Application of the Standardized Precipitation Evapotranspiration Index (SPEI) for drought analysis and monitoring: characteristics, recommendations and comparison with other indices

The complexity of drought quantification and analysis: • Droughts are difficult to pinpoint in time and space given different economic sectors and natural systems affected. • We identify a drought by its effects or impacts on different types of systems (agriculture, water resources, ecology, forestry, economy, etc.), but there is not a physical variable we can measure to quantify droughts. • Long-term drought objective metrics (streamflows, soil moisture, lake levels, etc.) are commonly not available. Moreover, using only objective metrics other relevant variables to determine drought severity (e.g. the atmospheric water demand) are not taken into account. • We use the so-called “DROUGHT INDICES” for drought quantification and analysis. Standardized Precipitation Evapotranspiration Index (SPEI): The SPEI uses the difference between precipitation and ETo. This represents a simple climatic water balance which is calculated at different time scales to obtain the SPEI. With a value for ETo, the difference between the precipitation (P) and PET for the month i is calculated according to: Di = Pi-EToi, The calculated D values are aggregated at different time scalesPeer Reviewe

Seasonal temperature trends on the Spanish mainland: a secular study (1916-2015)

Trends in seasonal mean values of maximum and minimum temperature are analysed in the Spanish mainland from the new MOTEDAS_century database. This new data set has been developed combining the digitalized archives from the Spanish Meteorological Agency (AEMET) with information retrieved from Annual Books published by the former Meteorological Agency dating back to 1916, and covers the period 1916-2015. In all four seasons, mean seasonal temperature of maximum (Tmax) and minimum (Tmin) increased. The raising occurred in two main pulses separated by a first pause around the middle of the 20th century, but differed among seasons and also between maximum and minimum temperature. Analysis of the percentage of land affected by significant trends in maximum temperature reveals two increasing phases in spring and summer for Tmax, and in spring, summer, and autumn for Tmin. However, winter Tmax only rose during the recent decades, and autumn Tmax in the first decades. Negative significant trends were found in extended areas in spring Tmax, and in spring, autumn, and summer Tmin, confirming the first pause around the 1940's-1960's. Trends of seasonal mean values of Tmax and Tmin are not significant for at least the last 25-35 years of the study period, depending on the season. The areas under significant positive trend are usually more extended for Tmin than Tmax at any season and period. Areas with significant trend expand and contract in time according to two spatial gradients: south‐east to north‐west (east‐west) for Tmax, and west to east for Tmin. We hypothesize a relationship between atmospheric prevalent advection and relief as triggering factors to understand spatial and temporal differences in seasonal temperatures at regional scale during the 20th century in the Iberian Peninsula

The Westerly Index as complementary indicator of the North Atlantic oscillation in explaining drought variability across Europe

This paper analyses the influence of different atmospheric circulation indices on the multi-scalar drought variability across Europe by using the Standardized Precipitation Evapotranspiration Index (SPEI). The monthly circulation indices used in this study include the North Atlantic oscillation (NAO), the East Atlantic (EA), the Scandinavian (SCAN) and the East Atlantic-Western Russia (EA-WR) patterns, as well as the recently published Westerly Index (WI), defined as the persistence of westerly winds over the eastern north Atlantic region. The results indicate that European drought variability is better explained by the station-based NAO index and the WI than by any other combination of circulation indices. In northern and central Europe the variability of drought severity for different seasons and time-scales is strongly associated with the WI. On the contrary, the influence of the NAO on southern Europe droughts is stronger than that exerted by the WI. The correlation patterns of the NAO and WI with the SPEI show a spatial complementarity in shaping drought variability across Europe. Lagged correlations of the NAO and WI with the SPEI also indicate enough skill of both indices to anticipate drought severity several months in advance. As long as instrumental series of the NAO and WI are available, their combined use would allow inferring European drought variability for the last two centuries and improve the calibration and interpretation of paleoclimatic proxies associated with drought

Recent changes of relative humidity: regional connections with land and ocean processes

We analyzed changes in surface relative humidity (RH) at the global scale from 1979 to 2014 using both observations and the ERA-Interim dataset. We compared the variability and trends in RH with those of land evapotranspiration and ocean evaporation in moisture source areas across a range of selected regions worldwide. The sources of moisture for each particular region were identified by integrating different observational data and model outputs into a Lagrangian approach. The aim was to account for the possible role of changes in air temperature over land, in comparison to sea surface temperature (SST), but also the role of land evapotranspiration and the ocean evaporation on RH variability. The results demonstrate that the patterns of the observed trends in RH at the global scale cannot be linked to a particular individual physical mechanism. Our results also stress that the different hypotheses that may explain the decrease in RH under a global warming scenario could act together to explain recent RH trends. Albeit with uncertainty in establishing a direct causality between RH trends and the different empirical moisture sources, we found that the observed decrease in RH in some regions can be linked to lower water supply from land evapotranspiration. In contrast, the empirical relationships also suggest that RH trends in other target regions are mainly explained by the dynamic and thermodynamic mechanisms related to the moisture supply from the oceanic source regions. Overall, while this work gives insights into the connections between RH trends and oceanic and continental processes at the global scale, further investigation is still desired to assess the contribution of both dynamic and thermodynamic factors to the evolution of RH over continental regions.Ministerio de Economía y Competitividad | Ref. PCIN-2015-220Ministerio de Economía y Competitividad | Ref. CGL2014-52135-C03-01Ministerio de Economía y Competitividad | Ref. CGL2014-60849-JINEuropean Commission | Ref. n. 69046

Increased Vegetation in Mountainous Headwaters Amplifies Water Stress During Dry Periods

The dynamics of blue and green water partitioning under vegetation and climate change, as well as their different interactions during wet and dry periods, are poorly understood in the literature. We analyzed the impact of vegetation changes on blue water generation in a central Spanish Pyrenees basin undergoing intense afforestation. We found that vegetation change is a key driver of large decreases in blue water availability. The effect of vegetation increase is amplified during dry years, and mainly during the dry season, with streamflow reductions of more than 50%. This pattern can be attributed primarily to increased plant water consumption. Our findings highlight the importance of vegetation changes in reinforcing the decrease in water resource availability. With aridity expected to rise in southern Europe over the next few decades, interactions between climate and land management practices appear to be amplifying future hydrological drought risk in the region.This work was supported by projects CGL2017-82216-R, PCI2019-103631, and PID2019-108589RA-I00 financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by AXIS (Assess-ment of Cross(X)-sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Dhais Peña-Angulo received a “Juan de la Cierva” postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC). Miquel Tomas-Burguera received a “Juan de la Cierva” postdoctoral contract (FJCI-2019-039261-I Spanish Ministry of Science and Innovation). C. Azorin-Molina and S. Grainger. acknowledge funding from the Irish Environmental Protection Agency grant 2019-CCRP-MS.60. C. Juez acknowl-edges funding from the H2020-MSCA-IF-2018 programme (Marie Sklodows-ka-Curie Actions) of the European Union under REA grant agreement, number 834329-SEDILAND

The complex influence of ENSO on droughts in Ecuador

48 Pags.- 1 Tabl.- 18 Figs. The definitive version is available at: http://link.springer.com/journal/382In this study, we analyzed the influence of El Niño–Southern Oscillation (ENSO) on the spatio-temporal variability of droughts in Ecuador for a 48-year period (1965–2012). Droughts were quantified from 22 high-quality and homogenized time series of precipitation and air temperature by means of the Standardized Precipitation Evapotranspiration Index. In addition, the propagation of two different ENSO indices (El Niño 3.4 and El Niño 1 + 2 indices) and other atmospheric circulation processes (e.g., vertical velocity) on different time-scales of drought severity were investigated. The results showed a very complex influence of ENSO on drought behavior across Ecuador, with two regional patterns in the evolution of droughts: (1) the Andean chain with no changes in drought severity, and (2) the Western plains with less severe and frequent droughts. We also detected that drought variability in the Andes mountains is explained by the El Niño 3.4 index [sea surface temperature (SST) anomalies in the central Pacific], whereas the Western plains are much more driven by El Niño 1 + 2 index (SST anomalies in the eastern Pacific). Moreover, it was also observed that El Niño and La Niña phases enhance droughts in the Andes and Western plains regions, respectively. The results of this work could be crucial for predicting and monitoring drought variability and intensity in Ecuador.This work was supported by the EPhysLab (UVIGO-CSIC Associated Unit) and the research projects I-COOP H2O 2013CD0006: “Test multisectorial y actividades demostrativa sobre el potencial desarrollo de sistemas de monitorización de sequías en tiempo real en la región del oeste de Sudamérica” financed by the Spanish National Research Council, CGL2011-27574-CO2-02, CGL2014-52135-C03-01 and Red de variabilidad y cambio climático RECLIM (CGL2014-517221-REDT), financed by the Spanish Commission of Science and Technology and FEDER, and “LIFE12 ENV/ES/000536-Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)” financed by the LIFE programme of the European Commission. Cesar Azorin-Molina was supported by the JCI-2011-10263 Grant. Arturo Sanchez-Lorenzo was supported by the JCI-2012-12508 Grant. Miquel Tomas-Burguera was supported by a doctoral grant by the Ministry of Economy and Competitiveness and Natalia Martin-Hernandez was supported by a doctoral grant by the Aragón Regional Government. E. Aguilar was funded by the Grant CCI-009-ATN/OC-12439-RG-2012 from the Banco Iberoamericano de Desarrollo.Peer reviewe

A global drought monitoring system and dataset based on ERA5 reanalysis: A focus on crop-growing regions

14 Pags.- 12 Figs. © 2022 The Authors. Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License.Drought monitoring systems are real-time information systems focused on drought severity data. They are useful for determining the drought onset and development and defining the spatial extent of drought at any time. Effective drought monitoring requires databases with high spatial and temporal resolution and large spatial and temporal coverage. Recent reanalysis datasets meet these requirements and offer an excellent alternative to observational data. In addition, reanalysis data allow better quantification of some variables that affect drought severity and are more seldom observed. This study presents a global drought dataset and a monitoring system based on the Standardized Precipitation Evapotranspiration Index (SPEI) and ERA5 reanalysis data. Computation of the atmospheric evaporative demand for the SPEI follows the FAO-56 Penman-Monteith equation. The system is updated weekly, providing near real-time information at a 0.5° spatial resolution and global coverage. It also contains a historical dataset with the values of the SPEI at different time scales since January 1979. The drought monitoring system includes the assessment of drought severity for dominant crop-growing areas. A comparison between SPEI computed from the ERA5 and CRU datasets shows generally good spatial and temporal agreement, albeit with some important differences originating mainly from the different spatial patterns of SPEI anomalies, as well as from employing long-term climate trends for different regions worldwide. The results show that the ERA5 dataset offers robust results and supports its use for drought monitoring. The new system and dataset are publicly available at the link https://global-drought-crops.csic.es/.This work was supported by projects PCI2019-103631 financed by the Spanish Commission of Science and Technology and FEDER and CROSSDRO project funded by AXIS (Assessment of Cross [X]- sectoral climate Impacts and pathways for Sustainable transformation), JPI- Climate co- funded call of the European Commission.Peer reviewe

Effect of reservoirs on streamflow and river regimes in a heavily regulated river basin of Northeast Spain

41 Pags.- 5 Tabls.- 13 Figs. The definitive version is available at: http://www.sciencedirect.com/science/journal/03418162Dams modify downstream hydrology because they alter natural river regimes and divert river flows. The Segre Basin is one of the main tributaries of the Ebro River in Northeastern Spain, and has a drainage area of 13,000 km2. In this study, we used data on long-term (1951–2013) river flows and climatic series to analyze the downstream cumulative effect of dams on natural river regimes and the disassociation between changes in climate and runoff in the Segre Basin. The headwaters of this basin are in the Pyrenees Mountains, and water flow has been highly regulated since the second half of the twentieth century due to the construction of numerous dams. We compared long-term monthly averages of upstream and downstream sectors, and assessed the relationship between the climatic and hydrological time series. Our results show that the progressive increase of the impounded ratio index (reservoir capacity) increased the disassociation between climate and runoff. This markedly exacerbated the negative trend in downstream runoff, so this decline that cannot be solely explained by climatic changes. Our results provide evidence that reservoirs can cause a significant decline in downstream runoff and significant alterations of natural river regimes.This work was supported by the research projects PCIN-2015-220, CGL2014-52135-C03-01 and Red de variabilidad y cambio climático RECLIM (CGL2014-517221-REDT) financed by the Spanish Commission of Science and Technology and FEDER and “LIFE12 ENV/ES/000536-Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)” financed by the LIFE programme of the European Commission.Peer reviewe

Gap Filling of Monthly Temperature Data and Its Effect on Climatic Variability and Trends

25 Pags.- 10 Figs.- 5 Tabls. Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-19-0244.s1. © 2019 American Meteorological Society. This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).Observational datasets of climatic variables are frequently composed of fragmentary time series covering different time spans and plagued with data gaps. Most statistical methods and environmental models, however, require serially complete data, so gap filling is a routine procedure. However, very often this preliminary stage is undertaken with no consideration of the potentially adverse effects that it can have on further analyses. In addition to numerical effects and trade-offs that are inherent to any imputation method, observational climatic datasets often exhibit temporal changes in the number of available records, which result in further spurious effects if the gap-filling process is sensitive to it. We examined the effect of data reconstruction in a large dataset of monthly temperature records spanning over several decades, during which substantial changes occurred in terms of data availability. We made a thorough analysis in terms of goodness of fit (mean error) and bias in the first two moments (mean and variance), in the extreme quantiles, and in long-term trend magnitude and significance. We show that gap filling may result in biases in the mean and the variance of the reconstructed series, and also in the magnitude and significance of temporal trends. Introduction of a two-step bias correction in the gap-filling process solved some of these problems, although it did not allow us to produce completely unbiased trend estimates. Using only one (the best) neighbor and performing a one-step bias correction, being a simpler approach, closely rivaled this method, although it had similar problems with trend estimates. A trade-off must be assumed between goodness of fit (error minimization) and variance bias.This work has been supported by the research projects CGL2014-52135-C3-1-R, CGL2014-52135-C3-3-R, CGL2017-83866-C3-1-R, CGL2017-83866-C3-3-R, and PCIN-2017-020, financed by the Spanish Commission of Science and Technology and EU ERDF, and INNOMED financed by the ERA-NET WaterWorks 2015 cofunded call of the European Commission, which is an integral part of the 2016 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI) as a result of a joint collaborative effort with the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE JPI), and funded by Agencia Estatal de Investigación of Spain, Research Promotion Foundation of Cyprus, Agence Nationale de la Recherche and Office national de l’eau et des milieux aquatiques of France, Ministry for Education, University and Research of Italy, Center of International Projects of Moldova, and Foundation for Science and Technology of Portugal with cofunding by the European Union. R.S.N. is funded by postdoctoral Grant FJCI-2017-31595 of the Juan de la Cierva Programe, funded by the Spanish Ministry of Science, Innovation and Universities, and EU ERDF.Peer reviewe

Computation of rainfall erosivity from daily precipitation amounts

42 Pags.- 2 Tabls.- 16 Figs. The definitive version is available at: https://www.sciencedirect.com/science/journal/00489697Rainfall erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known erosivity indices. One issue with this and other erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases.This work has been supported by research projects CGL2014-52135-C3-1-R andCGL2017-83866-C3-3-R, financed by the Spanish Ministerio de Economía, Industria y Competitividad (MINECO) and EU-FEDER. The work of M. Tomas-Burguera was supported by apredoctoral grant under the FPU program 2013 of the Spanish Ministerio de Educación, Cultura y Deporte.Peer reviewe