How to Read Standard Precipitation Evapotranspiration Index

Drought monitoring and analysis based on climatic indices

Drought is a major cause of agricultural, economical and ecology damage. Drought effects are apparent later on a long period with a shortage of precipitation, making it very hard to determine their onset, extent and terminate. Thus, it is hard to objectively quantify the characteristics of drought episodes in terms of their intensity, magnitude, elapsing and spatial extent. Much effort has been devoted to developing techniques for drought analysis and monitoring. Among these, the definition of quantitative indices is the most widespread arroyo, but subjectivity in the definition of drought has fabricated it very difficult to institute a unique and universal drought index. Near studies related to drought assay and monitoring systems have been conducted using either i) the Palmer Drought Severity Index (PDSI) (Palmer, 1965), based on a soil water balance equation, or ii) the Standardised Atmospheric precipitation Alphabetize (SPI; McKee et al., 1993), based on a atmospheric precipitation probabilistic approach.

The Palmer Drought Severity Index (PDSI)

The PDSI was a landmark in the development of drought indices. Information technology enables measurement of both wetness (positive value) and dryness (negative values), based on the supply and need concept of the water balance equation, and thus incorporates prior atmospheric precipitation, moisture supply, runoff and evaporation demand at the surface level. Many of the PDSI problems were solved by evolution of the cocky-calibrated PDSI (sc-PDSI) (Wells et al., 2004), which is spatially comparable and reports extreme wet and dry out events at frequencies expected for rare conditions. Nevertheless, the main shortcoming of the PDSI has not been resolved. This relates to its fixed temporal scale (betwixt nine and 12 months), and an autoregressive feature whereby alphabetize values are affected by the weather upwards to 4 years in the by (Guttman, 1998).

Multi-scalar drought indices: the Standardised Precipitation Alphabetize (SPI)

It is commonly accepted that drought is a multi-scalar phenomenon. McKee et al. (1993) clearly illustrated this essential characteristic of droughts through consideration of usable water resource including soil moisture, footing water, snowpack, river discharges, and reservoir storages. The time menses from the arrival of h2o inputs to availability of a given usable resource differs considerably. Thus, the fourth dimension calibration over which h2o deficits accrue becomes extremely important, and functionally separates hydrological, ecology, agronomical and other droughts. For this reason, drought indices must exist associated with a specific timescale to be useful for monitoring and management of unlike usable water resources. This explains the wide credence of the SPI, which is comparable in time and infinite (Hayes et al., 1999), and can be calculated at unlike time scales to monitor droughts with respect to different usable water resource.

The importance of including temperature data

Precipitation-based drought indices including the SPI rely on 2 assumptions: i) the variability of precipitation is much higher than that of other variables, such as temperature and potential evapotranspiration (PET), and ii) the other variables are stationary (i.e. they have no temporal trend). In this scenario the importance of these other variables is negligible, and droughts are controlled past the temporal variability in precipitation. Yet, some authors have warned against systematically neglecting the importance of the consequence of temperature on drought weather. Empirical studies have shown that temperature ascent markedly affects the severity of droughts.

The role of warming-induced drought stress is evident in contempo studies that have analysed drought impacts on net primary production and tree mortality (Williams et al., 2011; Martínez-Villalta et al., 2008; McGuire et al., 2010; Linares and Camarero, 2011). The strong role of temperatures on the drought severity was evident in the devasting 2003 central European heat wave, in which extreme loftier temperatures dramatically increased evapotranspiration and exacerbated summer drought stress (Rebetez et al., 2006), drastically reducing Aboveground Internet Primary Production (ANPP) (Ciais et al., 2005). Similar patterns were observed in the summer 2010 with a stiff heat wave that increased drought stress in forests and produced large forest fires in eastern Europe and Russian federation (Barriopedro et al., 2011). Thus, empirical studies have demonstrated that higher temperatures increment drought stress and enhance forest mortality under precipitation shortages (Adams et al., 2009). Warming processes are also probably the triggering factor of the decline in world agronomical productions observed in the last years (Lobell et al., 2011). Thus, to illustrate how warming processes are reinforcing drought stress and related ecological impacts worldwide, Breshears et al. (2005) enunciated the term global-change-blazon drought to refer to drought under global warming conditions.

In that location has been a full general temperature increment (0.five−2°C) during the last past 150 years, and climate alter models predict a marked increase during the 21st century. It is expected that this will have dramatic consequences for drought conditions, with an increase in water demand due to evapotranspiration.

Therefore, the use of drought indices which include temperature data in their formulation (such as the PDSI) is preferable, peculiarly for applications involving futurity climate scenarios. Withal, the PDSI lacks the multi-scalar character essential for both assessing drought in relation to different hydrological systems, and differentiating amid different drought types. Therefore a new drought alphabetize (the Standardized Precipitation Evapotranspiration Index; SPEI) has been formulated based on precipitation and PET. The SPEI combines the sensitivity of PDSI to changes in evaporation demand (acquired past temperature fluctuations and trends) with the simplicity of calculation and the multi-temporal nature of the SPI.

The SPI can not identify the role of temperature increase in future drought conditions, and independently of global warming scenarios can not account for the influence of temperature variability and the office of heat waves. The SPEI can account for the possible effects of temperature variability and temperature extremes beyond the context of global warming. Therefore, given the pocket-size additional information requirements of the SPEI relative to the SPI, use of the former is preferable for the identification, analysis and monitoring of droughts in any climate region of the globe.

A new drought index: the Standardised Precipitation-Evapotranspiration Index (SPEI)

The SPEI fulfils the requirements of a drought index since its multi-scalar graphic symbol enables information technology to be used past different scientific disciplines to detect, monitor and clarify droughts. Like the sc-PDSI and the SPI, the SPEI tin measure drought severity according to its intensity and duration, and can identify the onset and finish of drought episodes. The SPEI allows comparison of drought severity through time and infinite, since it can be calculated over a wide range of climates, as tin the SPI. Moreover, Keyantash and Dracup (2002) indicated that drought indices must be statistically robust and easily calculated, and take a clear and comprehensible calculation procedure. All these requirements are met by the SPEI. However, a crucial advantage of the SPEI over other widely used drought indices that consider the issue of PET on drought severity is that its multi-scalar characteristics enable identification of different drought types and impacts in the context of global warming.

Trouble overview

As an example, Figure 1 shows the evolution of the sc-PDSI and the SPI at different time scales from 1910 to 2007 at the Indore observatory (Bharat). The sc-PDSI has a unique fourth dimension scale, in which the longest and well-nigh severe droughts were recorded in the decades 1910, 1920, 1950, 1960 and 2000. These episodes are also clearly identified by the SPI at long fourth dimension scales (12−24 months). This provides evidence near the suitability of identifying and monitoring droughts using an index that only considers precipitation data. Moreover, this instance shows the advantage of the SPI over the sc-PDSI, since the different fourth dimension scales over which the SPI tin can be calculated allows the identification of dissimilar drought types. At the shortest time scales the drought serial show a high frequency of drought, and moist periods of brusk duration. In dissimilarity, at the longest fourth dimension scales the drought periods are of longer duration and lower frequency. Thus, short time scales are mainly related to soil water content and river discharge in headwater areas, medium fourth dimension scales are related to reservoir storages and belch in the medium grade of the rivers, and long fourth dimension-scales are related to variations in groundwater storage. Therefore, unlike fourth dimension scales are useful for monitoring drought conditions in different hydrological sub-systems.

Effigy 1. Time series of the sc-PDSI and three-, vi-, 12-, 18- and 24-month SPIs in Indore (India) (1910−2007). (clic for a larger version )

A reduction in atmospheric precipitation due to climatic change volition affect the severity of droughts. The influence of a reduction in precipitation on hereafter drought conditions is identified past both the sc-PDSI and the SPI. Figure two shows the development of the sc-PDSI and the xviii-calendar month SPI at the Albuquerque (New Mexico, USA) observatory between 1910 and 2007. Both indices were calculated using a hypothetical progressive precipitation decrease of 15% during this flow. Both the modeled SPI and sc-PDSI series showed an increment in the duration and magnitude of droughts at the finish of the century relative to the serial computed with existent information. As a effect of the atmospheric precipitation subtract, droughts recorded in the decades of 1970 to 2000 increased in maximum intensity, total magnitude and duration. In dissimilarity, the boiling periods showed the reverse behavior. Therefore, both indices have the capacity to tape changes in droughts related to changes in precipitation.

Figure 2. Time series of the PDSI and 18-month SPI at the Albuquerque (New Mexico, USA) observatory (1910−2007). Both indices were calculated from precipitation series containing a progressive reduction of 15% between 1910 and 2007. The difference between the indices is besides shown. (clic for a larger version)

Nonetheless, climate change scenarios also show a temperature increase during the 20th century. In some cases, such as the A2 greenhouse gas emissions scenario, the models predict a temperature increase that might exceed 4ºC with respect to the 1960−1990 boilerplate. Figure 3 shows the development of the sc-PDSI in Albuquerque, computed with real data between 1910 and 2007, just also considers a progressive increase of 2−4ºC in the mean temperature series. The differences between the sc-PDSI using real data and the two modeled series are also shown. This unproblematic experiment conspicuously shows an increase in the elapsing and magnitude of droughts at the end of the century, which is directly related to the temperature increase. A similar blueprint could non be identified using the SPI, demonstrating the shortcomings of this widespread index in addressing the consequences of climate alter.

Effigy 3. Time series of the sc-PDSI at Albuquerque (New Mexico, Us) between 1910 and 2007, and nether progressive temperature increase scenarios of 2ºC and 4ºC during the same period. The difference between the indices is also shown. (clic for a larger version)

Computation of the SPEI

The SPEI is really simple to calculate, and is based on the original SPI calculation procedure. The SPI is calculated using monthly (or weekly) atmospheric precipitation equally the input data. The SPEI uses the monthly (or weekly) deviation between precipitation and PET. This represents a simple climatic water balance which is calculated at different time scales to obtain the SPEI.

Climatic water residue (precipitation minus evapotranspiration)

A number of equations exist to model PET based on available data (e.grand. the Thornthwaite equation, the Penman-Monteith equation, the Hargreaves equation, etc), and the SPEI is not linked to any particular one.

In the original version of the SPEI we used the Thornthwaite equation (Thornthwaite, 1948), wich was applied to obtain the SPEIbase v1.0. In the SPEIbase v2.0 we used the FAO-56 Penman–Monteith equation (Allen et al. 1998.

With a value for PET, the deviation betwixt the precipitation (P) and PET for the month i is calculated:

,

which provides a elementary measure out of the water surplus or deficit for the analyzed month.

The calculated D_i values are aggregated at dissimilar time scales, following the same procedure as for the SPI.

Standardization of the variable

Selection of the about suitable statistical distribution to model the D series was difficult, given the similarity among the iv distributions (Pearson III, Lognormal, Log-logistic and General Extreme Value). The option was based on the beliefs at the about extreme values. Log-logistic distribution showed a gradual decrease in the bend for low values, and coherent probabilities were obtained for very low values of D, corresponding to 1 occurrence in 200 to 500 years. Additionally, no values were found below the origin parameter of the distribution.

The probability density office of a three parameter Log-logistic distributed variable is expressed as:

,

where α, β and γ are scale, shape and origin parameters, respectively, for D values in the range (γ > D < ∞).

Parameters of the Log-logistic distribution tin exist obtained following different procedures. Amongst them, the L-moment procedure is the nearly robust and easy approach (Ahmad et al., 1988). When 50-moments are calculated, the parameters of the Log-logistic distribution can be obtained following Singh et al. (1993):

,

,

,

where Γ(β) is the gamma part of β.

In Vicente-Serrano et al. (2010), when the log-logistic α, β and γ distribution parameters were calculated, the probability weighted moments (PWMs) method was used, based on the plotting-position approach (Hosking, 1990), where the PWMs of order s are calculated as:

where N is the number of data, Fⁱ is a frequency estimator post-obit the arroyo of Hosking (1990) and Di is the difference between Precipitation and Potential Evapotranspiration for the month i. yet, we take found that Using the Plotting Position formulae the standard difference of the SPEI serial change noticeably as a function of the SPEI fourth dimension-scale, which touch on spatial comparability of the SPEI values. On the contrary, if the PWMs are obtained by ways of the unbiased estimator given past Hosking (1986), the SD of the series does non change amidst the different SPEI time scales. The unbiased PWMs are obtained according to:

The method besides solves the problem found for the no solution of the SPEI model in some regions of the world. For these reasons, we recommend SPEI calculation using Unbiased PWMs.

The probability distribution function of D according to the Log-logistic distribution is and so given past:

With F(10) the SPEI can easily be obtained as the standardized values of F(10). For example, post-obit the classical approximation of Abramowitz and Stegun (1965):

,

where

,

for P≤0.5, P being the probability of exceeding a adamant D value, P=1-F(x). If P>0.5, P is replaced by 1−P and the sign of the resultant SPEI is reversed. The constants are: C0=2.515517, C1=0.802853, C2=0.010328, d1=one.432788, d2=0.189269, d3=0.001308. The average value of the SPEI is 0, and the standard difference is ane. The SPEI is a standardized variable, and it tin therefore exist compared with other SPEI values over time and infinite. An SPEI of 0 indicates a value corresponding to 50% of the cumulative probability of D, co-ordinate to a Log-logistic distribution.

Examples

Effigy 4 shows the sc-PDSI, and the three-, 12- and 24-monthly SPIs and SPEIs for Helsinki between 1910 and 2007. According to the sc-PDSI, the primary drought episodes occurred in the decades of 1930, 1940, 1970 and 2000. These droughts are also conspicuously identified by the SPI and the SPEI. Few differences were apparent between the SPI and the SPEI series, independently of the time calibration of analysis. This event shows that under climate conditions in which low interannual variability of temperature dominates, both drought indices respond mainly to the variability in precipitation.

Figure 4. sc-PDSI, 3-, 12- and 24-month SPI and SPEI at Helsinki (1910−2007). (clic for a larger version)

Effigy v shows the time series of the sc-PDSI obtained using original and modeled serial for the Valencia observatory (Kingdom of spain). The 18-month SPI and SPEI obtained with that series are too shown. Using the original information, the sc-PDSI identified the near of import droughts in the decades of 1990 and 2000. With a progressive temperature increment of 2ºC and 4ºC, the droughts increased in magnitude and duration at the stop of the century. The SPI did not place those severe droughts associated with a marked temperature increment, and information technology did not take into account the role of increased temperature in reinforcing drought conditions, as was shown past the sc-PDSI. In contrast, the main drought episodes were identified by the SPEI, with similar evolution to that observed for the sc-PDSI. Moreover, if temperature increased progressively by 2ºC or 4ºC, the reinforcement of drought severity associated with college h2o demand past PET was readily identified by the SPEI, with the fourth dimension series showing a high similarity to the sc-PDSI observed nether warming scenarios.

Figure 5. Time series of the sc-PDSI, and 18-month SPI and SPEI in Valencia (Spain). The original serial (1910−2007) and the sc-PDSI and SPEI were calculated for a temperature serial with a progressive increase of 2ºC and 4ºC throughout the analyzed flow. (clic for a larger version)

Effigy 6 compares the SPEI and the sc-PDSI nether a 4ºC temperature increase scenario throughout the analysis period at the Tampa (Florida, Usa) observatory. Under this warming scenario, the sc-PDSI shows quasi-continuous drought conditions between 1970 and 2000, with some small humid periods. The persistent drought weather condition during this period are also conspicuously identified by the SPEI, independent of the analysis time scale. Thus, the sc-PDSI provides the same data as the SPEI at fourth dimension scales of 7 to 10 months (R values between 0.850 and 0.857), but Figure 6 clearly shows that the SPEI as well provides data about drought weather condition at shorter and longer time scales.

Figure six. Time series of the sc-PDSI, and 1-, three-, 6-, 12-, xviii- and 24-month SPEI at Tampa (Florida, USA) under a 4ºC temperature increase scenario relative to the origin. (clic for a larger version)

References

Fundamental scientific literature for the SPEI:

• Vicente-Serrano S.M., Santiago Beguería, Juan I. López-Moreno, (2010) A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Alphabetize - SPEI. Periodical of Climate 23: 1696-1718.
• Beguería, Due south., Vicente-Serrano, Due south.M. y Angulo, M., (2010): A multi-scalar global drought data set: the SPEIbase: A new gridded product for the analysis of drought variability and impacts. Message of the American Meteorological Society. 91, 1351-1354
• Vicente-Serrano, S.Yard., Beguería, S., López-Moreno, J.I., Angulo, M., El Kenawy, A. (2010): A new global 0.5° gridded dataset (1901-2006) of a multiscalar drought index: comparing with electric current drought index datasets based on the Palmer Drought Severity Alphabetize. Periodical of Hydrometeorology. xi: 1033-1043
• Vicente-Serrano, S.K., Beguería, S. and Juan I. López-Moreno (2011). Annotate on "Characteristics and trends in various forms of the Palmer Drought Severity Alphabetize (PDSI) during 1900-2008" by A. Dai. Periodical of Geophysical Enquiry-Temper. 116, D19112, doi:10.1029/2011JD016410
• Vicente-Serrano, S.M., Santiago Beguería, Jorge Lorenzo-Lacruz, Jesús Julio Camarero, Juan I. López-Moreno, Cesar Azorin-Molina, Jesús Revuelto, Enrique Morán-Tejeda and Arturo Sánchez-Lorenzo. (2012) Performance of drought índices for ecological, agricultural and hydrological applications. Earth Interactions 16, 1-27.
• Beguería, S., Vicente-Serrano, S.M., Fergus Reig, Borja Latorre. Standardized Precipitation Evapotranspiration Index (SPEI) revisited (2014): parameter fitting, evapotranspiration models, kernel weighting, tools, datasets and drought monitoring. International Journal of Climatology, 34: 3001-3023
• Vicente-Serrano, S.M., Gerard Van der Schrier, Santiago Beguería, Cesar Azorin-Molina, Juan-I. Lopez-Moreno (2015). Contribution of atmospheric precipitation and reference evapotranspiration to drought indices under unlike climates. Journal of Hydrology 426: 42-54.
• Vicente-Serrano, S.M., Beguería, South., (2016) Comment on "Candidate Distributions for Climatological Drought Indices (SPI and SPEI)" by James H. Stagge et al. International Periodical of Climatology.36: 2120-213

Scientific literature using the SPEI:

• Abiodun, B.J., Ayobami T. Salami, Olaniran J. Matthew, Sola Odedokun (2012): Potential impacts of afforestation on climate change and extreme events in Nigeria. Climate Dynamics, DOI ten.1007/s00382-012-1523-9.
• Allen, K.J., J. Ogden, B. Grand. Buckley, E. R. Cook, P. J. Baker (2011): The potential to reconstruct broadscale climate indices associated with southeast Australian droughts from Athrotaxis species, Tasmania. Climate Dynamics 37: 1799-1821.
• Boroneant C., Ionita Thousand., Brunet 1000., Rimbu Northward. (2011). CLIVAR-SPAIN contributions: Seasonal drought variability over the Iberian Peninsula and its relationship to global sea surface temperature and big scale atmospheric circulation. WCRP OSC: Climate Enquiry in Service to Society, 24-28 October 2011, Denver, Usa.
• Camarero, J.J., Sangüesa, G., Alla, A.Q., González de Andrés, E., Maestro, 1000. y Vicente-Serrano, S.Grand., (2012): Los precedentes y la respuesta de los árboles a sequías extremas revelan los procesos involucrados en el decaimiento de bosques mediterráneos de coníferas. Ecosistemas, 21: 22-xxx.
• Deng F., Chen J.M. (2011). Recent global CO2 flux inferred from atmospheric CO2 observations and its regional analyses. Biogeosciences Discussions 38, 3497-3536, doi:10.5194/bgd-8-3497-2011.
• Drew, D.1000., Kathryn Allen, Geoffrey M. Downes, Robert Evans, Michael Battaglia and Patrick Baker (2012): Wood properties in a long-lived conifer reveal strong climate signals where ring-width series exercise not. Tree Physiology doi: x.1093/treephys/tps111.
• Hernández, S., Tarife, R., Gámiz-Fortis, Southward., Castro-Díez, Y. And Esteban-parra, Y., (2012): Study Of Drought In The Canary Islands Through The Assay Of Multiscale Indicesernández Barrera. en: Cambio climático. Extremos e impactos. Concepción Rodríguez Puebla, Antonio Ceballos Barbancho, Nube González Reviriego, Enrique Morán Tejeda y Ascensión Hernández Encinas (Eds.) Publicaciones de la Asociación Española de Climatología (AEC), 2012, Serie A, nº 8. Salamanca
• Li, Due west., Hou, Chiliad., Chen, H., Chen, X. (2012): Study on drought trend in southward China based on standardized atmospheric precipitation evapotranspiration alphabetize. Journal of Natural Disasters 21: 84-ninety
• Lorenzo-Lacruz, J., Vicente-Serrano, S.1000., López-Moreno, J.I., Beguería, S., García-Ruiz, J.M., Cuadrat, J.M. (2010) The bear on of droughts and water management on diverse hydrological systems in the headwaters of the Tagus River (central Espana). Journal of Hydrology, 386: thirteen-26.
• Martin-Benito, D., Hans Beeckman, Isabel Cañellas (2012): Influence of drought on tree rings and tracheid features of Pinus nigra and Pinus sylvestris in a mesic Mediterranean forest. European Journal of Wood Enquiry, DOI ten.1007/s10342-012-0652-three.
• McEvoy, D.J., Justin L. Huntington, John Abatzoglou, Laura Edwards (2012): An evaluation of multi-scalar drought indices in Nevada and Eastern California Earth Interactions doi: [spider web]
• Nadal-Romero, E., Vicente-Serrano, South.1000., Jiménez, I. (2012) Cess of badland dynamics using multi-temporal Landsat imagery: an instance from the Spanish Pre-Pyrenees. Catena 96: i-12.
• Nogués-Bravo, D., J.I. López-Moreno and S.1000. Vicente-Serrano (2012): Climate change and its impact. In Mediterranean Mountain Environments, First Edition. Edited by Ioannis N. Vogiatzakis. John Wiley & Sons, Ltd.
• Paulo, A.A., Rosa, R.D. and Pereira, Fifty.S. (2012): Climate trends and behaviour of drought indices based on precipitation and evapotranspiration in Portugal. Natural Hazards and Earth System Sciences, 12: 1481-1491.
• Potop, Five. and Možný, M., (2011): The Application A New Drought Index – Standardized Precipitation Evapotranspiration Alphabetize In The Czechia. In Středová, H., Rožnovský, J., Litschmann, T. (eds): Mikroklima a mezoklima krajinných struktur a antropogenních prostředí. Skalní mlýn, two. – 4.two. 2011, ISBN 978-80-86690-87-2
• Potop, 5., (2011): Development of drought severity and its impact on corn in the Democracy of Moldova. Theoretical and Practical Climatology 105: 469-483.
• Potop, 5., Možný, G. (2011): Examination of the effect of evapotranspiration as an output parameter in SPEI drought alphabetize in Key Bohemian region Šiška, B. – Hauptvogl, M. – Eliašová, M. (eds.). Bioclimate: Source and Limit of Social Evolution, International Scientific Conference, 6th – ninth September 2011, Topoľčianky, Slovakia
• Potop, V., Možný, K., Soukup, J., (2012): Drought at diverse time scales in the lowland regions and their impact on vegetable crops in the Czech republic. Agric Woods Meteorol 156: 121-133.
• Soo-Jin, S., Joong-Bae, A., Chi-Yung, T. (2013): Half-dozen month-atomic number 82 downscaling prediction of winter to jump drought in South Korea based on a multimodel ensemble. Geophysical Enquiry Letters, DOI: 10.1002/grl.50133
• Spinoni, J., T. Antofie, P. Barbosa, Z. Bihari, M. Lakatos, S. Szalai, T. Szentimrey, and J. Vogt (2013): An overview of drought events in the Carpathian Region in 1961-2010. Adv. Sci. Res., ten, 21-32.
• Su, H., Li, G. (2012): Low-frequency drought variability based on SPEI in clan with climate indices in Beijing. Shengtai Xuebao/ Acta Ecologica Sinica 32 (17) , pp. 5467-5475.
• Telesca, L.; Vicente-Serrano, S.M.; López-Moreno, J.I. (2012) Power spectral characteristics of drought indices in the Ebro river bowl at unlike temporal scales. Stochastic Environmental Research and Risk Assessment (SERRA).
• Toromani, E., Mitat Sanxhaku, Edmond Pasho (2011): Growth responses to climate and drought in silver fir (Abies alba) forth an altitudinal gradient in southern Kosovo. Canadian Journal of Forest Research, 41: 1795-1807.
• Vicente-Serrano, S.M., Lasanta, T., Gracia, C., (2010): Aridification determines changes in leaf action in Pinus halepensis forests under semiarid Mediterranean climate conditions. Agricultural and Wood Meteorology 150, 614-628.
• Vicente-Serrano, Southward.M., Juan I. López-Moreno, Luis Gimeno, Raquel Nieto, Enrique Morán-Tejeda, Jorge Lorenzo-Lacruz, Santiago Beguería and Cesar Azorin-Molina: (2011): A multi-scalar global evaluation of the bear on of ENSO on droughts. Periodical of Geophysical Inquiry-Atmosphere. 116, D20109, doi:10.1029/2011JD016039.
• Vicente-Serrano, S.M., Célia Gouveia, Jesús Julio Camarero, Santiago Beguería, Ricardo Trigo, Juan I. López-Moreno, César Azorín-Molina, Edmond Pasho, Jorge Lorenzo-Lacruz, Jesús Revuelto, Enrique Morán-Tejeda, Arturo Sánchez-Lorenzo (2012): Drought impacts on vegetation activity, growth and primary product in humid and arid ecoystems. en: Cambio climático. Extremos due east impactos. Concepción Rodríguez Puebla, Antonio Ceballos Barbancho, Nube González Reviriego, Enrique Morán Tejeda y Ascensión Hernández Encinas (Eds.) Publicaciones de la Asociación Española de Climatología (AEC), 2012, Serie A, nº viii. Salamanca. 691-699.
• Vicente-Serrano, S.Yard., Santiago Beguería, Jorge Lorenzo-Lacruz, Jesús Julio Camarero, Juan I. López-Moreno, César Azorín-Molina, Jesús Revuelto, Enrique Morán-Tejeda, Arturo Sánchez-Lorenzo (2012): Análisis comparativo de diferentes índices de sequía para aplicaciones ecológicas, agrícolas east hidrológicas. en: Cambio climático. Extremos e impactos. Concepción Rodríguez Puebla, Antonio Ceballos Barbancho, Nube González Reviriego, Enrique Morán Tejeda y Ascensión Hernández Encinas (Eds.) Publicaciones de la Asociación Española de Climatología (AEC), 2012, Serie A, nº 8. Salamanca. 679-689.
• Vicente-Serrano, South.M., Juan I. López-Moreno, Anita Drummond, Luis Gimeno, Raquel Nieto, Enrique Morán-Tejeda, Santiago Beguería and Javier Zabalza (2011). Effects of warming processes on droughts and water resources in the NW Iberian Peninsula (1930−2006) Climate Research. 48: 203-212.
• Vicente-Serrano, Due south.M., López-Moreno, J.I., Lorenzo-Lacruz, J., El Kenawy, A., Azorin-Molina, C., Morán-Tejeda, Due east., Pasho, E., Zabalza, J., Beguería, Southward. and Angulo-Martínez, 1000. (2011): The NAO touch on on droughts in the Mediterranean region. En Vicente-Serrano, South.G., y Trigo, R., (Eds.) Hydrological, socioeconomic and ecological impacts of the N Atlantic Oscillation in the Mediterranean region. Advances in Global Inquiry (AGLO) series. Springer-Verlag.
• Vicente-Serrano, Due south.M., Aidel Zouber, Teodoro Lasanta, and Yolanda Pueyo. (2012); Dryness is accelerating degradation of vulnerable shrublands in semiarid Mediterranean environments. Ecological Monographs, 82, 407-428.
• Vicente-Serrano, S.M., Santiago Beguería, Luis Gimeno, Lars Eklundh, Gregory Giuliani, Derek Weston, Ahmed El Kenawy, Juan I. López-Moreno, Raquel Nieto, Tenalem Ayenew, Diawoye Konte, Jonas Ardö and Geoffrey M.S. Pegram (2012). Challenges for drought mitigation in Africa: the potential use of geospatial data and drought information systems. Applied Geography, 34: 471-486.
• Wei-Guang L., Xue Y., Mei-Ting H., Hui-Lin Ch., Zhen-Li Ch. 2012. Standardized precipitation evapotranspiration alphabetize shows drought trends in Red china. Chinese Journal of Eco-Agriculture 20(5): 643−649.
• Wang Wenju, Cui Peng, Liu Min, Shen Lei, Li Xin, Qin Pengcheng 2012, Evolution Characteristics of Drought at Different Time Scales in Hubei Province in the Past fifty Years Chinese Agronomical Science Bulletin 28(29):279-284
• Wibig, J., (2012): Ibig Warunki Wilgotnościowe W Polsce W Świetle Wskaźnika Standaryzowanego Klimatycznego Bilansu Wodnego. Water-Surround-Rural Areas 2012 (IV–VI): 329–340
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