulations and future projections statistically downscaled from 21 CMIP5 models, to quantify the importance of AIE on extreme climate indices, specifically consecutive dry days (CDD), consecutive wet days (CWD), and simple daily intensity index (SDII). The 21 NEX‐GDDP CMIP5 models are classified into models with reliable (REM) and unreliable (UREM) monsoon climate simulated over India based on their simulations of the climate indices. The REM group is further decomposed based on whether the models represent only the direct (REMADE) or the direct and indirect (REMALL) aerosol effects. Compared to REMADE, including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Moreover, the REMALL simulated future CWD and CDD changes are mostly opposite to those in REMADE, indicating the substantial role of AIE in the future projection of dry and wet climates. These findings underscore the crucial role of AIE in future projections of the Indian hydroclimate and motivate efforts to accurately represent AIE in climate models.

Projected changes in extreme precipitation indices over the Lake Urmia basin in Iran

Abstract This study investigates the future changes in precipitation extreme indices in the Lake Urmia Basin during the period 2021–2100 compared to the base period (1987–2016), using the Coupled Model Intercomparison Project Phase 5 models. Trend analysis was performed using Mann–Kendall test and Sen's estimator. The output of these models was downscaled by the Long Ashton Research Station Weather Generator method for the representative concentration pathway (RCPs) 4.5 and 8.5. A model averaging technique was employed to create an ensemble model. The results showed that the average precipitation of the basin will decrease by the end of the 21st century. The projection also showed that the consecutive dry day's index increases based on both scenarios. However, other indices (maximum 1-day precipitation, maximum 5-day precipitation, very wet days, consecutive wet days, simple daily intensity index, and wet-day precipitation) are reduced compared to the base period. Moreover, the slope of significant trends in the RCP8.5 is greater and more severe than that in RCP4.5.

Use of Indices on Daily Timescales to Study Changes in Extreme Precipitation Across Nepal Over 40 Years (1976–2015)

AbstractThis paper presents a comprehensive picture of extreme precipitation across Nepal from 1976 to 2015 based on high‐quality daily precipitation observations from 28 precipitation stations spatially distributed across the country. We computed 11 extreme precipitation indices. The selected indices allow the analysis of extreme events (such as flood/inundation) related to heavy precipitation and evaluate the linkage with indications of agricultural production and drought (such as consecutive dry and wet days). Using a simple daily intensity index (SDII) and the number of days with daily precipitation ≥10 mm (R10), along with the total amount of annual precipitation (PRCPTOT), we summarized the distribution of the wet periods of the year at different locations. We divided the study period into two halves (T1: 1976–1995 and T2: 1996–2015) to examine the temporal mean, trends, and their shift. We emphasized the changes in those indices between these two periods across the country. Our results show that although the amount of precipitation (in terms of PRCPTOT and SDII) did not change significantly between the two periods (i.e., T1 and T2), there exists a clearly increasing trend of extreme precipitation indices in T2 compared to T1. In addition, the consecutive dry days show the rising tendency of elongated dry spells that can adversely affect plant/crop growth in the future. Our study recommends the need for effective management of both precipitation extremes (i.e., drier and wetter), for instance, by introducing irrigation facilities and flood mitigation measures.

Spatio–Temporal Variation of Extreme Climates and Its Relationship with Teleconnection Patterns in Beijing–Tianjin–Hebei from 1980 to 2019

Extreme climate events have a significant impact both on the ecological environment and human society, and it is crucial to analyze the spatial–temporal evolutionary trends of extreme climate. Based on the RClimDex model, this study used trend analysis, probability density function, and wavelet coherence analysis to analyze the spatiotemporal variation characteristics of extreme climate indices and their response mechanisms to teleconnection patterns. The results of the study show that: (1) All the extreme precipitation indices, except max 1-day precipitation amount, max 5-day precipitation amount, and extremely wet days increased, with no significant abrupt changes. The extreme warm indices increased and extreme cold indices decreased. The years with abrupt changes were mainly distributed between 1988 and 1997. (2) Spatially, the extreme precipitation indices of most meteorological stations decreased, except for the simple daily intensity index and the number of very heavy precipitation days. The extreme warm indices of most meteorological stations increased, and the extreme cold indices decreased. (3) Except for consecutive dry days, the frequency of extreme precipitation indices increased significantly, the severity and frequency of high-temperature events increased, while the frequency of low-temperature events increased, but the severity decreased. The results of rescaled range (R/S) analysis indicated that the climate in the Beijing–Tianjin–Hebei region will further tend to be warm and humid in the future. (4) The Polar/Eurasia Pattern, the East Atlantic Pattern, the Arctic Oscillation, and the East Atlantic/West Russian Pattern were most closely associated with extreme climate events in the Beijing–Tianjin–Hebei region. The multi-factor combination greatly enhanced the explanatory power of the teleconnection pattern for extreme climates.

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.

Spatial-Temporal Variations of Extreme Precipitation Characteristics and Its Correlation with El Niño-Southern Oscillation during 1960–2019 in Hubei Province, China

Extreme precipitation could result in many disasters, such as floods, drought, and soil erosion, further bringing severe economic loss. Based on the daily precipitation records during 1960–2019 of 26 stations obtained from the National Meteorological Science Data Center of China, 10 extreme precipitation indices (EPIs: annual total precipitation (PRCPTOT), max-1-day precipitation amount (RX1day), max-5-day precipitation amount (RX5day), number of heavy rain days (R10), number of very heavy rain days (R10), simple daily intensity index (SDII), consecutive dry days (CDD), continued wet days (CWD), very wet days (R95p) and extremely wet days (R99p)) were chosen and used to analyze the spatial-temporal variation of extreme precipitation within Hubei province, China, which is an important industrial and agricultural base in China. Finally, the correlation between El Niño-Southern Oscillation and EPIs was analyzed by cross-wavelet analysis. Results showed that the annual EPIs varied obviously during 1960–2019, and CWD decreased significantly (p < 0.05). The chosen EPIs were higher in eastern and southwestern Hubei compared to other regions, and RX1day, RX5day, R95p, and R99p were increased in most regions. The spatial-temporal variations of spring and summer EPIs were more obvious than those on an annual scale. In summer, all EPIs except CDD should increase in the near future. More attention should be paid to Wuhan, Enshi, and Macheng, where the RX1day, RX5day, R95p, and R99p will increase in these regions. Finally, the RX1day and R10 were positively correlated with MEI (p < 0.05), while the RX5day, CDD, CWD, and R99p were negatively correlated with MEI (p < 0.05). The extreme precipitation events within Hubei were affected by the El Niño-Southern Oscillation. The results could provide a possible driving factor for precipitation prediction and natural hazard prevention within Hubei province, China.