Wet Days Research Articles

REGIONÁLNE KLIMATICKÉ SCENÁRE FREKVENCIE VÝSKYTU DNÍ SO ZRÁŽKAMI NA SLOVENSKU

We present scenarios of how frequency of days with precipitation may change over the territory of Slovakia by 2050 and 2080 in the article. Climatic scenarios were created from three regional climate models EURO-CORDEX for the pessimistic emission scenario RCP8.5. We analysed the occurrence of the number of precipitation days for class intervals: 5 – 9.9 mm, 10 – 19.9 mm, 20 – 29.9 mm, 30 – 39.9 mm, as well as for open intervals ≥ 5 mm, ≥ 10 mm, ≥ 20 mm, ≥ 30 mm, and ≥ 40 mm. To correct systematic errors in climate models, we used data (daily precipitation totals) from the national network of rain gauges. The RCM models were corrected in two steps: first in terms of excessive occurrence of so-called “drizzle days”, and then in terms of systematic underestimation of daily sums across the entire distribution function (bias correction). The occurrence of days with daily rainfall exceeding 40 mm for the horizons of 2050 and 2080 is presented in the form of maps. As our analyses revealed, the annual frequency of days with daily rainfall exceeding 40 mm is expected to increase by approximately 20% by the middle of the 21st century and by up to 60% by the end of the century compared to the present. The paper also includes tables with relative changes and absolute frequencies of wet days for 75 selected locations in Slovakia.

Future Increase in Extreme Precipitation: Historical Data Analysis and Influential Factors

With global warming driving an increase in extreme precipitation, the ensuing disasters present an unsustainable scenario for humanity. Consequently, understanding the characteristics of extreme precipitation has become paramount. Analyzing observational data from 1961 to 2020 across 29 meteorological stations in Heilongjiang Province, China, we employed kriging interpolation, the trend-free pre-whitening Mann–Kendall (TFPW–MK) method, and linear trend analysis. These methods allowed us to effectively assess the spatiotemporal features of extreme precipitation. Furthermore, Pearson’s correlation analysis explored the relationship between extreme precipitation indices (EPIs) and geographic factors, while the geodetector quantified the impacts of climate teleconnections. The results revealed the following: (1) There has been a clear trend in increasing extreme precipitation over the last few decades, particularly in the indices of wet day precipitation (PRCPTOT), very wet day precipitation (R95P), and extremely wet day precipitation (R99P), with regional mean trends of 10.4 mm/decade, 5.7 mm/decade, and 3.4 mm/decade, respectively. This spatial trend showed a decrease from south to north. (2) Significant upward trends were observed in both spring and winter for the maximum 1-day precipitation (RX1day) and the maximum 5-day precipitation (RX5day). (3) The latitude and longitude were significantly correlated with the most extreme precipitation indices, while elevation showed a weaker correlation. (4) Extreme precipitation exhibited a nonlinear response to large-scale climate teleconnections, with the combined influence of factors having a greater impact than individual factors. This research provides critical insights into the spatiotemporal dynamics of extreme precipitation, guiding the development of targeted strategies to mitigate risks and enhance resilience. It offers essential support for addressing regional climate challenges and promoting agricultural development in Heilongjiang Province.

Projected future changes in extreme precipitation over China under stratospheric aerosol intervention in the UKESM1 climate model

Abstract. Extreme precipitation events are linked to severe economic losses and casualties in China every year; hence, exploring the potential mitigation strategies to minimize these events and their changes in frequency and intensity under global warming is of importance, particularly for the populous subregions. In addition to global warming scenarios, this study examines the effects of the potential deployment of stratospheric aerosol injection (SAI) on hydrological extremes in China based on the SAI simulations (G6sulfur) of the Geoengineering Model Intercomparison Project (GeoMIP) by the UK Earth System Model (UKESM1) simulations. G6sulfur is compared with simulations of the future climate under two different emission scenarios (SSP5-8.5 and SSP2-4.5) and a reduction in the solar constant (G6solar) to understand the effect of SAI on extreme precipitation patterns. The results show that under global warming scenarios, precipitation and extreme wet climate events during 2071–2100 are projected to increase relative to the control period (1981–2010) across all the subregions in China. Extreme drought events show a projected increase in southern China. The G6sulfur and G6solar experiments show statistically similar results to those under SSP2-4.5 in extreme precipitation intensities of China in UKESM1. These results are encouraging. The efficacy of SAI in decreasing extreme precipitation events and consecutive wet days is more pronounced than that of G6solar when compared to SSP2-4.5. While both G6sulfur and G6solar show drying at high-latitude regions, which is consistent with our understanding of the spin-down of the hydrological cycle under SRM. Given the limitations of the current model and the small ensemble size, and considering that the hydrological effects are less beneficial than those indicated for temperature, it is recommended that further, more comprehensive research be performed, including using multiple models, to better understand these impacts.

Modeling soil acidity (pH) dynamics under extreme agroclimatic conditions in Horro Guduru Wallaga Zone, northwestern Ethiopia.

Soil plays a critical role in nutrient availability, microbial activity, and fertility in agriculture. However, the effects of agroclimatic conditions on soil pH are not well understood, particularly in the Horro Guduru Zone of Ethiopia. This study aimed to investigate the soil pH under extremely wet and dry conditions across 3 shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Baseline agroclimatic data (1981-2010) and future projections (2041-2070) were obtained from the European Commission Climate Change Services. Soil pH data at a 250m resolution were extracted from the FAO-UNESCO global soil map. Missing values, multicollinearity, and outliers were addressed before modeling. Predictive models, including neural networks, generalized regression, and bootstrap forests, were validated, with the generalized regression model showing the best performance. The results indicate that soil pH decreases under consecutive dry‒wet conditions and increases with increasing maximum day temperatures across all scenarios. Soil pH is significantly influenced by the number of consecutive dry days, consecutive wet days, and maximum day temperature. The SSP1-2.6 and SSP2-4.5 scenarios resulted in improved pH levels, whereas SSP5-8.5 led to a decrease in soil pH, averaging 5.79 and decreasing to 5.54. These findings suggest that under SSP5-8.5, soil health and farming productivity may be compromised. This study emphasizes the need to adjust soil management practices based on prevailing climatic conditions to ensure soil health and agricultural sustainability.

The effect of day-to-day temperature variability on agricultural productivity

Abstract With rising extreme weather events due to climate change, the impact on agricultural production has become increasingly severe. Yet, there has been a significant gap in research that assesses the influence of day-to-day temperature variability on agricultural productivity on a global scale. Our study addresses this gap by exploring the effects of day-to-day temperature variability and the change of rainfall patterns on agricultural productivity worldwide from 1961 to 2018. The results reveal that day-to-day temperature variability not only has a direct, negative impact on agricultural Total Factor Productivity (TFP), but also influences it by modulating the effects of monthly average temperatures and wet days. One unit increase of day-to-day temperature variability leads to a 2% decrease in TFP. Day-to-day temperature variability neutralizes the impact of monthly average temperature on TFP, while exacerbating the impact of wet days on TFP. Furthermore, extreme rainfall events result in a consistent negative marginal effect across all countries/seasons/rainfall intervals. This study also identifies differentiated impacts across countries with varying income levels. Low-income regions’ TFP demonstrates markedly significant sensitivities to both monthly average temperatures and daily temperature fluctuations, which means less resilient. Furthermore, the impacts of general and extreme rainfall are comparatively less pronounced in high-income countries, indicating higher resilience to climate variability in these regions and a relative vulnerability to extreme weather events in low-income regions. Our findings illuminate the intricate and multifaceted role that daily temperature variability plays in agricultural productivity, providing a theoretical basis for understanding the heterogeneous impacts of climate change on agriculture and contributing insights into the broader discourse on climate resilience and agricultural sustainability.

Assessing Alterations of Rainfall Variability Under Climate Change in Zengwen Reservoir Watershed, Southern Taiwan

This study aims to detect changes in rainfall variability caused by climate change for various scenarios in the CMIP6 (Coupled Model Intercomparison Project Phase 6) multi-model ensemble. Projected changes in rainfall unevenness in terms of different timescale indices using three categories, namely WD50 (number of wettest days for half annual rainfall), SI (seasonality index), and DWR (ratio of dry-season to wet-season rainfall) are analyzed in Zengwen Reservoir watershed, southern Taiwan over near future (2021–2040) and midterm future (2041–2060) relative to the baseline period (1995–2014) under SSP2-4.5 and SSP5-8.5 scenarios. The projected rainfall for both baseline and future periods is derived from 25 GCMs (global climate models). The results indicate that noticeably deteriorated rainfall unevenness is projected in the Zengwen Reservoir watershed over future periods, which include decreased WD50, increased SI, and decreased DWR. Though there were noticeable differences in the rainfall projections by the different GCMs, the overall consensus reveals that uncertainties in future rainfall should not be ignored. In addition, WD50 has the greatest deviated relative change in mean, which implies that the short-timescale rainfall unevenness index is easily affected by climate change in the study area. Distributional changes in rainfall unevenness determined by simultaneously considering alterations in relative changes in mean and standard deviation indicated that there was no single dominant category. However, the top two categories, with summed frequencies exceeding 0.5, characterize different properties of rainfall unevenness indices. The top two categories of WD50 and SI commonly have decreased mean and increased mean, respectively, but nearly equal frequencies of the top two categories in DWR exhibit opposite variations. The proposed rainfall unevenness change detection approach provides a better understanding of the impacts of climate change on rainfall unevenness, which is useful for preparing adaptive mitigation measures for coping with disasters induced by climate change.

Global warming induced spread of the highest human fascioliasis hyperendemic area

BackgroundClimate change is driving the occurrence of several infectious diseases. Within a One Health action to complement the ongoing preventive chemotherapy initiative against human fascioliasis in the Northern Bolivian Altiplano hyperendemic area, field surveys showed a geographical expansion of its lymnaeid snail vector. To assess whether climate change underlies this spread of the infection risk area, an in-depth analysis of the long-term evolution of climatic factors relevant for Fasciola hepatica development was imperative.MethodsWe used monthly climatic data covering at least a 30-year period and applied two climatic risk indices, the water-budget-based system and the wet–day index, both of verified usefulness for forecasting fascioliasis transmission in this endemic area. To reveal the long-term trends of the climatic factors and forecast indices, we applied procedures of seasonal-trend decomposition based on locally weighed regression and trend analysis on the basis of linear models. To further demonstrate the changes detected, we depicted selected variables in the form of anomalies.ResultsThis study revealed a notorious climatic change affecting most of the hyperendemic area, with a strong impact on crucial aspects of the fascioliasis transmission. Trends in maximum and mean temperatures show significant increases throughout the endemic area, while trends in minimum temperatures are more variable. Precipitation annual trends are negative in most of the localities. Trends in climatic risk indices show negative trends at lower altitudes or when farther from the eastern Andean chain. However, monthly and yearly values of climatic risk indices indicate a permanent transmission feasibility in almost every location.ConclusionsWarmer temperatures have enabled lymnaeids to colonize formerly unsuitable higher altitudes, outside the endemicity area verified in the 1990s. Further, drier conditions might lead to an overexploitation of permanent water collections where lymnaeids inhabit, favoring fascioliasis transmission. Therefore, the present preventive chemotherapy by annual mass treatments is in need to widen the area of implementation. This study emphasizes the convenience for continuous monitoring of nearby zones for quick reaction and appropriate action modification.Graphical

On the present and future changes in Indian summer monsoon precipitation characteristics under different SSP scenarios from CMIP6 models

AbstractMonsoons are a vital part of the agriculture and economy of India which most of its population rely on for their livelihoods. It still is not clear how climate change will impact precipitation events over India due to the complexity of accurately modelling precipitation. Using twelve Coupled Model Intercomparison Project Six (CMIP6) models, we compared their performance to observed data taken from CRU as well as looking at the future changes in precipitation until the end of the twenty first century for the six precipitation homogenous regions over India. The individual models showed varying degrees of wet and dry biases and the ensemble mean of these models showed relatively lesser bias and improved spatial correlation. Out of 12 models, NorESM and MIROC6 models outperform other models in terms of capturing the spatial variability of precipitation over the Indian region. It is also found that due to lesser moisture transport from the adjoining seas represented through vertically integrated moisture transport (VIMT) analysis, there is consistent dry bias across the models. Further a comprehensive analysis of model performance across six homogeneous precipitation regions indicates that NorESM demonstrates better performance in the CNE and HR regions, EC-Earth excels in the PR, WC, and NE regions, while CMCC shows better performance specifically in the NW region compared to other models. Shared Socioeconomic Pathways (SSPs) were used for future projections and a slight increase in June, July, August, and September (JJAS) precipitation until the end of the century with SSP5-8.5 showing the largest increase. We found an increase in precipitation of 0.49, 0.74 and 1.4 mm/day under SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the far future. The northeast region was shown to receive the largest increase in precipitation (2.9 mm/day) compared to other precipitation homogenous regions and northwest will experience largest shift in precipitation. Interestingly, the number of wet days is expected to increase in the northwest region implying more VIMT towards the region. Our results indicate that monsoon precipitation extremes across all the homogenous regions will increase into the future with a higher severity under fossil-fuelled development, although the models still show large biases lowering confidence in our results.

Analyzing the Spatiotemporal Changes in Climatic Extremes in Cold and Mountainous Environment: Insights from the Himalayan Mountains of Pakistan

This study assessed the past changes in extreme precipitation and temperature events across the Himalayan Mountains of Pakistan. This cold and mountainous environmental region has witnessed a significant increase in climate-related disasters over the past few decades. Spatiotemporal changes in extreme temperature and precipitation events were analyzed using 24 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this study, in situ data of 16 national meteorological stations were obtained from the Pakistan Meteorological Department (PMD) for the past three decades (1991–2020). The significance of the trends was assessed using the modified Mann–Kendall (MMK) test, and the Theil–Sen (TS) slope estimator was used to estimate the slope of the trend. The results showed that there has been a consistent decline in the total precipitation amount across the Himalayan Mountains of Pakistan. The trend exhibited a decrease in the annual average precipitation at a rate of −6.56 mm/year. Simultaneously, there was an increasing trend in the annual average minimum and maximum temperatures at rates of 0.02 °C/year and 0.07 °C/year, respectively. The frequencies of consecutive wet days (CWDs) and maximum 5-day precipitation (RX5day) have decreased significantly, with decreasing rates of −0.40 days/year and −1.18 mm/year, respectively. The amount of precipitation during very wet days (R95p) and extremely wet days was decreased by −19.20 and −13.60 mm/decade, respectively. The warm spell duration (WSDI) and the frequency of warm days (TX90p) across the Himalayan Range both increased by 1.5 and 1.4 days/decade. The number of cold days (TX10p) and cold nights (TN10p) decreased by 2.9 and 3.4 days/decade. The average temperature of the hottest nights (TXn) and the diurnal temperature range (DTR) were increased by 0.10 and 0.30 °C/decade. The results indicated an increasing tendency of dry and warm weather in the Himalayan region of Pakistan, which could have adverse consequences for water resources, agriculture, and disaster management in the country. Therefore, it is essential to prioritize the implementation of localized adaptation techniques in order to enhance sustainable climate resilience and effectively address the emerging climate challenges faced by these mountainous regions.

Harvest and trade of wild edible russula griseocarnosa in North Vietnam

This study examines the harvest and trade of wild edible Russula griseocarnosa in north Vietnam. Russula griseocarnosa is an edible forest mushroom, but there is no reliable information on harvesting practices and trade in this species in Vietnam. This study aims to provide ethnomycological and marketing knowledge for this mushroom. Semi-structured interviews were conducted with mushroom collectors and traders over 2 years; harvest yields were obtained from local officials; and policy settings were sought from forest managers. About 85% of mushroom collectors were women of the Dao, Lo Ursula, Muong, Nung, Mong, San Chi, San Diu, and Tay ethnicities. The collecting season was bimodal, with the wet season beginning (May) and ending (September). The traders were Hoa, Kinh, Nung and Tay, and 57% were women. Fresh mushrooms sold for 9.1-10.5 US$/kg on dry days and 7.0-7.9 US$/kg on wet days. The best grade of dried product fetched 70.4-83.3 US$/kg. A number of interim policies have been established to encourage local people to protect forests and to help create a sustainable edible wild mushroom market. Our study provides the first detailed account of the role of edible wild mushrooms in northeast Vietnam, and this will inform the co-development of rural livelihood and sustainable forest management plans.

Research on short-term optimization and scheduling of multi-energy complementary systems based on forecast scenario dynamic correction

The inherent unpredictability and instability of renewable energy sources, such as wind and solar power, hinder the precise execution of power generation plans in complementary systems, posing significant challenges to their integration into power grids. Therefore, this study proposes a dynamic correction method for wind and solar output forecast scenarios in the short-term scheduling of wind-solar-hydro complementary systems. The method utilizes statistical analysis of forecast errors in wind and solar power outputs to characterize uncertainty patterns across different forecast levels and constructs a typical forecast scenario set based on single-day forecasts. This approach probabilistically models each scenario according to the temporal migration patterns of wind and solar power outputs and develops a neural network-based dynamic correction fusion model to refine the forecasts. Application of this method in a case study of the Yalong River Basin demonstrated that, after applying dynamic correction to the forecast scenarios, the mean absolute error in total wind and solar output predictions during the wet and dry seasons was reduced by 50.73 % and 47.95 %, respectively. Additionally, the dynamic correction reduced the maximum residual load on typical wet and dry days by 82.70 % and 62.37 %, respectively, and decreased the total intraday residual electricity by 91.17 % and 73.24 %, compared to single-day forecasts. The study concludes that the proposed dynamic correction method enhances power system stability and improves power generation efficiency and reliability.

Oberved trend of precipitation extreme in Kalimantan

<p style="line-height:200%;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph;"><span style="font-family:"Times New Roman";" lang="IN">It has been suggested that the global warming due to an increase in green house gas concentrations could lead to extreme climate events, such as </span><span style="color:#007BB8;font-family:"Times New Roman";" lang="IN">precipitation extreme and droughts</span><span style="font-family:"Times New Roman";" lang="IN">. This study is designed to analyze trend in the long-term time series of preciptation extreme in Kalimantan, focusing on observed data obtained from eight meteorological stations spanning from January 1985 to December 2022. </span><span style="background-color:white;color:#0D0D0D;font-family:"Times New Roman";" lang="EN-ID">Statistical analyses were employed to discern patterns and trends in extreme precipitation events. The findings reveal a discernible long-term trend characterized by fluctuations in precipitation extremes over the study period. The trend of total annual precipitation (PRCPTOT) has increased at all observation stations. Especially at SM. Nangapinoh and Supadio, the PRCPTOT trend increased significantly at the 99% and 90% levels. Meanwhile, the increase in consecutive wet days (CWD) trends only occurred at SM. Nangapinoh and Rahadi Oesman (West Kalimantan), and SM. Iskandar (South Kalimantan). A significant trend was found in SM. Nangapinoh (90%) and Iskandar (95%). In addition to experiencing an increase in the R20mm trend, there was a decrease in the trend. Significant trend increases at the 99% and 90% levels occurred in SM. Nangapinoh and Supadio.  In addition, an increase in CDD trend occurred only in SM. Rahadi Oesman and Tjilik Riwut, although not significant. Meanwhile, the majority of extreme precipitation indices have a greater correlation with ENSO than the IOD. </span></p>

Anthropogenic influence on precipitation in Aotearoa New Zealand with differing circulation types

This study quantifies the influences of anthropogenic forcing to date on precipitation over Aotearoa New Zealand (ANZ). Large ensembles of simulations from the weather@home regional climate model experiments are analysed under two scenarios, a natural (NAT) or counter-factual scenario which excludes human-induced changes to the climate system and an anthropogenic (ANT) or factual scenario. The impacts of anthropogenic forcing on precipitation are analysed in the context of large-scale circulation types characterized using an existing Self Organizing Map classification. The combined effect of both thermodynamics and dynamics are compared with values expected from the Clausius–Clapeyron (C–C) relation. Changes in the precipitation intensity due to greenhouse gas-forced temperature rise are lower than the expected C–C value. However extreme precipitation changes approach the C–C value for some circulation types. Specifically westerly flows enhance precipitation change across ANZ relative to the C–C rate, particularly over the West Coast. Conversely, northwesterly flows reduce the change over the North Island relative to the C–C value. Moreover, the wet day frequency generally reduces in the ANT scenario relative to NAT, reductions are largest on the West Coast of the South Island for westerly flows. Additionally, the frequency of days with extreme precipitation rises over ANZ for most circulation patterns, except in Northland and for northwesterly flows. This underscores the combined influence of dynamics and thermodynamics in shaping both precipitation intensity and frequency patterns across ANZ.

Added value of merging techniques in precipitation estimates relative to gauge-interpolation algorithms of varying complexity

Data-fusion techniques leverage the strengths of multisource precipitation data and can significantly enhance the accuracy of precipitation estimates. However, the extent to which these techniques improve precipitation estimates (i.e., added value) compared to interpolation algorithms and the factors driving this improvement remain unclear. To address these gaps, this study compared the performance of two merging techniques, i.e., double machine learning (DML) and geographically weighted regression (GWR), with multiple interpolation algorithms in estimating precipitation across China. The interpolation algorithms vary in complexity and include typical methods (IDW and Kriging), semi-physical methods (GIDS, DAYMET, and MicroMet), and climatologically aided interpolation (CAI). We quantified the added value of the merging techniques over these interpolation algorithms and investigated the driving factors using a data-driven approach. Results indicate that the merging techniques outperform all the interpolation algorithms, regardless of their complexity. The merging techniques provide greater added value in gauge-scarce regions (e.g., Northeast China) than in gauge-rich regions (e.g., Northwest China). The magnitude of the added value from merging techniques is significantly influenced by the choice of interpolation algorithms due to their varying performance. Additionally, our data-driven model reveals that factors such as the amount of precipitation, number of wet days, performance of precipitation products, and gauge density are key drivers that negatively affect the added value of merging techniques. This study highlights the importance of integrating multisource data to improve precipitation estimates, especially in regions with sparse gauges, rather than relying solely on gauge-only interpolation.

Trends of Extreme Temperature and Rainfall Indices for Addis Ababa City, Ethiopia

The extreme temperature and precipitation index trend analysis for Addis Ababa city is presented in this study. The Bole and Tikur Anbesa observatories, two stations in the capital city of Addis Ababa, provide the temperature and rainfall variation used in this analysis. The RClim-Dex software has been used to find climate extreme indices for Addis Ababa city stations based on climate data for the period 1954 to 2019. The annual total precipitation trend (PRCPTOT) at the 5% significant level exhibits a positive slope at the Addis Ababa observatory and a negative slope at the Addis Ababa Bole station. SDII trends indicate an increase at the 5% significant threshold for both stations. At the 5% significance level, the trends for both stations’ maximum and lowest temperatures show a positive slope. Conversely, both stations exhibit a negative slope in the diurnal temperature ranges. The RClim-Dex results indicate that, while rainfall intensity is decreasing, there is a tendency for annual rainfall indices to increase at the Addis Ababa observatory station, but to decrease at the Bole station. Additionally, there is an upward slope for consecutive wet days for all stations. Nevertheless, steady warming patterns are evident for both the maximum and minimum temperature indices. In contrast, declining trends are shown in the diurnal temperature ranges.

Projected changes in wet and dry extremes in the CMIP6 multi-model ensemble over the IGAD region of Eastern Africa

Wet and dry days, as well as wet and dry spells, are crucial pieces of information for rain-fed agriculture, food security, and numerous socioeconomic activities in East Africa. This study examines the projected changes in wet/dry days and wet/dry spells using data from the Multi-Model Ensemble (MME) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Statistical criteria and thresholds are used to project the changes in wet and dry days, as well as wet and dry spells, under the shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the annual, March–May (MAM), June–September (JJAS), and October-December (OND) seasons. The analysis indicates that the CMIP6 models generally overestimate the number of wet days (wet spells) across all seasons and scenarios at the 1 mm threshold. A projected 10–20% increase in total rainfall over the IGAD region is driven by an increase in rainfall intensity and the number of wet days under all scenarios during the near (2021–2050) and far (2071–2100) futures. A decrease in wet days and spells (increase in dry days and spells) is projected over South Sudan in JJAS, while an increase in wet spells is projected over Kenya, Somalia, and Sudan in OND during the near and far future compared to the baseline period. By the end of the century, wet days are projected to increase by approximately 20–30%, and dry spells are projected to decrease by 10–20% under the SSP1-2.6 and SSP5–8.5 scenarios. The EnsMean 10 CMIP6 tends to smooth out the extremes (wet or dry biases), creating a result that may appear more accurate but does not reflect the extremes or variations projected by individual models over South Sudan, Uganda, Kenya and Ethiopia. The projected patterns of wet and dry spells under SSP1-2.6 highlight the importance of mitigation efforts to limit temperature rise to below 1.5°. It is necessary to model how shifts in extreme dry and wet conditions will likely affect climate-sensitive sectors, such as agriculture and food security, in order to make well-informed decisions.

Statistical downscaling model for future projection of daily IDF relationship by Markov chain and kernel density estimator

ABSTRACT Changes in climate might have a significant impact on rainfall characteristics, including extreme rainfall. This study aims to project the future daily rainfall, preserving most of the rainfall characteristics, including extreme rainfall incorporating climate changes. This paper presents two hybrid semi-parametric statistical downscaling models for future projection of IDF curves. The precipitation flux from seven scenarios of ten GCMs and observed daily rainfall data are considered as predictors and predictand variables, respectively. At site, daily rainfall occurrence is modeled using a two-state first-order Markov chain. Rainfall amounts on each wet day are modelled using a univariate nonparametric kernel density estimator. Two types of amount generation models are presented in this study. The bounded model (KDE-SP) is developed, considering the support for the kernel distribution as positive. In the unbounded model (KDE-Ext), the wet days are reclassified as extreme and non-extreme rainy days. A significant increasing trend can be observed in the future projected intensity–duration–frequency relationships. The maximum increment using empirical distribution is observed as 93.21 and 80.93% on a 5-year return period in the far future for the SSP5-8.5 scenario, using KDE-Ext and KDE-SP models, respectively. Although both methods show similar results, the KDE-Ext model performs better in simulating extreme rainfall.

Spatial and temporal analysis and trends of extreme precipitation over the Mississippi River Basin, USA during 1988–2017

Study regionMississippi River Basin. Study focusUsing daily precipitation records of 769 meteorological stations over the Mississippi River Basin (MRB), the spatial-temporal variability and trend of nine extreme precipitation indices were estimated and statistically assessed using the Mann-Kendall test. Factors likely to influence the spatial pattern and trends of precipitation extremes indices were also checked. New hydrological insights for the regionThe spatial pattern of the extreme precipitation indices exhibits a southeast to Northwest dipole, with the maximum values recorded over the southeastern part of the domain (exception being for Consecutive Dry Days, CDD which shows otherwise) driven by the southerly moisture transport toward the southeast. The spatial pattern of the extreme precipitation is controlled by the topography. The results also show that, on average, almost all the indices (except CDD) exhibit an increasing trend. The total wet day precipitation exhibits a significant increasing trend. Spatially, most of the significant increasing (decreasing) trends of the extreme's precipitation-except CDD- are located over the Upper (South) MRB where there is a significant sign toward cooling (warming) conditions. This supports the view that changing climate towards warming (cooling) conditions is significantly affecting precipitations extremes over the MRB. The relationships between large-scale teleconnections and extreme precipitation show that Pacific North America significantly increases (decreases) frequency and intensity indices over the Northwest (southeast) MRB, whereas the Pacific Decadal Oscillation does increase the frequency and intensity indices over the southeast. El Niño Southern Oscillation significantly increases the frequency and intensity indices over the entire MRB, with consequences to infrastructure failures, increasing vulnerable populations, risk zones and relocations populations.

Maize yield and Fall armyworm damage responses to genotype and sowing date-associated variations in weather conditions

Due to climate disruption and delayed onset of rains, maize is often sown late, outside the optimal window, increasing the impact of pests such as fall armyworm and reducing yields. Identifying optimal sowing dates and the best genotypes is crucial to maximise yields while limiting fall armyworm (FAW) damage. Thus, this study was undertaken to evaluate yield and FAW damage variation in relation to sowing-date weather conditions to determine the optimal sowing window. The experiment had a split-plot design with four genotypes ('PVAH-1 L', 'PVAH-3 L', 'PVAH-6 L' and 'SAM 4 VITA') and four sowing dates (30 November to 14 January and delayed by 15, 30 and 45 days) during the three consecutive cropping seasons. Abundant rainfall and a high number of wet days increase yields while reducing FAW damage. The genotype shows that 'PVAH-1 L' has ear resistance (3.24) and leaf partial resistance (4.05) to FAW, with a high yield (6.54 t.ha−1). In contrast, 'PVAH-3 L' (4.27 and 5.05), 'PVAH-6 L' (4.24 and 4.37) and 'SAM 4 VITA' (4.25 and 4.00) show partial resistance to FAW in both ears and leaves, but have relatively lower yields, except for 'PVAH-6 L' (6.29 t.ha−1). Maize sown on 15 December had a high yield (8.76 t.ha−1), similar to those sown on 30 November. However, sowing on 30 December and 14 January reduced yields by 2 t.ha−1 and 7 t.ha−1 respectively, while increasing FAW infestation and damage. Therefore, in the Lubumbashi region, due to the delayed onset of rains and climatic disturbances, the sowing period can be extended to 30 December, with an optimal window extending from 30 November to 15 December. To maximise yields and limit FAW damage, it is recommended that 'PVAH-1 L' be sown on 15 December, 'SAM 4 VITA' on 30 November or 15 December and 'PVAH-6 L' on 30 November or 15 December.

Future changes in extremes across China based on NEX-GDDP-CMIP6 models

This paper evaluates the NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP) CMIP6 models’ performance in simulating extreme climate indices across China and its eight subregions for the period 2081–2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. The models effectively reproduce the spatial patterns of extreme high temperatures, especially in northern China. They show enhanced capabilities in accurately simulating the maximum daily maximum temperature (TXx) and the number of high temperature days (T35). They improve the cold bias of the TXx index in Northwest China and warm bias in South China. In terms of precipitation, the models demonstrate strong performance, evidenced by significant spatial correlations in total wet day precipitation (PTOT) simulations. They reduce the biases of PTOT and simple daily intensity (SDII) compared to CMIP6 models. Regionally, they enhance PTOT accuracy along southern coasts and in Yunnan, better captures very heavy precipitation days (R20) in the Southwest region, max 5-day precipitation (RX5D) in North China and Southwest region, and SDII in the Northeast region and Yunnan. Under SSP5-8.5 scenario, significant impacts include increased TXx in Northwest China, more heatwave days in Southwest China, and more T35 in South China. Extreme precipitation will become more frequent in South and East China, with the greatest intensity increases in Southwest China (SWC1). North China will see fewest consecutive dry days (CDD) indices, while consecutive wet days (CWD) will prominently rise in SWC1.