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ABSTRACT It has been broadly reported that future climate change will most likely affect the spatio‐temporal distribution of water resources and consequently droughts. There is a prevailing notion that an increase in temperature and frequency of heat waves are expected to result in more intense droughts in the coming years. In this study, we aimed to evaluate the effect of the potential evapotranspiration (PET) method selection on future drought projections over Poland. In our study, simulations of the Soil and Water Assessment Tool (SWAT) model were conducted, utilising an ensemble of six EURO‐CORDEX projections, spanning the period from 2006 to 2100 under the RCP8.5 scenario. Two model setups with two different PET methods (Penman‐Monteith—PM and Hargreaves—HAR) were used. For drought conditions evaluation we selected the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) for meteorological drought, Standardized Streamflow Index (SSI) for hydrological drought, and Standardized Soil Moisture Index (SMI) for agricultural drought. The meteorological and hydrological droughts were calculated using a 12‐month time aggregation window, while agricultural drought was calculated using a 3‐month window. Climate projections revealed that by 2080s annual mean temperature and precipitation increase is expected by up to +3.4°C and +10.3% respectively. Under future climate conditions duration and severity of meteorological droughts are projected to decrease. PM method leads to a higher PET increases (1.35 mm year−1) than the HAR method (1.1 mm year−1) throughout the century which entail diverging signal of change for agricultural and hydrological droughts. PM‐ and HAR‐based simulations indicate increase in the total duration and cumulative severity of agricultural droughts, buthowever, for HAR‐based projections, the increase is much less. For hydrological droughts the signal of change is similar for both PET methods, but considerably distinct in magnitude. Considering the entire simulation period, by the end of the century cumulative severity of hydrological droughts is projected to decrease, with a much more pronounced decline for HAR (70% reduction) than for the PM method (35% reduction). Our study demonstrated that methodological choices are crucial to the assessment of future drought risk under climate change. [ABSTRACT FROM AUTHOR]

In this study, we propose a probabilistic metric, the formative drought rate (FDR), to quantify drought propagation. It is the probability that a meteorological drought in precipitation forms a hydrological drought in streamflow. Drought events were identified based on the standardized precipitation index and streamflow drought index, respectively, at 1‐, 3‐, 6‐ and 12‐month timescales. The method was tested in three river basins in Turkey (Kucuk Menderes, Gediz and Ergene). In each river basin, meteorological stations were coupled with streamflow gauging stations to form pairs of stations depending on their distance from each other and the length of their common record periods. The FDR was calculated across all timescales for each pair of stations. It was found capable to describe the river basin‐specific spatial and temporal variability of drought propagation. As the FDR is defined in the form of probability, it is expected to be a useful metric for quantifying propagation from meteorological to hydrological drought. Thus, it carries a potential for scientific research and practice in water resources management. [ABSTRACT FROM AUTHOR]

Climate change modifies monsoon rainfall patterns, leading to significant impacts on future meteorological droughts. This study investigated the spatiotemporal pattern of future precipitation, drought features, and the dynamic mechanisms governing their evolution induced by teleconnected atmospheric circulation in the Haihe River Basin, situated in the north China monsoon region. The investigation was conducted under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 scenarios of CMIP6, utilizing the Standardized Precipitation Index and an improved run theory at seasonal and annual scales. The findings revealed that high‐emission scenarios cause a wetter climate trend, particularly in autumn and winter, related to the attenuation of the East Asian Trough from climate warming. The uneven intra‐annual precipitation patterns within the basin, induced by the Asian monsoon climate, are expected to improve in the far future, particularly under the SSP2‐4.5 scenarios. The drought frequency, intensity, and duration of future meteorological drought are expected to improve compared to historical conditions under all three scenarios. SSP2‐4.5 shows the shortest duration and lightest intensity of annual droughts. Extreme droughts are primarily driven by strong subsidence with low‐level moisture divergence. However, in the far future, the eastward shift of the western Pacific subtropical high results in reducing moisture transport from the western Pacific, leading to increasing contribution of advection term to atmospheric drying, particularly under the SSP1‐2.6. In winter, the increase in radiative forcing leads to enhanced evapotranspiration from the Eurasian continent, providing additional moisture and alleviating drought, especially in the far future under the SSP1‐2.6 and SSP2‐4.5 scenarios. Plain Language Summary: Climate change is causing changes in rainfall patterns during monsoon season, which will affect droughts in the future. This study looked at how rainfall, wet and dry trends, and droughts will change in the Haihe River Basin in north China from 2025 to 2099 under different climate scenarios. The study found that models predicting high greenhouse gas emissions show more rainfall and a trend toward wetter conditions, particularly in autumn and winter. The scenario with moderate emissions shows the shortest and lightest droughts. The study also found that droughts are caused by subsidence and low‐level moisture divergence, and in the future, the reduction of moisture transport from the western Pacific will contribute to more atmospheric drying. Finally, during winter, increased radiative forcing causes significant moisture supply from evapotranspiration originating from the Eurasian continent during the drought period. Overall, this study shows how climate change will affect droughts in the future and provides important information for policymakers and stakeholders to plan for the future. Key Points: High radiative forcing cause attenuation of East Asia Trough and increased precipitation in north China monsoon regionSSP2‐4.5 scenario shows the shortest duration and lightest intensity of annual droughtsIn far future, eastward shift of western Pacific subtropical high enhances advection's role in atmospheric drying, especially in SSP1‐2.6 [ABSTRACT FROM AUTHOR]

The relationship between biodiversity and the stability of ecosystem functioning over time has been widely studied. The current global context has refloated this topic for biodiversity's role in buffering the effects of different disturbances. In general, the results of these studies show that ecosystem functioning is more stable over time in more diverse systems. However, these results are derived from empirical research on small‐scale studies, where species and disturbances conditions are manipulated. In this work, we used climate and floristic information data obtained from surveys over an extended period on Flooding Pampa grasslands (Argentina) with a remotely sensed indicator of the stability of net primary productivity at a regional scale over a broad temporal range to evaluate the relationship between species diversity and the stability of ecosystem functioning under different water conditions. We found a close correlation between Normalized Difference Vegetation Index responses of natural grasslands and climate variability in the study area. Besides, grasslands with higher species richness and diversity showed greater stability in ecosystem functioning at different water conditions. The results obtained could be relevant in natural resource management for the close relationship between diversity–stability in a local and regional productive context characterized by a simplified landscape of space and time. [ABSTRACT FROM AUTHOR]

Current methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments. Plain Language Summary: Drought is affected by both rainfall and evapotranspiration. Drought indices represent drought severity compared to normal conditions as a function of time. Some drought indices are based on rainfall alone and some use both rainfall and evapotranspiration in the calculations. To understand future drought risk, simulations from climate models are needed. Unfortunately, different climate models often disagree on amounts and patterns of rainfall in the future, the disagreement being considerably more on rainfall than for evapotranspiration. In this study, we attempt to reduce the impact of these differences by developing a new method to estimate future drought. We used a mathematical method known as wavelets to estimate drought indices based on rainfall. Evapotranspiration is then used directly from the climate model and combined with the rainfall based drought index to create one overall drought index. We used projections from multiple climate models to understand if our new method led to a greater agreement in how often and severe future droughts may be. Our results confirm that the new method offers greater consistency in drought projections for the future. Key Points: Current methods for projecting drought ignore differences in uncertainty between P and ET simulationsA new basis for projecting drought is proposed that explicitly accounts for relative uncertainty between P and ETOur results show that the new method offers greater consistency in drought projections for the future [ABSTRACT FROM AUTHOR]

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Water availability is an important driver of bird population change, and its effects are likely to increase in coming decades under climate change. Here we assess effects of temperature, precipitation, and water area on wintering bird populations in Miyangaran Wetland in southwestern Iran. Modeling methods including, generalized linear model (GLM) and hierarchical partitioning were used to examine the relative importance of variables. The number of wintering species, inhabiting the wetland, varied among years, ranging from 10 to 48 species. The total number of wintering birds showed a significant decreasing trend. A significant increasing trend was obtained for shorebirds, while waterfowl species were significantly decreased. The GLM showed that species abundance, richness, and diversity were significantly correlated with the standardized precipitation index (SPI), annual precipitation, and normalized difference water index (NDWI). Hierarchical partitioning analysis also identified NDWI, SPI, and annual precipitation as the most important variables with average independent effects of 35, 36 (p <.01) and 17% (p <.05), respectively. Our results revealed that the water area plays a major role in determining the structure of bird diversity and abundance, affecting both waterfowl and shorebirds. [ABSTRACT FROM AUTHOR]

Drought is a multifaceted climate phenomenon. Due to recent climate warming, the risk of drought has increased. Regional drought management requires accurate regional drought characterization to prepare early warning drought mitigation policies. Therefore, a more flexible and efficient procedure is necessary for characterizing drought at a regional level. This study provides a new spatiotemporal weighting scheme for integrating precipitation data from various meteorological stations located in a specific region. Consequently, this article presents a new generalized regional drought indicator—The Spatiotemporal Weighted Efficient Drought Index (STWEDI). The methodology of STWEDI consists of two stages: (1) The first stage combines the meteorological data of various stations located in a certain region under the proposed weighting scheme, and (2) the second stage standardizes the cumulative distribution function (CDF) of mixture probability models. In application, this research estimated the time series data of STWEDI of seven regions containing a varying number of meteorological stations. To assess the performance of STWEDI, we compared the time series data of STWEDI with the most commonly used Standardized Precipitation Index (SPI) using the Pearson correlation. Results show that STWEDI is strongly correlated with individual SPIs in all the clusters and scales. Moreover, we found that STWEDI is strongly associated with SPI at all time scales. These statistical measures show that STWEDI is the more effective drought indicator for regional drought monitoring. [ABSTRACT FROM AUTHOR]

This study assesses the intensity, spatial distribution and temporal variability of extreme precipitation events (EPEs) on different time scales relevant to agriculture and water resources during the 1901–2016 period over Argentina's Austral Chaco (ACh). EPEs were identified using a nonparametric approach for the Standardized Precipitation Index (SPI). The leading modes of SPI's variability were detected using the Singular Spectrum Analysis. We also compile and control the quality of precipitation observations in a scarcely monitored region. The limited number of stations conditions the interpolation of the GPCC v2018 database here used, mainly during the early twentieth century. The EPEs characterized by SPI at a 24‐month time scale, which favour long‐lasting water excesses or deficits with hydrological impacts, underwent two differentiated long‐term periods: a dry one before 1960 and a wet one from 1960 to 2005. Consistently, seasonal wet EPEs were more frequent during the wet period while seasonal droughts were more frequent and with larger spatial extent during the dry period. Seasonal EPEs, represented by the SPI at 3‐month time scale, can impact agriculture during the crops' critical growth period. Since 2006, the long‐term wetting trend has been reversed. If this behaviour continues into the coming decades, more droughts and less wetness might be expected. The EPEs present a large interannual variability with 6.3‐ and 9‐year significant cycles that, combined with long‐term trends, favoured extremely wet/dry EPEs. Most of the wet/dry EPEs occurred during El Niño/La Niña events. Although this relationship is not straightforward, we found a discriminant ENSO signal between October of the year when an ENSO event starts and February of the next year. Finally, severely dry/wet EPEs tend to have large spatial extent in the ACh region. The central‐eastern ACh region experiences the most extreme wet and dry conditions, which makes this area more prone to extreme EPEs. [ABSTRACT FROM AUTHOR]

We studied spatial and temporal patterns of precipitation–temperature (P–T) relationships through correlations between monthly standardized precipitation index (SPI) and monthly temperature anomalies in individual climatic seasons over Europe. In the observed data (represented by E‐OBS), positive correlations (wet–warm/dry–cold relationships) prevail during winter over most of Europe, while negative values (dry–warm/wet–cold) are dominant in summer. In the next step, an ensemble of seven regional climate models (RCMs) from the CORDEX project driven by the ERA‐Interim reanalysis were examined as to their reproduction of the regional patterns of the P–T correlations. In winter, the RCMs yielded overly strong positive P–T correlations over northern Europe, while the correlations were too weak in the south compared to observed data. During summer, the biases were generally larger; the RCMs were able to capture the overall negative P–T correlations but these tended to be too weak over northern Europe. This deficiency was found to be linked to simulated differences in shortwave radiation (a proxy for cloud cover) between dry and wet months. In western, central, and southeastern Europe, by contrast, most RCMs yielded too strong negative correlations in summer, and overly large decreases of relative humidity during dry months probably contributed to these errors. The results pointed up issues that should be addressed as the reported RCMs' deficiencies may lower credibility of projected compound dry–hot events in climate change scenarios. [ABSTRACT FROM AUTHOR]

Flood is known to be the leading cause of natural disasters globally leaving disastrous and devastating damages in its wake. Flood risk and their time of occurrence is usually difficult to monitor and predict without appropriate tools for continuous monitoring. Extreme and frequent rainfall is one of the major causes of flood disasters. Assessment of flood hazards and its subsequent analysis using the standardized precipitation index (SPI) is very effective in the prediction of flood risk with the strength of the SPI being the use of rainfall as the only input variable. The SPI is found to be valuable in prediction of meteorological and hydrological floods such as flash floods, groundwater floods and dam burst. The knowledge of the occurrence of these flooding events can be very beneficial in preventing extensive damages to property, infrastructure, agriculture, and loss of life. The aim of this study is to assess flood hazard in eThekwini metropolitan area by examining the trend of flood events from 1985 to 2016 by the use of the SPI and its potential to predict flood risk in the study area. The results show that SPI properly explains the development of the conditions leading up to the occurrence of floods events in the analyzed period. Thus, is indispensable in the assessment of flood risk leading to an accurate prediction. The knowledge of the pattern and trends obtained from the SPI analysis is valuable to decision-makers for efficient flood risk management plans in the promotion of preventive actions for mitigating the impacts of floods. [ABSTRACT FROM AUTHOR]

It is a good practice to follow common guidelines in the computation of Standardized Precipitation Index (SPI) data sets as part of operational drought monitoring systems. In the European Drought Observatory (https://edo.jrc.ec. europa.eu/), reference statistics are computed following the World Meteorological Organization Guidelines on the Calculation of Climate Normals, where a definition of the reference period for monitoring applications is introduced as the most recent 30-year period finishing in a year ending with 0. In this study, the temporal consistency of the SPI time series computed using the fifthgeneration European Centre for Medium-range Weather Forecast reanalyses model precipitation data set is tested over Europe to quantify the effect of the transition from one baseline period (1981–2010) to another (1991–2020) and to evaluate the capability of these static baselines to reproduce the behaviour of non-stationary SPIs (nSPIs). The results of the comparison suggest that the threshold commonly used to identify droughts (SPI = −1) is only marginally affected by the change in reference period (mean absolute deviation, MAD = 0.15 ± 0.1) for short-term SPI, whereas larger differences (MAD up to 0.6) can be observed over certain areas (i.e., Southern Italy and Eastern Europe) for longer accumulation periods (i.e., SPI-9 or SPI-12). Examples show that changes in drought classification from extremely dry (SPI < −2) to moderately dry (SPI < −1) are not uncommon, which may lead to misinterpretation by users. Finally, analyses against nSPI highlight an overall good correspondence between stationary and nSPIs, even if both static baselines displayed difficulties in reliably capturing the magnitude of nSPI for the entire 10-year period for which they should be used. In this regard, it has been demonstrated that more spatially uniform results can be achieved with 5-year updates, with a good matching (MAD <0.25) for SPI-1 and an acceptable matching (MAD < 0.50) for SPI-12 over more than 80% of Europe. [ABSTRACT FROM AUTHOR]

Droughts are recurring phenomena in the north‐western Himalaya causing severe socioeconomic hardships. Our understanding on temporal and spatial occurrence of such extreme droughts in long‐term perspective is constrained due to limited short‐term weather records. Toward fulfilling such a large data void, we developed a network of ring‐width chronologies of Himalayan cedar from seven moisture stressed high‐elevation sites in Kishtwar, Jammu & Kashmir. Moisture responsive ring‐width chronologies were used to develop the annual Standardized Precipitation Index (SPI from previous year June to current year May (SPI12‐May)) extending back to 1613 CE. The SPI reconstruction revealed annual to decadal‐scale variability in droughts, which strikingly were more frequent and of larger magnitude during 1760s–2017 CE in comparison to the earlier part of the reconstruction (1613–1750s CE). Our SPI12‐May record revealed strong consistency with other tree‐ring‐based hydrological records from the western Himalaya. Spatial correlation analyses revealed strong linkage of the SPI with Tropical Pacific Ocean surface temperature. Key Points: Annual Standardized Precipitation Index from prior year June to current year May was reconstructed for the first time for Jammu & KashmirHigh‐magnitude droughts were found more frequent since later part of LIA (1760s–2017 CE) in comparison to earlier part (1613–1750s CE)Dry periods observed in early part (1613–1750s CE) were shorter in comparison to that in later part of the reconstruction [ABSTRACT FROM AUTHOR]

We applied the informational approach of the Fisher‐Shannon method to study the time series of Standardized Precipitation Index (SPI) derived from monthly rainfall data at 133 gauging stations in Northeast (NE) Brazil. The climate in the study region ranges from tropical humid in the coastal area Zona de Mata to tropical semiarid inland Sertão, passing through a sub‐humid transition region Agreste. The time span is from 1962 to 2012, and SPI was calculated for 1, 3, 6, and 12 ‐ month time scales. We calculate information quantifiers Shannon entropy power (SEP) and Fisher information measure (FIM) that quantify signal disorder and structural organization, respectively, and define its position in the Fisher Shannon plane (FS). We find that the SPI‐1 series that describes short term dry/wet conditions display a higher degree of organization (higher FIM) and a lower degree of disorder (lower SEP) than SPI's that describe medium and long term conditions (SPI‐3, SPI‐6, and SPI‐12). By analysing the spatial distribution of information quantifiers, we find that short term dry/wet conditions (SPI‐1) show the lowest disorder and highest organization degree in the deep inland Sertão region and highest disorder and lowest organization degree in the coastal Zona de Mata region, while long term conditions (SPI‐12) show the opposite trend. For medium‐term conditions (SPI‐3 and SPI‐6), the SEP is highest and FIM lowest in the transition sub‐humid Agreste region. Our findings indicate that information theory‐based methods, particularly FS have the potential to discriminate among different climatic regimes. [ABSTRACT FROM AUTHOR]

We analysed long‐term variability and trends in meteorological droughts across Western Europe using the Standardized Precipitation Index (SPI). Precipitation data from 199 stations spanning the period 1851–2018 were employed, following homogenisation, to derive SPI‐3 and SPI‐12 series for each station, together with indices on drought duration and severity. Results reveal a general absence of statistically significant long‐term trends in the study domain, with the exception of significant trends at some stations, generally covering short periods. The largest decreasing trends in SPI‐3 (i.e., increasing drought conditions) were found for summer in the British and Irish Isles. In general, drought episodes experienced in the last two or three decades have precedents during the last 170·years, emphasizing the importance of long records for assessing change. The main characteristic of drought variability in Western Europe is its strong spatial diversity, with regions exhibiting a homogeneous temporal evolution. Notably, the temporal variability of drought in Western Europe is more dominant than long‐term trends. This suggests that long‐term drought trends cannot be confirmed in Western Europe using precipitation records alone. This study provides a long‐term regional assessment of drought variability in Western Europe, which can contribute to better understanding of regional climate change during the past two centuries. [ABSTRACT FROM AUTHOR]

Drought patterns are better understood by analysing the joint dependence among long‐term drought characteristics: severity, duration, and frequency. Changes in these drought characteristics are also likely due to potential climate change, for instance, increased drought severity and duration, increased frequency of drought events, and increased spatial extent of drought impacts. In this study, we performed a regional‐scale analysis of potential changes in meteorological drought characteristics in a tropical region in India under a warming future climate scenario. The meteorological drought characteristics were computed using the Standardized Precipitation Index. The Coordinated Regional Downscaling Experiment South Asia Regional Climate Model future precipitation simulation under the RCP 4.5 scenario was bias‐corrected and utilized for future drought frequency analysis. Regionalization of meteorological drought characteristics was performed using the simple k‐means clustering technique to identify the homogeneous regions for baseline and future periods. Bivariate copula was utilized to model the dependence among the drought characteristics in order to develop the S‐D‐F curves. The changes in regionalization and regional S‐D‐F curves under future potential climate change were then investigated. The results of the study suggest an increase in drought severity by 2–3% for long‐term droughts (duration more than 5 months) in the future as indicated by upward shift and changes in the slope of the regional S‐D‐F curves. There is a projected increase in the occurrence of long‐term droughts in the future when compared to short‐term (duration of 1 to 3 months) droughts. We also observed a considerable spatial shift of drought hotspots, the severe drought‐prone regions lie in the eastern part of the study region in the future. Future projections of regional S‐D‐F curves and Spatio‐temporal drought characteristics obtained from the proposed framework may be utilized for devising drought mitigation strategies. [ABSTRACT FROM AUTHOR]

Meteorological droughts have large impacts on society and the environment. A better understanding and quantification of their occurrences can be highly relevant for the development of proper climate change mitigation, adaptation and resilience strategies. Here we examine meteorological droughts from observed data covering the 1971–2000 period for the Fulda catchment in Germany by means of the Standardized Precipitation Index. The joint dependency of drought duration and severity is modelled by a copula function, which relates their univariate distributions in a functional relationship. Recurrence intervals are further calculated as a function of the joint relationship and univariate marginals. Future projections are investigated in which downscaled EURO‐CORDEX Regional Climate Model (RCM) projections for the period 2021–2050 are used together with the three Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5, in order to analyse and compare future joint patterns of duration and severity of events. We find that drought duration and severity present a clear interdependency supporting the choice of a bivariate model. Results suggest substantial differences in the future joint relationship duration–severity. Depending on the RCM and RCP, drought patterns show different magnitude of changes in the future. The projected changes are different for the different returns periods. RCP8.5 shows more severe events and longer drought durations than RCP2.6 and RCP4.5. The uncertainties of the projected patterns also depend on the RCP and RCM and are larger for higher return periods. [ABSTRACT FROM AUTHOR]

Iran has a predominantly arid and semi‐arid climate where the drought hazards and their variability are the crucial concerns for water resources management. This study assesses the characteristics and trends of meteorological droughts features in different climate regions of Iran. Monthly rainfall data are analysed using Standardized Precipitation Index (SPI) to reconstruct meteorological droughts for different time steps (1, 3, 6, 9 and 12). The drought events are decomposed into three features, namely Severity (S), Duration (D) and Peak (P) to assess their spatial variations in different climate regions. The Mann–Kendall (MK) test is used to detect the trends in drought features in different climate regions. The percentage area (PA) of increasing/decreasing trend in drought features is computed to assess the spatial variations of trends in each region. Besides, SPI is compared with Standardized Precipitation Evapotranspiration Index (SPEI) to understand the importance of evapotranspiration on meteorological droughts. The high correlations between SPI and SPEI indicate that the results obtained using SPI would not vary if a similar analysis is conducted using SPEI. The results obtained using SPI reveal that short‐duration droughts (6 months or less) are more frequent and severe in the northwest and the north along the Caspian plains while the longer‐duration droughts (more than 6 months) are more frequent and severe in the south, southeast and southwest. Overall, the northwest region of Iran is found most susceptible to droughts in terms of S, P and D. Moderate (40% < PA < 60%) or significant (PA > 60%) increase in the drought features are observed in all climate regions for shorter SPI (3 months or less) except northwest, while no drought feature is found to increase significantly for SPI‐12. Overall, the higher increase in drought S, P and D are found in the central arid region and at higher altitudes. [ABSTRACT FROM AUTHOR]

Spatiotemporal behaviors of meteorological droughts are basic for understanding the development and driving forces of droughts. Currently, there are a limited number of approaches mainly used to analyze the spatiotemporal evolution of large‐scale soil moisture/hydrological drought events at global or continental scales. These behavior description approaches on the spatiotemporal dynamics of drought are not perfect and should be improved further. To this end, a new approach was proposed to investigate the migration of meteorological droughts based on topological spatial relations. It was used to identify drought clusters, migration trajectories, and migration directions of meteorological droughts. In the Poyang Lake basin (24°29′ to 30°04′N, 113°34′ to 118°28′E), China, monthly precipitation data were available from 1960 to 2007. The one‐month standardized precipitation index was used to characterize meteorological droughts. The proposed approach identified a total of 77 drought events and 454 drought clusters during the study period. Their drought centroids were tracked and analyzed. The migration distances of drought events exhibited a significant linear relationship with the drought duration (R2 > 0.8, slope of 165 km per month). More than 70% of drought events had a migrating displacement greater than 1° (approximately 100 km). These results indicated that the meteorological drought events traveled actively and occurred on a widespread basis in the basin. Moreover, two main patterns of drought cluster migration (northward and southward) were identified. The approach and findings can provide some important implications to investigate the migration of meteorological droughts in other regions. Key Points: A new approach was developed to investigate meteorological drought migrationMigration distances of droughts had a significant linear relationship with the drought duration (R2 > 0.8)Most drought events displaced greater than 100 km toward north or south in the Poyang Lake basin [ABSTRACT FROM AUTHOR]

It is generally accepted that El Niño–Southern Oscillation (ENSO) is the main modulator of rainfall variability over Northern South America in the interannual scale. Assuming that, an index is proposed to quantify this expected interannual variability in time series of rainfall. The result is the exposure assessment to the effects of droughts, measured with the Standardized Precipitation Index (SPI) for the monthly scale. The SPI is calculated from rainfall series, and wavelet analysis is used to estimate the variance for different frequencies present in the signal. The Wavelet Interannual Variability Index (WIVI) is calculated as the sum of the wavelet coefficients over a predetermined range of modes of variability (with periods longer than 2 year and shorter than 8 years). The index was tested using a dataset of rainfall records from Tocantins state, Central Brazil. Most of the series ranged from 1974 to 2012. On average, the series had 3.2% of gaps which were not filled to avoid the effect of artificial trends on the data. The state lies mostly over the Cerrado biome and is a new frontier of agricultural development in Brazil. According to the results, the Northern region is under higher exposure of interannual variability, with higher values of WIVI. The assessment is in agreement with large‐scale features of South American climate, specifically considering the influences of the Pacific and Atlantic Oceans and their patterns of sea surface temperature (SST). Interannual variability of rainfall was quantified by applying wavelet decomposition to time series of Standardized Precipitation Index (SPI). The wavelet variance for time scales of 2–8 years was quantified for 36 stations over Tocantins, Central Brazil. The variability within those scales was normalized as the Wavelet Index of Interannual Variability (WIVI). According to results, the Northern region is under higher exposure of interannual variability, with higher values of WIVI. The assessment is in agreement with large‐scale features of South American climate, specifically considering the influences of the Pacific and Atlantic Oceans and their patterns of sea surface temperature (SST). [ABSTRACT FROM AUTHOR]

Summary: Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple soil moisture datasets and remotely sensed greenness. This reveals substantial impacts of soil moisture alone that reduce GPP by up to 40% at sites located in sub‐humid, semi‐arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying soil, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly‐based drought indices. Counter to common assumptions, fLUE reductions are largest in drought‐deciduous vegetation, including grasslands. Our results highlight the necessity to account for soil moisture limitation in terrestrial primary productivity data products, especially for drought‐related assessments. [ABSTRACT FROM AUTHOR]

ABSTRACT: Enhancing the capability of both standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) for quantifying wet and dry events under distinct climate conditions is of paramount importance. The different recommendations of recent studies regarding the best distribution to calculate the SPEI and the lack of studies addressing the effect of different parameters estimation methods on the SPI motivated us to apply and adapt distinct testing methodologies to select candidate models for calculating these standardized drought indices (SDI). The study is based on two data sets. The first represents a tropical–subtropical region of Brazil. The second comprises the same weather stations that were used for developing the original version of the SPEI. The study also emphasized the performance of the models within the range of typical SDI values [−2.0 : 2.0]. Along with goodness‐of‐fit tests, we calculated the mean absolute errors between the indices values estimated from the candidate distributions, and their corresponding theoretical values derived from the standard normal distribution. The two‐parameter gamma and the generalized extreme value distributions are, respectively, recommended for general use in SPI and SPEI algorithms (1–12‐month timescales). The unbiased probability weighted moments are recommended to estimate the distributions parameters. The study also described a trade‐off between choosing the best model for the central part and for the tails of the distributions. This trade‐off suggests that the methodologies used to select models for the SDI algorithms may have to decide which part of the distribution (central or tails) should be emphasized. The behaviour of the errors among different wet/dry categories showed that both indices were only capable of representing drought and floods in a similar probabilistic way within the range [−2.0 : 2.0]. This feature supports our decision to emphasize model performances within such range. [ABSTRACT FROM AUTHOR]

ABSTRACT: Ocean and atmospheric patterns associated with the most severe and most persistent droughts in the Mexican Altiplano region are explored. The standardized precipitation index on a time scale of 12 months was used and compared with time series of other atmospheric conditions to identify major dry episodes from 1970 to 2012. The time series were derived from the second mode of a varimax‐rotated empirical orthogonal functional analysis from the mean summer rainfall series observed at weather stations located in the Mexican Altiplano from 1970 through 2000. The results showed that the longest and most persistent episode occurred from 1998 through 2001, while the most severe dry episode occurred in 2011–2012, when the Pacific Decadal Oscillation was in a negative phase and the Atlantic Multidecadal Oscillation in a positive phase, both in combination with a La Niña event. A large upper‐level anticyclone over the north‐central United States is related to droughts in the Altiplano. Under these conditions, the droughts were linked to a large anticyclone positioned over the central‐north of the United States, with consequent negative anomalies of specific humidity over central‐northern Mexico. Concurrent with droughts in the interior of Mexico, are positive rainfall anomalies along the central Pacific coast of Mexico near the study area. Under different atmospheric conditions, opposite anomalous behaviour between the coast and interior can exist. The mechanism driving this see‐saw behaviour between the west coast of Mexico and the Altiplano is explored. [ABSTRACT FROM AUTHOR]

ABSTRACT: Weather‐pattern, or weather‐type, classifications are a valuable tool in many applications as they characterize the broad‐scale atmospheric circulation over a given region. This study analyses the aspects of regional UK precipitation and meteorological drought climatology with respect to a new set of objectively defined weather patterns. These new patterns are currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. Weather pattern definitions and daily occurrences are mapped to Lamb weather types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Standardized precipitation index (SPI) and drought severity index (DSI) series are calculated for a range of aggregation periods and seasons. Monthly weather‐pattern frequency anomalies are calculated for SPI wet and dry periods and for the 5% most intense DSI‐based drought months. The new weather‐pattern definitions and daily occurrences largely agree with their respective LWTs, allowing comparison between the two classifications. There is also broad agreement between weather pattern and LWT changes in frequencies. The new data set is shown to be adequate for precipitation‐based analyses in the UK, although a smaller set of clustered weather patterns is not. Furthermore, intra‐pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in this context. Six of the new weather patterns are associated with drought over the entire UK, with several other patterns linked to regional drought. It is demonstrated that the new data set of weather patterns offers a new opportunity for classification‐based analyses in the UK. [ABSTRACT FROM AUTHOR]

Controls on the spatio-temporal extent of groundwater flooding are poorly understood, despite the long duration of groundwater flood events and distinct social and economic impacts. We developed a novel approach using statistical analysis of groundwater level hydrographs and impulse response functions (IRFs) and applied it to the 2013/2014 Chalk groundwater flooding in the English Lowlands. We proposed a standardized index of groundwater flooding which we calculated for monthly groundwater levels for 26 boreholes in the Chalk. We grouped these standardized series using k-means cluster analysis and cross-correlated the cluster centroids with the Standardized Precipitation Index accumulated over time intervals between 1 and 60 months. This analysis reveals 2 spatially coherent groups of standardized hydrographs that responded to precipitation over different timescales. We estimated IRF models of the groundwater level response to effective precipitation for 3 boreholes in each group. The IRF models corroborate the Standardized Precipitation Index analysis showing different response functions between the groups. We applied identical effective precipitation inputs to each of the IRF models and observed differences between the hydrographs from each group. It is suggested this is due to the hydrogeological properties of the Chalk and of overlying relatively low permeability superficial deposits (recent unconsolidated sediments overlying the bedrock, such as clays and tills), which are extensive over 1 of the groups. The overarching controls on groundwater flood response are concluded to be a complex combination of antecedent conditions, rainfall, and catchment hydrogeological properties. These controls should be taken into consideration when anticipating and managing future groundwater flood events. The approach presented is generic and parsimonious and can be easily applied where sufficient groundwater level and rainfall data are available. [ABSTRACT FROM AUTHOR]

ABSTRACT Tajikistan is an exceptionally vulnerable Central Asian country in the face of climate change due to its' high exposure and sensitivity to climate change combined with poor adaptation capacity. Therefore, comprehensive climate data are needed for efficient planning of mitigation and adaptation. Here, the key spatio-temporal features of Tajikistan's climate were analysed based on historical data from 52 stations with the longest time-series covering nearly 80 years. In addition, bias-corrected CMIP5 climate models were used to simulate future temperature and precipitation conditions across a range of time periods and greenhouse gas emission scenarios. The results show that the mean annual temperatures have risen in Tajikistan since the 1930s, average rate of change being approximately 0.1 °C decade−1. For precipitation, only a weak signal of change could be detected. The high-resolution (30 arc seconds) spatial analyses reveal the strong control of topography on Tajikistan's climate with such coupling leading mean annual air temperatures to vary from −13.7 °C (eastern mountain regions) to 17.3 °C (low-altitude plains). Similarly, the mean annual precipitation vary from less than 50 mm to over 1000 mm, spring being the wettest of the seasons. The future climate simulations suggest drastic increase in air temperatures; at the end of this century the temperature multi-model mean can be approximately 2 °C (Representative Concentration Pathways, RCP2.6) to 7 °C ( RCP8.5) higher compared to 1961-1990. Precipitation scenarios indicate increase in the eastern part of the country and decrease in the west. Finally, the applied spatial uncertainty analyses stress the need for the future development of the climate station network especially in high-elevation areas associated with complex topography. These results show the pronounced climatic variation in Tajikistan and the expected near- and long-term changes in air temperature and precipitation conditions, thus forming a sound basis for future planning of national mitigation and adaptation activities. [ABSTRACT FROM AUTHOR]

ABSTRACT: Spatio‐temporal drought characteristics related to weak, moderate, and strong El Niño events in the Indonesian region were investigated. The empirical orthogonal function (EOF) patterns of normalized monthly precipitation and Standardized Precipitation Index (SPI) for the period 1950–2010 were analysed; the influence of El Niño events are better represented by SPI. The effects of El Niño events on meteorological drought were investigated based on a composite analysis. The effects of El Niño were more obvious during June–July–August and September–October–November, while they were not significant in March–April–May especially during weak and moderate El Niño events. Spatial distributions of affected areas varied by season, intensity of El Niño events, and local factors. During moderate and strong El Niño events, meteorological drought was more common in southern Sumatra, Java, Bali, Nusa Tenggara, southeastern Kalimantan, Sulawesi, Maluku, and Papua. Based on the spatial and temporal variability of droughts, the monthly or seasonal meteorological drought outlook can be improved by using SPI focusing more on the regions and seasons that are affected by El Niño events. The multi‐model ensemble (MME) forecast of SPI performed better for the target months of September to November even at medium lead times. The months are consistent with the period of serious El Niño effects on drought, implying that serious drought conditions during the most harshly affected season can be predicted using the MME forecast. [ABSTRACT FROM AUTHOR]

This work created a 250-year historic drought catalogue by applying the Standardized Precipitation Index (SPI) to the Island of Ireland precipitation network (1850-2015) and a reconstructed precipitation series from 1765. Documentary sources from newspaper archives spanning the last 250 years, together with other historical sources are used to (1) add confidence to the quantitative detection of drought episodes and (2) gain insight to the socio-economic impacts of historic droughts. The results show that Ireland is drought prone but recent decades are unrepresentative of the longer-term drought climatology. A large decline in 30-year accumulated SPI-12 values is evident from around the 1990s onwards. During the years 1850–2015 seven major drought rich periods were identified with an island-wide fingerprint in 1854–1860, 1884–1896, 1904–1912, 1921–1923, 1932–1935, 1952–1954 and 1969–1977. These events exhibit substantial diversity in terms of drought development, severity and spatial occurrence. Two exceptionally long events are found in the record: the continuous drought of 1854–1860 and the drought of 1800–1809 (in fact a series of three droughts with brief interludes). Over the last 250 years, droughts have resulted in agricultural hardship, water resource crises and failures and preceded some of the major famines of the eighteenth and nineteenth centuries. This work shows that newspaper archives can be used to trace the progression of drought events and impacts and we thus advocate their wider use in corroborating quantitative assessments. The resulting catalogue challenges prevailing perceptions about drought in Ireland while strengthening the evidence base for future drought and water resource planning across the island. [ABSTRACT FROM AUTHOR]

Correlations between wet/dry variations and El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO) across the Pearl River basin were analysed at annual and seasonal time scales using the Pearson correlation and moving correlation coefficient. The basin was divided into homogeneous climate zones using the fuzzy C-means method. The influences of ENSO, NAO, IOD, and PDO on annual and seasonal wet/dry variations and impacts of warm and cold episodes of these indices on Standardized Precipitation Index-based dry and wet conditions were investigated. Furthermore, the mechanisms of the influences were tried to be analysed from the perspective of moisture transmission. The results indicated that: (1) IOD, NAO, and ENSO are the principle factors driving annual, wet, and dry seasonal precipitation variations, respectively. Moreover, opposite correlations can be identified between precipitation and IOD, NAO, and ENSO in the same/previous year; (2) Correlations between precipitation variations at different time scales and climate indices are strongly stable. Furthermore, climate indices tend to have more significant and stable impacts on precipitation variations during the dry season than on annual precipitation and precipitation variations during the wet season; (3) There exist differentmoisture convergence and divergence phenomena in the Pearl River basin under anomalous year of climate indices, so the annual, wet, and dry seasonal precipitation characteristic will be affected; (4) ENSO exhibits remarkable impacts on the variations of dry conditions; and meanwhile ENSO and IOD have significant impacts on the variations of wet conditions. In general, the cold phase of climate indices tends to trigger a higher risk of droughts, and the warm phase of climate indices tends to cause higher rates of extreme wet conditions. Taking different climate indices as predictors, this study may help forecast precipitation variations at different time scales. [ABSTRACT FROM AUTHOR]

ABSTRACT Correlations between wet/dry variations and El Niño-Southern Oscillation ( ENSO), North Atlantic Oscillation ( NAO), Indian Ocean Dipole ( IOD), and Pacific Decadal Oscillation ( PDO) across the Pearl River basin were analysed at annual and seasonal time scales using the Pearson correlation and moving correlation coefficient. The basin was divided into homogeneous climate zones using the fuzzy C-means method. The influences of ENSO, NAO, IOD, and PDO on annual and seasonal wet/dry variations and impacts of warm and cold episodes of these indices on Standardized Precipitation Index-based dry and wet conditions were investigated. Furthermore, the mechanisms of the influences were tried to be analysed from the perspective of moisture transmission. The results indicated that: (1) IOD, NAO, and ENSO are the principle factors driving annual, wet, and dry seasonal precipitation variations, respectively. Moreover, opposite correlations can be identified between precipitation and IOD, NAO, and ENSO in the same/previous year; (2) Correlations between precipitation variations at different time scales and climate indices are strongly stable. Furthermore, climate indices tend to have more significant and stable impacts on precipitation variations during the dry season than on annual precipitation and precipitation variations during the wet season; (3) There exist different moisture convergence and divergence phenomena in the Pearl River basin under anomalous year of climate indices, so the annual, wet, and dry seasonal precipitation characteristic will be affected; (4) ENSO exhibits remarkable impacts on the variations of dry conditions; and meanwhile ENSO and IOD have significant impacts on the variations of wet conditions. In general, the cold phase of climate indices tends to trigger a higher risk of droughts, and the warm phase of climate indices tends to cause higher rates of extreme wet conditions. Taking different climate indices as predictors, this study may help forecast precipitation variations at different time scales. [ABSTRACT FROM AUTHOR]

The standardized precipitation index (SPI) is an important yet easy-to-calculate means to describing wet or dry conditions in very different climates. In this work, we develop a new scheme for improved one-month lead-time forecasts of this index over Russia. As a basic seasonal forecasting model, we utilize the semi-implicit semi-Lagrangian vorticity-divergence (SL-AV) model of the Hydrometeorological Centre of Russia and the Institute of Numerical Mathematics of the Russian Academy of Sciences. Based on hindcast simulations of this model, we demonstrate its relatively poor skills in obtaining direct one-month lead-time SPI forecasts in the region of interest. In order to improve the accuracy of these forecasts, we use mean sea-level pressure and 500 hPa geopotential height fields from model output of the same SL-AV hindcasts to identify informative predictors for the local SPI values, based on the observation that the cross-correlation structure between the three different fields reveals relevant interdependencies between precipitation, mean sea-level pressure and 500 hPa geopotential height in different regions. Using this information in terms of regression models for obtaining both, deterministic and probabilistic forecasts provides a significant improvement of the SPI forecast skills, pointing to the potential for implementing the proposed scheme in operational one-month lead-time precipitation forecasts. [ABSTRACT FROM AUTHOR]

ABSTRACT East China ( EC) has experienced a number of severe droughts in recent decades. Understanding the characteristics of droughts and their persistence will provide operational guidelines for water resource management and agricultural production. This study uses a logistic regression model to measure the probability of drought occurrence in the current season given the previous season's Standardized Precipitation Index ( SPI) and Southern Oscillation Index ( SOI) as well as drought persistence. Results reveal large spatial and seasonal variations in the relationship between the previous season's SPI and the drought occurrence probability in a given season. The drought persistence averaged over the entire study area for all the four seasons is approximately 34% with large variations from season to season and from region to region. The East and Northeast regions have the largest summer drought persistence (∼40%) and lowest fall drought persistence (∼28%). The spatial pattern in winter and spring drought persistence is dissimilar with stronger winter and weaker spring drought persistence in the Southwest and Northeast relative to other regions. Logistic regression analysis indicates a stronger negative relationship in summer-to-fall (or between fall drought occurrence and summer SPI) than other inter-season relationships. Most of the stations with significant relationships in summer-to-fall lie in the East and the southern part of the North region. Significant negative relationships in winter-spring and fall-winter are primarily located on the western portion of the study area. A significant negative spring-summer relationship exists in the North region. Further, spring drought occurrence has a statistically significant positive relationship with winter SOI in the Southeast. Fall drought occurrence and summer SOI are negatively correlated in the northwest portion of EC (i.e. the border of North and Southwest regions). Overall, this study demonstrates that the impact of previous season SPI and SOI on current season drought varies substantially from region to region and from season to season. This study also shows stronger drought persistence in summer than in other seasons. In other words, the probability of fall drought occurrence is closely related to summer moisture conditions in the EC. [ABSTRACT FROM AUTHOR]

Drought indicators are used as triggers for action and so are the foundation of drought monitoring and early warning. The computation of drought indicators like the standardized precipitation index (SPI) and standardized streamflow index (SSI) require a statistical probability distribution to be fitted to the observed data. Both precipitation and streamflow have a lower bound at zero, and their empirical distributions tend to have positive skewness. For deriving the SPI, the Gamma distribution has therefore often been a natural choice. The concept of the SSI is newer and there is no consensus regarding distribution. In the present study, twelve different probability distributions are fitted to streamflow and catchment average precipitation for four durations (1, 3, 6, and 12 months), for 121 catchments throughout the UK. The more flexible three- and four-parameter distributions generally do not have a lower bound at zero, and hence may attach some probability to values below zero. As a result, there is a censoring of the possible values of the calculated SPIs and SSIs. This can be avoided by using one of the bounded distributions, such as the reasonably flexible three-parameter Tweedie distribution, which has a lower bound (and potentially mass) at zero. The Tweedie distribution has only recently been applied to precipitation data, and only for a few sites. We find it fits both precipitation and streamflow data nearly as well as the best of the traditionally used three-parameter distributions, and should improve the accuracy of drought indices used for monitoring and early warning. [ABSTRACT FROM AUTHOR]

ABSTRACT The increase in concern about drought in the Seyhan−Ceyhan River Basins in Turkey has brought about the need to define temporal and spatial drought variations. Unlike previous studies that cover this region in the current literature, sufficient amounts of data from 14 meteorological and 12 streamflow stations are used in the form of 3, 6 and 12 month timescales allowing both meteorological and hydrological drought analyses to be conducted in detail. The standardized precipitation index and streamflow drought index methods are employed for the drought analyses presented. The location, period and occurrence level of drought events for this region are specified and presented graphically. The correlation between hydrological and meteorological droughts suggests that there is a specific lag time for the droughts such that a meteorological drought emerges in the following year as a hydrological drought. Accordingly, the predicted hydrological drought period may allow for better planning of the efficient use of water resources and hydroelectric and agricultural production in this region. [ABSTRACT FROM AUTHOR]

ABSTRACT Prediction of droughts has a great importance in the management and planning of water resources. This study developed an adaptive neuro-fuzzy inference system ( ANFIS) based model for prediction of droughts, and evaluated its applicability in the seven homogeneous rainfall zones of the East Asian monsoon region (20°-50°N, 103°-149°E). Standardized Precipitation Index ( SPI) was used to characterize the drought events. SPI series were computed for each zone using a 30-year (1978-2007) gridded rainfall dataset (0.5° grid resolution) at the corresponding grid points. The influence of sea surface temperature anomalies ( SSTA) on droughts was assessed using a lagged-correlation between global gridded SSTA (0.2° grid resolution) and the SPI of each zone. SSTA were used as a potential predictor variable based on the premise that the land-sea thermal contrast is a major driver of the monsoon. The model was trained and validated using a 25-year (1978-2002) dataset, with different configurations to obtain the optimum model structure and a set of suitable predictors. The performance of the model was demonstrated by comparing the model simulated results with the observed drought index and drought categories using a 5-year (2003-2007) independent checking dataset. The model predicted the drought categories accurately for 50 to 70% cases in checking period for different zones. The results showed the viability of the proposed model for drought prediction with substantial enhancement in accuracy when past SSTA were used as a predictor compared with the use of only past SPI data. [ABSTRACT FROM AUTHOR]

ABSTRACT Features and interdecadal variability of droughts were assessed over the East Asian monsoon region (20°-50°N, 103°-149°E) using the standardized precipitation index ( SPI) and the gridded rainfall data set (1978-2007) at 0.5° resolution. To assess the spatial and temporal patterns of droughts, seven homogeneous rainfall zones that exhibit unique rainfall regimes and long-term variability over the region were used. The statistical analysis method known as the theory of runs was used to identify and characterize drought events. A run was defined as a portion of SPI drought series in which all values were below the selected threshold level. Run-length and run-sum were used to define the drought duration and drought severity, respectively. The study revealed unique drying and wetting patterns for different zones in the region. Interdecadal analysis of droughts over the past three decades revealed a significant increase in drought duration and severity in the low rainfall zones, whereas a significant decrease appeared in the high rainfall zones. In particular, the duration and severity dwindled to zero with no major drought event over the eastern and the East Sea coastal region of Japan during the last decade (1998-2007). These patterns pose serious threats of increasing droughts in the low rainfall zones and flooding in the high rainfall zones. The spectral analysis, using the Fast Fourier Transform, was performed to identify the cyclic patterns of SPI time series in each zone, which revealed dominant cycles of 15, 7.5, and 3.4 years in the different zones. These results suggest the possible influence of interdecadal Pacific Oscillations and North Atlantic Oscillations on droughts in the region, although these relations remain a challenging task. [ABSTRACT FROM AUTHOR]

ABSTRACT This article addresses the regional impacts of climate change on precipitation and meteorological drought over Southern South America ( SSA) through a CMIP5 multi-model ensemble based on 15 General Circulation Models ( GCMs) forced under two different greenhouse gas concentration pathways ( RCP4.5 and RCP8.5). An assessment of the biases in the representation of the precipitation annual cycle was performed over the 1979-2008 period over five regions within SSA, based on a comparison between the GCMs precipitation outputs with the Global Precipitation Climatology Centre ( GPCC) dataset. The multi-model ensemble well reproduces the shape of the annual cycle of precipitation over most of SSA, although the monthly totals were overestimated (underestimated) over the North-West and South (North-East and Central-East) regions. Changes in precipitation and meteorological drought characteristics were identified by the difference for early (2011-2040) and late (2071-2100) 21st century values with respect to the 1979-2008 baseline, using the standardized precipitation index as a short- and long-term drought indicator. Future climate conditions are expected to modify the regional characteristics of meteorological droughts over SSA, but the range of uncertainty in the expected changes is high. A significant increase in the number of drought events in all the regions for most of the 21st century sub-periods is projected for the multi-model ensemble. The mean duration of drought events will be shorter, with no significant changes in the severity of droughts and the occurrence of multi-decadal changes in the number of critical dry months is likely, although the significance in the changes depends on the region, future time horizon and greenhouse gas concentration pathways. These results overlap with a projected increase in precipitation over most of the regions, which has a strong seasonality and, therefore, will have some implications upon the future meteorological drought developments and the agricultural and hydrological practices in SSA. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation Index ( SPI), a well-reviewed meteorological drought index recommended by the World Meteorological Organization ( WMO), and its more recent climatic water balance variant, the Standardized Precipitation-Evapotranspiration Index ( SPEI), both rely on selection of a univariate probability distribution to normalize the index, allowing for comparisons across climates. Choice of an improper probability distribution may impart bias to the index values, exaggerating or minimizing drought severity. This study compares a suite of candidate probability distributions for use in SPI and SPEI normalization using the 0.5° × 0.5° gridded Watch Forcing Dataset ( WFD) at the continental scale, focusing on Europe. Several modifications to the SPI and SPEI methodology are proposed, as well as an updated procedure for evaluating SPI/ SPEI goodness of fit based on the Shapiro-Wilk test. Candidate distributions for SPI organize into two groups based on their ability to model short-term accumulation (1-2 months) or long-term accumulation (>3 months). The two-parameter gamma distribution is recommended for general use when calculating SPI across all accumulation periods and regions within Europe, in agreement with previous studies. The generalized extreme value distribution is recommended when computing the SPEI, in disagreement with previous recommendations. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, the spatio-temporal variability and trends of droughts across Bolivia between 1955 and 2012 were investigated using two climate drought indices: the Standardized Precipitation Index ( SPI), which is based on precipitation data, and the Standardized Precipitation Evapotranspiration Index ( SPEI), which is based on the difference between the precipitation and the reference evapotranspiration ( ETo). We found that the average drought conditions across the country showed a temporal behaviour mainly characterized by decadal variations. The spatial pattern of drought evolution showed marked differences between the Amazonian region and the Bolivian Altiplano. Both regions showed different drought periods, a lower frequency of drought variability in the Amazon region and trends towards drier conditions in the Altiplano, mainly due to a higher atmospheric water demand as a consequence of increased ETo. We also showed that inclusion of ETo, obtained from maximum and minimum temperature records, increased the spatial heterogeneity of the drought evolution in relation to the evolution observed when only precipitation droughts were considered. The SPEI, the calculation of which includes precipitation and ETo, indicated intensification in drought severity in the last years analysed relative to the pattern found when precipitation droughts alone were considered, and also indicated an increase in the magnitude and duration of drought events. The potential for increasing drought conditions under various climate change scenarios is discussed. [ABSTRACT FROM AUTHOR]

ABSTRACT The Standardized Precipitation Index ( SPI) has been widely used to monitor the drought status. In the estimation of SPI, an appropriate probability distribution function ( APDF) is required to fit precipitation series with different timescales, such as 3-month, 9-month and so on. Then the standard normal distribution function is used to transform the cumulative probability derived from APDF to calculate the SPI value. Obviously, the estimation accuracy of SPI particularly depends on the sample that influences the selection of PDF and parameter estimation. However, the size of the observed sample is usually small, and it cannot perfectly present the statistical property of the population, which usually causes the uncertainty on calculation of SPI and in turn results in the uncertainty of drought assessment. Due to the existence of gap between the estimated SPI and the true SPI, when one obtains the estimation of SPI, it is necessary to know the reliability or uncertainty of the estimated SPI. In this article, a new method based on the Bootstrap method was put forward to analyse the impact of sampling uncertainty on the estimation of SPI and drought assessment. Through repeating samples from the original sample, a large number of Bootstrap samples are constructed, and based on each Bootstrap sample, the corresponding estimation of SPI for the given event can be obtained. Therefore, the sampling distribution of SPI for the given event can also be derived, on which both the point estimation and interval estimation of SPI for the given precipitation event can be provided, and the overall assessment of a drought event could be achieved. The case study shows that, compared with the original SPI calculation method, the proposed method not only provides equivalent assessment results, but also has the capability to assess the impact of sampling uncertainty on the uncertainty of SPI calculation and drought assessment. [ABSTRACT FROM AUTHOR]

ABSTRACT Xinjiang is the largest arid inland region in China and its agricultural development plays an important role in the socio-economy of China. Drought is one of the major natural hazards in this region and has impacts on agriculture. In this study, spatiotemporal patterns of droughts are analysed based on monthly precipitation datasets from 52 meteorological stations covering the period of 1961-2008 using the Rotated Empirical Orthogonal Functions ( REOF) technique and also the Mann-Kendall trend test ( MK test). Results of the study indicate that: (1) a wetting tendency is observed in recent years, particularly in northern Xinjiang. Seasonally, the wetting tendency is found mainly in winter. The modes of REOF can be divided by the Tianshan mountain and the principal component ( PC) of the northern Xinjiang mode in winter has a significant increasing trend. (2) The area of drought-affected regions is decreasing in northwestern Xinjiang and along the northern side of Tianshan mountain. Seasonally, the area of drought-affected regions is significantly decreasing in winter. (3) The wetting tendency of Xinjiang may alleviate the drought influence. However, uneven spatial patterns of precipitation changes may enhance drought impacts. Besides, drought-related impacts can be greatly alleviated where irrigation conditions are favourable. [ABSTRACT FROM AUTHOR]

Drought is one of the most frequent natural disasters in Bangladesh which severely affect agro-based economy and people's livelihood in almost every year. Characterization of droughts in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development. In this study, standardized precipitation index is used to understand the spatial distribution of meteorological droughts during various climatic seasons such as premonsoon, monsoon, and winter seasons as well as cropping seasons such as Pre-Kharif (March-May), Kharif (May-October), and Rabi (December-February). Rainfall data collected from 29 rainfall gauge stations located in different parts of the country were used for a period of 50 years (1961-2010). The study reveals that the spatial characteristics of droughts vary widely according to season. Premonsoon droughts are more frequent in the northwest, monsoon droughts mainly occur in the west and northwest, winter droughts in the west, and the Rabi and Kharif droughts are more frequent in the north and northwest of Bangladesh. It is expected that the findings of the study will support drought monitoring and mitigation activities in Bangladesh. [ABSTRACT FROM AUTHOR]

Drought is a complex and highly destructive natural phenomenon that affects portions of the United States almost every year, and severe water deficiencies can often become catastrophic for agricultural production. Evapotranspiration ( ET) by crops is an important component in the agricultural water budget; thus, it is advantageous to include ET in agricultural drought monitoring. The main objectives of this study were to (1) conduct a literature review of drought indices with a focus to identify a simple but simultaneously adequate drought index for monitoring agricultural drought in a semiarid region and (2) using the identified drought index method, develop and evaluate time series of that drought index for the Texas High Plains. Based on the literature review, the Standardized Precipitation-Evapotranspiration Index ( SPEI) was found to satisfy identified constraints for assessing agricultural drought. However, the SPEI was revised by replacing reference ET with potential crop ET to better represent actual water demand. Data from the Texas High Plains Evapotranspiration network was used to calculate SPEIs for the major irrigated crops. Trends and magnitudes of crop-specific, time-series SPEIs followed crop water demand patterns for summer crops. Such an observation suggests that a modified SPEI is an appropriate index to monitor agricultural drought for summer crops, but it was found to not account for soil water stored during the summer fallow period for winter wheat. [ABSTRACT FROM AUTHOR]

Given that forests represent the primary terrestrial sink for atmospheric CO2, projections of future carbon (C) storage hinge on forest responses to climate variation. Models of gross primary production ( GPP) responses to water stress are commonly based on remotely sensed changes in canopy 'greenness' (e.g., normalized difference vegetation index; NDVI). However, many forests have low spectral sensitivity to water stress ( SSWS) - defined here as drought-induced decline in GPP without a change in greenness. Current satellite-derived estimates of GPP use a vapor pressure deficit ( VPD) scalar to account for the low SWSS of forests, but fail to capture their responses to water stress. Our objectives were to characterize differences in SSWS among forested and nonforested ecosystems, and to develop an improved framework for predicting the impacts of water stress on GPP in forests with low SSWS. First, we paired two independent drought indices with NDVI data for the conterminous US from 2000 to 2011, and examined the relationship between water stress and NDVI. We found that forests had lower SSWS than nonforests regardless of drought index or duration. We then compared satellite-derived estimates of GPP with eddy-covariance observations of GPP in two deciduous broadleaf forests with low SSWS: the Missouri Ozark ( MO) and Morgan Monroe State Forest ( MMSF) AmeriFlux sites. Model estimates of GPP that used VPD scalars were poorly correlated with observations of GPP at MO ( r2 = 0.09) and MMSF ( r2 = 0.38). When we included the NDVI responses to water stress of adjacent ecosystems with high SSWS into a model based solely on temperature and greenness, we substantially improved predictions of GPP at MO ( r2 = 0.83) and for a severe drought year at the MMSF ( r2 = 0.82). Collectively, our results suggest that large-scale estimates of GPP that capture variation in SSWS among ecosystems could improve predictions of C uptake by forests under drought. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, wet and dry periods were analysed using the Standardized Precipitation Index ( SPI) on timescales of 1, 3, 6 and 12 months ( SPI1, SPI3, SPI6 and SPI12) at ten stations in Serbia during the period 1949-2011. The onset of severe wetness and drought during the period 1999-2001 was presented. The distributions of the monthly precipitation amounts for each station were modelled by a gamma distribution. The severity and duration of the wet and dry periods were calculated. The generalized Pareto distribution ( GPD) was applied to the SPI values for the timescales of 1 and 12 months during the period 1936-2011 in Belgrade. Furthermore, the severity and duration of the wet and dry periods for timescales of 1 and 12 months were analysed using the extreme value theory. Using the time series of the SPI, it was found that the frequency of droughts in the southern part of Serbia was higher than in the other parts of the country. The obtained results indicated that the GPD could be an appropriate distribution for SPI values less than −1 at the 1- and 12-month timescales, and for the severity of the wet and dry periods at the 1-month timescale. [ABSTRACT FROM AUTHOR]

In this paper, we perform a detailed regional analysis of the link between meteorological drought indices and streamflow for a comprehensive Austrian data set of 47 small to medium-size catchments in humid-temperate climate. Four drought indices considering different components of the catchment water balance are tested. We assess the quality of the link using rank correlation analysis, and the probability of detecting low-flow events by hit-scores. Overall, correlations range between 0.4 and 0.8 and differ significantly between regions. A stratified analysis shows that the link is much stronger (i) for summer low flows and droughts than for anomalies in general, and (ii) for more humid than more arid conditions. Under more humid conditions streamflow droughts of small to medium-size catchments are to a large extent generated by climate forcing and therefore well represented by a simple meteorological index. Under increasingly dry conditions, the climate signal gets less predictive, and storage properties of the underground become more important. A simple soil moisture accounting scheme (such as those of the Palmer index) can considerably improve the correlations. Overall, we conclude there is a significant link between meteorological drought and streamflow drought, except for catchments where groundwater storage and snow processes are important. The results are encouraging and provide a wealth of information which can profitably be used to set up statistical prediction models to estimate low flows from meteorological time series. [ABSTRACT FROM AUTHOR]

By using the Variable Infiltration Capacity model with Palmer Drought Severity Index (VIC-PDSI) model and Standardized Precipitation Index (SPI), spatiotemporal trends of climate variation during the main growing seasons for plants of Loess Plateau between 1971 and 2010 were detected and characterized. The VIC-PDSI model is established by combining the VIC model with PDSI. The simulation results and the grids system of VIC were applied to substitute for the two-layer bucket-type model to do the hydrological accounting, which could improve the physical mechanism of PDSI and expand its application range. Our results suggest that the climate of the study area has experienced a drying and warming trend during the past four decades. Apart from some individual years and regions, there was a perpetuation of water deficit over the Plateau both in spring and summer. The drought frequency increased from southeast to northwest in spring, while the drought frequency decreased from southeast to northwest in summer. The climate in the southern part of the Loess Plateau, accounting for 23.3% of the study region, showed a significant drying and warming trend in spring over the past four decades. The climate variability detected by VIC-PDSI model shows good agreement with that monitored by SPI. Since a large part of the study region frequently suffered from water shortage during the main growing seasons for plants, people living in such drought-prone areas should take measures to prevent the negative effects on agricultural production, reforestation, and regional food security caused by drought. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The aim of this paper is to test the ability of neural network approaches to hindcast the spring standardized precipitation index on a 6-month time scale (SPI6) in Portugal, based on winter large-scale climatic indices. For this purpose, the linkage of the spring SPI time series with the winter North Atlantic Oscillation (NAO) and the sea surface temperature (SST) was investigated by means of maps of the correlation coefficient for the period from October 1910 to September 2004. The results indicate that the winter NAO is a good predictor for the SPI6 of the spring (SPI6 finishing in April, May and June, SPI6April, SPI6May and SPI6June, respectively) for the northern, central and southern regions of Portugal. The winter SST1 (area of the Mediterranean Sea) must only be considered for the northern region, and the winter SST3 (area of the North Atlantic between Iberia and North America) only for the southern region. Spatial maps of predictive SPI6 for April, May and June were created and validated. The neural models explained more than 81% of the total variance for the SPI6April and SPI6May and more than 64% of the total variance for the SPI6June. Probability maps were also developed considering the values predicted by the neural methods for the spring months and all drought categories (moderate, severe and extreme). These maps indicating the probability of droughts can provide valuable support for the integrated planning and management of water resources throughout Portugal. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT The implications of geographical factors (i.e. elevation, freshwater area, urbanization, and proximity to coast) and climatic factors (i.e. air temperature, wind speed, relative humidity, and solar radiation) with regard to drought trend were investigated by analyzing the monthly averaged daily climate data (i.e. precipitation, temperature, wind speed, relative humidity, and solar radiation) recorded from 1973 to 2006 at 53 climatological stations in South Korea. In addition, geographical characteristics were identified by GIS analysis and land cover data were analyzed for these 53 stations. Further, multivariate regression analyses were performed by setting the Standardized Precipitation Index ( SPI) trend as the dependent variable and the geographical and climatic factors as the independent variables. The results indicated that, overall, the droughts in South Korea are becoming more severe in spring for the short- and seasonal term; however, the droughts in all seasons are becoming less severe for the long term. The effects of geographical and climatic factors on drought trends are also closely related to seasonality and drought duration for short-, seasonal-, and medium-term droughts, but not for long-term droughts. Therefore, for efficient water resources management for drought preparedness, drought duration and seasonality should also be considered along with geographical and climatic characteristics of a region. [ABSTRACT FROM AUTHOR]

Assessing the risk, the severity and the likely evolution of droughts are key tasks for improving preparedness of regions prone to drought conditions, and mitigation of drought consequences. The access to real-time and high-quality climatic information is essential for this purpose. Different climatic databases are being developed and made available on real time by climatic research institutions, but their capability for quantifying droughts characteristics including severity, or spatio-temporal variability, is uncertain given their low spatial resolution. In this study, we assessed the capability of three databases with contrasted spatial resolution for measuring spatial and temporal variability of drought occurrence. The standardized precipitation index, calculated for each database, showed that the low resolution datasets allow an acceptable measurement of the magnitude, intensity and duration of droughts, while failing mostly in detecting the spatial patterns of the specific drought episodes. Moreover, the capability of the datasets for assessing the impacts of droughts on surface hydrology and tree growth was examined. Results confirmed the usefulness of the drought index for assessing drought impacts on water resources and forest ecosystems even when low resolution databases are used. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]