Standardized Precipitation Evaporation Index Research Articles

Prediction of droughts over Pakistan using machine learning algorithms

Climate change has increased frequency, severity and areal extent of droughts across the world in the last few decades magnifying their adverse impacts. Prediction of droughts is immensely helpful in early warning and preparing the most vulnerable communities to their adverse impacts. For the first time, this study investigated the potential of developing drought prediction models over Pakistan using three state-of-the-art Machine Learning (ML) techniques; Support Vector Machine (SVM), Artificial Neural Network (ANN) and k-Nearest Neighbour (KNN). Three categories of droughts; moderate, severe, and extreme considering two major cropping seasons called Rabi and Kharif were estimated using Standardized Precipitation Evaporation Index (SPEI) and then predicted using the predictor data obtained from the National Centres for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) reanalysis database. Also, for the first time in drought modelling, a novel feature selection approach called Recursive Feature Elimination (RFE) was used for identifying optimum sets of predictors. In validation, SVM-based models were able to better capture the temporal and spatial characteristics of droughts over Pakistan compared to those by ANN and KNN-based models. KNN which was used in developing drought models for the first time displayed limited performance in comparison to that by SVM and ANN-based drought models, in validation. It was found that in the Rabi season SPEI is positively correlated with relative humidity over the Mediterranean Sea and the region north of the Caspian Sea. In the Kharif season, SPEI is positively correlated with the humid region over the south-eastern part of the Bay of Bengal and the regions north of the Mediterranean and Caspian Seas. In developing a drought prediction model for Pakistan, relative humidity, temperature and wind speed should be considered with a domain which encompasses the Mediterranean Sea, the region north of the Caspian Sea, the Indian Ocean and the Arabian Sea.

Responses of the Terrestrial Ecosystem Productivity to Droughts in China

The terrestrial ecosystem productivity (hereafter, TEP) is a key index of global carbon cycles and a fundamental constraint of carbon sequestration capacity, and also an important measure of ecosystem services and food security. However, the TEP has been significantly affected by the long-lasting droughts. Identifying the spatial relationship between droughts and the TEP is crucial for enhancing ecosystem services in China. Here the net primary production (hereafter, NPP) derived from the Carnegie-Ames-Stanford Approach model (CASA-NPP) and two drought indices, namely the Standard Precipitation Index (hereafter, SPI) and the Standard Precipitation Evaporation Index (hereafter, SPEI), are used to examine the spatial relationship between droughts and the NPP in China for the period of 1982-2012. Our main results have shown that: (1) China’s annual NPP has increased slowly from 3.82 to 4.35 PgC per year (hereafter, PgC/yr), while droughts have become much severer from 1982 to 2012; (2) On the 3-month timescale, the NPP in arid and semi-arid ecosystems has decreased at a rate of 1.28 TgC per month with per “unit” decrease in the drought index (indicating drier conditions). (3) Overall, the NPP in China has increased 5.71 TgC per month with per “unit” increase in the drought index (indicating wetter conditions); The contribution of this NPP increase is mainly from forests and farmlands; (4) The SPEI is a relatively more effective and sensitive index in representing China’s droughts. In southern China, the lagging period for the NPP response to droughts is about 3-month, while a 6-month lagging period is found in the arid and semi-arid ecosystems in northern China.

An approach to characterise spatio-temporal drought dynamics

The spatiotemporal monitoring of droughts is a complex task. In the past decades, drought monitoring has been increasingly developed, while the consideration of its spatio-temporal dynamics is still a challenge. This study proposes a method to build the spatial tracks and paths of drought, which can enhance its monitoring. The steps for the drought tracks calculation are (1) identification of spatial units (areas), (2) centroids localisation, and (3) centroids linkage. The spatio-temporal analysis performed here to extract the areas and centroids builds upon the Contiguous Drought Area (CDA) analysis. The potential of the proposed methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. The method to calculate the drought tracks allows for identification of drought paths delineated by an onset and an end in space and time. Tracks, severity and duration of the drought are identified, as well as localisation (onset and end position), and rotation. The response of the drought tracking method to different combinations of parameters is also analysed. Further research is in progress to set up a model to predict the drought tracks for particular regions across the world, including India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).

Spatiotemporal Trends and Attribution of Drought across China from 1901–2100

Investigating long-term drought trends is of great importance in coping with the adverse effects of global warming. However, little attention has been focused on studying the detailed spatial variability and attribution of drought variation in China. In this study, we first generated a 1 km resolution monthly climate dataset for the period 1901–2100 across China using the delta spatial downscaling method to assess the variability of the Standardized Precipitation Evaporation Index (SPEI). We then developed a simple approach to quantifying the contributions of water supply (precipitation) and demand (potential evapotranspiration, PET) on SPEI variability, according to the meaning of the differentiating SPEI equation. The results indicated that the delta framework could accurately downscale and correct low-spatial-resolution monthly temperatures and precipitation from the Climatic Research Unit and general circulation models (GCMs). Of the 27 GCMs analyzed, the BNU-ESM, CESM1-CAM5, and GFDL-ESM2M were found to be the most accurate in modeling future temperatures and precipitation. We also found that, compared with the past (1901–2017), the climate in the future (2018–2100) will tend toward significant droughts, although both periods showed a high spatial heterogeneity across China. Moreover, the proportion of areas with significantly decreasing SPEI trends was far greater than the proportion of those with increasing trends in most cases, especially for northwestern and northern China. Finally, the proposed approach to quantifying precipitation and PET contributions performed well according to logical evaluations. The percentage contributions of precipitation and PET on SPEI variability varied with study periods, representative concentration pathway scenarios, trend directions, and geographic spaces. In the past, PET contributions for significant downward trends and precipitation contributions for significantly upward trends accounted for 95% and 72%, while their future contributions were 57 ± 22%–149 ± 20% and 95 ± 27%–190 ± 58%, respectively. Overall, our results provide detailed insights for planning flexible adaptation and mitigation strategies to cope with the adverse effects of climate drought across China.

The climatic response of baldcypress (Taxodium mucronatum Ten.) in San Luis Potosi, Mexico

Baldcypress, a long-lived species of riparian habitats, is sensitive to interannual climate variability. The annual radial growth is associated with the Standardized Precipitation Evaporation Index (SPEI) and Palmer Drought Severity Index (PDSI), both depending on a water balance. The baldcypress (Taxodium mucronatum Ten.) is a long-lived species and Mexico’s national tree. The objective of this study was to analyze the dendroclimatic response of baldcypress at sites located on the transitional zone between the semiarid and subtropical regions of Mexico, known as the Middle Zone of the state of San Luis Potosi, Mexico. The first site, Los Peroles (LPO), is a wetland where the oldest known baldcypress specimens in Mexico are found. The second site, Rio Verde (RVDE), is a riparian site located along a tributary of the Moctezuma River that drains into the Gulf of Mexico. Two ring-width chronologies were analyzed. The first one from LPO with a length of 633 years and the second one from RVDE with a length of 423 years. The species responded positively to the seasonal January–July precipitation (r = 0.52, p = 0.001) and negatively to the maximum monthly temperature of January, May, and June (r = − 0.42, −0.46, and −0.34; respectively). The average chronology was significantly associated with the mean January–August Standardized Precipitation Evaporation Index (SPEI) (r = 0.62, p = 0.000). An SPEI reconstruction was developed for the period 1575–1996 where the most prolonged and severe droughts were found at 70-year peaks in the El Nino Southern Oscillation frequency band. The annual radial growth of the species is a suitable indicator of the January–August water availability, and is important information that can be used to establish water management strategies for this semiarid environment and to preserve millennial baldcypress specimens.

A novel generalized combinative procedure for Multi-Scalar standardized drought Indices-The long average weighted joint aggregative criterion

Drought hazards have complex climatic and spatio-temporal features. Therefore, its accurate monitoring is a challenging task in hydrological research. In recent, the use of standardized drought indices for drought monitoring is common in practice. However, the existence of several drought indices creates chaotic problems in data mining and decision making. This article presents a new weighting scheme for combining multiple drought indices. We propagated steady-state probabilities of Markov chain as weights in the Probabilistic Weighted Joint Aggregative Index (PWJADI) criterion. Hence, to aggregate drought characterization two or more indices, averaged long term behavior of drought classification states observed in the individual drought index is considered as a weighting characteristic. The proposed algorithm is rather general and can be used for any standardized type of drought indices. The new procedure is named as Long Averaged Weighted Joint Aggregative Criterion (LAWJAC). In this research, we focused on the three multi-scalar drought indices namely, Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Standardized Precipitation Temperature Index (SPTI). The selection of these indices is due to their similar computational procedures. In the evolution of LAWJAC, three meteorological stations of the Northern Area of Pakistan are considered. A comparison of LAWJAC is made with PWJADI. Results show significant deviations between existing and proposed methods. By the rationale of the proposed algorithm, these deviations strongly advocate the use of LAWJAC for more accuracy in drought characterization.

Characterisation of the dynamics of past droughts

Drought is a complex natural phenomenon. The description of the way in which drought changes (moves) in space may help to acquire knowledge on its drivers and processes to improve its monitoring and prediction. This research presents the application of an approach to characterise the dynamics of drought. Tracks, severity, duration, as well as localisation (onset and end position), and rotation of droughts were calculated. Results of calculated droughts were compared with documented information. Data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor was used to identify droughts in India as an example for the period 1901–2013. Results show regions where droughts with considerable coverage tend to occur. Paths, i.e. consecutive spatial tracks, of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Results of this research are being used to build a model to predict the spatial drought tracks, incl. India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).

Analysis for Spatio-Temporal Variation Characteristics of Droughts in Different Climatic Regions of the Mongolian Plateau Based on SPEI

Continuous climate warming in the last few decades has led to global climate anomalies, resulting in frequent drought events in arid/semiarid regions with fragile and sensitive ecological environment. The Mongolian Plateau (MP) is located at the mid-latitude arid/semiarid climate region, which is deemed as the most sensitive region in response to global climate change. In order to understand the spatiotemporal characteristics of droughts in Mongolian Plateau under changing climate, we divided the study area into three climatic regions via Köppen climate classification. Then, the seasonal and annual drought trends were analyzed by standardized precipitation evaporation index (SPEI), which is a function of monthly mean temperatures, highest temperatures, lowest temperatures and precipitations, collected from the 184 meteorological stations from 1980 to 2015. Mann–Kendall (MK) test was employed to detect if there is an abrupt change of annual drought, while the empirical orthogonal function method (EOF) was adopted to investigate the spatiotemporal characteristics of droughts across the Mongolian Plateau. Results from MK test illustrated that the SPEI-12 exhibited statistically significant downward trends (a < 0.05) for all three climatic regions of the Mongolian Plateau. EOF spatial analysis indicated that Region III experienced the most severe drought from 1980 to 2015. During the 35 years period, an abrupt change of drought was detected in 1999. Before year 1999, the climate was relatively humid. However, the entire region became more arid after year 1999, reflected by remarkably increased frequency and intensity of drought. SPEI-3 revealed the trend of drought at seasonal scale. We found that drought became more severe in spring, summer, and fall seasons for the entire MP. However, winter became more humid. Different climate regions exhibited quite different drought seasonality: Region I experienced a severe arid trend in summer and fall. For Region II and III, summer became more arid. All three regions became more humid in winter season, especially for Region I, with the Sen’s slope of 0.0241/a.

Investigating relationship between drought severity in Botswana and ENSO

Influences of El Nino southern oscillation (ENSO) on weather systems have increased the frequency and amplitude of extreme events over the last century. This even continues to exacerbate the already warming earth, with 2014–2016 which coincided with the strongest El Nino years observed as the warmest period in recent past. This study presents an approach of characterizing droughts at various timescales and establishes teleconnections between ENSO and drought severity in Botswana. The study uses Standardized Precipitation Evaporation Index (SPEI) at timescales of 1, 3, 6, 12 and 24 month to characterize droughts and Pearson’s correlations to study the teleconnections between SPEIs and ENSO. Results from the study reveal that extreme droughts are a rare occurrence in Botswana though it is more prone to moderate droughts at 12 month SPEI with a probability of 19% in the north. The highest severe drought probability was 7% recorded in the east. Linear trends indicate increasing dryness of around 0.8% per decade. These results have demonstrated that warm sea surface temperatures combined with negative Southern Oscillation Index correspond to persistent negative SPEI values and thus are likely to result in dry conditions. Significant correlations were observed in the mid austral summer in December and January. Due to this relationship, the drought early warning systems could use ENSO as one of the instruments for predicting drought over the study area and hence in its management.

The Temporal Variability of Rainfall and Streamflow into Lake Nakuru, Kenya, Assessed Using SWAT and Hydrometeorological Indices

Temporal variability analysis of rainfall and river discharges is useful in determining the likelihood of the occurrence of extreme events such as drought or flooding for the purposes of developing policies to mitigate their effects. This study investigated the temporal variability of rainfall and discharges into Lake Nakuru, Kenya using meteorological drought indicators and hydrological drought indicators from 1981 to 2018. The standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) were used to characterize meteorological drought, while the streamflow drought index (SDI) was used to characterize hydrological drought. A SWAT model was applied for the prediction of streamflow on five tributaries of Lake Nakuru (Njoro, Ngosur, Nderit, Larmudiac, and Makalia Rivers). The model was successfully calibrated on Njoro River at the upstream of river gauging station 2FCO5 from 1984 to 1996, and the parameters were validated from 1997 to 2007. The SUFI-2 algorithm was applied in SWATCup to perform the calibration of the model. The model performance was considered satisfactory in daily time step (NSE = 0.58, R2 = 0.58 during calibration and NSE = 0.52, R2 = 0.68 during validation). The average annual water balance revealed that out of 823 mm received annual precipitation, 154 mm was surface runoff and 178 mm was the annual average water yield. The average annual actual evapotranspiration (ET) was 607 mm. The results for the temporal variation of the SPI and SDI for the five subcatchments indicated that the drought events identified by the 12-month SPI/SPEI were almost all identified by the 12-month SDI. At the catchment scale, SPI showed an equal distribution of wet and dry periods, with 50.00% of positive anomalies and 50.00% of negative anomalies being observed from 1981 to 2018, while SDI observes a high frequency of dry periods (52.63%) and a lower frequency of wet periods (47.37%). There is a higher frequency of wet periods compared to dry periods for both indices from 2009 to 2010 at 60.00% and 40.00% for SPI and 90.00% and 10.00% for SDI, respectively. Both indices observed that 1984 and 2000 were severely dry years (SPI/SPEI < −2.00), while 2018 was severely wet (SPI/SPEI > 2.00). The results for the variability in rainfall and streamflow indices revealed that the last 10 years (2009–2018) were wetter than the period from 1981 to 2008.

Spatio-temporal variation of drought characteristics, water resource availability and the relation of drought with large scale climate indices: A case study of Jhelum basin, India

With the intensification of global warming and anthropogenic activities, warming or drying episodes with respect to water deficit has brought severe challenges across the globe. It becomes imperative to understand the drought characteristics as well as water resource availability in such regions. In this paper, we employ trend analysis to investigate the tendency of Potential Evapotranspiration (PET) and Climatic Water Balance (CWB) from 1979 to 2017 in the Jhelum river basin. We applied Thornthwaite parameterization which is based on latitude and monthly mean temperature for the computation of PET. The results showed that annual PET of Srinagar and Quazigund stations has increasing tendency while annual CWB has decreasing tendency for all the selected stations in the basin. The trend test applied to monthly PET and CWB indicate that monthly PET has increasing tendency in most of the months while monthly CWB has decreasing tendency in most of the months for all the selected stations in the basin. In addition to this, the drought events were characterized using Standardized Precipitation Evaporation Index (SPEI) based on 3, 6 and 12-month time scales. The SPEI was also applied to investigate the spatio-temporal characteristics of drought during the study period in the basin. The spatial patterns of drought severity were derived based on the Average Recurrence Interval (ARI). The temporal analysis revealed that an average of 65 long term, 69 intermediate term and 74 short term drought events were observed across the basin. However the spatial characteristics indicated that drought becomes more severe with long return periods in the western part of the basin. This study also investigated the influence of large scale climatic variables on 3 month SPEI over the basin using cross wavelet analysis. The results show that the drought characteristics are significantly influenced by both the ENSO and ENSO-M events throughout the Jhelum basin.

The synergy between drought and extremely hot summers in the Mediterranean

In the last years a large number of weather driven extreme events has occurred worldwide with unprecedented socio-economic impacts and are expected to increase, in both frequency and intensity, under future global-warming conditions. In this context early identification and predictability of such events are paramount as they mostly affect several socio-economic activities. Despite the effort in monitoring and evaluation of these extreme events, a quantitative assessment of their interaction is still a challenge. We propose to analyze if the occurrence of extremely hot days/nights in the summer is preceded by drought events in spring and early summer throughout the Mediterranean area. This was investigated by correlating the number of hot days and nights in the regions hottest months with a drought indicator on the prior months. Drought characterization was performed using both the Standardized Precipitation Evaporation Index (SPEI) and the Standardized Precipitation Index (SPI) for the 3-, 6- and 9-months time scales, considering the period 1980–2014 with a spatial resolution of 0.5°. The number of hot days and nights per month (NHD and NHN, respectively) is determined for the same period and spatial resolution. Results show that the most frequent hottest months for the Mediterranean region occur in July and August. Most regions exhibit statistically significant negative correlations, i.e. high (low) NHD/NHN following negative (positive) SPEI/SPI values, and thus a potential for NHD/NHN early warning. This analysis allowed to identify the Iberian Peninsula, northern Italy, northern Africa and the Balkans as the main hotspots of predictability of extreme hot temperatures in the summer preceded by the occurrence of drought events in the spring or early summer.

Assessment of geostatistical methods for spatiotemporal analysis of drought patterns in East Texas, USA

Drought is one of the most complex and least understood climate-related natural hazards. Active drought mitigation and contingency plan formulation often require a reliable drought distribution map. This study analyzed different spatial interpolation techniques to produce drought distribution map in East Texas, USA. Deterministic [inverse distance weighting (IDW) and spline], and geostatistical [ordinary kriging (Gaussian (KG) and spherical (KS))] interpolation techniques were employed as candidate methods for evaluation. Thirty-four years (1980–2013) of weather station data (N = 47) were used to calculate a 12-month Standardized Precipitation Evaporation Index (SPEI). The dataset was randomly divided into test data (70%, N = 33) and validation data (30%, N = 14). The resulting SPEI maps were cross-checked and validated through a validation dataset by calculating error matrices. The results indicate that KG tends to perform well in relatively drier conditions while IDW shows mixed results, performing well both in dry and wet conditions. The overall power of the four techniques to map 12-month drought conditions resulted in the order of IDW > KG > KS > spline.

STANDARDIZED PRECIPITATION EVAPOTRANSPIRATION INDEX (SPEI) AS A TOOL TO DETERMINE THE HYDROLOGICAL DYNAMIC OF PLAIN REGIONS (ARGENTINA)

The Standardized Precipitation Evaporation Index (SPEI) in a 12-months timescale was used to define wet, normal and dry cycles in four representative sites of climatic sub regions of the Central Plains of Argentina. The studied period was 1980-2016. Hydrological dynamic was defined by analysis of shallow lakes areal variations and hydrological deficits and exceeds calculation. Data ranges and frequency of data were statistically analyzed. Along the studied period, the frequency of normal and humid years was higher than the dry ones. Nineteen eighty-five, 2008 and 2009 were some dry years, defined by SPEI. Effectively, regarding hydrological dynamic, there was noted a decrease (from 0.9 % to 96 %) of the shallow lakes areas during the aforementioned periods, in relation with their area during the humid ones. Hydrological deficit were noted during the 1995 and 2008 dry years in the site of the north and the rest of the sites, respectively. Also, exceeds amounts showed a response to the climatological cycles, during extremely wet years (i.e.: 1998 (NW of the studied area), 2001 (center), 2002 (NE) and 2009, in the northern site).

Comparison of the Performance of Six Drought Indices in Characterizing Historical Drought for the Upper Blue Nile Basin, Ethiopia

The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system. This study compares six drought indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Soil Moisture Deficit Index (SMDI), Aggregate Drought Index (ADI), and Standardized Runoff-discharge Index (SRI), and evaluates their performance with respect to identifying historic drought events in the UBN basin. The indices were calculated using monthly time series of observed precipitation, average temperature, river discharge, and modeled evapotranspiration and soil moisture from 1970 to 2010. The Pearson’s correlation coefficients between the six drought indices were analyzed. SPI and SPEI at 3-month aggregate period showed high correlation with ETDI and SMDI (r > 0.62), while SPI and SPEI at 12-month aggregate period correlate better with SRI. The performance of the six drought indices in identifying historic droughts: 1973–1974, 1983–1984, 1994–1995, and 2003–2004 was analyzed using data obtained from Emergency Events Database (EM-DAT) and previous studies. When drought onset dates indicated by the six drought indices are compared with that in the EM-DAT. SPI, and SPEI showed early onsets of drought events, except 2003–2004 drought for which the onset date was unavailable in EM-DAT. Similarly, ETDI, SMDI and SRI-3 showed early onset for two drought events and late onsets in one-drought event. In contrast, ADI showed late onsets for two drought events and early onset for one drought event. None of the six drought indices could individually identify the onsets of all the selected historic drought events; however, they may identify the onsets when combined by considering several input variables at different aggregate periods.

Developing drought impact functions for drought risk management

Abstract. Drought management frameworks are dependent on methods for monitoring and prediction, but quantifying the hazard alone is arguably not sufficient; the negative consequences that may arise from a lack of precipitation must also be predicted if droughts are to be better managed. However, the link between drought intensity, expressed by some hydrometeorological indicator, and the occurrence of drought impacts has only recently begun to be addressed. One challenge is the paucity of information on ecological and socioeconomic consequences of drought. This study tests the potential for developing empirical drought impact functions based on drought indicators (Standardized Precipitation and Standardized Precipitation Evaporation Index) as predictors and text-based reports on drought impacts as a surrogate variable for drought damage. While there have been studies exploiting textual evidence of drought impacts, a systematic assessment of the effect of impact quantification method and different functional relationships for modeling drought impacts is missing. Using Southeast England as a case study we tested the potential of three different data-driven models for predicting drought impacts quantified from text-based reports: logistic regression, zero-altered negative binomial regression (hurdle model), and an ensemble regression tree approach (random forest). The logistic regression model can only be applied to a binary impact/no impact time series, whereas the other two models can additionally predict the full counts of impact occurrence at each time point. While modeling binary data results in the lowest prediction uncertainty, modeling the full counts has the advantage of also providing a measure of impact severity, and the counts were found to be reasonably predictable. However, there were noticeable differences in skill between modeling methodologies. For binary data the logistic regression and the random forest model performed similarly well based on leave-one-out cross validation. For count data the random forest outperformed the hurdle model. The between-model differences occurred for total drought impacts and for two subsets of impact categories (water supply and freshwater ecosystem impacts). In addition, different ways of defining the impact counts were investigated and were found to have little influence on the prediction skill. For all models we found a positive effect of including impact information of the preceding month as a predictor in addition to the hydrometeorological indicators. We conclude that, although having some limitations, text-based reports on drought impacts can provide useful information for drought risk management, and our study showcases different methodological approaches to developing drought impact functions based on text-based data.

Evaluating climate signal recorded in tree-ring δ13 C and δ18 O values from bulk wood and α-cellulose for six species across four sites in the northeastern US.

We evaluated the applicability of tree-ring δ13 C and δ18 O values in bulk wood - instead of the more time and lab-consuming α-cellulose δ13 C and δ18 O values, to assess climate and physiological signals across multiple sites and for six tree species along a latitudinal gradient (35°97'N to 45°20'N) of the northeastern United States. Wood cores (n=4 per tree) were sampled from ten trees per species. Cores were cross-dated within and across trees at each site, and for the last 30years. Seven years, including the driest on record, were selected for this study. The δ13 C and δ18 O values were measured on two of the ten trees from the bulk wood and the α-cellulose. The offsets between materials in δ13 C and δ18 O values were assessed. Correlation and multiple regression analyses were used to evaluate the strength of the climate signal across sites. Finally the relationship between δ13 C and δ18 O values in bulk wood vs α-cellulose was analyzed to assess the consistency of the interpretation, in terms of CO2 assimilation and stomatal conductance, from both materials. We found offsets of 1.1‰ and 5.6‰ between bulk and α-cellulose for δ13 C and δ18 O values, respectively, consistent with offset values reported in the literature. Bulk wood showed similar or stronger correlations to climate parameters than α-cellulose for the investigated sites. In particular, temperature and vapor pressure deficit and standard precipitation-evaporation index (SPEI) were the most visible climate signals recorded in δ13 C and δ18 O values, respectively. For most of the species, there was no relationship between δ13 C and δ18 O values, regardless of the wood material considered. Extraction of α-cellulose was not necessary to detect climate signals in tree rings across the four investigated sites. Furthermore, the physiological information inferred from the dual isotope approach was similar for most of the species regardless of the material considered.

Ecosystem Resilience to Drought and Temperature Anomalies in the Mekong River Basin

Climate change is leading to an increasing in the frequency and intensity of extreme weather events, which significantly affect ecosystems stability. In this study, ecological stability metrics in response to wet/dry events and warm/cold events on vegetation greenness were assessed using an auto-regressive model of NDVI in the Mekong River basin (around 759,000 km2) where large ecological and climatic gradients exist. Gridded temperature, and the Global Standard Precipitation Evaporation Index (SPEI) and antecedent NDVI were used as model predictors. The forest in north Laos was more resilient to the temperate and wet/dry anomalies events than other regions in the basin. Drought reduced green biomass in north Laos, northeast Thailand and Myanmar, but in these tropical climate regions’ the vegetation biomass was also more responsive by higher temperatures. Vegetation in northeast Thailand, Cambodia and the Mekong delta were less sensitive to the temperature anomalies effect compared to other part of Mekong River basin. The map of resistance and resilience metrics can help to determine the most vulnerable regions to extreme events for policy makers.

Drought Trends in the Iberian Peninsula over the Last 112 Years

The Iberian Peninsula (IP) is a drought-prone area located in the Mediterranean which presents a significant tendency towards dryness during the last decades, reinforcing the need for a continuous monitoring of drought. The long-term evolution of drought in the IP is analyzed, using the Standardized Precipitation Evaporation Index (SPEI) and the Standardized Precipitation Index (SPI), for the period of 1901–2012 and for three subperiods: 1901–1937, 1938–1974, and 1975–2012. SPI and SPEI were calculated with a 12-month time scale, using data from the Climatic Research Unit (CRU) database. Trends in the drought indices, precipitation, and reference evapotranspiration (ET0) were analysed and series of drought duration, drought magnitude, time between drought events, and mean intensity of the events were computed. SPI and SPEI significant trends show areas with opposite signals in the period 1901–2012, mainly associated with precipitation trends, which are significant and positive in the northwestern region and significant and negative in the southern areas. Additionally, SPEI identified dryer conditions and an increase in the area affected by droughts, which agrees with the increase in ET0. The same spatial differences were identified in the drought duration, magnitude, mean intensity, and time between drought events.

Millennium-Long Tree-Ring Chronology Reveals Megadroughts on the Southeastern Tibetan Plateau

ABSTRACT Millennium-aged trees are rare in natural forests. Here we present an 1184-year-long tree-ring width chronology from living juniper trees in the Biru area on the southeastern Tibetan Plateau. Growth-climate response analysis shows that the Biru chronology is significantly and positively correlated with late-spring (May-June) Standardized Precipitation Evaporation Index (SPEI) (r = 0.67, n = 53, p < 0.01). The tree-ring chronology explains 44.5% of the total variance of SPEI during the period AD 1957–2010. Reconstruction of May-June SPEI shows that there was a two-century-long megadrought during the late 13th to late 15th Centuries, and a seven-decade-long megadrought during AD 1630s to 1690s. Comparisons with other moisture records in the region suggest that the two-century megadrought identified in our reconstruction might be a widespread phenomenon most likely reflecting a stage of reduced Southwest Asian Summer Monsoon. Our results provide new evidence on the megadrought events on the Tibetan ...