Changes In Rainfall Research Articles

Extreme rainfall and landslides as a response to human-induced climate change: a case study at Baixada Santista, Brazil, 2020

In March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20–42% of the total losses and damages. The greatest economic losses occurred in Guarujá (42%), followed by São Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guarujá (107%), São Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guarujá has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.

Regional climate change: consensus, discrepancies, and ways forward

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.

Hydroclimatic scenario generation using two-stage stochastic simulation framework

Climate change poses significant challenges for decision-making processes across a range of sectors. From the water resources planning and management perspective, the interest is often in evaluating the performance of a water supply system in a future state considering the potential changes in rainfall and streamflow characteristics. With observed climate change signals, scenario-based projections of rainfall and streamflow simulations are crucial for evaluating the potential impacts of climate change on water resource systems. Given the complexity of the existing approaches, their applications for generating scenario-based projections of streamflow and rainfall are limited. We developed a non-parametric bootstrapping approach, NPScnGen, for future scenario generation of any hydroclimatic variable. The developed approach is flexible and can be used with any physical hydrological or data-driven stochastic model that provides simulations of hydroclimatic variables of interest for the historical climate condition. In NPScnGen, samples of any set of time-series characteristics, such as mean and standard deviation, are generated from a multivariate Gaussian process for the considered scenario, and then bootstrapping is performed to select the closest sample from the historical simulation of that hydroclimatic variable. We have also proposed a modified wavelet-based model, Wavelet-HMM, and used that model to synthetically generate historical climate time-series as a baseline. We present the application of the developed framework consisting of historical climate simulation and future climate projection approaches on rainfall and streamflow datasets for the Tampa Bay region in Florida.Plain Language Summary: Water resources managers require a wide range of hypothetical but potential changes in hydroclimatic variables such as streamflow and rainfall to evaluate the sustenance of water supply systems in future. Existing scenario generation approaches are limited by either the complexity of statistical models or dependency on climate models which have their own limitations. In such a scenario, the developed non-parametric scenario generation framework in this study, NPScnGen, can be very useful. The developed framework can be applied with any sophisticated time-series generation model that can generate synthetic hydroclimatic traces for baseline climate condition, and it is also flexible in generating a wide range of potential climate change scenarios. We show the application of the framework on both streamflow and rainfall datasets.

How is climate change altering the precipitation pattern over Northwestern India?

AbstractClimate change in India is causing devastating downpour events and shifts in spatial characterization, with apparent regional differences. The Northwest India (NWI), which is in the Aravalli rain shadow zone and was formerly drier, has attracted great attention in recent years due to its changing rainfall patterns. This study throws light on the astonishing behaviour of ISMR over NWI with the advent of intensified rainfall over the region in the recent time frame. The Pettitt test of change point (CP) detection is utilized in this analysis to measure the change in rainfall patterns over a period of 70 years, from 1950 to 2019. This analysis suggests significant variation in the CP's time frame for different months of ISMR. The earliest change was noticed in July, while the latest was for September for the mean as well as for the intense precipitation. Moreover, we found a maximum change in the precipitation during the peak monsoon month (i.e., July and August). The difference in the precipitation of different percentile values before and after CP indicates a decrease (increase) in low (high) intensity precipitation for all the months and seasons as a whole with varying magnitude. The highest reduction of low‐intensity precipitation is noticed for the months of July and August, while the highest increase of high‐intensity precipitation (>95th percentile) is noticed for June and September. The heavier precipitation contributes largely to the mean increase of precipitation, expecting to receive more mean and precipitation extremes in the future. Unlike the increasing precipitation trend, potential evapotranspiration (source of local moisture) shows a declining trend, revealing the negative association among them and the possibility of enhanced advection of remote moisture responsible for enhanced precipitation over NWI. The enhanced vertically integrated moisture transport of the Arabian Sea and Bay of Bengal and its strengthening relationship with precipitation further confirm the contribution of remote moisture to intensified precipitation over NWI, though the in‐depth dynamical cause remains unclear. The increased Convective Available Potential Energy for entire monsoon seasons as a whole and individual months facilitates a favourable condition for enhanced convective activity over the region, resulting in strengthening the precipitation.

Changes in Future Rainfall over Southeast Asia Using the CMIP6 Multi-model Ensemble

A multi-model ensemble from the new CMIP6 models was utilized to determine the future changes in precipitation over Southeast Asia (SEA; longitude: 90°E–140°E, latitude: 15°S–30°N). The changes are computed for the three (3) future time slices (2021–2040, 2041–2060, and 2081–2100) under four (4) different scenarios based on the Shared Socioeconomic Pathways (SSPs): 1-2.6, 2-4.5, 3-7.0, and 5-8.5. Our results indicate that future rainfall in the SEA-averaged region could increase by about 4%, 5%, 6%, and 9% towards the end of the century relative to the present-day average (1995–2014) under SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5, respectively. Among all scenarios, SSP3-7.0 widely shows remarkably dry conditions whereas SSP5-8.5 suggests extremely wet conditions on different time scales. A clear dissociation of wet and dry areas is expected in the far-term period (2081–2100). Changes in the annual cycle indicate that monsoon rainfall could experience significant increases. The study also emphasizes the importance of moisture flux convergence (MFC) in determining precipitation patterns across different seasons and regions. The results suggest that MFC plays a crucial role in the projected increase or decrease of rainfall in SEA regions. Spatial correlation of future global mean temperature (GMT) and rainfall have a high positive (negative) correlation in the north (south) latitudes. Changes in rainfall are found to be sensitive to GMT. The responses to future rainfall changes per degree Celsius of warming are at the rate of 8.9%, 6.3%, 3.6%, and 2.7% under SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5, respectively.

Shrimp Farmer’s Perceptions and Adaptation Strategies to the Threat of Climate Change in Bangil District, East Java

As one of the main stakeholders in the fishing industry, shrimp farmers are directly affected by climate change that causes changes in seawater temperature, rainfall, and unpredictable weather patterns. Related to the issue, the threat to income may be felt by shrimp farmers. Therefore, in this study, we highlight two objectives as a basis for drawing up this article, namely (1) to understand shrimp farmers’ perceptions of climate change, and (2) to identify factors that influence farmers’ decisions in implementing adaptation strategies. There are two types of analysis that we use, namely descriptive analysis and logit regression analysis. Our findings show that the majority of shrimp farmers consider climate change manifested in the form of changes in rainfall patterns (96.21%), increases in pond water temperatures (98.71%), decreases in yield production (95.41%), and increases in shrimp pests and diseases (96.23%). The results of our logit regression analysis show that the decision-making factors of shrimp farmers are significantly influenced. These results indicate that the implementation of adaptation strategies in the fisheries sector can generate significant growth and reduce the risks posed by climate change. As a result, the government needs to encourage the development of more suitable climate change adaptation strategies for small-scale shrimp farmers in Indonesia.

Impact of Soil Moisture Dynamics and Precipitation Pattern on UK Urban Pluvial Flood Hazards Under Climate Change

AbstractThe diversity of flood‐generating mechanisms superimposed on catchment physiographic features with non‐stationary meteorological drivers makes future flood hazard assessment a grand challenge. To date, many studies have examined patterns in rainfall and streamflow, but far fewer have investigated trends in the other drivers of flooding. The complex transfer function between precipitation and flooding makes it potentially misleading to simply look at the change in rainfall to express the hazard. Furthermore, there are very few studies that have directly used output from km‐scale climate models in flood modeling. Coarse resolution climate data sets may not credibly resolve local climate and weather extremes. Changes in rainfall distribution and antecedent moisture over extended time periods due to climate change have so far been ignored when assessing urban pluvial flood risk. In this paper, an urban flood hazard assessment framework using the latest 2.2 km resolution UK Climate Projections Local is proposed. Global warming induced changes in pluvial flood risks under RCP8.5 are projected, focusing on the impact of changing precipitation patterns and soil moisture dynamics on flood generation. Results indicate a strong increase in the frequency of occurrence of extreme floods, and the resultant future (2060–2080) annual flood volume is expected to increase up to 52.6% relative to 1980–2000 over a major UK urban region, and these patterns are likely to hold more generally elsewhere in the UK. Shifts to a later occurrence of extreme flooding is identified under global warming. Previous studies that have neglected soil moisture dynamics are unlikely to give accurate flood estimates.

Identification of climate change and its impact on maize (Zea mays L.) production in Majalengka Regency

Maize is one of the multipurpose crop whose yields can be affected by climate change. Climate change includes increasing air temperature, increasing sea levels, and changes in rainfall can be a threat and cause a decrease in maize yields. This could be caused by climate change factors. This research aims to identify climate change and analyze the correlation between climate change and maize production in the Majalengka Regency. Maize plantations in the 26 sub-districts are still fluctuative. The method used in this research is quantitative descriptive using trend analysis and correlation. The data used include temperature, rainfall, harvested area, production, and productivity of maize obtained from (i) the Meteorological, Climatological, and Geophysical Agency of Majalengka Regency, (ii) Statistics Indonesia, (iii) Food Security, Agriculture, and Fisheries Office of Majalengka Regency, and other related sources. The results of this research showed that climate change in Majalengka Regency was marked by an increase in temperature trend and a decrease in rainfall trend. The correlation between temperature and changes in maize production in Majalengka Regency was significant, while rainfall was not significant.

ASSESSING THE DYNAMICS OF CLIMATE CHANGE IN KHULNA CITY: A COMPREHENSIVE ANALYSIS OF TEMPERATURE, RAINFALL, AND HUMIDITY TRENDS

The study analyzes the temperature, rainfall, and humidity dynamics of Khulna city from 1981 to 2020. The average rainfall changes are 0.0018 mm, with an increase in daytime temperatures of 0.0223°C each year. Nighttime temperatures also show an increasing trend, with an increase in minimum temperatures of 0.0342°C each year. The overall humidity decreases, indicating less humid weather. The decadal average rainfall ranges from 5.13 mm from 1981 to 2000, with a drop to 4.65 mm between 1991 and 2000. The maximum temperature ranges from 30.94°C to 31.31°C, with a slight increase to 22.17°C between 2011 and 2020. The decadal average humidity ranges from 81.28% from 1991–2000 to 80.72% from 2001–2010. Pre-monsoon average rainfall declines by 5.13 mm, indicating a drier season. The monsoon season has an inclining trend of 0.0239 mm, with a promising increment of rain resulting in a wetter monsoon. Post-monsoon average rainfall increases by 0.0121 mm, resulting in a wetter season each year. The winter season has a slight decline of -0.0043 mm at 6°C. The variability of temperature, rainfall, and humidity patterns in Khulna city reveals a correlation between rainfall and temperature, which indirectly impacts crop yield. Better observational rainfall, humidity, and temperature data are necessary for effective agriculture and crop production. The estimated value for the average temperature (maximum) from 1981 to 2020 is 0.023°C, suggesting that if the year increases by one year, the average maximum temperature increases by 0.023°C.

Occurrence, distribution, and sources of organophosphate esters (OPEs) in the air of the Indo-China Peninsula Based on a Passive Air Monitoring Network

Organophosphate esters (OPEs) are a class of emerging and ubiquitous contaminants that are attracting increasing attention, and their large-scale use as flame retardants and plasticizers has led to their pervasive presence in the environment, although their broader impacts remain unknown. In this study, 11 OPEs were measured in the atmosphere of Southeast Asia and Southwest China during 2016. The ∑11OPEs were higher in this region (78.0–1670 pg/m3, mean 458 pg/m3) than in many remote areas, lower than in developed regions, and comparable to levels in many developing country cities. Generally, the ∑11OPEs were higher in urban (105–1670 pg/m3, mean 538 pg/m3) than in suburban (78.0–1350 pg/m3, mean 388 pg/m3). Seasonal variations of OPEs in the air were more pronounced in Cambodia and Laos, especially for Triphenyl Phosphate (TPHP). Seasonal variations of ∑11OPEs in most regions correspond to changes in temperature and rainfall. Biomass burning may be also a factor in facilitating OPE emissions from biomass materials or soil into the atmosphere of Southeast Asia. The random forest analysis showed that among these, rainfall had the greatest effect on the seasonal variation of atmospheric OPE concentrations, followed by biomass burning and temperature. The inter-regional variation of ∑11OPEs in Southeast Asia was related to population and economic development in each region. Airflow trajectories indicated that the OPEs in this region were mainly from local sources. The health risk assessment revealed that the inhalation exposure risks of OPEs to the residents in the study areas were very low during the sampling period, but may be increasing.

Probabilistic projections of temperature and rainfall for climate risk assessment in Vietnam

ABSTRACT In this study, we developed a probabilistic model using the surrogate mixed model ensemble (SMME) method to project temperature and rainfall in Vietnam under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The SMME model combines patterns from 31 global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their weighted model surrogates. Testing for the period of 2006–2018 demonstrated the SMME's ability to encompass observed temperature and rainfall changes. By the end of the 21st century, there is a 5% probability of average temperature increase exceeding 6.29 °C, and a 95% probability of minimum temperature increasing by more than 2.21 °C during 2080–2099 under RCP8.5 compared to 1986–2005. Meanwhile, rainfall is projected to slightly increase, with an average rise of 6.12% at the 5% probability level. The study also quantified the contributions of uncertainty sources – unforced, forced, and scenario-related – to the projection results, revealing that unforced uncertainty dominates the total signal at the beginning of the 21st century and gradually decreases, while forced uncertainty remains relatively moderate but increases gradually over time. As we approach the end of the century, scenario uncertainty dominates, accounting for 75–80% of the total signal.

Modeling streamflow responses to land use and land cover change using MIKE SHE model in the upper Omo Gibe catchment of Ethiopia

AbstractThe land use and land cover (LULC) changes in the upper Gibe catchment were studied intricately using the MIKE SHE model and analyzed through statistical tests. The Mann–Kendall test was used to identify potential trends, while the Pettit test was used to detect abrupt changes in rainfall, temperature, and streamflow. A set of three maps of LULC change (1990, 2003, and 2018) was developed to observe how they affect the hydrological pattern of the catchment. Statistical analyses were conducted at mean annual time scales to establish relationships among the anomalies. It has been noted that certain temperature gauges showed statistically significant increases in temperature. Change points in the 1980s and 1990s were identified during the study period. The annual streamflow trends displayed an increasing trend that was not that significant. LULC changes contributed to increased surface runoff, attributed to agricultural, settlement, and water body expansion, as well as reductions in bare land, forest, shrubland, and grassland. The MIKE SHE model performed well during both the calibration and validation periods on the monthly time scale. The alterations in LULC had a noticeable impact on stream flows during both wet and dry seasons, resulting in increased mean monthly stream flows during the wet season and decreased flows during the dry season. The MIKE SHE study and related statistical analysis is quite a different method to perceive the results.

Using UNSEEN approach to attribute regional UK winter rainfall extremes

AbstractThree out of the five highest daily winter rainfall totals on record over Northern England have occurred from 2015 onwards. Heavy rainfall events in the winters of 2013–2014, 2015–2016 and 2019–2020 led to more than 2.8‐billion‐pounds of insurance losses from flooding in the UK. Has the frequency of these events been influenced by human‐induced climate change? Winter rainfall in the UK is extremely variable year‐to‐year, which makes the attribution of rainfall extremes particularly challenging. To tackle this problem, we introduce an UNprecedented Simulated Extreme Ensemble (UNSEEN) approach for the attribution of such extremes, thereby increasing the data available, and apply this approach to five recent flooding events on a regional scale. Using this method, for all five events we found a significant climate signal in the extreme regional rainfall totals immediately preceding the flooding. Results were fairly similar for each—with the events being found to become from 1.4 to 2.6 times more likely. An alternative attribution method that uses a different model with substantially less data did not find significant increases, reinforcing the need for very large amounts of data to detect significant changes in extreme rainfall against a noisy background of natural variability. We also examine how extreme rainfall is changing more broadly across English regions in winter, finding that 1‐in‐10 to 1‐in‐90‐year winter rainfall totals have changed significantly in Northern England. The high volume of data using UNSEEN has enabled us to examine the dynamics of these events, showing that daily extremes in winter are likely to have increased across all the circulation patterns responsible for high rainfall in English regions.

Water Erosion Response to Rainfall Type on Typical Land Use Slopes in the Red Soil Region of Southern China

Land use and rainfall are two important factors affecting soil erosion processes. The red soil region of southern China is a representative region with high rainfall amounts and rapidly changing land use patterns where the water erosion process is sensitive to changes in land use and rainfall. To comprehensively understand the water erosion response to land use and rainfall in this region, a 6-year in situ experiment based on eight plots (bare land and seven typical land uses) was conducted from 2015 to 2020. The 320 rainfall events were divided into 4 types, and there were 3 main rainfall types. The runoff of different rainfall types was primarily determined by the rainfall amount, while the soil erosion of different rainfall types was primarily determined by the rainfall intensity. High-intensity rainfall contributed the most to both total runoff and soil erosion. Compared with bare land, the seven typical land uses reduced runoff and soil erosion by more than 75%. Grassland, cropland, and forest with low vegetation coverage experienced high runoff and soil erosion, while shrubland most effectively reduced runoff and soil erosion. The combination of land use and rainfall type significantly affected the annual average runoff depth, soil erosion modulus, and soil loss coefficient. Rainfall types can change the relationship between runoff and soil erosion for different land uses. The runoff and soil erosion of bare land were highly correlated with rainfall characteristics, while vegetation weakened this relationship under short- or moderate-duration rainfall. To effectively reduce water erosion, high-intensity rainfall should receive special attention, and all land uses should ensure that vegetation is well developed, especially understory vegetation.

Plant responses to changing rainfall frequency and intensity

Plant responses to changing rainfall frequency and intensity

Projected changes in rainfall and temperature using CMIP6 models over the Okavango River basin, southern Africa

The Okavango River Basin (ORB) which includes a renowned World Heritage site Okavango Delta, contains highly biodiverse ecosystems. Most of its rural population relies on rain-fed subsistence farming. Limited research has been done on future changes in rainfall and temperature in this region. Here, such changes are analysed for the periods 2030–2059 (near term) and 2070–2099 (long term), relative to the historical period 1985–2014. The analysis is based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), under the shared socioeconomic pathways (SSPs) SSP245 and SSP585. The projected changes vary over the ORB. For rainfall, the models project some significant decreasing trends over Ngamiland in the central/southern ORB, but none are significant over the northern ORB. The significant trends during the near term period include those projected by the model average (-30.60 mm/decade) in March–April (MA) under the SSP585 scenario. Some significant decreasing rainfall trends have also been found in December-February (DJF), but none are significant in October–November (ON). DJF is the main growing season whereas ON (MA) is the onset (end) of the rainy season. For temperature, significant warming trends have been found over both Ngamiland and the northern ORB, with the strongest warming in ON particularly during the near term period. For this ON season over the northern ORB, under the SSP585 (SSP245) scenario, the model average projects a warming trend of 0.56 °C/decade (0.67 °C/decade). These findings may help with the management of agricultural activities, water resources and the highly biodiverse ecosystems in the ORB.

Are our lands getting wetter or drier? A perspective from Climate scenario data in Upper Brantas

Changes in rainfall are considered influential for sustaining the land resources in tropical agriculture. Many areas in East Java are classified as top priorities in adaptation and mitigation programs, especially in the agricultural and water resources sectors. Climate change impacts have been documented through global-scale General Circular Models (GCM). This study examined the future estimated climate generated from the CMIP5 model of the globe’s climate of four greenhouse gas scenarios in the Brantas Hulu watershed and studied the consequences for the agricultural climate suitability. The study exploited the 18 climate calculation models at the regional level by focusing on RCP 4.5, the development scenario considered in Indonesia’s climate action. During the 2000-2099 predictions, temperature and precipitation increased in all models. If this increase continues to be experienced without efforts for proper mitigation and adaptation plans, the disruption in agricultural products might pose a major consequence for food security.

Climate change and its impact on infectious diseases in Asia.

Climate change, particularly increasing temperature, changes in rainfall, extreme weather events and changes in vector ecology, impacts the transmission of many climate-sensitive infectious diseases. Asia is the world's most populous, rapidly evolving and diverse continent, and it is already experiencing the effects of climate change. Climate change intersects with population, sociodemographic and geographical factors, amplifying the public health impact of infectious diseases and potentially widening existing disparities. In this narrative review, we outline the evidence of the impact of climate change on infectious diseases of importance in Asia, including vector-borne diseases, food- and water-borne diseases, antimicrobial resistance and other infectious diseases. We also highlight the imperative need for strategic intersectoral collaboration at the national and global levels and for the health sector to implement adaptation and mitigation measures, including responsibility for its own greenhouse gas emissions.

Climate change projection using statistical downscaling model over southern coastal Iran

Iran is highly vulnerable to climate change, particularly evident in shifting precipitation and temperature patterns, especially in its southern coastal region. With these changing climate conditions, there is an urgent need for practical and adaptive management of water resources and energy supply to address the challenges posed by future climate change. Over the next two to three decades, the effects of climate change, such as precipitation and temperature, are expected to worsen, posing greater risks to water resources, agriculture, and infrastructure stability. Therefore, this study aims to evaluate the alterations in mean daily temperature (Tmean) and total daily rainfall (rrr24) utilizing climate change scenarios from both phases 5 and 6 of the Coupled Model Inter-comparison Project (CMIP5 and CMIP6, respectively) in the southern coastal regions of Iran (Hormozgan province), specifically north of the Strait of Hormuz. The predictions were generated using the Statistical Downscaling Model (SDSM) and National Centre for Environmental Prediction (NCEP) predictors, incorporating climate change scenarios from CMIP5 with Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 and CMIP6 with Shared Socioeconomic Pathways (SSPs) 1, 2, and 5. The analysis was conducted for three distinct time periods: the early 21st century (2021–2045), middle 21st century (2046–2071), and late 21st century (2071–2095). The results indicated that the CMIP5 model outperformed the CMIP6 model in simulating and predicting Tmean and rrr24. In addition, a significant increase in Tmean was observed across all the scenarios and time periods, with the most pronounced trend occurring in the middle and late 21st century future periods. This increase was already evident during the base period of 2021–2045 across all scenarios. Moreover, the fluctuations in precipitation throughout the region and across all scenarios were significant in the three examined future periods. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of Tmean (+1.22 °C) in Bandar Lengeh station in 2071–2095 period. The lowest change magnitude of Tmean among CMIP5 scenarios was found in RCP4.5 (−1.94 °C) in Ch station in 2046–2070 period. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of rrr24 (+150.2 mm) in Chabahar station in 2071–2095 period. The lowest change magnitude of rrr24 among CMIP5 scenarios was found in RCP8.5 (−25.8 mm) in Bandar Abbas station in 2046–2070 period. In conclusion, the study reveals that the coastal area of Hormozgan province will experience rising temperatures and changing rainfall patterns in the future. These changes may lead to challenges such as increased water and energy consumption, heightened risks of droughts or floods, and potential damage to agriculture and infrastructure. These findings offer valuable insights for implementing local mitigation policies and strategies and adapting to emerging climate changes in Hormozgan's coastal areas. For example, utilizing water harvesting technologies, implementing watershed management practices, and adopting new irrigation systems can address challenges like water consumption, agricultural impacts, and infrastructure vulnerability. Future research should accurately assess the effect of these changes in precipitation and temperature on water resources, forest ecosystems, agriculture, and other infrastructures in the study area to implement effective management measures.

Potential for historically unprecedented Australian droughts from natural variability and climate change

Abstract. In drought-prone Australia, multi-year droughts have detrimental impacts on both the natural environment and human societies. For responsible water management, we need a thorough understanding of the full range of variability in multi-year droughts and how this might change in a warming world. But research into the long-term frequency, persistence, and severity of Australian droughts is limited. This is partly due to the length of the observational record, which is short relative to the timescales of hydroclimatic variability and hence may not capture the range of possible variability. Using simulations of Australian precipitation over the full past millennium (850–2000), we characterise the nature of multi-year meteorological droughts across Australia and include a particular focus on the Murray–Darling Basin (MDB), the largest agricultural region in Australia. We find that simulated Australian droughts in the 20th century (1900–2000) are within the bounds of pre-industrial natural variability in terms of drought intensity, severity, and frequency. A tendency for droughts to last longer in southwestern and eastern Australia (including the MDB) in the 20th century, compared with the pre-industrial period, suggests an emerging anthropogenic influence, consistent with projected rainfall changes in these regions. Large volcanic eruptions tend to promote drought-free intervals in the MDB. Model simulations of droughts over the last millennium suggest that future droughts across Australia could be much longer than what was experienced in the 20th century, even without any human influence. With the addition of anthropogenic climate change, which favours drought conditions across much of southern Australia due to reduced cool-season rainfall, it is likely that future droughts in Australia will exceed recent historical experience.