Standardized Precipitation Evaporation Index Research Articles

Effects of NAO on the climatic water budget at the Polish southern Baltic coast

Abstract: Effects of the North Atlantic Oscillation (NAO) on the Standardized Precipitation-Evaporation Index (SPEI), a metric of the climate-dependent water budget, in the Polish part of the southern Baltic coastal areas were analysed. The analyses were based on monthly NAO index values calculated by P.D. Jones, T. Jonsson, and D. Wheeler for 1951-2010 and on monthly temperatures and sums of precipitation in Szczecin, Ustka, and Elbląg used to calculate SPEI. No strong effects of NAO on the water budget could be demonstrated. Intensified air mass advection from the west was found to slightly reduce SPEI in the Polish coastal zone. In winter (particularly in February), the reduction was caused by the temperature increase during air mass advection from the west, which increases evaporation. In summer (mainly in August), the effect was due to the decreased sum of precipitation, the reduction, however, not being significant. Therefore, NAO effects on the water budget are considered weak and irregular.

Effects of NAO on the climatic water budget at the Polish southern Baltic coast

Abstract Effects of the North Atlantic Oscillation (NAO) on the Standardized Precipitation-Evaporation Index (SPEI), a metric of the climate-dependent water budget, in the Polish part of the southern Baltic coastal areas were analysed. The analyses were based on monthly NAO index values calculated by P.D. Jones, T. Jonsson, and D. Wheeler for 1951-2010 and on monthly temperatures and sums of precipitation in Szczecin, Ustka, and Elbląg used to calculate SPEI. No strong effects of NAO on the water budget could be demonstrated. Intensified air mass advection from the west was found to slightly reduce SPEI in the Polish coastal zone. In winter (particularly in February), the reduction was caused by the temperature increase during air mass advection from the west, which increases evaporation. In summer (mainly in August), the effect was due to the decreased sum of precipitation, the reduction, however, not being significant. Therefore, NAO effects on the water budget are considered weak and irregular.

A quantitative analysis to objectively appraise drought indicators and model drought impacts

Abstract. Drought monitoring and early warning is an important measure to enhance resilience towards drought. While there are numerous operational systems using different drought indicators, there is no consensus on which indicator best represents drought impact occurrence for any given sector. Furthermore, thresholds are widely applied in these indicators but, to date, little empirical evidence exists as to which indicator thresholds trigger impacts on society, the economy, and ecosystems. The main obstacle for evaluating commonly used drought indicators is a lack of information on drought impacts. Our aim was therefore to exploit text-based data from the European Drought Impact report Inventory (EDII) to identify indicators that are meaningful for region-, sector-, and season-specific impact occurrence, and to empirically determine indicator thresholds. In addition, we tested the predictability of impact occurrence based on the best-performing indicators. To achieve these aims we applied a correlation analysis and an ensemble regression tree approach, using Germany and the UK (the most data-rich countries in the EDII) as test beds. As candidate indicators we chose two meteorological indicators (Standardized Precipitation Index, SPI, and Standardized Precipitation Evaporation Index, SPEI) and two hydrological indicators (streamflow and groundwater level percentiles). The analysis revealed that accumulation periods of SPI and SPEI best linked to impact occurrence are longer for the UK compared with Germany, but there is variability within each country, among impact categories and, to some degree, seasons. The median of regression tree splitting values, which we regard as estimates of thresholds of impact occurrence, was around −1 for SPI and SPEI in the UK; distinct differences between northern/northeastern vs. southern/central regions were found for Germany. Predictions with the ensemble regression tree approach yielded reasonable results for regions with good impact data coverage. The predictions also provided insights into the EDII, in particular highlighting drought events where missing impact reports may reflect a lack of recording rather than true absence of impacts. Overall, the presented quantitative framework proved to be a useful tool for evaluating drought indicators, and to model impact occurrence. In summary, this study demonstrates the information gain for drought monitoring and early warning through impact data collection and analysis. It highlights the important role that quantitative analysis with impact data can have in providing "ground truth" for drought indicators, alongside more traditional stakeholder-led approaches.

Spatio-temporal Analysis of Hydrological Drought at Catchment Scale Using a Spatially-distributed Hydrological Model

Lately, drought is more intense and much more severe around the globe, causing more deaths than other hazards in the past century. Drought can be characterized quantitatively for its spatial extent, intensity and duration by using drought indicators. Several indicators have been developed in order to characterize drought, being the most widespread the Standardized Precipitation Index (SPI). Nevertheless, due to its known limitations, other indicators have been proposed. In this paper, evaporation and runoff simulations of a basin were used to evaluate the variation and performance of different meteorological and hydrological drought indicators in identifying drought. Daily simulations of evaporation and runoff were computed by using a distributed hydrological model of a catchment located in the southeast of Mexico. After calibration of the hydrological model, we calculated at different time steps the drought indicators: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Standardized Evapotranspiration Deficit Index (SEDI) and Standardized Runoff Index (SRI). Furthermore, the so-called Non-Contiguous Drought Analysis (NCDA) was carried out to compare the skill of each indicator to identify drought. Results show that meteorological drought indicators do not identify all drought events for the time steps of 1 and 3 months. For 3-, 6- and 9-month time steps, meteorological drought indicators tend to identify the onset with a lag. For long-time steps of 12 and 24, the use of agricultural and hydrological droughts indicators is recommended, since these indicators can identify prolonged drought periods. The results suggest that for a better monitoring of drought in a catchment, it is important the joint evaluation and the use of not only meteorological drought indicators but also hydrological and agricultural ones, in order to identify drought events and their spatio-temporal evolution.

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Use of the Standardized Precipitation Evapotranspiration Index (SPEI) to Characterize the Drying Trend in Southwest China from 1982–2012

In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize the drought conditions in Southwest China from 1982–2012. The SPEI was calculated by precipitation and temperature data for various accumulation periods. Based on the SPEI, the multi-scale patterns, the trend, and the spatio-temporal extent of drought were evaluated, respectively. The results explicitly showed a drying trend of Southwest China. The mean SPEI values at five time scales all decreased significantly. Some moderate and severe droughts were captured after 2005 and the droughts were even getting aggravated. By examining the spatio-temporal extent, the aggravating condition of drought was further revealed. To investigate the performance of SPEI, correlation analysis was conducted between SPEI and two remotely sensed drought indices: Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI). The comparison was also conducted with the Standardized Precipitation Index (SPI). The results showed that for both SMCI and VCI, the SPI and SPEI had approximate correlations with them. The SPEI could better monitor the soil moisture than the SPI in months with significant increase of temperature. The correlations between the VCI and SPI/SPEI were lower; nevertheless, the SPEI was slightly superior to the SPI.

Tree ring δ18O's indication of a shift to a wetter climate since the 1880s in the western Tianshan Mountains of northwestern China

AbstractCentral Asian droughts have drastically and significantly affected agriculture and water resource management in these arid and semiarid areas. Based on tree ring δ18O from native, dominant Schrenk spruce (Picea schrenkiana Fisch. et Mey.), we developed a 300 year (1710–2010) standard precipitation‐evaporation index (SPEI) reconstruction from January to August for China's western Tianshan Mountains. The regression model explained 37.6% of the variation in the SPEI reconstruction during the calibration period from 1950 to 2010. Comparison with previous drought reconstructions confirmed the robustness of our reconstruction. The 20th century has been a relatively wet period during the past 300 years. The SPEI showed quasi 2, 5, and 10 year cycles. Several pluvials and droughts with covariability over large areas were revealed clearly in the reconstruction. The two longest pluvials (lasting for 12 years), separated by 50 years, appeared in the 1900s and the 1960s. The most severe drought occurred from 1739 to 1761 and from 1886 to 1911 was the wettest period since 1710. Compared to previous investigations of hydroclimatic changes in the western Tianshan Mountains, our reconstruction revealed more low‐frequency variability and indicated that climate in the western Tianshan Mountains shifted from dry to wet in 1886. This regime shift was generally consistent with other moisture reconstructions for the northeastern Tibetan Plateau and northern Pakistan and may have resulted from a strengthened westerly circulation. The opposite hydrological trends in the western Tianshan Mountains and southeastern Tibetan Plateau reveal a substantial influence of strengthened westerlies and weakening of the Indian summer monsoon.

Climatic controls of aboveground net primary production in semi-arid grasslands along a latitudinal gradient portend low sensitivity to warming.

Although climate models forecast warmer temperatures with a high degree of certainty, precipitation is the primary driver of aboveground net primary production (ANPP) in most grasslands. Conversely, variations in temperature seldom are related to patterns of ANPP. Thus forecasting responses to warming is a challenge, and raises the question: how sensitive will grassland ANPP be to warming? We evaluated climate and multi-year ANPP data (67 years) from eight western US grasslands arrayed along mean annual temperature (MAT; ~7-14 °C) and mean annual precipitation (MAP; ~250-500 mm) gradients. We used regression and analysis of covariance to assess relationships between ANPP and temperature, as well as precipitation (annual and growing season) to evaluate temperature sensitivity of ANPP. We also related ANPP to the standardized precipitation evaporation index (SPEI), which combines precipitation and evapotranspiration to better represent moisture available for plant growth. Regression models indicated that variation in growing season temperature was negatively related to total and graminoid ANPP, but precipitation was a stronger predictor than temperature. Growing season temperature was also a significant parameter in more complex models, but again precipitation was consistently a stronger predictor of ANPP. Surprisingly, neither annual nor growing season SPEI were as strongly related to ANPP as precipitation. We conclude that forecasted warming likely will affect ANPP in these grasslands, but that predicting temperature effects from natural climatic gradients is difficult. This is because, unlike precipitation, warming effects can be positive or negative and moderated by shifts in the C3/C4 ratios of plant communities.

Identification and simulation of space–time variability of past hydrological drought events in the Limpopo River basin, southern Africa

Abstract. Droughts are widespread natural hazards and in many regions their frequency seems to be increasing. A finer-resolution version (0.05° × 0.05°) of the continental-scale hydrological model PCRaster Global Water Balance (PCR-GLOBWB) was set up for the Limpopo River basin, one of the most water-stressed basins on the African continent. An irrigation module was included to account for large irrigated areas of the basin. The finer resolution model was used to analyse hydrological droughts in the Limpopo River basin in the period 1979–2010 with a view to identifying severe droughts that have occurred in the basin. Evaporation, soil moisture, groundwater storage and runoff estimates from the model were derived at a spatial resolution of 0.05° (approximately 5 km) on a daily timescale for the entire basin. PCR-GLOBWB was forced with daily precipitation and temperature obtained from the ERA-Interim global atmospheric reanalysis product from the European Centre for Medium-Range Weather Forecasts. Two agricultural drought indicators were computed: the Evapotranspiration Deficit Index (ETDI) and the Root Stress Anomaly Index (RSAI). Hydrological drought was characterised using the Standardized Runoff Index (SRI) and the Groundwater Resource Index (GRI), which make use of the streamflow and groundwater storage resulting from the model. Other more widely used meteorological drought indicators, such as the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evaporation Index (SPEI), were also computed for different aggregation periods. Results show that a carefully set-up, process-based model that makes use of the best available input data can identify hydrological droughts even if the model is largely uncalibrated. The indicators considered are able to represent the most severe droughts in the basin and to some extent identify the spatial variability of droughts. Moreover, results show the importance of computing indicators that can be related to hydrological droughts, and how these add value to the identification of hydrological droughts and floods and the temporal evolution of events that would otherwise not have been apparent when considering only meteorological indicators. In some cases, meteorological indicators alone fail to capture the severity of the hydrological drought. Therefore, a combination of some of these indicators (e.g. SPEI-3, SRI-6 and SPI-12 computed together) is found to be a useful measure for identifying agricultural to long-term hydrological droughts in the Limpopo River basin. Additionally, it was possible to undertake a characterisation of the drought severity in the basin, indicated by its time of occurrence, duration and intensity.

Comparison of drought indicators derived from multiple data sets over Africa

Abstract. Drought monitoring is a key component to mitigate impacts of droughts. Lack of reliable and up-to-date precipitation data sets is a common challenge across the globe. This study investigates different data sets and drought indicators on their capability to improve drought monitoring in Africa. The study was performed for four river basins located in different climatic regions (the Oum er-Rbia in Morocco, the Blue Nile in eastern Africa, the Upper Niger in western Africa, and the Limpopo in southeastern Africa) as well as the Greater Horn of Africa. The five precipitation data sets compared are the ECMWF ERA-Interim reanalysis, the Tropical Rainfall Measuring Mission satellite monthly rainfall product 3B-43, the Global Precipitation Climatology Centre gridded precipitation data set, the Global Precipitation Climatology Project Global Monthly Merged Precipitation Analyses, and the Climate Prediction Center Merged Analysis of Precipitation. The set of drought indicators used includes the Standardized Precipitation Index, the Standardized Precipitation-Evaporation Index, and Soil Moisture Anomalies. A comparison of the annual cycle and monthly precipitation time series shows a good agreement in the timing of the rainy seasons. The main differences between the data sets are in the ability to represent the magnitude of the wet seasons and extremes. Moreover, for the areas affected by drought, all the drought indicators agree on the time of drought onset and recovery although there is disagreement on the extent of the affected area. In regions with limited rain gauge data the estimation of the different drought indicators is characterized by a higher uncertainty. Further comparison suggests that the main source of differences in the computation of the drought indicators is the uncertainty in the precipitation data sets rather than the estimation of the distribution parameters of the drought indicators.

Addressing drought conditions under current and future climates in the Jordan River region

Abstract. The Standardized Precipitation–Evaporation Index (SPEI) was applied in order to address the drought conditions under current and future climates in the Jordan River region located in the southeastern Mediterranean area. In the first step, the SPEI was derived from spatially interpolated monthly precipitation and temperature data at multiple timescales: accumulated precipitation and monthly mean temperature were considered over a number of timescales – for example 1, 3, and 6 months. To investigate the performance of the drought index, correlation analyses were conducted with simulated soil moisture and the Normalized Difference Vegetation Index (NDVI) obtained from remote sensing. A comparison with the Standardized Precipitation Index (SPI), i.e., a drought index that does not incorporate temperature, was also conducted. The results show that the 6-month SPEI has the highest correlation with simulated soil moisture and best explains the interannual variation of the monthly NDVI. Hence, a timescale of 6 months is the most appropriate when addressing vegetation growth in the semi-arid region. In the second step, the 6-month SPEI was derived from three climate projections based on the Intergovernmental Panel on Climate Change emission scenario A1B. When comparing the period 2031–2060 with 1961–1990, it is shown that the percentage of time with moderate, severe and extreme drought conditions is projected to increase strongly. To address the impact of drought on the agricultural sector, the irrigation water demand during certain drought years was thereafter simulated with a hydrological model on a spatial resolution of 1 km. A large increase in the demand for irrigation water was simulated, showing that the agricultural sector is expected to become even more vulnerable to drought in the future.

Standardized precipitation evaporation index (SPEI)-based drought assessment in semi-arid south Texas

The coastal semi-arid region of south Texas is known for its erratic climate that fluctuates between long periods of drought and extremely wet hurricane-induced storms. The standard precipitation index (SPI) and the standard precipitation evaporation index (SPEI) were used in this study in conjunction with precipitation and temperature projections from two general circulation models (GCMs), namely, the National Center for Atmospheric Research (NCAR) Parallel Climate Model (PCM) and the UK Meteorological Office Hadley Centre model (HCM) for two emission scenarios—A1B (~720 ppm CO2 stabilization) and B1 (~550 ppm CO2 stabilization) at six major urban centers of south Texas spanning five climatic zones. Both the models predict a progressively increasing aridity of the region throughout the twenty-first century. The SPI exhibits greater variability in the available moisture during the first half of the twenty-first century while the SPEI depicts a downward trend caused by increasing temperature. However, droughts during the latter half of the twenty-first century are due to both increasing temperature and decreasing precipitation. These results suggest that droughts during the first half of the twenty-first century are likely caused by meteorological demands (temperature or potential evapotranspiration (PET) controlled), while those during the latter half are likely to be more critical as they curtail moisture supply to the region over large periods of time (precipitation and PET controlled). The drought effects are more pronounced for the A1B scenario than the B1 scenario and while spatial patterns are not always consistent, the effects are generally felt more strongly in the hinterlands than in coastal areas. The projected increased warming of the region, along with potential decreases in precipitation, points toward increased reliance on groundwater resources which are noted to be a buffer against droughts. However, there is a need for human adaptation to climate change, a greater commitment to groundwater conservation and development of large-scale regional aquifer storage and recovery (ASR) facilities that are capable of long-term storage in order to sustain groundwater availability. Groundwater resource managers and planners must confront the possibility of an increased potential for prolonged (multi-year) droughts and develop innovative strategies that effectively integrate water augmentation technologies and conservation-oriented policies to ensure the sustainability of aquifer resources well into the next century.