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Article

Assessing Regional Climate Models (RCMs) Ensemble-Driven Reference Evapotranspiration over Spain

by
Patricia Olmos Giménez
and
Sandra G. García-Galiano
*
Department of Civil Engineering, R&D Group of Water Resources Management, Universidad Politécnica de Cartagena, Paseo Alfonso XIII, 52, 30203 Cartagena, Spain
*
Author to whom correspondence should be addressed.
Water 2018, 10(9), 1181; https://doi.org/10.3390/w10091181
Submission received: 30 May 2018 / Revised: 6 August 2018 / Accepted: 1 September 2018 / Published: 4 September 2018
(This article belongs to the Special Issue Hydrological Impacts of Climate Change and Land Use/Land Cover Change)
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Abstract

:
The present work applies a novel methodology of combining multiple Regional Climate Models (RCMs) (or ensemble) that are based on the seasonal and annual variability of temperatures over Spain, which allows for the quantification and reduction of uncertainty in the projections of temperature based-potential evapotranspiration. Reference evapotranspiration (ETo) is one of the most important variables in water budgets. Therefore, the uncertainties in the identification of reliable trends of reference evapotranspiration should be taken into account for water planning and hydrological modeling under climate change scenarios. From the results over Spain, the RCMs ensemble reproduces well the yearly and seasonal temperature observed dataset for the time reference period 1961–1990. An increase in the ensemble-driven ETo for time period 2021–2050 over Spain is expected, which is motivated by an increase in maximum and minimum temperature, with the consequent negative impacts on water availability.

1. Introduction

Projecting the impacts of climate change on temperature and rainfall has become an important issue [1,2,3]. Besides precipitation, potential evapotranspiration (PET) is the most important variable for determining the water balances at different spatial scale (e.g., basin, region).
Reliable PET projections are needed for hydrologic modelling under climate change scenarios. However, in order to evaluate its impact on water balances, PET can only be estimated indirectly, i.e., by considering some other directly measurable weather elements currently obtained by climatic models [4].
It is remarkable that PET is considered equivalent to the reference evapotranspiration ETo by several authors [5,6,7], being assumed as the evapotranspiration rate of a short green crop (grass), completely shading the ground, of uniform height, and with adequate water availability in the soil profile. The main ambiguities and limitations with PET is that many types of crops can fit into the description of a short green crop. Conversely, the concept of reference evapotranspiration ETo [8] is better suited for agronomical purposes and it refers to the evapotranspiration from a standardized vegetated surface (with an assumed crop height of 0.12 m, a fixed surface resistance of 70 s·m−1 and an albedo of 0.23), well-watered grass of uniform height, actively growing, and completely shading the ground. Under these fixed conditions, the evaporative demand of the atmosphere can be considered to be independent of crop type, crop development, and management practices and dependent by meteorological conditions only.
ETo (and the consequent ETc—maximum crop evapotranspiration) largely affects agricultural productivity. A reliable estimate of ETo in agricultural regions is highly valuable for implementing water management practices that will improve yields and promote sustainable agricultural practices [9,10]. Moreover, it is a very important issue in areas affected by water scarcity and droughts. Currently, this concept is used in hydrology as a robust input parameter to model at basin scale [11]. Moreover, the concept of ETo is interesting to define the values of evapotranspiration that were measured under different climates [12]. Consequently, special attention should be taken in the estimation of precipitation, ETo and their interactions.
The Penman-Monteith (PM) equation [13], which was adopted by the Food and Agriculture Organization (FAO), has been agreed as the standard model to estimate reference evapotranspiration (ETo). However, the high requirement of meteorological variables (i.e., air temperature, relative humidity, solar radiation, and wind speed) represents a barrier in its application (specifically in areas where these meteorological variables are not currently measured). Therefore, these requirements constitute disadvantages in comparison with other methods with a less number of parameters [4].
Djaman et al. [14] applied sixteen ETo equations with a lower requirement of meteorological variables, when the weather datasets were scarce. Various authors [15,16,17] have demonstrated that radiation-based methods are appropriate for estimating ETo. The Hargreaves-Samani equation [18] constitutes an alternative model, when only air temperature and radiation datasets are available. Tabari [15] has checked that the Hargreaves-Samani equation turned out to be the most precise model under warm humid and semi-arid climatic conditions. However, other authors [19,20] have suggested overestimations of ETo when Hargreaves-Samani equation is applied in humid regions with a low ET rate, and underestimations in dry regions with high ET rate. To solve this issue, various authors [21,22,23,24,25] have proposed local adjustments of Hargreaves-Samani equation to improve its performance. In a recent study Tegos et al. [26] have shown that using a parametric model with explanatory variables temperature and extraterrestrial radiation, and parameters that are estimated through calibration, satisfactory PET results are obtained for the climate regime of Spain.
The maximum and minimum temperatures are required to estimate ETo projections that are based on the Hargreaves-Samani equation. Regional Climate Models (RCMs) are increasingly used to assess the impact of climate change. Due to biases that are associated with climate models projections, the combination of information provided by an ensemble approach from different RCMs simulations instead of only one specific RCM [27,28,29,30], is a way to reduce the involved uncertainties.
An ensemble approach has been recently used by [31,32] that is based on sixteen RCMs to generate rainfall and temperature projections. On the other hand, Xu et al. [33] have obtained projections of temperature and precipitation over China, under the Representative Concentration Pathways (RCP) scenarios, from an average of eleven Global Climate Models (GCMs). Diallo et al. [34] have suggested that multi-model RCM ensemble of temperature and precipitation based on different driving GCMs, help to compensate systematic errors from both the nested and the driving models.
Different works can be found in the literature oriented to assess the climate change on PET using RCMs or GCMs. Prudhomme et al. [6] have compared the future changes of PET from 12 equations in Great Britain with a RCM, getting a large variation in the magnitude of changes. Zhang et al. [35] have assessed the future change of ETo over China, from three GCMs under IPCC SRES A1B and B1 scenarios, demonstrating the expected important increases of ETo. Obada et al. [36], working with three RCMs in West Africa, have demonstrated gradual increases of annual PET until 2100, for the RCP4.5 and RCP8.5 scenarios.
Nevertheless, in almost all studies, the ability of RCMs in evaluating climate variables has been focused on precipitation and temperature, but not in the estimate of ETo. Moreover, in almost all studies of RCM-driven ETo the authors do not consider an ensemble of climate models. For this reason, the RCMs ensemble driven-ETo based on a locally calibrated simple model (like as Hargreaves-Samani equation), is paving the way in the reduction of involved uncertainties for projecting future changes.
In this paper, an indirect method to take into account the bias (or systematic errors) of temperature values has been applied. The simulated projections by RCMS with higher bias in relation to the historical dataset were penalized (less weight was given to them in the ponderation equation). The building of RCMs ensemble from a large number of climate model, has been based on a performance evaluation to reproduce the historical period (1961–1990). To reach this objective, a novel approach that was proposed by Olmos Giménez el al. [31] to calculate the reliability factor has been applied to build the maximum and minimum temperature ensembles from the RCMs projections. The methodological approach is evaluated based on its ability to reproduce the spatial and seasonal variability of temperatures for the baseline period 1961–1990. The estimated changes of ETo and temperature values between the baseline period and future period 2020–2050 are discussed. The main aim of this work is to improve the RCMs driven-ETo estimations over Spain, for increasing the reliability of water availability projections over continental Spain.

2. Material and Methods

2.1. Study Area and Datasets

The study area is located in continental Spain (Figure 1). The climate of Spain varies across the country, according to geographical situation and orographic conditions. When considering the climatological regimes of continental Spain, three zones are delimited (Figure 1) to study the impacts of climate change on ETo. From Figure 1, Zone 1 corresponds to Cantabrian coast, while Zone 2 is named Levante region (east of continental Spain); and, Zone 3 corresponds to the south of Spain (Andalusian region). The Northern part of Spain (especially the regions of Basque Country, Asturias, Cantabria, and Galicia) presents an oceanic climate characterized by mild winters and warm summers. The climate and landscape are determined by the Atlantic Ocean winds whose moisture gets trapped by the mountains circumventing the Spanish Atlantic coast.
The Mediterranean climate is characterized by two wet periods, spring and autumn, separated by hot and dry summers. The summer months present high evaporation and only occasional rainy events. High intensity rainfall events are usually recorded in autumn season. The south eastern part of Spain (mainly in region of Murcia and south of Valencian community) presents a semiarid climate. In contrast to the Mediterranean climate, the dry season continues beyond the end of summer season.
Observational grids of daily maximum and minimum temperature for Spain, have been obtained from the dataset named Spain02 (V2.1 version) from University of Cantabria (Santander, Spain) [37]. This dataset presents 0.2° of spatial resolution, for the time period 1961–2007.
The RCMs have been provided by the European project ENSEMBLES [38], with spatial resolution 0.25°, while considering the emissions scenarios that are described in the Special Report on Emission Scenarios of the IPCC [39]. A grid with 906 sites (or grid points) over Spain (Figure 1), has been considered to evaluate and validate the RCMs performances to build the ensemble of temperatures. The selected RCMs for scenario A1B [39] with a spatial resolution of 0.25° over continental Spain in the time period 1961–2050 have been: C4IRCA3, CNRM/RM5.1, DMI/ARPEGE, DMI/ECHAM5-r3, DMI/BCM, ETHZ/CLM, ICTP/REGCM3, KNMI/RACMO2, METNO/BCM, METNO/HADCM3Q0, MPI-M/REMO, OURANOS/MRCC4.2.1, SMHI/BCM, SMHI/ECHAM5-r3, SMHI/HadCM3Q3, and UCLM/PROMES.

2.2. Building the PDFs Ensemble

In the present work, an approach based in the reliability ensemble averaging (REA) method [29,40], already validated for ensembles of rainfall over Spain by Olmos Giménez et al. [31], has been implemented to address some limitations of the original approach. The upgrade essentially is oriented to modify the weights estimation method. Firstly, the convergence-based reliability factor of the original method was not considered, since it introduces great uncertainty. Secondly, the bias analysis was improved by considering the bias assessment for yearly and seasonal scales. The bias evaluates the performance of the model in reproducing the present climate (period 1961–1990). The assessment of the reliability factor is considered as an indirect way to take into account the bias, because the projections of RCMs with greater bias present less weight in the ponderation equation.
To calculate the reliability factor (Ri), the following equation is applied [31,32]:
R i = ( R W i n t e r R S p r i n g R S u m m e r R A u t u m n R Y e a r l y ) 1 / 5 ,   1 ≤ i ≤ number   of   RCMs
where the factors RWinter, RSpring, RSummer, RAutumn, and RYearly measure the model´s ability to reproduce each selected variable (in this case, the mean, maximum, and minimum temperatures) for the time reference period 1961–1990. These factors are calculated, as follows: firstly, the corresponding time series are extracted for each site (pixel of RCMs grid) both from the RCMs and observed datasets (906 sites over Spain). Secondly, seasonal and yearly cumulative density probabilities functions (CDFs) are fitted to the corresponding time series (from RCMs and observed dataset). The Weibull plotting position formula has been used to estimate the CDF. Thirdly, to measure the goodness of fit among the CDFs of observed dataset and RCMs, the p value of two-sample Smirnov-Kolmogorov test (TSSK) [41] is applied following the methodological approach that was proposed by Giraldo Osorio and García Galiano [3]. The TSSK test evaluates the similarity between the probability density functions that are associated with two independent samples, quantifying the maximum distance between the CDFs. The R value is obtained on each site (906 sites over continental Spain), then an interpolation method has been applied to obtain the spatial distribution. The Ri values varies from 0 to 1, where Ri = 1 represents the highest reliability.
The maps of normalized reliability factor (Pm) have been calculated with the equation that was proposed by [40] for each RCM in function of Ri.
P m i = R i / ∑ i = 1 , N R i
where i = 1 to N, and N is the number of RCMs.
Then, the PDFs ensembles have been obtained for the time period 2021–2050, while using Pm as the weighting factor of the RCMs. The process of building PDFs ensembles is explained in detail by [31].

2.3. Assessing Spatio-Temporal Distributions of ETo

The estimation of spatio-temporal distribution of ETo has been performed from the observational dataset (time period 1961–1990), and from the RCM ensemble (time periods 1961–1990 and 2021–2050).
The equation that was proposed by Hargreaves and Samani (1985) [18] to estimate ETo is expressed, as follows,
E T o = C R a ( T m + 17.8 ) ( T m a x − T m i n ) 0.5
where ETo is the reference evapotranspiration (mm day−1); Ra is the water equivalent of the monthly-averaged daily extraterrestrial radiation (mm day−1), obtained from Ra = 37.6 dr (ω senφ senδ + cosφ cosδ senω), according to [13] where 1 mm day−1 = 0.408 MJ m−2 day−1; dr is the inverse relative distance Earth-Sun; ω is the hour angle (rad); φ is the latitude (rad); δ is the solar declination (rad); Tmax and Tmin are the monthly-averaged maximum and minimum values of daily air temperature (°C); and Tm is the monthly-averaged daily temperature, calculated as the average of Tmax and Tmin; and, C is the Hargreaves coefficient, which original value is 0.0023. In this work, a calibrated coefficient (C′) to southeast of Spain by Maestre Valero et al. (2013) [23] has been considered, with C′ = 0.00285. The coefficient used is slightly higher than that of Hargreaves because southeast of Spain is considered to be a semiarid region, where original Hargreaves coefficient presents underestimations.
The Equation (4) adjusted to the Southeast of Spain [23] has been finally applied, as follows,
E T o = C ′ R a ( T m + 17.8 ) ( T m a x − T m i n ) 0.5

3. Results

3.1. RCMs Ensemble

In this work, the RCM ensemble for the mean, maximum, and minimum monthly temperatures variables have been obtained. The spatial distributions of values of reliability factor (Ri), built by applying the TSSK p value for mean temperature, are presented in Figure 2. These maps give a general idea about the reliability of each RCM to simulate the temperatures.
From Figure 2, the models with best performance to replicate the mean temperature have been DMI/ECHAM-r3, ETHZ/CLM, KNMI/RACMO2, METNO/HadCM3Q0, and MPIM/REMO. While, the models with worst performances have corresponded to OURANOS/MRCC4.2.1, DMI/BCM, SMHI/BCM, METNO/BCM, and SMHI/HadCM3Q3, presenting the lowest values of R over Spain. One of the RCMs (MPIM/REMO) presents an outstanding performance according to the R factor, although this model does not capture with enough reliability the observed patterns in the southwest corner of Spain. However, there are other models, such as DMI/BCM and DMI/ECHAM-r3, which present a good reliability in that area (southwest corner of Spain).
Once Pm maps have been calculated for each RCM, the PDF ensemble for each site of the study area has been built. Monthly maps of mean, minimum, and maximum temperature for the periods 1961–1990 and 2021–2050, have been obtained.
The CDFs of mean, maximum and minimum temperature for the time reference period 1961–1990 have been obtained from observed dataset and RCMs. The CDFs and CDF ensemble that was obtained on site 36 (North of Spain) for each season and yearly mean temperature are presented in Figure 3 as a representative example, since the performance varies for each site and RCM. From Figure 3, it can be seen that CDF ensemble presents good performance with respect to observed data CDF. The best performance occurs for yearly mean temperature (0.765 p value). However, the best performances vary for each analyzed season, sites, and variables.

3.2. Assessing the Plausible ETo and Temperature Scenarios for 2021–2050

The ETo values applying the Equation (4) has been computed for each grid cell over Spain. Time series of ETo are calculated for the period 2021–2050 once the RCMs temperature ensembles have been obtained. Figure 4 presents the standardized monthly ETo and maximum and minimum temperature for the period 2021–2050. Positive trend in the three variables is evident from the Figure 4.
It is important to know the expected trends of both ETo and temperatures. Therefore, the maps of RCM ensemble of mean annual ETo and maximum and minimum temperature for the period 2021–2050 and scenario A1B (Figure 5a,c,e), and the variation with respect to the time reference period 1961–1990 over Spain (Figure 5b,d,f), have been evaluated.
The maximum and minimum temperatures (Figure 5a,c) have a spatial distribution with a strong increasing gradient from north to south. The maximum temperature is expected between a minimum value of 14 °C in the Pyrenees, to values of 26 °C in Andalusia region. South of Spain presents the highest values of minimum temperatures, with maximum values greater than 12 °C, and north of Spain shows the lowest values, with minimum values less than 4 °C in the Pyrenees region.
The map for ETo (Figure 5e) also shows an increasing gradient from north to south, with maximum values of mean ETo (1800 mm) in the southeast of the Spanish territory. From Figure 5b, the maximum temperature is predicted to increase from 10 to 15% in the north of Spain, and from 5 to 10% in the south. While in the Mediterranean coast, increases of 5% are expected. A general increase in the minimum temperature (Figure 5d) between 20 and 30% is expected for the major part of Spain, except for areas of the Mediterranean and Andalusia regions where increases of 10% are expected. While in the Pyrenees and Iberian System are identified the highest increases (greater than 60%). Finally, an increase of mean annual ETo (Figure 5f) from 0 to 5%, reaching 10% in some areas, is predicted for Spain, except for areas of the Mediterranean region.
The variability of the simulated seasonal mean ETo for the three selected areas of Spain: Zone 1, Zone 2, and Zone 3 represented in Figure 1, has been evaluated considering the time reference period observed (1961–1990) and future period (2021–2050). The results are presented in Figure 6.
From Figure 6a (winter season) and 6d (autumn season), only slight variations of mean ETo for the future period in comparison to time reference period are projected for the three zones. However, for spring season (Figure 6b), an increase in the mean ETo is expected in Levante zone and also in Andalusian zone. In the case of summer season (Figure 6c), the variation of mean ETo for the future period is relevant in comparison to time reference period, where the largest increment is expected for the Andalusian region. In conclusion, the major increments of ETo values are expected in spring and summer seasons, for the Levante and Andalucia zones.

4. Discussion and Conclusions

Evapotranspiration process returns a high percent of precipitation to the atmosphere over Spain (especially relevant in the South and Southeast of Spain), and it is an important meteorological component when hydrological models are used for projecting future changes on water cycle. Therefore, robust projections of ETo, as well as rainfall, are needed to obtain reliable hydrological projections.
The FAO [13] recommend that the Penman-Monteith method could be used as a standard equation to calculate ETo. But, the availability of the required information to apply PM method is not enough in some places, such as developing countries or in large areas (e.g., Spain). For this reason, there is a large amount of works in the literature that evaluate the performance of ETo equations, that only require temperature and radiation data, against the PM equation [14,15,16,17,18,22,23]. Some authors, like Oudin et al. [7], have demonstrated that the PET formulae based on temperature and radiation tend to provide the best inputs to rainfall–runoff models. For this reason, a simple equation temperature-based ETo with few climate variables has been applied to increase the reliability of climate projections. The use of this equation of ETo with a calibrated coefficient is supported by works, such as [21,22,23,24,25].
In addition to the uncertainties due to choice the ETo method estimation, the selection of the climate model (GCM or RCM) is an important source of uncertainty for future projections of changes, as demonstrated Kingston et al. [42], while comparing six PET methods that were obtained from five different GCMs. However, a large part of the existing works is based on only one RCM or GCM instead of a RCM ensemble. For example, authors such as Obada et al. (2017) [36] have obtained projections of ETo in Benin using PM equation for Representative Concentration Pathways scenarios (RCP4.5 and RCP8.5), but only working with three RCMs. The Obada et al. (2017) [36] results indicate that annual ETo gradually increase and reach its maximum on 2100.
Zhang et al. [35] have calculated future change in ETo with PM equation and projections from three GCMs under A1B and B1 scenarios. These works present some strengths (e.g., they used the PM equation and more than one scenario), but their weaknesses correspond to a low number of climate models and not in consideration of the climate model ensemble.
In comparison with other works, the improvement of the present study is to address the estimation of ETo from a RCM ensemble, with the aim to reduce the involved uncertainties. The RCM ensembles of mean, maximum, and minimum temperatures have been built applying a novel methodology that was proposed by [31], based on seasonal and annual variability of these simulated variables from sixteen RCMs (driven by different GCMs for scenario A1B). This methodology has been used successfully in the Southeast of Spain to reduce uncertainties in the runoff projections by Olmos Giménez et al. (2018) [32].
While considering the climate effects of IPCC [39] and RCP scenarios, [43] indicate that the median of the projected warming to the end of the century under the SRES A1B scenario is found between the RCP6 and RCP8.5 scenarios. Moreover, analyzing mean temperature change [44] is found a very high spatial correlation between RCP8.5 and A1B results, with 0.82–0.97 for temperature changes, and even stronger correlation towards the end of the century.
Taking into account the built RCM ensemble of temperatures, the climate projections of ETo have been estimated. An increase of seasonal mean ETo of 5% due to an increase of maximum and minimum temperatures over Spain, is projected. This increase in ETo occurs in summer especially. These results are in line with the trends that were obtained by Vicente-Serrano et al. (2014) [45] for Spain in the time period 1961–2011 by applying various ETo estimation equations. The results of the present study are similar to the findings in others regions such as China and West Africa [35,36]. The PET’s changes in West Africa are expected from 2 to 7% for 2011–2040 and RCP4.5 and RCP8.5 scenarios [36]. The results of Zhang et al. (2013) [35] show that the air temperature increment would dominate the change in ETo, and ETo values will increase for 2050s when compared with the annual mean of period 1960–1990 (increment of 4.42–16.21% for 2050). However, other authors like McVicar et al. (2012) [46] working with trends in observed terrestrial near-surface wind speeds and its implications for evaporation, have uncovered for the last decades the declining rates of evaporative demand (by reviewing papers reporting trends in measured pan evaporation and estimated ETo).
Finally, the work presents a robust methodology to estimate RCMs ensemble-driven ETo projections, and underlines its importance in the study of climate change impacts on hydrological cycle processes, with a reduced requirement of climatic variables.

Author Contributions

The authors contributed equally to the work.

Funding

Fundación Séneca grant number 19527/PI/14.

Acknowledgments

The authors acknowledge the financial support of Fundación Séneca grant number 19527/PI/14, Agency of Science and Technology of Murcia Region in the framework of PCTIRM 2011–2014, Comunidad Autónoma de la Región de Murcia (Spain).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of location of 906 sites and study zones (Zone 1, Zone 2, and Zone 3) of continental Spain.
Figure 1. Map of location of 906 sites and study zones (Zone 1, Zone 2, and Zone 3) of continental Spain.
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Figure 2. Maps of R values for average temperature, estimated by applying the Smirnov-Kolmogorov test (two-sample Smirnov-Kolmogorov test (TSSK) p value) for each Regional Climate Models (RCM).
Figure 2. Maps of R values for average temperature, estimated by applying the Smirnov-Kolmogorov test (two-sample Smirnov-Kolmogorov test (TSSK) p value) for each Regional Climate Models (RCM).
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Figure 3. Cumulative density probabilities functions (CDFs) of mean temperature on site 36 from observed dataset (in black), ensembles (dashed black), and RCMs (in colour), for time reference period 1961–1990.
Figure 3. Cumulative density probabilities functions (CDFs) of mean temperature on site 36 from observed dataset (in black), ensembles (dashed black), and RCMs (in colour), for time reference period 1961–1990.
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Figure 4. Standardized monthly data over continental Spain of RCM ensembles of ETo and maximum and minimum temperature for time period 2021–2050.
Figure 4. Standardized monthly data over continental Spain of RCM ensembles of ETo and maximum and minimum temperature for time period 2021–2050.
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Figure 5. Spatial distribution of: (a,c,e) ensemble of mean annual maximum and minimum temperature, and ETo from RCMs for period 2021–2050; and, (b,d,f) Difference maps (%) between the periods 2021–2050 and 1961–1990 observational dataset, assessed as [100 × (mapEns − mapObs)/mapObs].
Figure 5. Spatial distribution of: (a,c,e) ensemble of mean annual maximum and minimum temperature, and ETo from RCMs for period 2021–2050; and, (b,d,f) Difference maps (%) between the periods 2021–2050 and 1961–1990 observational dataset, assessed as [100 × (mapEns − mapObs)/mapObs].
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Figure 6. Boxplots of mean ETo simulated for time period 2021–2050 and from observational dataset for time reference period 1961–1990, for the Cantabrian coast (Z1), east of continental Spain (Z2), and Andalusian zone (Z3): (a) Winter, (b) Spring, (c) Summer, and (d) Autumn seasons.
Figure 6. Boxplots of mean ETo simulated for time period 2021–2050 and from observational dataset for time reference period 1961–1990, for the Cantabrian coast (Z1), east of continental Spain (Z2), and Andalusian zone (Z3): (a) Winter, (b) Spring, (c) Summer, and (d) Autumn seasons.
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Giménez, P.O.; García-Galiano, S.G. Assessing Regional Climate Models (RCMs) Ensemble-Driven Reference Evapotranspiration over Spain. Water 2018, 10, 1181. https://doi.org/10.3390/w10091181

AMA Style

Giménez PO, García-Galiano SG. Assessing Regional Climate Models (RCMs) Ensemble-Driven Reference Evapotranspiration over Spain. Water. 2018; 10(9):1181. https://doi.org/10.3390/w10091181

Chicago/Turabian Style

Giménez, Patricia Olmos, and Sandra G. García-Galiano. 2018. "Assessing Regional Climate Models (RCMs) Ensemble-Driven Reference Evapotranspiration over Spain" Water 10, no. 9: 1181. https://doi.org/10.3390/w10091181

APA Style

Giménez, P. O., & García-Galiano, S. G. (2018). Assessing Regional Climate Models (RCMs) Ensemble-Driven Reference Evapotranspiration over Spain. Water, 10(9), 1181. https://doi.org/10.3390/w10091181

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