Statistical Bias Correction Research Articles

Historical Spatial and Temporal Climate Trends in Southern Ontario, Canada

AbstractSpatial and temporal trends in historical temperature and precipitation extreme events were evaluated for southern Ontario, Canada. A number of climate indices were computed using observed and regional and global climate datasets for the area of study over the 1951–2013 period. A decrease in the frequency of cold temperature extremes and an increase in the frequency of warm temperature extremes was observed in the region. Overall, the numbers of extremely cold days decreased and hot nights increased. Nighttime warming was greater than daytime warming. The annual total precipitation and the frequency of extreme precipitation also increased. Spatially, for the precipitation indices, no significant trends were observed for annual total precipitation and extremely wet days in the southwest and the central part of Ontario. For temperature indices, cool days and warm night have significant trends in more than 90% of the study area. In general, the spatial variability of precipitation indices is much higher than that of temperature indices. In terms of comparisons between observed and simulated data, results showed large differences for both temperature and precipitation indices. For this region, the regional climate model was able to reproduce historical observed trends in climate indices very well as compared with global climate models. The statistical bias-correction method generally improved the ability of the global climate models to accurately simulate observed trends in climate indices.

Future changes in water requirements of Boro rice in the face of climate change in North-West Bangladesh

Understanding future changes in crop water requirements and irrigation demand in the context of climate change is essential for long-term water resources management and agricultural planning. This study investigates the impacts of climate change on future water requirements of dry season Boro rice. Climate scenarios for four North-West districts of Bangladesh were constructed from the outputs of five global circulation models using a combination of statistical downscaling and bias correction. The generated climate data were used as input for CropWat to estimate water requirements of Boro rice for 2050s and 2080s (using 30year average climate data). Reference crop evapotranspiration (daily ETo) is increasing in the future, mainly due to higher temperatures. Potential crop water requirement (∑ETC) of Boro rice, however, will reduce by 6.5% and 10.9% for RCP 4.5 and 8.5, respectively for 2050s; and by 8.3% and 17.6% for RCP 4.5 and 8.5, respectively for 2080s compared to the reference period (1980–2013). ΣETC will decrease because of a lower number of growing days due to the phenological response of rice to higher temperatures. Low rainfall accessibility under a shortened Boro season leads to an increase in the amount of irrigation water required to satisfy crop evapotranspiration demand. Although daily water requirements will increase, the total net irrigation requirement of Boro rice will decrease by 1.6% in 2050s and 7.4% in 2080s for RCP 8.5 scenario on average for all models and districts. Estimated net irrigation requirements showed high variations for different climate models, mainly due to a high variation in the projected rainfall. For improved water management planning, close monitoring and periodic evaluations are necessary to understand future directions of change in rainfall amounts and distribution.

An Iberian climatology of solar radiation obtained from WRF regional climate simulations for 1950–2010 period

The mesoscale Weather Research and Forecasting (WRF) Model is used over the Iberian Peninsula to generate 60years (1950–2010) of climate data, at 5km resolution, in order to evaluate and characterize the incident shortwave downward radiation at the surface (SW↓), in present climate.The simulated values of SW↓ in the period 2000–2009 were compared with data measured in Spanish and Portuguese meteorological stations before and a statistical BIAS correction was applied using data from Clouds and the Earth's Radiant Energy System (CERES), on board four different satellites. The spatial and temporal comparison between WRF results and observations show a good agreement for the analyzed period, although the model overestimates observations. This overestimation has a mean normalized bias of about 7% after BIAS correction (or 17% for original WRF output). Additionally, the present simulation was confronted against another previously validated WRF simulation performed with different resolution and set of parametrizations, showing comparable results. WRF adequately reproduces the observational features of SW↓ with correlation coefficients above 0.8 in annual and seasonal basis.60years of simulated SW↓ over the Iberian Peninsula were produced, which showed annual mean values that range from 130W/m2, in the northern regions, to a maximum of around 230W/m2 in the southeast of the Iberian Peninsula (IP). SW↓ over IP shows a positive gradient from north to south and from west to east, with local effects influenced by topography and distance to the coast.The analysis of the simulated cloud fraction indicates that clear sky days are found in >30% of the period at the southern area of IP, particularly in the Algarve (Portugal) and Andalusia (Spain), and this value increases significantly in the summer season for values above 80%.

A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes.BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

Impact of climate change on hydrological conditions in a tropical West African catchment using an ensemble of climate simulations

Abstract. This study evaluates climate change impacts on water resources using an ensemble of six regional climate models (RCMs)–global climate models (GCMs) in the Dano catchment (Burkina Faso). The applied climate datasets were performed in the framework of the COordinated Regional climate Downscaling Experiment (CORDEX-Africa) project.After evaluation of the historical runs of the climate models' ensemble, a statistical bias correction (empirical quantile mapping) was applied to daily precipitation. Temperature and bias corrected precipitation data from the ensemble of RCMs–GCMs was then used as input for the Water flow and balance Simulation Model (WaSiM) to simulate water balance components.The mean hydrological and climate variables for two periods (1971–2000 and 2021–2050) were compared to assess the potential impact of climate change on water resources up to the middle of the 21st century under two greenhouse gas concentration scenarios, the Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results indicate (i) a clear signal of temperature increase of about 0.1 to 2.6 °C for all members of the RCM–GCM ensemble; (ii) high uncertainty about how the catchment precipitation will evolve over the period 2021–2050; (iii) the applied bias correction method only affected the magnitude of the climate change signal; (iv) individual climate models results lead to opposite discharge change signals; and (v) the results for the RCM–GCM ensemble are too uncertain to give any clear direction for future hydrological development. Therefore, potential increase and decrease in future discharge have to be considered in climate change adaptation strategies in the catchment. The results further underline on the one hand the need for a larger ensemble of projections to properly estimate the impacts of climate change on water resources in the catchment and on the other hand the high uncertainty associated with climate projections for the West African region. A water-energy budget analysis provides further insight into the behavior of the catchment.

A combined statistical bias correction and stochastic downscaling method for precipitation

Abstract. Much of our knowledge about future changes in precipitation relies on global (GCMs) and/or regional climate models (RCMs) that have resolutions which are much coarser than typical spatial scales of precipitation, particularly extremes. The major problems with these projections are both climate model biases and the gap between gridbox and point scale. Wong et al. (2014) developed a model to jointly bias correct and downscale precipitation at daily scales. This approach, however, relied on pairwise correspondence between predictor and predictand for calibration, and, thus, on nudged simulations which are rarely available. Here we present an extension of this approach that separates the downscaling from the bias correction and in principle is applicable to free-running GCMs/RCMs. In a first step, we bias correct RCM-simulated precipitation against gridded observations at the same scale using a parametric quantile mapping (QMgrid) approach. In a second step, we bridge the scale gap: we predict local variance employing a regression-based model with coarse-scale precipitation as a predictor. The regression model is calibrated between gridded and point-scale (station) observations. For this concept we present one specific implementation, although the optimal model may differ for each studied location. To correct the whole distribution including extreme tails we apply a mixture distribution of a gamma distribution for the precipitation mass and a generalized Pareto distribution for the extreme tail in the first step. For the second step a vector generalized linear gamma model is employed. For evaluation we adopt the perfect predictor experimental setup of VALUE. We also compare our method to the classical QM as it is usually applied, i.e., between RCM and point scale (QMpoint). Precipitation is in most cases improved by (parts of) our method across different European climates. The method generally performs better in summer than in winter and in winter best in the Mediterranean region, with a mild winter climate, and worst for continental winter climate in Mid- and eastern Europe or Scandinavia. While QMpoint performs similarly (better for continental winter) to our combined method in reducing the bias and representing heavy precipitation, it is not capable of correctly modeling point-scale spatial dependence of summer precipitation. A strength of this two-step method is that the best combination of bias correction and downscaling methods can be selected. This implies that the concept can be extended to a wide range of method combinations.

A Statistical Bias Correction Tool for Generating Climate Change Scenarios in Indonesia based on CMIP5 Datasets

Providing information regarding future climate scenarios is very important in climate change study. The climate scenario can be used as basic information to support adaptation and mitigation studies. In order to deliver future climate scenarios over specific region, baseline and projection data from the outputs of global climate models (GCM) is needed. However, due to its coarse resolution, the data have to be downscaled and bias corrected in order to get scenario data with better spatial resolution that match the characteristics of the observed data. Generating this downscaled data is mostly difficult for scientist who do not have specific background, experience and skill in dealing with the complex data from the GCM outputs. In this regards, it is necessary to develop a tool that can be used to simplify the downscaling processes in order to help scientist, especially in Indonesia, for generating future climate scenario data that can be used for their climate change-related studies. In this paper, we introduce a tool called as “Statistical Bias Correction for Climate Scenarios (SiBiaS)”. The tool is specially designed to facilitate the use of CMIP5 GCM data outputs and process their statistical bias corrections relative to the reference data from observations. It is prepared for supporting capacity building in climate modeling in Indonesia as part of the Indonesia 3rd National Communication (TNC) project activities.

Evaluation of Statistical Downscaling of North American Multimodel Ensemble Forecasts over the Western United States

AbstractThe skill of two statistical downscaled seasonal temperature and precipitation forecasts from the North American Multimodel Ensemble (NMME) was evaluated across the western United States at spatial scales relevant to local decision-making. Both statistical downscaling approaches, spatial disaggregation (SD) and bias correction spatial disaggregation (BCSD), exhibited similar correlative skill measures; however, the BCSD method showed superior tercile-based skill measures since it corrects for variance deflation in NMME ensemble averages. Geographic and seasonal variations in downscaled forecast skill revealed patterns across the complex topography of the western United States not evident using coarse-scale skill assessments, particularly in regions subject to inversions and variability in orographic precipitation ratios. Similarly, differences in the skill of cool-season temperature and precipitation forecasts issued when the fall El Niño–Southern Oscillation (ENSO) signal was strong versus ENSO-neutral years were evident across topographic gradients in the northwestern United States.

Statistical Bias Correction of Fifth Coupled Model Intercomparison Project Data from the CGIAR Research Program on Climate Change, Agriculture and Food Security - Climate Portal for Mount Makulu, Zambia

Statistical Bias Correction of Fifth Coupled Model Intercomparison Project Data from the CGIAR Research Program on Climate Change, Agriculture and Food Security - Climate Portal for Mount Makulu, Zambia

Statistical bias correction method applied on CMIP5 datasets over the Indian region during the summer monsoon season for climate change applications

This study makes use of temperature and precipitation from CMIP5 climate model output for climate change application studies over the Indian region during the summer monsoon season (JJAS). Bias correction of temperature and precipitation from CMIP5 GCM simulation results with respect to observation is discussed in detail. The non-linear statistical bias correction is a suitable bias correction method for climate change data because it is simple and does not add up artificial uncertainties to the impact assessment of climate change scenarios for climate change application studies (agricultural production changes) in the future. The simple statistical bias correction uses observational constraints on the GCM baseline, and the projected results are scaled with respect to the changing magnitude in future scenarios, varying from one model to the other. Two types of bias correction techniques are shown here: (1) a simple bias correction using a percentile-based quantile-mapping algorithm and (2) a simple but improved bias correction method, a cumulative distribution function (CDF; Weibull distribution function)-based quantile-mapping algorithm. This study shows that the percentile-based quantile mapping method gives results similar to the CDF (Weibull)-based quantile mapping method, and both the methods are comparable. The bias correction is applied on temperature and precipitation variables for present climate and future projected data to make use of it in a simple statistical model to understand the future changes in crop production over the Indian region during the summer monsoon season. In total, 12 CMIP5 models are used for Historical (1901–2005), RCP4.5 (2005–2100), and RCP8.5 (2005–2100) scenarios. The climate index from each CMIP5 model and the observed agricultural yield index over the Indian region are used in a regression model to project the changes in the agricultural yield over India from RCP4.5 and RCP8.5 scenarios. The results revealed a better convergence of model projections in the bias corrected data compared to the uncorrected data. The study can be extended to localized regional domains aimed at understanding the changes in the agricultural productivity in the future with an agro-economy or a simple statistical model. The statistical model indicated that the total food grain yield is going to increase over the Indian region in the future, the increase in the total food grain yield is approximately 50 kg/ ha for the RCP4.5 scenario from 2001 until the end of 2100, and the increase in the total food grain yield is approximately 90 kg/ha for the RCP8.5 scenario from 2001 until the end of 2100. There are many studies using bias correction techniques, but this study applies the bias correction technique to future climate scenario data from CMIP5 models and applied it to crop statistics to find future crop yield changes over the Indian region.

Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes

For reliable prediction of sediment yield in a watershed, fine-scale projections for hydro-climate components were first obtained using the statistical bias correction and downscaling scheme based on the combination of an Artificial Neural Network (ANN), Nonstationary Quantile Mapping (NSQM) and Stochastic Typhoon Synthesis (STS) sub-modules. Successively, the hydrologic runoff and sediment yield from the land surfaces were predicted through the long-term continuous watershed model, Soil and Water Assessment Tool (SWAT), using the bias-corrected and downscaled Regional Climate Model (RCM) output under the Intergovernmental Panel on Climate Change’s (IPCC’s) A1B climate change scenario. The incremental improvement of the combined downscaling process was evaluated successfully during the baseline period, which provides projected confidence for the simulated future scenario. The realistic simulation of sediment yield is closely related to the rainfall event with high intensity and frequency. During the long-term future period, the Coefficient of River Regime (CORR) reaches 353.9 (27.2% increase with respect to baseline). The projection for annual precipitation by 2040 and 2100 is a 25.7% and a 57.2% increase with respect to the baseline period, respectively. In particular, the increasing CORR rate (33.4% and 72.5%) during the flood season is much higher than that for the annual total amount. However, the sediment yield is expected to increase by 27.4% and 121.2% during the same periods, which exhibits steeper trends than the hydrologic runoff. The June, July, August (JJA) season occupies 83.0% annual total sediment yield during the baseline period, which is similar during the projection period. The relative change of sediment yield is 1.9-times higher than that of dam inflows.

Lidar-Based Height Correction for the Assimilation of Atmospheric Motion Vectors

AbstractThis study uses lidar observations from the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite to correct operational atmospheric motion vector (AMV) pressure heights. This intends to reduce the height assignment error of AMVs for their use in data assimilation. Additionally, AMVs are treated as winds in a vertical layer as proposed by several recent studies. Corrected and uncorrected AMV winds are evaluated using short-term forecasts of the global forecasting system of the German Weather Service. First, a direct lidar-based height reassignment of AMVs with collocated CALIPSO observations is evaluated. Assigning AMV winds from Meteosat-10 to ~120-hPa-deep layers below the lidar cloud top reduces the vector root-mean-square (VRMS) differences of AMVs from Meteosat-10 by 8%–15%. However, such a direct reassignment can only be applied to collocated AMV–CALIPSO observations that compose a comparably small subset of all AMVs. Second, CALIPSO observations are used to derive statistical height bias correction functions for a general height correction of all operational AMVs from Meteosat-10. Such a height bias correction achieves on average about 50% of the reduction of VRMS differences of the direct height reassignment. Results for other satellites are more ambiguous but still encouraging. Given that such a height bias correction can be applied to all AMVs from a geostationary satellite, the method exhibits a promising approach for the assimilation of AMVs in numerical weather prediction models in the future.

Improving global rainfall forecasting with a weather type approach in Japan

ABSTRACT An automated version of the weather type classification scheme was performed over Japan to characterize daily circulation conditions. A daily gridded field of mean sea-level pressure (MSLP) from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis dataset (ERA-interim) and the THORPEX Interactive Grand Global Ensemble (TIGGE) daily forecast dataset were used. The weather type is advantageous as it provides an opportunity to improve global rainfall prediction by refining statistical bias correction. We distinguished 11 weather types: anticyclone, cyclone, hybrid and eight purely wind directions. The results indicate that the main weather types contributing to the total volume of rainfall are cyclone, hybrid, purely westerly and northwest winds. A gamma-based bias correction decreases the global rainfall forecast root mean square by 10%, while specific weather type gamma bias correction accounts for 5–10% root mean square error reduction, with a total decrease of errors up to a maximum of 20%. Both global and weather type bias corrections improve the extreme dependency scores (EDS), but for different extreme rainfall thresholds. The study advocates the use of weather type bias-correction methods for extreme event rainfall intensity corrections higher than 100 mm/d. EDITOR A. Castellarin ASSOCIATE EDITOR A. Jain

Assessing distribution‐based climate model bias correction methods over an alpine domain: added value and limitations

ABSTRACTClimate impact assessment and decision making in the light of projected future climate change require accurate and robust climate scenarios at the local scale. The latter are targeted by statistical bias correction (BC) and downscaling of climate model output. A nowadays well‐established technique is quantile mapping (QM). Here, we apply several different implementations of empirical QM, a parametric (and computationally more expensive) QM method for daily precipitation, and a mean‐BC to an ensemble of regional climate scenarios over the topographically structured terrain of Switzerland. The performance of these methods in the current climate and their long‐term stability are analysed with respect to distributional and temporal statistics as well as climate impact indices for daily temperature and precipitation. We select an optimal QM implementation, study its effect on the inter‐variable consistency, and compare its climate change signal (CCS) to that of a delta‐change (DC) method. The results demonstrate that QM effectively reduces raw model biases. The most important improvements are corrected magnitudes and, for precipitation, also wet‐day frequencies. The quantile‐dependent bias removal is superior to the mean‐BC with respect to distribution‐tail statistics. Temporal statistics and climate impact indices are also improved. There is no performance benefit from the parametric QM method. The selected empirical QM implementation substantially improves the joint temperature–precipitation distribution and maintains the temperature–precipitation cross‐correlation function as represented by raw climate model data. The CCS analysis reveals the superiority of QM over the DC method with respect to distribution‐tail characteristics and temporal statistics. This work reveals empirical QM as a reliable and stable method for BC and downscaling from state‐of‐the‐art regional climate models to local weather stations over alpine terrain, confirming and expanding previous studies.

Predicting Heat Stress in Cotton Using Probabilistic Canopy Temperature Forecasts

Heat stress can reduce crop yield or even cause total crop failure. The ability to predict heat stress in advance would allow growers time to implement protective measures, helping to avoid such losses. This study presents a strategy for producing probabilistic heat stress forecasts for well‐watered cotton (Gossypium hirsutum L.) in the Camilla, GA, region. Multiple linear regression was used to develop a cotton canopy temperature model based on predicted air temperature, humidity, solar radiation, and wind speed. The European Centre for Medium‐Range Weather Forecasts Ensemble Prediction System was used to predict the meteorological variables used in the canopy temperature model, which produced 10‐d probabilistic canopy temperature forecasts for each day of observations during 2014. A statistical mean bias correction was applied to improve on the raw model forecasts. The forecasts were found to be skillful, with relative operating characteristic areas greater than 0.5. The bias‐corrected forecasts were found to increase skill. A heat stress warning system was then created using the forecasts. Additionally, an economic analysis was performed as an example of how probabilistic forecasts can be used to aid producers with financial decisions pertaining to weather‐related risks. Creating heat stress advisories for crops provide growers time to protect assets. Coupling crop and atmospheric models translates weather data into usable information for growers. Probabilistic canopy temperature forecasts can be used to evaluate on‐farm weather‐related risks.

Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

Downscaling of monthly precipitation using CMIP5 climate models operated under RCPs

ABSTRACTDownscaling 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.

Circulation pattern-based assessment of projected climate change for a catchment in Spain

We present an approach for evaluating catchment-scale hydro-meteorological impacts of projected climate change based on the atmospheric circulation patterns (ACPs) of a region. Our approach is motivated by the conjecture that GCMs are especially good at simulating the atmospheric circulation patterns that control moisture transport, and which can be expected to change in response to global warming. In support of this, we show (for the late 20th century) that GCMs provide much better simulations of ACPs than those of precipitation amount for the Upper Guadiana Basin in central Spain. For the same period, four of the twenty GCMs participating in the most recent (5th) IPCC Assessment provide quite accurate representations of the spatial patterns of mean sea level pressure, the frequency distribution of ACP type, the ‘number of rainy days per month’, and the daily ‘probability of rain’ (they also reproduce the trend of ‘wet day amount’, though not the actual magnitudes). A consequent analysis of projected trends and changes in hydro-climatic ACPology between the late 20th and 21st Centuries indicates that (1) actual changes appear to be occurring faster than predicted by the models, and (2) for two greenhouse gas emission scenarios (RCP 4.5 and RCP 8.5) the expected decline in precipitation volume is associated mainly with a few specific ACPs (primarily directional flows from the Atlantic Ocean and Cantabric Sea), and with decreasing probability of rain (linked to increasing temperatures) rather than wet day amount. Our approach is a potentially more insightful alternative for catchment-scale climate impacts assessments than the common approach of statistical downscaling and bias correction.

Modeling Soil Salinity in Greenhouse Cultivations Under a Changing Climate With SALTMED

Soil salinity is a major soil degradation threat especially for arid coastal environments where it hinders agricultural production, thus imposing a desertification risk. In the prospect of a changing climate, soil salinity caused by brackish water irrigation introduces additional uncertainties regarding the viability of deficit irrigation and intensive cultivation practices such as greenhouse cropping. Here, we propose a modification of the SALTMED leaching requirement model to account for greenhouse cultivation conditions. The model is applied in the RECARE Project Case Study of Timpaki, a semiarid region in south-central Crete, Greece, where greenhouse horticulture is an important land use. Excessive groundwater abstractions toward irrigation have resulted in a drop of the groundwater level in the coastal part of the aquifer, thus leading to seawater intrusion and in turn to soil salinization. Crop yield and soil profile electrical conductivity (EC) sensitivity to initial soil EC (up to 2 dS m−1) and irrigation water EC (up to 3 dS m−1) are modeled for the locally popular horticultural crops of Solanum lycopersicum, Solanum melongena, and Capsicum annuum. Climate model data obtained from nine general circulation models for the “worst case” representative concentration pathway of 8.5 W m−2 of the fifth phase of the Coupled Model Intercomparison Project are corrected for bias against historical observations with the Multisegment Statistical Bias Correction method and used to estimate crop yield and soil profile EC sensitivity in a warmer future. Results show that the effects of climate change on S. lycopersicum greenhouse cultivations of Timpaki will be detrimental, whereas S. melongena and C. annuum cultivations may show greater resilience.

Hybrid approach in statistical bias correction of projected precipitation for the frequency analysis of extreme events

A general circulation model (GCM) can be applied to project future climate factors, such as precipitation and atmospheric temperature, to study hydrological and environmental climate change. Although many improvements in GCMs have been proposed recently, projected climate data are still required to be corrected for the biases in generating data before applying the model to practical applications. In this study, a new hybrid process was proposed, and its ability to perform bias correction for the prediction of annual precipitation and annual daily maxima, was tested. The hybrid process in this study was based on quantile mapping with the gamma and generalized extreme value (GEV) distributions and a spline technique to correct the bias of projected daily precipitation. The observed and projected daily precipitation values from the selected stations were analyzed using three bias correction methods, namely, linear scaling, quantile mapping, and hybrid methods. The performances of these methods were analyzed to find the optimal method for prediction of annual precipitation and annual daily maxima. The linear scaling method yielded the best results for estimating the annual average precipitation, while the hybrid method was optimal for predicting the variation in annual precipitation. The hybrid method described the statistical characteristics of the annual maximum series (AMS) similarly to the observed data. In addition, this method demonstrated the lowest root mean squared error (RMSE) and the highest coefficient of determination (R2) for predicting the quantiles of the AMS for the extreme value analysis of precipitation.