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This paper analyzes rainfall data to characterize the rainy season and define rice and maize crop calendars for current and future conditions in Madagascar. The daily rainfall data are taken from observational climate records and climate model simulations from the CMIP6 under the SSP245 and SSP585 scenarios. Rainy season characteristics are calibrated to fit rice and maize crop growth stages. The comparison between the past (1950–2018) and the future (2030–2100) highlights changes in the onset and cessation dates, which happen later and earlier, respectively. This causes the reduction of the rainy season duration, which affects the rice and maize crop calendars, especially its sowing or seeding periods. The worst (best) case is mainly observed in the southeast (southwest). On the one hand, the southwestern region may need to adapt to grow rice and maize crops with short or medium crop cycles in the future. In the Highland or Central land, the length of the sowing or seeding period increases. On the other hand, the North and East face a significant reduction in the length of the sowing or seeding period. Rice endures more than maize. Growing rice crops twice a year may not be possible in the future. But rather, we observe minor changes in the West. Our analysis suggests the imperative necessity to advise smallholder farmers to rely on short crop cycle varieties of rice and maize crops. Predominantly, the harvesting period is postponed. It is recommended to carefully consider our results for the definition of food policies. [ABSTRACT FROM AUTHOR]

A model‐circulation increment‐based dynamic statistical technique (MIDST) is proposed in this paper to improve the prediction of summer rainfall over southern China (SC) where quite low prediction skills have been found in the Beijing Climate Center Climate System Model version 1.1 with a moderate resolution (BCC_CSM1.1m). The results show that BCC_CSM1.1m can hardly represent the variability of the summer rainfall anomaly and its year‐to‐year increment over SC, and the skillful predictions are mostly confined over the middle reaches of the Yangtze River. Using the dynamic model output and statistical method, the MIDST is established to capture the coupled modes between the year‐to‐year increments of the summer rainfall anomaly and the associated simultaneous three‐dimensional coupled air‐sea circulation predictors. The cross‐validation indicates that the prediction skills of the MIDST are evidently improved for both the summer rainfall increment prediction and summer rainfall anomaly prediction compared with the direct BCC_CSM1.1m prediction. The skillful prediction can persist for long forecast leads over most regions except southwestern China. As the major predictability source of seasonal prediction, the intense response to changes in the circulation related to the El Niño‐Southern Oscillation (ENSO) is well captured, and thus, the performance improvement of the MIDST is primarily due to its more realistic representation of the incremental circulation related to the ENSO. [ABSTRACT FROM AUTHOR]

The frequency of flash floods resulting from heavy rainfall over West Africa has increased in recent years with serious socio‐economic consequences. Therefore, the need to utilize numerical weather prediction models to forecast heavy rainfall events reliably is also rising at many operational meteorological centres in West Africa. This paper evaluates the performance of the Consortium for Small‐scale Modelling (COSMO) model of the German Meteorological Services (DWD) in predicting rainfall over West Africa for high‐impact rainfall events that occurred between 19 and 26 August 2017. The paper aims to investigate the synoptic forcings modulating daily rainfall variability during that period. Results show that COSMO simulates adequately the spatio‐temporal variability of rainfall distribution over West Africa, though with inherent biases. COSMO displays a decreasing skill in producing spatial rainfall distribution as rainfall amounts tend to 30 mm and above. Additionally, areas of heavy rainfall, mostly about 100–300 km southwest of the core of the Africa Easterly Jet (AEJ), often coincide with areas of decreasing mean sea level pressure of at least 0.6 hPa and areas of increasing convective available potential energy of at least 500 J/kg. Although not in all cases, the trough of the Africa Easterly Wave (AEW) is always located to the east of these areas. We show that not every storm, especially east of the prime meridian, is associated with an AEW trough. COSMO is able to reproduce the atmospheric dynamics modulating the daily rainfall variability, in addition to capturing the daily propagation of the AEW trough, and the core of the AEJ. However, the reproducibility skill of the model in predicting atmospheric dynamics may not transform into the predictive skill of the model in producing rainfall. Nevertheless, operational forecasters may be able to determine likely areas of heavy rainfall by estimating the position of the AEJ core based on the position of areas of the least falling pressure from COSMO. Finally, the incorporation of the fractions skill score metric based on the neighbourhood approach could also assist operational forecasters to decide at which scale a severe weather alert can be issued. [ABSTRACT FROM AUTHOR]

This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [ABSTRACT FROM AUTHOR]

We assess the impact of three bias correction approaches on present day means and extremes, and climate change signal, for six climate variables (precipitation, minimum and maximum temperature, radiation, vapour pressure and mean sea level pressure) from dynamically downscaled climate simulations over Queensland, Australia. Results show that all bias‐correction methods are effective at removing systematic model biases, however the results are variable and season‐dependent. Importantly, our results are based on fully independent cross‐validation, an advantage over similar studies. Linear scaling preserves the climate change signals for temperature, while quantile mapping and the distribution‐based transfer function modify the climate change signal and patterns of change. The Perkins score for all the values above the 95th percentile and below the 5th percentile was used to evaluate how well the climate model matches the observational data. Bias correction improved Perkins score for extremes for some variables and seasons. We rank the bias‐correction methods based on the Kling–Gupta efficiency (KGE) score calculated during the validation period. We find that linear scaling and empirical quantile mapping are the best approaches for Queensland for mean climatology. On average, bias correction led to an improvement in the KGE score of 23% annually. However, in terms of extreme values, quantile mapping and statistical distribution‐based transfer function approaches perform best, and linear scaling tends to perform worst. Our results show that, except linear scaling, all approaches impact the climate change signal. [ABSTRACT FROM AUTHOR]

An analysis of artificial rainbow observations in ZhaoSu region from 2017 to 2019 shows that rainbows mainly occur between 16:00 and 22:00 (LST) from April to September. The analysis based on the meteorological observation in the same period shows that precipitation, temperature, wind force and cloud variety contribute to rainbow formation. Approximately 90% of rainbows appear one hour after rainfall, and 100% of rainbows occur when the Beaufort wind scale is less than level 8 following Beaufort wind scale (less than 20.9 m/s), the temperature is greater than 8°C, and the cloud amount is greater than 40%, respectively. A rainbow probabilistic forecast model is constructed based on five meteorological factors. The forecast ability of the model is independently assessed by comparing rainbow forecasts and its observation in 2020. The Brier score is 0.20, indicating that the objective model is effective for rainbow forecasts. [ABSTRACT FROM AUTHOR]

We have investigated cloud formation over the South China Sea in the upper troposphere and its impacts on the radiation budget of the troposphere and ocean surface under monsoon surges using satellite and reanalysis products and an atmospheric numerical model. During strong surges (SS), the upper tropospheric (UT) cloud formation in central South China Sea shows a clear diurnal cycle, peaking around 12–14 local solar time when the incoming solar radiation is the strongest. The enhanced UT clouds attenuate the incoming solar radiation in the upper troposphere reducing the net downward shortwave radiation flux at the surface by approximately 28% in SS compared with no surge case. In contrast the downward longwave radiation flux is enhanced mainly from the middle to upper troposphere in SS. The cooling because of the diminished incoming shortwave radiation overwhelms the warming due to the longwave radiation at the ocean surface. This cloud radiative forcing is observed partly over the cold tongue in sea surface temperature (SST) in the South China Sea. Numerical simulations with a 1‐dimensional slab ocean model suggests that the monsoon‐induced UT cloud radiative forcing contributes partially to maintain and reinforce the SST cold tongue with a tendency of about 0.08 K in 6 days or about 1/6 of one standard deviation of the Cold Tongue Index in December. The latent heat flux due to the monsoon surge is still the most significant factor maintaining the cold tongue. [ABSTRACT FROM AUTHOR]

An emergent consequence of climate change is its potential impacts on transatlantic flight routes and durations, with studies suggesting that future optimal paths will likely lead to decreased (increased) eastbound (westbound) journey times. However, these earlier works all rely on a class of flight planning algorithms (based on the so‐called 'shooting method') that are fundamentally different to those typically used by the aviation industry (based on the so‐called 'network method'). To help resolve any ambiguities associated with these differences and better align both the academic and operational communities, we have therefore conducted an evaluation of methods of estimating time‐optimal flight routes to relate the result obtained from the shooting method with the result obtained from the network method, using identical data from an ensemble of eight different climate models included within the CMIP5 project. Our findings suggest that, contrary to the existing literature, journey times will likely be shorter, irrespective of direction of travel and season, although the magnitude of this difference is negligible given the typical duration of transatlantic flights. Trajectory prediction (TP) results are largely dependent on the characteristics of the ensemble, their method of generation, the projected climate scenario and the temporal periods considered. Importantly, we conclude the choice of TP models is not a crucial factor in assessing the impact of climate change on flight durations—as long as this sample size (i.e., number of trajectories used in deriving statistics) is large. [ABSTRACT FROM AUTHOR]

In this study we evaluate the reliability of the Regional Climate Model (RegCM4.6) by performing several sensitivity tests at 50 km horizontal resolution using 10 convection schemes or combination of convection schemes. Emphasis on model output is made for the September-October--November 2017-2019 seasonal period. Part of the Central African region, five sub-regions, was identified. The simulated temperatures are compared to the daily climatology of near-surface temperature of the European Centre for Medium-Range Weather Forecasts Reanalysis fifthgeneration and the National Center for Environmental Prediction datasets, while the simulated precipitations are compared to the precipitation of the Global Precipitation Climatology Project and Climate Hazards Group Infrared Precipitation with Stations datasets. In most analyses, the Grell scheme with Arakawa--Schubert closure assumption tends to have the best index of agreement but its spatial distribution shows underestimation of rainfall. Generally, the Emanuel convection scheme is more suitable to represent rainfall and temperature over Central Africa. Compared to our previous study (Komkoua Mbienda et al., International Journal of Climatology, 2017, 37, 328-342), the present study shows that we have to pay attention to the choice of convective scheme when using any version of RegCM4 released for climate study over Central Africa. This choice is strictly related to the RegCM version released, the study years and the season. It is important to note, however, that the results presented are a preliminary study of the response to the selected convection schemes. The analysis uses a limited sample of climate model simulation (three model years for each convection scheme). Follow-up work, featuring longer duration climate simulations and a full assessment of statistical significance, is therefore required to confirm the robustness of the analysis presented. [ABSTRACT FROM AUTHOR]

Errors in the physics schemes and parameters of a land surface model can lead to large errors/bias in simulated soil moisture. In addition, large bias in simulated soil moisture may be caused by soil lateral water flow that is not described in a land surface model. In the present study, three model output statistics (MOS) approaches (a linear regression based MOS, a rescaling based MOS and a cumulative distribution function matching based MOS) are proposed to correct these biases and errors at stations that have soil moisture observations. Results show that the biases/errors in simulated soil moisture were significantly corrected by the three MOS approaches. The best performance was obtained for the linear regression MOS model consisting of four separate linear regression MOS models for different seasons throughout the year (MOS-SN). However, for the linear regression MOS and the rescaling MOS, a few months' data may also be used for MOS correction when the same seasonal period is used for both model fitting and prediction. With respect to goodness of fit and performance, the linear regression MOS model is better than the rescaling MOS model, and the MOS-SN is slightly better than the nonlinear cumulative distribution function matching approach. For sites where soil moisture observations are much shorter, or more intermittent, than meteorological observations, the linear regression MOS approaches can be used to reconstruct missing historic soil moisture data. All MOS approaches can also be used to correct the errors/bias in forecast soil moisture from weather or climate models at sites with soil moisture observations. [ABSTRACT FROM AUTHOR]

The active phase of the East Asian summer monsoon (EASM) in Taiwan during May and June, known as Meiyu, produces substantial precipitation for water uses in all sectors of society. Following a companion study that analysed the historical increase in the Meiyu precipitation, the present study conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan. Higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group. Despite the difference in model performance, both groups projected a substantial increase in the Meiyu precipitation over Taiwan. In the higher skill group, weak circulation changes and reduced low‐level convergence point to a synoptically unfavourable condition for precipitation. In the lower skill group, intensified low‐level southwesterly winds associated with a deepened upper level trough enhance moisture pooling. Thus, the projected increase in Meiyu precipitation will likely occur through the combined effects of (1) the extension of a strengthened North Pacific anticyclone enhancing southwesterlies; and (2) more systematically, the Clausius–Clapeyron relationship that increases precipitation intensity in a warmer climate. The overall increase in the Meiyu precipitation projected by climate models of variable performance supports the observed tendency toward more intense rainfall in Taiwan and puts its early June 2017 extreme precipitation events into perspective. [ABSTRACT FROM AUTHOR]

Turbulence observations over an Indian region are used to examine the observational behaviour of stability‐correction functions for momentum (φm) and heat (φh) under stable conditions within the framework of Monin–Obukhov similarity theory. The φm is observed to follow the linear functional form in the case of near‐neutral to moderately stable conditions. However, φm is found to increase with a relatively slower rate as compared with the linear form in strongly stable conditions. A large scatter in φh is observed with the near‐neutral condition, and they tend to level off in very stable conditions. The optimized co‐efficients appearing in the functional form of φm suggested by Grachev et al. in 2007 are derived using turbulence observations. The present study recommends the use of these optimized functional forms of φm and φh for the estimation of surface fluxes over an Indian region in atmospheric models. [ABSTRACT FROM AUTHOR]

Groundwater recharge depends generally on precipitation. In this study, a geographical information system (GIS) procedure was applied to assess the climate change effect on groundwater recharge in the Grand Est region of France. The analysis comprises high‐resolution climate models that reflect the long‐term climatological regime. The hydrological properties include aquifers, land‐cover and terrain morphology data, which were used to develop the potential infiltration map of the study area. Two parameters, the De Martonne aridity index and effective precipitation, were combined into a 5 × 5 matrix to assess the climate change effect on groundwater recharge during the past (1990s), present (2020s) and future (2050s) periods. The present and future intense aridization and the depletion of the effective precipitation (< 650 mm) reveal the negative effects of climate change on aquifers' recharge in the Grand Est region. The areas with high and very high climate effects will increase in the 2020s and 2050s. These areas extend mainly in the western, north‐central and northeastern parts of the region occupying the Rhine, Aube and Marne valleys. The medium effect could be found in the central, southern and northwestern parts, while the low impact on groundwater recharge was verified in the northwestern and southeastern parts of the region. The area with low effects of climate change extends mostly in the Vosges and Ardennes mountains. These findings contribute to the long‐term hydrogeological studies in the Grand Est region. [ABSTRACT FROM AUTHOR]

With the aim of developing a forecasting system for the prediction of adverse meteorological conditions in the vicinity of complex terrain for aircraft landing at Hong Kong International Airport, the linearized, quasi‐empirical FLOWSTAR model has been configured to represent a series of eight landing events (2015–2017); half of these events reported significant wind shear and, for two, the landing attempt was aborted. Meteorological data used as input to the model have been derived from Hong Kong Observatory radiosonde measurements; these are the most reliable available despite being recorded at a location distant from the airport and at one or more hours before or after landing events. In general, the model predicts stronger wind shear for cases where adverse conditions have been reported. Analysis of the magnitude of wind shear generated as a result of wind veering and backing helps to quantify the importance of nonlinear flow features such as trapped lee waves. The model is able to represent strong downslope flows and recirculating flow regions (rotors) generated by the mountainous region located upwind of the airport. Comparisons of model predictions against LIDAR traces indicate that the model replicates wind shear for 50% of cases where it was recorded, although the location and magnitude of the wind shear usually differs between the modelled and measured datasets; the lack of agreement is likely to be because the input meteorological conditions are not wholly representative of the upstream conditions and the LIDAR trace does not exactly coincide with the aircraft trajectory. When airports are located in hilly terrain, near‐ground flow features such as wind shear and turbulence periodically cause disruption to aircraft movements. The FLOWSTAR model has been configured to represent favourable and unfavourable landings at Hong Kong International Airport; the model predicts high wind shear at some locations along the flight path (black dotted line). Quantitative analyses of wind shear generated as a result of wind veering and backing demonstrate the importance of flow features such as trapped lee waves. [ABSTRACT FROM AUTHOR]

One risk associated with rocket‐launching activities is the inhalation of exhaust gases, which can occur either in the vicinity of the launch pad or, depending on the way these gases are transported and dispersed through the atmosphere, at locations far from the launch pad. Immediately after the ignition of the rocket motors, a large, hot cloud is formed near the ground, composed of carbon monoxide (CO) and carbon dioxide (CO2), hydrogen chloride (HCl) and also particulate material composed of aluminium oxide (Al2O3) in the case of rockets driven by a solid propellant. The Weather Research and Forecasting Model (WRF), an atmospheric mesoscale model, was modified in the present study to simulate the transport, dispersion and chemistry mechanisms (with the addition of reactions involving HCl and other chlorinated compounds) of these gases released during a launch. Simulations of the dispersion of effluents were performed for different rainfall regimes (dry and rainy periods) as well as for atmospheric thermal stability (day‐ and night‐time conditions) for a specific rocket (the Veículo Lançador de Satélites, VLS) at Brazil's Alcântara Launch Center (Centro de Lançamento de Alcântara, CLA). The results show that the most critical levels of HCl and CO occurred on the launch pad (Setor de Preparação de Lançamento, SPL) from the time of the launch (t0) to 10 min. The village of Alcântara, which is 9 km from the launch pad, could be affected according to the wind direction. The concentration of HCl reached a level in the range of 2200–3800 ppmv for both dry and wet periods respectively, and these concentrations can be very hazardous to humans. A large cloud of toxic gas, such as the one shown, is formed at the surface level a few moments after the ignition of rockets. This hot cloud expands and rapidly covers the launch pad facility and can be transported to nearby cities. In order to predict the concentrations of gases after a launch, numerical air‐quality models need appropriate methods for handling rocket emissions in order to solve this problem. [ABSTRACT FROM AUTHOR]

The past few decades have witnessed a change in precipitation patterns around the world. This change includes an increase in heavy precipitation and a decrease in light and moderate precipitation. Global/regional warming has been attributed as one of the major factors causing the observed change in precipitation patterns. Past studies over the Indian region have not focused much on quantification of the impact of warming on changes in precipitation patterns. The present research quantifies the impact of warming on precipitation patterns over the Indian region. For this purpose, rain gauge‐based high‐resolution gridded precipitation data from the India Meteorological Department (IMD) for 113 years between 1901 and 2013 are used to link the changes in precipitation patterns over India during the South West (SW) monsoon season with warming using a new method that focuses on inter‐annual differences rather than time series. It has been reported that during the SW monsoon period, there is an increase of about 51.85% ± 19% in the top 10% precipitation for each degree (K) increase in temperature. Decreases of about 15–30%/K in low and moderate precipitation have also been reported per 1 K increase in temperature. The impact of warming is more severe over Western and North Eastern India and during July and August. An increase in heavy rainy days of about 46.53% ± 14% per 1 K increase in temperature has been reported over the Indian region. Low and moderate rainy days show a decrease of about 24.67% ± 9.53% for a unit degree increase in temperature. Results reported in this research point out increasing trends in droughts and floods due to increased heavy precipitation and decreased low and moderate precipitation in a warming environment. Study focuses on examination of precipitation patterns in changing climate over India. Results report an increase in heavy precipitation and decrease in low and moderate precipitation. Impacts of ENSO and IOD have been examined separately. Seasonal variation of heavy precipitation anomaly over Indian region (I) along with global temperature anomaly. Smoothing has been done to eliminate the fluctuation. [ABSTRACT FROM AUTHOR]

ABSTRACT The spatial climatic characteristics of the Himalayas are complex and a challenge for regional climate models ( RCMs). There is no doubt that some form of correction before any application of RCM simulations is a must. In recent years, simple bias correction techniques have been overshadowed by more popular and complex bias correction techniques. In this study an attempt is made to compare the performance of a simple and of a comparatively complex correction technique for hydrological analysis at a monthly resolution in the Kaligandaki River Basin of Nepal. The research workflow consists of bias correction of temperature and precipitation using a simple technique (linear scaling) and a comparatively complex one (quantile mapping). The performance at monthly resolution is evaluated against observed meteorological data while a combined evaluation is made via hydrological model response analysis. The wetter and colder RCM estimates were significantly improved after bias correction. The hydrological modelling response also shows the importance of the bias correction of the RCMs. However, no significant difference was observed between the outputs of linear scaling and quantile mapping which exhibited almost identical performances. Hence, this study has a novel conclusion that a simple method, such as linear scaling, is sufficient for hydrological analysis at monthly resolution. [ABSTRACT FROM AUTHOR]

ABSTRACT In recent years (2013-2016), extreme weather events have caused substantial disruption to Great Britain's ( GB's) railway infrastructure. In the coming decades this vulnerability is unlikely to subside as the effects of climate change become more intense. Railway stakeholders in GB are strongly engaged with understanding climate change impacts on the railway system and how the industry could adapt to these impacts. Since 2010, Network Rail and RSSB have supported research into these topics under the Tomorrow's Railway and Climate Change Adaptation programme. Under this programme, an analogue study was performed to determine whether lessons could be learned from other countries' weather management. Two types of analogue were used to identify suitable locations. First, climate data from 20 models of the Coupled Model Intercomparison Project phase 5 ( CMIP5) were used to identify regions with similar present-day climate to that projected for GB in the future. The analogue locations were found to be largely insensitive to the climate indicators and the methods used to compare climate at different locations. Next, railway networks in many countries were studied to find those with similar physical and operational characteristics to the GB network. The regions with both climate and railway analogues are France, the Netherlands, Belgium, Germany and Denmark. As part of a wider aim to support the GB railway network's weather resilience and climate change adaptation ( WR/ CCA) activities, focused stakeholder engagement has been undertaken with representatives of most of these countries' railways. This targeted approach is complementary to a broader collation of existing WR/ CCA measures used globally. [ABSTRACT FROM AUTHOR]

ABSTRACT Downscaling of general circulation model ( GCM) outputs extracted from CMIP5 datasets to monthly precipitation for the Gediz Basin, Turkey, under Representative Concentration Pathways ( RCPs) was performed by statistical downscaling models, multi- GCM ensemble and bias correction. The output databases from 12 GCMs were used for the projections. To determine explanatory predictor variables, the correlation analysis was applied between precipitation observed at 39 meteorological stations located over the Basin and potential predictors of ERA-Interim reanalysis data. After setting both artificial neural networks and least-squares support vector machine-based statistical downscaling models calibrated with determined predictor variables, downscaling models producing the most suitable results were chosen for each meteorological station. The selected downscaling model structure for each station was then operated with historical and future scenarios RCP4.5, RCP6.0 and RCP8.5. Afterwards, the monthly precipitation forecasts were obtained from a multi- GCM ensemble based on Bayesian model averaging and bias correction applications. The statistical significance of the foreseen changes for the future period 2015-2050 was investigated using Student's t test. The projected decrease trend in precipitation is significant for the RCP8.5 scenario, whereas it is less significant for the RCP4.5 and RCP6.0 scenarios. [ABSTRACT FROM AUTHOR]