Future Rainfall Projections Research Articles

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.

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.

Assessing the resilience of stormwater ponds under climate change: A case study on Grace Lake, Florida, unveiling flood control and water quality implications

Stormwater ponds have emerged as a crucial solution in Florida to accommodate excess runoff from new developments and mitigate environmental pollution. Climate change affects rainfall and evapotranspiration, likely altering the hydrologic process. It is unclear how effective stormwater ponds will be in a changing climate, especially where surface water and groundwater interaction is highly expected. To address these concerns, this study employs a fully coupled surface water and groundwater model that was calibrated for the Grace Lake, located in central Florida. Utilizing the Multi-Model Ensemble method and GCM data, future rainfall projections were generated for the mid-century (2040–2070). The results indicate a projected 13 % increase in rainfall for the mid-century under the Representative Concentration Pathway 4.5 (RCP 4.5). This increase led to a rise of 13 cm in the seasonal high water elevation of Grace Lake, reducing its reliability as a flood control mechanism by 13 %. Moreover, downstream areas from the lake are expected to experience higher flood stages in the future, with the duration of road flooding extending from less than three days (base period) to approximately seven days (future period). This increase in surface runoff raises concerns for water quality, with nitrogen and phosphorus loads projected to surge up to 67 % in the future period.

Analysis of Historical and Future Rainfall Projections in India's Lower Godavari Basin

Changes in evaporation and precipitation in the climate are precursors to changes in the water cycle due to the increase in temperatures due to climate change. In recent years, enormous changes in precipitation patterns have led to an increase in extreme rainfall events, reflecting flash floods in smaller areas. To understand this, you must study the rainfall patterns of the district and the smaller watersheds. In the present study, we have observed the trend of rainfall in the lower Godavari basin, which was part of the Godavari River from 1970 to 2019, using IMD 0.25X0.25 ° gridded rainfall data. According to the study, during the winter season and before the monsoon, there is no significant increasing or decreasing trend. In the post-monsoon season they are showing a negative and decreasing trend with a magnitude of 4 mm/year. But in the monsoon season, one end (the right end near the river mouth) of the basin or watershed is showing an increasing trend, while the left part is showing a significantly decreasing trend. In addition to that, future projections of the CMIP5 two scenarios data from 2000 to 2100 to understand the precipitation patterns in future projections. Lower Godavari basin from 2020–2040 and 2040-2100 with the RCP 4.5 and RCP 8.5 scenarios. With the RCP 4.5 scenario, the first 40 years (2020–2040) of the future projected mean seasonal rainfall are 4.5 mm/year higher in the western part of the sub-basin than in the eastern and river mouth regions.

Evaluating real-time control of stormwater drainage network and green stormwater infrastructure for enhancing flooding resilience under future rainfall projections

Traditional stormwater control measures are designed to handle system loadings induced by fixed-size storm events. However, climate change is predicted to alter the frequency and intensity of flooding events, stimulating the need to explore another more adaptive flooding solution like real-time control (RTC). This study assesses the performance of RTC to mitigate impacts of climate change on urban flooding resilience. A simulated, yet realistic, urban drainage system in Salt Lake City, Utah, USA, shows that RTC improves the flooding resilience by up to 17% under climatic rainfall changes. Compared with green stormwater infrastructure (GSI), RTC exhibits a lower resistibility, lower flooding failure level, and higher recovery rate in system performance curves. Results articulate that keeping RTC's performance consistent under ‘back-to-back’ storms requires a tradeoff between upstream dynamical operation and downstream flooding functionality loss. This research suggests that RTC provides a path towards smart and resilient stormwater management strategy.

Diagnosis of the rainfall-wheat yield relationship in the current and future climate change conditions in Eastern Algeria

Future projections indicate that rain-fed agriculture in North Africa is among the most vulnerable in the world in the context of future climate change. This article aims to diagnose the relationship between rainfall and wheat yield in both current and future climatic situations in a semi arid agro-climatic conditions represented by the region of Bordj Bou Arreridj. For the current situation, we used 15 years (1995–2009) of recorded rainfall and durum wheat yield series. Future rainfall projections (2071–2100) were generated by the MED-CORDEX climate model version CCLM4-8-19 under RCP 6.0 scenario. Simulated data over the observed period and that of the future on the maximum evapotranspiration (ETM) of durum wheat and the water deficit (WD) accumulated over the cycle as well as future yields are obtained using a simple agro meteorological crop simulation model, previously validated. In both current and future situations, precipitations, ETM, WD and yields data are first analyzed, then yields are related by regression to three components of rainfall: annual rainfall, cumulative rainfall over the crop cycle (November–June) and cumulative rainfall during spring (March–May). In the observed climate, annual precipitation averages 382.3 ± 96.3 mm, cumulative rainfall over the crop cycle (November–June) averages 278.3 mm and cumulative rainfall during spring is 101.9 mm. These last decrease to 303.7 ± 99.4, 232.3 and 83.3 mm in the future situation. Observed yields (1995–2009) averages1.9 ± 0.64 q/ha in the observed situation and decrease to 15.5 ± 0.54 q/ha in future climate. ETM are low and WD values are high in the current climate, with a worsening of the situation in the future climate, particularly during spring. The correlation between yields and précitations is always positive in both weather conditions, but the best R2 are 0.65 and 0.82 and concern spring rains. In semi-arid regions, cumulative rainfall towards the end of the growing season is currently impacting the grain yield of durum wheat and will become more decisive in the context of future climate change.

Comparative assessment on climate prediction from CMIP5 and CMIP6 models over Hulu Terengganu, Malaysia

The uncertainties of climate change in the future year cause the contribution factors and greenhouse gasses (GHGs) effects on the local climates need to be revised. The development of new climate scenarios in the 6th Coupled Model Intercomparison Project (CMIP6) is consistent with the technological exploration and increment of GHGs dispersion compared to the consideration factors in CMIP5. The purpose of this study was to compare the performance of CMIP5 (based on Representative Concentration Pathways, RCPs) and CMIP6 (based on Shared Socioeconomic Pathways, SSPs) in simulating seasonal rainfall and estimating trends in Hulu Terengganu, Malaysia. The linear scaling (LS) method was used in this study to treat the gaps between observed and simulated results, and the climate trend was examined using the Mann-Kendall (MK) and Sen’s Slope tests. The results show that the SSPs scenario outperforms the RCPs in simulating historical rainfall (2015-2020) by producing a higher r value and a smaller percentage difference. According to the MK test, there was no significant trend in projected rainfall across all scenarios (2020-2099). Based on Sen’s Slope test, RCP 4.5 and RCP 8.5 show an increasing trend for all rainfall stations. However, all SSP scenarios show a declining trend in projected rainfall, with SSP1-2.6 producing the largest declining trend magnitude. In contrast, when compared to observed rainfall from the baseline period (1988-2017), the SSPs scenario indicates the potential for a greater increase in future annual rainfall projections than the RCPs scenario. All SSP scenarios show an increasing annual rainfall magnitude in 2040-2069 (Δ2050). However, the annual rainfall for SSP2-4.5 and SSP5-8.5 began to decrease in 2070-2099 (Δ2080). Meanwhile, RCP 2.6 has the greatest reduction in annual rainfall projections for both projected time periods when compared to other scenarios. It can be concluded that although all SSPs scenarios show a declining trend in projected rainfall from 2020 to 2099, the total annual rainfall projected for SSPs remains higher than RCPs in Δ2050 and Δ2080 periods.

Investigation and evidence of high-episodic groundwater recharge events in tropical hard-rock aquifers of southern India

Processes controlling groundwater recharge have been a topic of pursuit in the hydrological research community. The groundwater recharge in hard-rock aquifers is significantly impacted by rainfall patterns, aquifer characteristics, weathering/soil conditions, topography, land use, and land cover. Analysis of the recharge process in tropical semi-arid hard-rock aquifer regions of southern India is crucial due to several factors, including (a) a heavily tailed monsoon system prevailing in the region, which is characterized by very few episodic storm events; (b) heterogeneity of aquifers in terms of fractures; and (c) the presence of several man-made irrigation lakes/tanks along with the drainage network. This study uses a lumped unconfined aquifer model to estimate the groundwater recharge for nine locations in Gundlupet taluk and 150 locations in Berambadi Experimental Watershed (EWS) in the south Indian state of Karnataka. Analysis of estimated recharge factors identifies 30 high-episodic recharge events out of 292 observations (around 10%) in Gundlupet taluk and 80 out of 150 locations in 2017 in Berambadi EWS. Partial information correlation (PIC) analysis is used to select the significant predictors out of potential predictors based on rainfall intensity distribution and climatological indices. PIC analysis reveals that the number of rainfall events with 15–30 mm daily rainfall intensity are most significant for normal recharge events in Gundlupet taluk and Berambadi EWS. The combined information on daily rainfall distribution, daily rainfall events of 20–40 mm, and the number of La Niña months in a particular year can explain the variability of high-episodic recharge events in Gundlupet taluk. These high-intensity rainfall events can be potential sources of alternate recharge pathways resulting in faster indirect recharge, which dominates the diffused recharge and results in high-episodic recharge events. Rainfall intensity distribution and climatological indices contain the potential information required to disaggregate normal and high-episodic recharge factors for future rainfall projections, which is useful for future groundwater level projections.

Long-term rainfall projection based on CMIP6 scenarios for Kurau River Basin of rice-growing irrigation scheme, Malaysia

Rainfall is a vital component in the rice water demand model for estimating irrigation requirements. Information on how the future patterns are likely to evolve is essential for rice-growing management. This study presents possible changes in the future monthly rainfall patterns by perturbing model parameters of a stochastic rainfall using the change factor method for the Kerian rice irrigation scheme in Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were employed to project rainfall from 2021 to 2080. The results show that the stochastic rainfall generator performed well at preserving the statistical properties between simulated and observed rainfall. Most scenarios predict the increasing trend of the mean monthly rainfall with only a few months decreasing in April and May occurring in off (dry) season. The future patterns 2051–2080 show a significant increasing trend during main (wet) season compared to the near future period (2021–2050). The projected future rainfall under SSP1-2.6 and SSP2-4.5 are higher than SSP5-8.5 from January to July, and December but lower from August to November. The projected annual rainfall will significantly increase toward 2080 during the main-season but uniform during the off-season except under SSP5-8.5, which is significantly decreasing. The output results are essential for rice farmers and water managers to manage and secure future rice irrigation water under the impact of future climate change. The projected changes in rainfall on the river basin demand further study before concluding the impact consequences for the rice sector.Article highlightsThe rainfall generator performs well in simulating future rainfall based on an ensemble of five different GCMs considering three different scenarios emission (low, medium, and high) caused by greenhouse gas and radiative forcing.The future rainfall projection predicted that off (dry) season would become wet, and main (wet) season would become wetter due increase in monthly and annual rainfall.The outcomes of this paper are beneficial for rice farmers and water managers of the study area to manage their rice cultivation and water release from the reservoir schedules to avoid losses due to flood and drought.

Determination the optimal prediction scenario for the future precipitation rates in the Al-Najaf Governorate in Iraq

The world suffers from the effect of climate change which complicates the prediction of future events. Precipitation, temperature, andgeological properties of soil such as drought and relative humidity are among the most important transactions affected by climate change. Statistical downscaling models are recently used to predict future events of precipitation and temperature. In the current study, astatistical downscaling model is built for Al-Najaf Governorate, Iraq, depending on the daily record of observed rainfall data from 2010 to 2020 with the aim of predicting the future rainfall's projections of ​​the decades from 2030 to 2100. Identification the best, ideal and precise scenario that fits the study area to predict the future rainfall values is done. Calibration the statistical downscaling model of Al-Najaf Governorate has given good results comparable to the observations. Overall the three scenariosCanESM2_rcp2.6, CanESM2_rcp4.5 and CanESM2_rcp8.5, CanESM2_rcp8.5 scenario results the best values ​​of standard deviations. Therefore, this scenario is qualified to be the best scenario for anticipating the future rainfall's events. Results indicate a slight increase in rainfall for the decades 2030-2070. In Autumn season (December 2080), the rainfall will increase by 18mm, while rainfall records a 23mm increment in Winter season (Januarys 2080, 2090, and 2100). The implicit years of the predicted decades in Winter and Autumn seasons, rainfall ranges (10-18)mm (Autumn) and (15-23)mm (Winter).

Representative grid location-multivariate adaptive regression spline (RGL-MARS) algorithm for downscaling dry and wet season rainfall

The high dimensionality of predictor variables reduces the predictive accuracy of statistical downscaling models. Principal component analysis (PCA) is one of the extensively used approaches for reducing the dimensionality of the predictors. However, PCA reduces the efficiency of downscaling models when a nonlinear predictor-predictand relationship exists. To solve this issue, the representative grid location (RGL) approach was used to minimise the dimension of the predictor variables. Thus, a novel RGL-MARS (Multivariate Adaptive Regression Spline) based downscaling model was proposed in the current study. The proposed model was compared with PCA-MLR (Principal Component Analysis-Multiple Linear Regression), MARS, and PCA-MARS downscaling methods. Eight general circulation models (GCMs) were considered, out of which only CAN-ESM2 (second-generation Canadian Earth System Model) GCM was found suitable for the study area. Three criteria, i.e., correlation coefficient (CC), mutual information (MI), and decision tree (DT), were used for the selection of dominant predictors. It was observed that CMD (CC + MI + DT) predictors, when used with the RGL-MARS downscaling method, showed the best performance (NSE = 74–89%; root mean square error (RMSE) = 10–40 mm). The results revealed that rainfall simulated by the RGL-MARS model captured the standard deviation and coefficient of variation in the observed rainfall, whereas the rest failed to do so. The RGL-MARS model solved the issue of underprediction of wet season rainfall and overprediction of dry season rainfall. The RGL-MARS addressed the issue with an improved NSE of 62–89% and RMSE of 18–45 mm; thus, the result exhibited that integration of MARS with the RGL perform better compared to the PCA.. This study also demonstrated that downscaling model outcomes were more reliable for the wet than the dry season. The future rainfall projection indicated that the rainfall fluctuation might be more in the dry season than the wet season. The proposed downscaling method can improve the accuracy of rainfall projections under various climatic conditions, subsequently, coupled with a range of hydrological and land surface models to better understand the catchment characteristics and the water balance dynamics in future climate studies.

Landscape evolution of the Wenchuan earthquake-stricken area in response to future climate change

The Wenchuan earthquake caused rapid and gradual changes to the local geomorphology in the earthquake-stricken area. Extreme rainfall events can have strong impacts on local geomorphic evolution and alter the risks of subsequent geohazards and flooding. However, there is still lack of understanding of long-term geomorphic and fluvial evolution in face of changing future climate. In this paper, we first perform the future extreme rainfall projection and then analyze the landscape evolution variation under future climate change with the CAESAR-Lisflood model. A new approach to applying the ‘NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)’ dataset at the local scale is presented, and a future landscape evolution analysis is conducted. We used spatial (tailoring process) and statistical temporal downscaling methods with NEX-GDDP data to project future extreme rainfall in combination with the CAESAR-Lisflood model to simulate the landscape evolution in response to climate change. The results showed that the future rainfall projection model can provide the most robust and effective representative of future climate change scenarios at the local scale, the geological and geomorphological environment of the basin continues to change dynamically after the earthquake, and nearly 27 years (~2035) are needed before the geomorphology of the basin stabilizes, and then, the yearly yield remains at an average rate of 1 × 104 m3. Post-earthquake mountainous area response and recovery characteristics to a changing future climate can provide important information on future geohazard projections and measures to be taken.

Constraining Climate Model Projections of Regional Precipitation Change

AbstractAs communities prepare for the impacts of climate change, policy makers and stakeholders increasingly require locally resolved projections of future climate. Statistical downscaling uses low‐resolution outputs from climate models and historical observations to both enhance the spatial resolution and correct for systematic biases. By examining the downscaled rainfall over land, we show that although bias corrections are effective in reducing biases in the current climate, they do not reduce the intermodel spread in future rainfall projections. This failure stems from the strong dependence of future rainfall change upon the current climatological rainfall patterns. Even after bias corrections are applied, the downscaled projections of precipitation change retain this dependence upon their native climatology. However, we show that this dependence can be exploited; even very simple methods to subset models according to their ability to resolve the observed rainfall climatology can substantially reduce the intermodel spread in rainfall projections.

Comparison of dynamical and statistical rainfall downscaling of CMIP5 ensembles at a small urban catchment scale

Downscaling of future projections of climatic variables from global climate models (GCMs) to urban catchment scales is required for stormwater studies as existing GCMs are unable to predict the rainfall at high temporal and spatial resolutions. In the current study, the capability of dynamical and statistical downscaling methods is evaluated and compared using the rainfall data of current climate and future extreme events at a small urban catchment scale. Regional climate model (RCM) and statistical downscaling model (SDSM) are utilized to downscale rainfall data from twelve GCMs under three representative concentration pathways (RCPs) (i.e., RCP2.6, RCP 4.5 and RCP 8.5). The daily rainfall data (1985–2015) of Lucas Creek catchment located in Auckland, New Zealand is used as a baseline/current climate. The future downscaled rainfall data is analyzed in the 2090s (2071–2100). The results showed that both the methods performed well in downscaling the current climate. For future projections, SDSM underestimated mean daily rainfall at the start of the annual cycle and overestimated towards the middle of the year compared to RCM. Similarly, monthly variance and skewness were overestimated for some months by SDSM. The GCMs of both the methods also showed variations in the future rainfall projections amongst themselves. However, significant alterations in the future rainfall were observed compared to the current climate. Rainfall frequency analysis was performed by applying the Gumbel distribution to the baseline and downscaled data for 1, 2, 3, 4, 5, 10, 20, 30, 50 and 100 years return periods. The investigation revealed that RCM and SDSM show similar results for low return periods and different results for high return periods for the current and future climate. Both the methods forecasted an increase in magnitudes of future events however, RCM projections were lower compared to SDSM. The results illustrate the downscaling abilities of both the methods at a small urban catchment scale however, the contrasting implication in downscaling the rainfall data is related to their different downscaling mechanisms.

A review of past and projected changes in Australia's rainfall

There has been much attention given to the spatial and temporal characteristics of changes in mean and extreme rainfall over Australia during the past century. As Australia is the second driest continent on Earth, reliable projections around the trends and variability in future rainfall are crucial for policymakers and water resource management. This article comprehensively reviews the current published literature on trends in Australia's rainfall from pre‐instrumental and instrumental records, the climatic drivers of Australia's rainfall variability, attribution of the long‐term trends, extreme rainfall attribution methods with particular reference to a recent case study (2010–2012 east Australia rainfall event) and projected changes of mean and extreme rainfall over Australia during the 21st century. Notable trends in the observational record of rainfall in Australia are a decrease in mean rainfall in southwest and southeast Australia and an increase in northwest Australia since 1950. The general consensus of research into Australia's future rainfall is that mean rainfall will continue to decrease in southwest Australia in a warming world, while changes over northern and eastern Australia remain uncertain. There are still significant knowledge gaps around the causes of observed trends in rainfall both in the mean and extremes, the ability of climate models to accurately represent rainfall in the Australian region and future rainfall projections. These gaps are identified, and avenues for future research directions are proposed.This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change

Tropical Pacific SST and ITCZ Biases in Climate Models: Double Trouble for Future Rainfall Projections?

AbstractThe double Intertropical Convergence Zone bias remains a persistent problem in coupled general circulation model simulations. Due to the strong sea surface temperature (SST)‐convection relationship in the tropics, precipitation biases are sensitive to background SST. Using historical simulations of 24 coupled general circulation models and an atmospheric general circulation model, we show that cold equatorial SST biases at least exacerbate double Intertropical Convergence Zone biases in the Pacific. A linear regression model is used to demonstrate that improved predictability of precipitation trends is possible with such model‐dependent information as mean‐state SST biases accompanying projected SST trends. These results provide a better understanding of the root of the double Intertropical Convergence Zone bias and a possible path to reduced uncertainty in future tropical precipitation trends.

Relationship between the Future Projections of Sahel Rainfall and the Simulation Biases of Present South Asian and Western North Pacific Rainfall in Summer

Confident model projections of regional climate, in particular precipitation, could be very useful for designing climate change adaptation, particularly for vulnerable regions such as the Sahel. However, there is an extremely large uncertainty in the future Sahel rainfall projections made by current climate models. In this study, we find a close relationship between the future Sahel rainfall projections and present rainfall simulation biases in South Asia and the western North Pacific in summer, using the historical simulations and future projections of phase 5 of the Coupled Model Intercomparison Project (CMIP5). This future–present relationship can be used to calibrate Sahel rainfall projections since historical simulation biases can be much more reliably estimated than future change. The accordingly calibrated results show a substantial increase in both precipitation and precipitation minus evaporation in the future Sahel, in comparison with the multimodel ensemble (MME) result. This relationship between the historical rainfall bias and future Sahel rainfall projection is suggested to lie with the different schemes of convective parameterization among models: some schemes tend to result in both overestimated (underestimated) historical rainfall in South Asia (the western North Pacific) and enhanced future Sahel rainfall projection, while other schemes result in the opposite.

Fuzzy measure-based multicriteria land assessment for rainfed maize in West Africa for the current and a range of plausible future climates

Crop production in West Africa is largely under rainfed conditions, making the system vulnerable to the impacts of climate change. However, the impact of future climate on the geographic range of many crops in West Africa is still uncertain. This is exacerbated by considerable uncertainty in projecting future West African climate by global circulation models (GCMs). In this paper, the current land suitability for rainfed maize (Zea mays L.) production in West Africa and how it would change in response to a range of future climate changes were assessed. A novel non-additive approach involving the integration of a fuzzy measure modelling procedure and the Choquet integral to deal with non-linear relationships among criteria was introduced. The supervised and unsupervised variants of the approach and the Ordered Weighted Averaging (OWA) aggregators were evaluated and compared using data on the current climate, soil characteristics, and actual maize yield estimates at some locations across the region. The supervised fuzzy measure modelling outperformed the unsupervised variant and the OWA operators, demonstrating the importance of considering interaction among criteria and incorporating human reasoning in multicriteria analysis. Consequently, the supervised modelling approach was used to estimate suitability scores for rainfed maize for the current and a range of plausible future climates which were generated from unique combinations of temperature and rainfall percentile values (5th, 50th and 95th) from 25 and 32 GCMs projections under the RCPs 2.6 and 8.5 scenarios, respectively. Under the current climate in West Africa, the combined suitable/highly suitable areas for rainfed maize production are in the humid savanna zones. In contrast, the forest region to the south exhibited moderate suitability with the semi-arid savanna zones being marginally suitable. Relative to the current climate in which 91.3% of West Africa is at least moderately suitable for maize production, suitability for maize will change in at least 43% of West Africa in response to future climate change. Across all scenarios and years, future suitability in the semi-arid region of the Sudano-Sahelian savannas shows strong sensitivity to uncertainty in rainfall projections. However, regardless of the trajectory of future rainfall projection, the 95th percentile temperature projection will decrease suitability in about 15% of the West African region by 2080 under the RCP8.5 scenario. Because of the incorporation of uncertainties of future climate projections in this analysis, the results from this study can be used in the development of flexible climate adaptation strategies.

Rainfall and air temperature projections for Sharjah City, United Arab Emirates

Climate change has significant effects on hydrological cycle and water resources. Sharjah City in the United Arab Emirates (UAE) is located in one of the most water-scarce regions of the world. Despite the importance of climate change projection and impact studies, those studies specifically for Sharjah City are rare. In this study, first, monthly rainfall and temperature projections were generated for near and far future. Then, trend analysis was conducted to detect trends in future rainfall and temperature projections for Sharjah City. Findings of this study showed that mean air temperature in Sharjah City is expected to rise up to 12-17% for near and far future respectively, whereas average annual rainfall is projected to decrease in the range of 1-53%. Trend analysis showed significant increasing air temperature trends and non-significant decreasing rainfall trends. This study will contribute to climate change impact and policy making studies in Sharjah City.

Evaluating the reliability of stormwater treatment systems under various future climate conditions

Water Sensitive Urban Design (WSUD) stormwater systems, also known as Low Impact Development (LID) systems or Nature Based Solutions (NBS), are currently implemented based on the underlying assumption of statistical stationarity of rainfall, which threatens to become outdated under climatic uncertainty. This paper applies a new downscaling method to examine the implications of climate change on future rainfall and evaluate the reliability of WSUD stormwater infrastructure in pollution reduction, flow frequency mitigation and reliability as an alternative water supply. A variety of future atmospheric scenarios are considered as part of this comprehensive assessment by analysing an ensemble of eight different downscaled General Circulation Models (GCMs). High resolution catchment-scale rainfall projections for Melbourne, Australia were generated using a scheme called High-resolution Downscaling of Rainfall Using STEPS (HiDRUS) at a fine 1 km and 6-min scale for more precise analysis with uncertainty estimates. Statistical analyses show that, in general, the climate models predict a drier future with fewer rainfall events and longer dry periods when comparing the simulated near future (2040–2049) periods against the base-line period (1995–2004). The difference simulated between historical and future rainfall projections show minimum difference of WSUD performance in pollution removal and flow frequency reduction, with slightly lower harvesting reliability (<3%) observed under future climate; high variabilities, however, were observed across GCM simulations, indicating big uncertainties of system reliability under various conditions, e.g. design wetland sizes may vary from 2.5% to 4.0% of the impervious catchment area according to different future projects across GCMs. Larger WSUD systems are recommended to ensure reliable performance of pollution removal, as well as harvesting reliability under simulated future conditions. The significance of considering an ensemble of different GCMs as opposed to many scenarios generated by a single ‘best’ climate model was also demonstrated for the robust estimation of uncertainty in future WSUD reliability. This work highlights important considerations for the future design, management and quantitative evaluation of WSUD reliability.