Precipitation Projections Research Articles

Performance Evaluation of CMIP6 Climate Model Projections for Precipitation and Temperature in the Upper Blue Nile Basin, Ethiopia

The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p < 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures.

Understanding the Zonal Variability in Projections of Sahel Precipitation

AbstractThe uncertainty in precipitation projections across the Sahel has persisted across generations of climate models. Many projections show a zonal dipole in the sign of precipitation change, with wetting across the Central Sahel and drying across the Western Sahel. We analyze the outputs from an ensemble of current climate models to explain why some produce this dipole and understand the inter‐model variability in the transition region for these models. Models projecting the dipole tend to shift the Atlantic Intertropical Convergence Zone (ITCZ) to the south, while models that do not tend to shift the ITCZ to the north. We find that the strong relationship between the change in Sahel precipitation and surface temperature‐based indices is highly driven by the non‐dipole models. These indices do not explain most of the variance in where models transition. This suggests that understanding zonal variability in Sahel precipitation change must go beyond these temperature‐based analysis.

Projection of climate change impact on main climate variables and assessment of the future of Köppen–Geiger climate classification in Iran

AbstractHuman society and the environment are facing significant challenges due to climate change. Climate change is projected to impact main climate variables, such as temperature and precipitation. Changes in main climate variables affect climate classification and alter climate zone maps. In this research, first, the projection of temperature and precipitation in 30 main stations of Iran under the Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from Coupled Model Intercomparison Project Phase Six (CMIP6) for the end of the twenty-first century (2071–2099) was carried out. Then, the future of climate zone maps was assessed in Iran by Köppen-Geiger climate classification. The evaluation of the model data based on observation data for the period of 1991–2020 showed an acceptable correlation, with R-square and RMSE values in the ranges of 0.67–0.96 and 2.44–8.38, respectively. Results showed that the temperature in the future period (2071–2099) will increase by 1–4.7 °C under all scenarios compared to the historical study period (1991–2020), while the precipitation will either increase or decrease depending on the season and the specific climate change scenario. Assessment of future climate classifications revealed that the BW (arid desert) and BS (semi-arid steppe) categories will increase, as classified by Köppen-Geiger, will increase. At the same time, Ds and Cs (dry summer) classifications will decrease in during the study period over Iran. These findings provide policymakers with some insights into how to deal with the impacts of climate change in the future and implement some measures now.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Correction: Cool season precipitation projections for California and the Western United States in NA-CORDEX models

Correction: Cool season precipitation projections for California and the Western United States in NA-CORDEX models

Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios

Flash floods are highly destructive, and their frequency and intensity are expected to escalate due to climatic changes. This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. To predict FFS, we evaluated twelve flood-influencing variables: elevation (EL), slope (SL), aspect (AS), drainage density (DD), distance to stream (DS), topography roughness index (TRI), stream power index (SPI), topographic wetness index (TWI), soil permeability (SP), precipitation (PR), land use and land cover (LULC) and normalized difference vegetation index (NDVI). Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. Our findings indicated that the areas at high to very high risk of flooding are projected to increase significantly under both the SSP1-2.6 and SSP5-8.5 scenarios. Initially, around 38 % of the studied region had high to very high flood susceptibility, but this is expected to rise to 40–42 % over the projected time periods. These spatial delineations of flood-prone areas can provide guidance for developing effective mitigation and adaptation strategies to address the adverse impacts of flash flooding in the hilly river basins of Bangladesh.

How Climate Change Will Shape Pesticide Application in Quebec’s Golf Courses: Insights with Deep Learning Based on Assessing CMIP5 and CMIP6

The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase 5 (CMIP5) to the latest CMIP6 phase has drawn the attention of professionals, including engineers, decision makers, and golf course managers. This study evaluates how climate projections from CMIP6, using Canadian Earth System Models (CanESM2 and CanESM5), impact pesticide application trends on Quebec’s golf courses. Through the comparison of temperature and precipitation projections, it was found that a more substantial decline in precipitation is exhibited by CanESM2 compared to CanESM5, while the latter projects higher temperature increases. A comparison between historical and projected pesticide use revealed that, in most scenarios and projected periods, the projected pesticide use was substantially higher, surpassing past usage levels. Additionally, in comparing the two climate change models, CanESM2 consistently projected higher pesticide use across various scenarios and projected periods, except for RCP2.6, which was 27% lower than SSP1-2.6 in the second projected period (PP2). For all commonly used pesticides, the projected usage levels in every projected period, according to climate change models, surpass historical levels. When comparing the two climate models, CanESM5 consistently forecasted greater pesticide use for fungicides, with a difference ranging from 65% to 222%, and for herbicides, with a difference ranging from 114% to 247%, across all projected periods. In contrast, insecticides, growth regulators, and rodenticides displayed higher AAIR values in CanESM2 during PP1 and PP3, showing a difference of 28% to 35.6%. However, CanESM5 again projected higher values in PP2, with a difference of 1.5% to 14%.

Precipitation changes during the last glacial period in the Ili Basin, northern Central Asia, as inferred from the records of loess dolomite

Understanding the climatic evolution in Central Asia (CA) and its drivers is crucial for informed decision-making and predicting global changes due to its significant contribution as a global dust source. Unfortunately, our current understanding of the pre-Holocene precipitation patterns in CA is lacking due to the limited availability of reliable proxy indicators, and our knowledge of future precipitation projections in the region, based on paleoclimatic dynamics, is also quite limited. In this study, we analyzed variations in carbonate and dolomite contents of a loess section in the Ili Basin, northern CA, to reveal precipitation changes during the last glacial period. The results showed that changes in carbonate minerals were mainly influenced by the source material supply, driven by reduced precipitation and eluviation during glacial period. We thereby established a precipitation index by removing the influence of provenance signals from the dolomite records. The index indicated lower precipitation during mid-marine isotope stage (MIS) 3 compared to MIS2, likely due to meridional shifts and intensity changes of the westerlies caused by changes in precession and obliquity, with precession playing a major role. Through the comparison of the precipitation index with the δ18O records of the Greenland ice core on a millennial timescale, it was observed that the precipitation in northern CA exhibited a positive correlation with the North Atlantic Oscillation (NAO) mode due to migration of the westerlies. By leveraging our understanding of orbital- and millennial-scale precipitation patterns, we utilized the random forest (RF) regression model and the autoregressive integrated moving average model to forecast precipitation changes for the upcoming 5000–10,000 years. The results indicated a variable pattern marked by a general upward trend, suggesting the possibility of favorable development of agricultural-based economies in the Ili River Valley. People should realize that some integrated measures are designed to improve resilience of agricultural sector in the region and enhance its capacity to adapt to challenges posed by climate change. However, more extensive research is necessary to verify these results through thorough examination and comparisons of loess sections in our research location.

Modelling extreme precipitation projections under the effects of climate change: case study of the Caspian Sea

ABSTRACT Climate change is expected to alter climate extremes. This paper evaluates future changes in extreme precipitation events over the Caspian Sea, where the changes are noticeable due to its geographical location and remoteness from the ocean. Using bias-corrected data from the Coupled Model Intercomparison Project Phase 6 and extreme value analysis, this study shows increased precipitation from 18.48 mm/month in 1980–2014 to 19.50–19.82 mm/month in 2015–2100, depending on the climate scenario. Additionally, extreme events in the future are projected to increase over the sea due to climatic changes, depicting the emergence of droughts/floods that could occur more frequently in the region.

A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China.

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.

Constraining Regional Hydrological Sensitivity Over Tropical Oceans

AbstractRegional hydrological sensitivity (i.e., precipitation change per degree local surface warming) contributes substantially to the uncertainty in future precipitation projections over tropical oceans. Here, we investigate the sensitivity of relative precipitation (P*, precipitation divided by the basin average precipitation) to local sea surface temperature (SST) change by dissecting it into three components, namely the sensitivity of P* to relative SST (SSTrel, SST minus the tropical mean SST) changes, the sensitivity of P* to surface convergence changes, and the sensitivity of surface convergence to SST gradient changes. We show that the relationships between P* and SSTrel, and between P*, surface convergence, and SST gradients are largely constant during climate change. This allows us to constrain regional hydrological sensitivity based on present‐day SST‐precipitation relationships. The sensitivity of surface convergence to SST gradient changes is a main source of uncertainty in regional hydrological sensitivity and is likely underestimated in GCMs.

An ensemble approach to downscale climatological variables via screening of clustered ANNs

ABSTRACT The statistical downscaling of global circulation models presents a significant challenge in selecting appropriate input variables from a vast pool of predictors. To address the issue, we developed ensemble approach based on the Combining Multiple Clusters via Similarity Graph (COMUSA), which integrates k-means and self-organizing maps (SOMs) methods with the mutual information (MI)-random sampling approach. This innovative feature extraction technique demonstrated a 21% improvement in the classification efficacy of large-scale climatic variables. When comparing feature extraction methods, the combination of MI-random sampling and ensemble clustering yielded more accurate results than SOM clustering alone. The most efficient artificial neural networks (ANNs)-based downscaling model was employed to project near- and mid-future precipitation and temperature (2025–2035, 2035–2045), revealing varied outcomes under different scenarios (SSP3-7.0 and SSP5-8.5). Under SSP3-7.0 and SSP5-8.5, annual mean precipitation values are projected to decrease by 2–3 and by 4–5%. Also, projected annual mean temperature values indicate an increase in 21-27 and 29-35% under SSP3-7.0 and SSP5-8.5 scenarios. Integrating COMUSA ensemble clustering with MI-random sampling enhances the estimation accuracy of the ANN downscaling model, contributing to accurate projections of future precipitation and temperature values.

Identifying urban prone areas to flash floods: The case of Santa Cruz de Tenerife

Floods are the natural hazard that causes the largest annual losses in the world Urban expansion and population growth have made cities the most hazardous areas, mainly due to poor planning, occupation of the drainage network and soil sealing. Santa Cruz de Tenerife is one of the many cities worldwide threatened by this phenomenon. Its typically Mediterranean rainfall pattern, characterized by extreme precipitation events in short periods of time, together with its orography of steep ravines, short length and width, as well as its disorderly growth, make it a space prone to the occurrence of flash floods. In addition, the increase in torrential rainfall as a consequence of climate change and the tendency towards greater irregularity in precipitation is considerably intensifying the problem. This paper studies the characteristics of these episodes and the black spots inventoried in its General Management Plan (PGO for its initials in Spanish). On the other hand, flood modeling is carried out based on the rainfall characterization, whose maximum flows, in total, range between 500 and 1600 m3/s. Finally, a methodology that allows integrating both analyses to obtain a detailed hazard map is proposed as an alternative to the more traditional flood hazard analyses. A design storm of 288 mm is applied and the data is validated against the largest rainfall event on record, March 31, 2002. It has been shown that in an urban drainage network, the main watercourses and those that have disappeared due to urbanization represent areas susceptible to flooding and are the sectors that should be emphasized during the implementation of risk reduction measures. Finally, emphasis is placed on the need to integrate future climate projections of precipitation to better define the maximum flood flows.

Future projection of extreme precipitation using a pseudo-global warming method: A case study of the 2013 Alberta flooding event

The June 2013 extreme precipitation event in Alberta resulted in devastating flash floods that caused significant economic losses and societal disruption. In this study, two high-resolution experiments were conducted using the Weather Research and Forecasting (WRF) model to study the change of the 2013 Alberta extreme precipitation event in a warmer climate. The control experiment was forced with 6-hourly ERA-Interim reanalysis data, while the sensitivity experiment was forced with perturbed ERA-Interim reanalysis data with climate change signals derived from ten global climate models under the Representative Concentration Pathway 8.5 emission scenario. The results indicate that the 2013 Alberta extreme precipitation event is projected to exhibit two significant characteristics in a warming climate. First, precipitation is expected to increase over the Canadian Rocky Mountain region and eastern British Columbia. Second, the precipitation is expected to decrease over the Alberta and Saskatchewan Prairies. Future changes in the extreme precipitation event are associated with changes in the cyclone evolution, moisture transport, and atmospheric stability change caused by climate change. We also found that the increase in atmospheric stability due to the decrease of relative humidity in the lower atmosphere cause less precipitation to form over the plains and later enhance the orographic precipitation in the Canadian Rockies. In addition to the general increase of precipitable water under global warming, this mechanism causes the storm's precipitation to be more concentrated near the Canadian Rockies. The findings from this study could be beneficial for understanding future changes in extreme precipitation events that share similar characteristics.

Projection of precipitation under RCP4.5 and RCP 8.5 in central and southern regions of Iraq

Climate change has significant impacts on natural systems, especially in terms of altering hydrological patterns due to changing precipitation and melting snow and ice. This study examines projected changes in precipitation for central and southern Iraq under the RCP4.5 and RCP8.5 scenarios for the periods 2046–2065 and 2081–2100 using CMIP5 climate model HadGEM2-AO output. The results revealed that under RCP4.5, there was large spatial variation in the projected precipitation over the region during 2064-2065, ranging from as low as 27 mm in western part to as high as 1541 mm in the eastern part. The spatial variability as well as precipitation amount decreased considerably during 2081-2100 periods. Under RCP8.5 the projected precipitation was only 14-164 mm in 2046-2065 and 36-532 mm in 2081-2100 periods. Thus, under RCP8.5 scenario anticipated precipitation will be quite low.

Internally Driven Variability of the Angola Low is the Main Source of Uncertainty for the Future Changes in Southern African Precipitation

AbstractVariations in southern African precipitation have a major impact on local communities, increasing climate‐related risks and affecting water and food security, as well as natural ecosystems. However, future changes in southern African precipitation are uncertain, with climate models showing a wide range of responses from near‐term projections (2020–2040) to the end of the 21st century (2080–2100). Here, we assess the uncertainty in southern African precipitation change using five Ocean‐Atmosphere General Circulation single model initial‐condition large ensembles (30–50 ensemble members) and four emissions scenarios. We show that the main source of uncertainty in 21st Century projections of southern African precipitation is the internal climate variability. In addition, we find that differences between ensemble members in simulating future changes in the location of the Angola Low explain a large proportion (∼60%) of the uncertainty in precipitation change. Together, the internal variations in the large‐scale circulation over the Pacific Ocean and the Angola Low explain ∼64% of the uncertainty in southern African precipitation change. We suggest that a better understanding of the future evolutions of the southern African precipitation may be achieved by understanding better the model's ability to simulate the Angola Low and its effects on precipitation.

A Critical Evaluation and Future Projection of Extreme Precipitation Over South Korea in Observation-Based Products and a High-Resolution Model Simulation

A Critical Evaluation and Future Projection of Extreme Precipitation Over South Korea in Observation-Based Products and a High-Resolution Model Simulation

Rising groundwater levels in Dare County, North Carolina: implications for onsite wastewater management for coastal communities

ABSTRACT Onsite wastewater treatment systems (OWTS) are a common wastewater treatment approach in coastal communities. Vertical separation distance (VSD) requirements between the drainfield and groundwater aim to ensure aerated soils for wastewater treatment. When the VSD declines, OWTS can fail. This study evaluated groundwater response to sea level rise (SLR) and the implications for OWTS. A groundwater monitoring network (13 wells) was used to evaluate groundwater depth in Dare County, North Carolina. Groundwater levels were measured with water level meters and pressure transducers. Trends in groundwater depth and SLR were analyzed to evaluate the influence of SLR on groundwater depth. From 1984–2022, mean groundwater levels have risen (∼7.6 mm/year) in response to SLR. Currently, sites at &amp;lt;2.7 m land elevation are most likely to have groundwater depths &amp;lt;1 m and inadequate VSD. Based on current precipitation and NOAA intermediate SLR projections, groundwater depth projections suggest that OWTS at lower elevations are more likely to experience groundwater inundation by 2040–2060. SLR has resulted in reduced VSD causing diminished wastewater treatment capacity in low-lying areas. OWTS VSD requirements are typically static due to regulatory constraints. Future management approaches should consider adapting to rising coastal groundwater levels because of increasing wastewater contamination risks.

Podnebne projekcije temperature zraka in padavin za porečja Ledave, Pesnice in Vipave do konca 21. stoletja

As part of the project ‚CeVoTak‘, Integrated Management of Small Water Retention and Soil Erosion Prevention Measures in Agricultural Catchments, we studied changing temperature and precipitation conditions up to the year 2100. The study was conducted on agricultural lands in the catchments of the Ledava and Pesnica rivers in the sub-Pannonian region and the Vipava river in the sub-Mediterranean region. A common climate database was used to create the climate projections - RCM (Regional Climate Model) simulations from the project EURO-CORDEX and scenarios RCP (Representative Concentration Pathway), RCP2.6, 4.5 and 8.5. The projections were prepared for three time periods 2011-2040; 2041-2070 and 2071-2100 for 6 different regional climate models for average, minimum and maximum air temperatures and precipitation. Analysis of the ensemble of model simulations for all scenarios shows similar results for the basin of all rivers, an increase in temperature (maximum in winter, minimum in spring), with high confidence for all scenarios and periods. Projections of precipitation are less reliable, but show an increase in annual precipitation due to the winter increase. The use of climate change projections with expert interpretation is essential for determining the vulnerability of individual areas and building resilience through the implementation of climate change adaptation.

The combined impact of climate change scenarios and land use changes on water resources in a semi-arid watershed

This study proposes a combined approach involving climate change scenarios and varied land use and land cover (LULC) projections in a semi-arid watershed (El Grou) located in Morocco. The Soil and Water Assessment Tool (SWAT) model was coupled with land use scenarios generated using the Cellular Automata-Markov model for three dates (2030, 2040, and 2050) and future climate projections from the CMIP5 model under RCP 4.5 and 8.5 scenarios. Future precipitation and temperature projections indicate a decrease in annual precipitation by 5 % to 34 % and an increase in average temperatures, particularly during the summer months. LULC changes reveal a significant decline in agricultural lands and forests, with an expansion of bare soil by 2050. These changes, analyzed through three SWAT models representing different time periods, indicate a decrease in surface runoff by 41 % to 73 %, a reduction in total water yield by 21 % to 53 %, and a decline in groundwater recharge, posing significant challenges for water resource management and ecosystem health. The study underscores the need for integrated land and water management strategies, incorporating climate change adaptation measures, to ensure sustainable water resource utilization and build resilience in the El Grou watershed and similar semi-arid regions.