Rainfall Projections Research Articles

Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach

Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. To make an initial approach, Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) were employed to forecast rainfall from 2012 to 2020. The RNN model showed strong alignment with the observed patterns, accurately predicting rainfall seasonality. However, median comparisons revealed fair approximations with discrepancies. The Root Mean Square Error (RMSE) ranged from 6.7 mm to 11.2 mm, and the coefficient of determination (R2) was low in some series. Extensive analyses showed a low Wilmott agreement and high Mean Absolute Percentage Error (MAPE), highlighting limitations in projecting anomalies and days without rain. Despite challenges, this study lays a foundation for future advancements in climate modeling and water resource management in the BLA.

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.

Regionally Coupled Climate Model ROM Projects More Plausible Precipitation Change Over Central Equatorial Africa

AbstractUnraveling plausible future rainfall change (ΔPr) patterns is crucial for tailoring societies' responses to climate change‐induced hazards. This study compares rainfall projections from the regionally coupled ocean model (ROM) and its atmospheric component, the regional atmospheric model REMO, over Central Equatorial Africa (CEA). Both models are forced by the Earth system model MPI‐ESM‐LR following the Representative Concentration Pathway 8.5. Results reveal increased rainfall across most of CEA, with ROM projecting more widespread and intensified wetting than REMO, although REMO produces more precipitation under future conditions, underscoring the influence of historical biases on REMO's projection. Examining processes underpinning changes unveils strong controls of sea and land surface temperature changes in ΔPr differences between the two models. Specifically, ROM mitigates warming more over the Atlantic than over CEA landmass compared to REMO, inducing enhancement of the Congo Basin cell and increased precipitable water content through specific humidity, affecting deep convection. Both models project enhanced Sahel and Kalahari thermal lows, with ROM better depicting the Kalahari low's warmer nature than the Sahel low. The resulting temperature gradients strengthen the northern and southern shallow meridional Hadley overturning circulation. ROM simulates the wetter conditions than REMO, attributed to its weaker northern Hadley Cell, which restricts the likelihood of northward moisture divergence toward the Sahel. Additionally, differences in mid‐tropospheric moisture convergence differentiate between ROM and REMO's wetness relative to the historical period and under future conditions. ROM projections are more plausible, in association with the reliability of its added value under the historical climate and mechanisms underlying Δpr.

Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China

In recent decades, global warming has significantly altered both the spatial and temporal distribution of rainfall patterns. This change has heightened the risk of rainfall-induced landslides, which are the most prevalent natural disasters in the mountainous regions of southwestern China. These events pose unpredictable and severe threats to the region, making it essential to forecast future rainfall trends and assess how landslide susceptibility will respond to these changes. Understanding these dynamics is crucial for developing effective strategies to mitigate and adapt to the changing rainfall patterns that influence landslides. This study focuses on Sichuan Province, China, and uses annual cumulative rainfall (ACR) as a key dynamic variable to create landslide susceptibility maps (LSMs). The goal is to explore the evolving relationship between rainfall and landslide susceptibility and use future rainfall projections to predict these risks. To achieve this, a historical landslide geospatial database was compiled across five temporal categories: 2000, 2001–2005, 2006–2010, 2011–2015, and 2016–2020. The extreme learning machine (ELM) was applied to generate LSMs for the years 2000 to2020, while an elasticity framework was used to assess how sensitive landslide susceptibility is to rainfall variations. To project future scenarios, a long short-term memory (LSTM) model was employed to project the ACR for 2030, using monthly rainfall data from 2000 to 2020. This projected ACR was then used to estimate future landslide susceptibility. Results showed a marked increase in high landslide susceptibility areas: 5.6 % by 2005, 0.3 % by 2010, 0.2 % by 2015, and 12.9 % by 2020, all relative to the year 2000. The elasticity analysis revealed that from 2000 to 2020, a 1 % change in rainfall would cause an average 1.35 % change in landslide susceptibility. Looking forward to 2030, the projected rise in ACR is expected to lead to a 2.44 % increase in areas of high landslide susceptibility. Multiple validation techniques were applied to ensure reliability and robustness of these findings.

Statistical Modelling and Projection of Future Rainfall using SARIMA and Hybrid SARIMA-GARCH Models in Various Zones of Kerala

Water is an important natural resource considered as basic need for all living things around the world. The volume of pure water present in the Earth is regulated by the amount of rainfall received over the years. Sudden climatic changes are observed in throughout the world which led to flood, drought and uneven rainfall over the years. In this study, SARIMA and SARIMA-GARCH models are applied for forecasting rainfall in different zones of Kerala. The presence of heteroscedasticity in residuals obtained from SARIMA model was identified using ARCH-LM test and it was eliminated by applying SARIMA-GARCH model to the same. The ARCH-LM test results confirmed the presence of heteroscedasticity in residuals. The comparison of models used for predicting rainfall revealed that hybrid SARIMA-GARCH model is more efficient in projecting future values of rainfall in the northern and southern zones of Kerala whereas SARIMA model is showing more accuracy in the central zone of Kerala even in the presence of heteroscedasticity of residuals. The comparison of rainfall forecasted in different zones of Kerala clearly indicated that rainfall is higher in the northern zone whereas lower in the southern zone. In the northern and central zones, the rainfall showed a peak from June to September and almost negligible rainfall from December to February. The outperformed model in each zones of Kerala was applied for projection of future rainfall for next 5 years (2021-2025). Compare to previous years, the rainfall in the northern and central zones is expected to decrease whereas in southern zone of Kerala, rainfall will be almost same.

Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin.

Investigating the Impact of Climate Change Impacts on Direct-seeded Rice Production in Middle Gujarat Using the InfoCrop-rice Model

Rice (Oryza sativa L.) is a global food staple, essential for human nutrition and calorie intake, particularly in low- and middle-income countries. This study assesses the impact of climate change on direct-seeded rice (DSR) production in Middle Gujarat (India) using the InfoCrop-Rice model. The study evaluates potential climate scenarios under Representative Concentration Pathways (RCP) 4.5 and RCP 8.5, focusing on projected changes in temperature, rainfall, and sunshine hours, and their effects on rice yield and phenology. Field data from Kharif 2023 were used to calibrate the model, which projected significant increases in maximum and minimum temperatures, variable changes in rainfall, and consistent reductions in sunshine hours. By 2100, maximum temperatures are expected to rise by up to 6°C under RCP 8.5, and minimum temperatures could increase by as much as 7.5°C. Rainfall projections show high variability, with potential reductions and increases depending on the scenario and time period. Sunshine hours are projected to decline by approximately 6.6% under both RCP scenarios. The model simulations indicate a notable decline in rice yields, with reductions ranging from 18% to 36% by 2100. These results underscore the severe impact of climate change on DSR productivity. The study highlights the need for adaptive strategies, including improved water management, resilient crop varieties, and sustainable agricultural practices, to mitigate the adverse effects of climate change and ensure the sustainability of direct seeded rice production in Middle Gujarat.

Introducing a climate, demographics, and infrastructure multi-module workflow for projected flood risk mapping in the greater Pamba River Basin, Kerala, India

Enhanced understanding of extreme events is essential for comprehensive risk assessment promoting informed decision-making and early warning to ensure the resilience against challenges imposed by changing climate. This study proposes a novel multi-module integration framework for the comprehensive evaluation of future flood risk in the Greater Pamba River basin, Kerala, India. The integrative modelling framework is developed by assembling a Cellular Automata-Markov Chain (CAMC) based and Use/Land Cover (LULC) projections, Reliability Ensemble Averaging (REA) of Global Climate Model (GCM) rainfall projections, Bayesian approach-based population projection, and spatial analysis of projected infrastructure and Digital Elevation Models (DEMs). By seamlessly interweaving the associated datasets, we illustrate the intricacies of flood vulnerability, exposure, and frequency across multiple return periods (2, 5, 10, and 100, 200 and 500 year) leading to the creation of diagnostic and prognostic high-resolution flood risk maps. The resultant flood analytics and analyses shown that around 36 % of the villages/municipalities and between 70 and 80 % of the critical facilities like schools, hospitals, and flood relief camps are falling under high-risk zones, for the 100-year return period flood. This finding is in line with Kerala's devastating flood episode in 2018, which exhibited characteristics of a nearly 100-year return period flood. In light of a changing climate, population growth, and shifting landscapes, these approaches provide a roadmap for more informed decision-making and adaptive flood risk management measures.

The episodic dynamics of salt transport in the Waitematā Estuary, a well-mixed estuary in New Zealand

This study investigates the response of salt transport to freshwater discharge events and tides in the Waitematā Estuary. The salt transport is assessed by both observations and numerical simulations using a gridded salt balance and isohaline salt transport. The Waitematā Estuary represents a unique type of estuary in parameter space: it varies between well-mixed and strain-induced periodic stratification during the spring-neap tidal cycle. Spring tides often lead to a net inflow of salt when the freshwater discharge is below average. Higher freshwater discharge (>10 m3s-1) results in net downstream salt transport regardless of the amplitude of the tides. The estimates of estuarine parameters show that the whole estuary becomes partially-mixed when the freshwater discharge increases by another order of magnitude (100 m3s-1). Based on future regional climate projections of less average rainfall with more intense events and rising temperatures, the estuary is expected to experience less frequent but stronger salt outflow. This study contributes observations from a previously undocumented part of estuarine parameter space.

Rainfall projections under different climate scenarios over the Kaduna River Basin, Nigeria

This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges.

Robust changes to the wettest and driest days of the year are hidden within annual rainfall projections: a New Zealand case study

Understanding how the statistical properties of daily rainfall will respond to a warming climate requires ensembles of climate model data which are much larger than those typically available from existing centennial-scale modelling experiments. While such centennial-scale experiments are very useful to explore scenario uncertainty in twenty-first century climate, ensemble size constraints often result in regional climate change assessments restricting their focus to annual- or season-mean rainfall projections without providing robust information about changes to the most extreme events. Here, we make use of multi-thousand member ensembles of regional climate model output from the Weather@Home project to explicitly resolve how the wettest and driest days of the year over New Zealand will respond to simulations of a 3 °C world, relative to simulations of the climate of the recent past (2006–15). Using a novel framework to disentangle changes during the wettest and driest days of the year, we show that many regions which show negligible change in annual mean rainfall are in fact experiencing significant changes in the amount of rain falling during both the wettest and driest spells. Exploring these changes through the lens of drought risk, we find many agricultural regions in New Zealand will face significant changes in the frequency of low-rainfall extremes in a warmer world.

The Hawai‘i Climate Data Portal (HCDP)

Abstract The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community.

Unveiling Climate Trends and Future Projections in Southeastern Brazil: A Case Study of Brazil’s Historic Agricultural Heritage

The intricate relationship between climate and society in a given region demands a profound understanding of climate patterns, especially in agricultural areas like Diamantina, Minas Gerais (MG), recognized by the Food and Agriculture Organization (FAO) as the birthplace of the first Globally Important Agricultural Heritage System (GIAHS) in Brazil, situated in the southwest region of the country. Given the growing concerns about climate change, we conducted a meticulous analysis of the climatic characteristics of Diamantina-MG. To achieve this, we examined historical meteorological data from 1973 to 2022, employing the Mann–Kendall and Sen’s slope tests to analyze trends. Additionally, we utilized three global climate models (GCMs) under different shared socioeconomic pathways (SSPs) to predict future climate scenarios (2021–2100) based on the projections of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Furthermore, we used Köppen and Thornthwaite climate classification methodologies to characterize both the current and future climate conditions of the region. Our results indicate that, historically, Diamantina-MG has experienced significant increases in minimum temperature, indicating a warmer climate in recent decades. For temperature, the projections show a consensus among models, projecting a continuous increase, potentially reaching up to 5.8 °C above the historical average temperature (19.2 °C) by the end of the century. Regarding rainfall projections, they show greater uncertainty, with discrepancies among models observed until 2060. However, specifically for the second half of the century (2060–2100), the models agree that there will be increases in annual rainfall. Regarding the climatic types of the region, we found that the current Köppen Cwb and Thornthwaite B3rB’3a’ classifications could shift to Aw and B1wA’a’, representing a humid tropical savanna climate with longer periods of water deficiency, considering the impacts resulting from increased air temperature and evapotranspiration. In summary, the study’s results indicate that climate changes are occurring and are likely to intensify in the Jequitinhonha Valley region, MG, in the future. The analysis of these data, from the perspective of the Brazilian GIAHS sustainability, reveals the importance of considering adaptation and mitigation measures to ensure the resilience of agricultural systems and local communities in the region that face these significant environmental changes.

Rainfall projection using CIMP6 models of extreme area in Johor

Rainfall projection using CIMP6 models of extreme area in Johor

Projections of future streamflow for Australia informed by CMIP6 and previous generations of global climate models

Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts.

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.

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.

Assessment of Projected Temperature and Precipitation in the Northern Province of Sri Lanka through Statistically Downscaled CMIP6 Projection


 
 
 The issue of climate change has emerged as a paramount concern for the global community, with several nations grappling with the far-reaching effects thereof on their respective territories. Among these nations, Sri Lanka is ranked as the country facing the greatest potential threat. In the Northern Province of Sri Lanka, which has experienced a rebirth after years of internal conflict with the aid of international agencies, projections regarding climate change indicate a diverse array of potential impacts. To arrive at these projections, a comprehensive analysis was conducted, which leveraged downscaled data derived from the Coupled Model Inter-comparison Projects, Phase 6 (CMIP6), obtained via the grid for the Northern Province. Various models were employed to scrutinize this data, and to validate the findings, an analysis was performed by comparing the model-simulated past climate data to observed data. Multimodal ensembles provided insights into unique temperature and precipitation patterns under varying emis- sion scenarios, including the Shared Socioeconomic Pathways (SSP) 4.5 and 8.5, between 2020 and 2100. Under the SSP2 4.5 scenario, for instance, the temperature increase would total 1.13°C, accompanied by 106.19mm of augmented rainfall. By contrast, under the SSP5-8.5 scenario, temperature would increase by 1.81°C, with a projection of 159.6mm increase in rainfall. Moreover, spatially, the future changes in temperature and rainfall for the Northern Province of Sri Lanka display consequential variations. Specifically, the western part is projected to witness higher rates of temperature and rainfall increase than the eastern part. However, it should be noted that variations exist in the values of the projections of temperature and rainfall across the different models. Regardless, the region must brace itself for elevated temperatures, resulting in heatwaves and an augmented frequency of scorching days, indicating an urgent need for policymakers and communities to incorporate these findings when developing and implementing climate adaptation strategies that aim to mitigate climate change’s adverse impact in the area of study.
 
 

Performance evaluations of CMIP6 model simulations and future projections of rainfall and temperature in the Bale Eco-Region, Southern Ethiopia

Performance evaluations of CMIP6 model simulations and future projections of rainfall and temperature in the Bale Eco-Region, Southern Ethiopia

Historical trends and future projections of annual rainfall from CMIP6 models in Ho Chi Minh City, Vietnam

Climate risks have posed a major threat to many local communities living in low-lying coastal megacities across the globe, including Ho Chi Minh City, Vietnam. Hence, this study first aimed to contribute towards a comprehensive understanding of temporal trend patterns of annual rainfall and absolute extremes in Ho Chi Minh City over the last 4 decades (1980-2022) through multiple non-parametric statistical trend tests. We employed the quantile delta mapping (QDM) method to develop daily bias-corrected rainfall data based on the outputs in the latest Coupled Model Intercomparison Project phase 6 (CMIP6) under 8 shared socio-economic pathway (SSP) greenhouse gas emission scenarios. Evaluation of model performance was implemented by repeatedly omitting 5 successive years in turn for estimating testing errors. The outcomes indicate the high applicability of well-calibrated transfer functions, even for high quantiles, to the production of future rainfall scenarios. The projected changes in annual rainfall and absolute extremes were obtained by estimating multi-model medians from CMIP6 models for future periods (i.e. 2021-2040, 2041-2060, 2061-2080, and 2081-2100), with reference to the base period (1995-2014). In general, annual rainfall in Ho Chi Minh City is projected to increase substantially, and Thu Duc station consistently shows the highest increases in annual rainfall. Projected changes are approximately 30.9% (8.3 to 77.8%) under the high-end scenario (i.e. SSP5-8.5) by the end of the 21st century. It is expected that these findings will yield several solid arguments for mitigating climate-related risks in Ho Chi Minh City.