Standardized Precipitation Index Data Research Articles

A STUDY OF THE STANDARDIZED PRECIPITATION INDEX (SPI) AND MACHINE LEARNING TECHNIQUES FOR DROUGHT PREDICTION IN THE STATE OF PARAÍBA, BRAZIL

This study aimed to identify and analyze droughts in Paraíba, using the Standardized Precipitation Index (SPI) and machine learning algorithms for predicting SPI for the subsequent years (2020-2021) at six rainfall stations distributed across the mesoregions of Paraíba. The Precipitation data were downloaded from the Global Precipitation Climatology Centre (GPCC) and the National Oceanic and Atmospheric Administration (NOAA) database, covering the period from 1991 to 2019. Three machine learning algorithms were selected based on their ability to fit historical SPI data: Extra Trees Regressor, Gradient Boosting Regressor, and Random Forest Regressor. The applied machine learning models yielded satisfactory results, with the Extra Trees Regressor consistently producing the highest R² value across all stations, indicating high data explainability. The predictions were analyzed to determine their accuracy and reliability, providing valuable insights into precipitation variability and drought occurrence in different mesoregions of Paraíba. In conclusion, this study contributed to understanding climate variability and its implications in Paraíba, offering valuable insights into drought occurrence and the importance of adaptive approaches to mitigate adverse impacts. The application of SPI and machine learning techniques proved effective in analyzing and predicting precipitation, providing an objective approach to characterizing drought and rainfall intensity in specific regions.

Drought Assessment in Greece Using SPI and ERA5 Climate Reanalysis Data

The present work aims to assess the spatial variability and the trends of the annual rainfall and meteorological drought in the entire territory of Greece utilising the ERA5 reanalysis precipitation dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF), which spans from January 1940 to December 2022 (an 83-year period). Drought assessment took place based on the Standardized Precipitation Index (SPI) for timescales ranging from 1 month to 12 months. Evaluation was carried out by calculating SPI using observed rainfall data from five meteorological stations. The annual rainfall and drought severity trends for timescales of 1 (SPI-1), 3 (SPI-3), 6 (SPI-6) and 12 (SPI-12) months were analysed using the Theil–Sen slope method and the Mann–Kendall trend test. The results indicate significant, both increasing and decreasing, annual precipitation trends at the 95% significance level for the Aegean Islands, western Crete and western mainland of Greece. The results also indicate significant drought trends for SPI-12 for the Aegean Islands and western Peloponnese. Trend analysis for SPI-1, SPI-3 and SPI-6 indicate a mixture of non-significantly increasing wetting trends and increasing drought trends at the national scale. In conclusion, the ERA5 dataset seems to be a valuable tool for drought monitoring at the spatial scale.

رصد ظاهرة الجفاف باستخدام الاستشعار عن بعد ونظم المعلومات الجغرافية في محافظة واسط / العراق

Drought considered being the most vulnerable natural hazard that affects a large number of people. Drought has nonstructural effects that spread across a vast geographic area, endangering human lives. The Normalized difference vegetation index (NDVI) based on Landsat 8 imaging and the meteorological-based Standardized Precipitation Index (SPI) were used to monitor the drought in Wasit governorate eastern Iraq for 2013 and 2018. To accurately assess the drought-affected area and provide appropriate drought-proofing, the Arc GIS Desktop 10.5 program was utilized to examine the spatial and temporal variance of drought across the study area. Using NDVI analysis in the study area, the vegetation area decreased by 7 %. Drought classification based on 12-month SPI indicated the drought of the near-normal type. The SPI data analysis data show that in 2013, SPI values were lower in Badrah station's northern part of the study area. While in 2018, the lower SPI value - was 0.54033 in Ali al Gharbi terrestrial station. This result is considered near normal according to the SPI usual range. These findings could aid drought management programs and reveal the genuine drought situation in the area.

An improved daily standardized precipitation index dataset for mainland China from 1961 to 2018

The standardized precipitation index (SPI), one of the most commonly used drought indicators, is widely used in the research areas of drought analysis and drought prediction in different fields such as meteorology, agriculture, and hydrology. However, its main disadvantage is the relatively coarse time resolution of one month. To improve the time resolution of SPI to identify flash droughts, we have refined the traditional SPI calculation method and developed a new multi-scale daily SPI dataset based on data from 484 meteorological stations in mainland China from 1961 to 2018. SPI data from three different sites (located in Henan, Yunnan, and Fujian Provinces) at the three-month timescale were analyzed by comparing with historically recorded drought events. We found that the new multi-scale daily SPI can effectively capture drought events in different periods and locations and identify the specific start and end times of drought events. In short, our SPI dataset appears reasonable and capable of facilitating drought research in different fields.

The current and projected drought-caused loss of the spring wheat yield in the south of European Russia

The study focuses on the linkage between droughts at the beginning of the crop growing season and the yield of spring wheat in southern regions of European Russia. Its extrapolation into the future helps to analyze possible climatic risks in the spring wheat growing. It was found that moderate droughts from May to June, detected by using Standardized Precipitation Index data, in most cases have led to low yields. It was revealed that changes in the air moisture conditions in late spring and early summer in the period 1991-2018 compared with the previous thirty-year period were generally more favorable for the cultivation of spring wheat in most southern regions of European Russia. These positive changes were due to a decrease in the frequency of droughts. At the same time, a significant increase in the frequency of such droughts was observed in Samarskaya and Orenburgskaya oblasts, where the largest sown areas of spring wheat are situated. According to model projections of the future climate, similar changes in the air moisture conditions in the study area will generally remain favorable by the middle of the 21st century. Unfavorable regional forecasts associated with a possible increase in the frequency of droughts at the start of the crop growing season are predicted in Kuban, the east of the Volga region, and the north of the Lower Volga region and the southeast of the Central Black Earth region, in the North-Western Caspian Sea region.

Spatiotemporal assessment of meteorological drought using satellite-based precipitation data over Iraq

Iraq is a semi-arid country, which suffers the impact of recurrent droughts. However, studies related to the characterization of drought and risk evaluation in Iraq are scarce due to the lack of accurate climatic datasets. The present work seeks to examine the feasibility of utilizing Precipitation Data based on Remotely Sensed information from satellites (PDRS) in Iraq to monitor droughts. Two monthly PDRS are collected, namely CHIRPS for the period 1983-2016, and TRMM for the period 1998-2017 were used to calculate the Standardized Precipitation Index (SPI) for various timescales (SPI-3, SPI-6, and SPI-12) of different climate zones in the region. The findings obtained have been checked using data from sparsely scattered ground meteorological stations (GS). Although the PDRS was found to be capable of capturing estimated precipitation by GS data at different climatic zone, the two PDRS products demonstrated different responses to GS data. While the TRMM revealed a strong correlation for the droughts estimated with GS data, the CHIRPS data showed a milder correlation with the GS data. Besides, good consistency was observed in the time series of SPIs calculated with GS and PDRS data. Overall, the TRMM was found to measure the dry classes more accurately while CHIRPS was found to be better at various dry and wet classes in Temporal Coincidence (TC) terms. In addition, the TRMM – SPI data showed a better correlation in detecting the drought characteristics for Z-I and Z-II, however, the CHIRPS revealed a stronger correlation for Z-III. The findings suggested the suitability of TRMM precipitation for drought analysis and monitoring in Iraq Zones I and II, and the use of CHIRPS precipitation data for Z-III.

Reconstructing Extreme Precipitation in the Sacramento River Watershed Using Tree‐Ring Based Proxies of Cold‐Season Precipitation

AbstractExtreme precipitation and consequent floods are some of California's most damaging natural disasters, but they are also critical to the state's water supply. This motivates the need to better understand the long‐term variability of these events across the region. This study examines the possibility of reconstructing extreme precipitation occurrences in the Sacramento River Watershed (SRW) of Northern California using a network of tree‐ring based moisture proxies across the Western US. We first develop a gridded reconstruction of the cold‐season standardized precipitation index (SPI) west of 100°W. We then develop an annual index of regional extreme precipitation occurrences in the SRW and use elastic net regression to relate that index to the gridded, tree‐ring based SPI. These regressions, built using SPI data across the SRW only and again across a broader region of the Western US, are used to develop reconstructions of interannual variability in extreme precipitation frequency back to 1400 CE. The SPI reconstruction is skillful across much of the West, including the Sacramento Valley and Central Oregon. The reconstructed SPI also captures historical interannual variations in extreme SRW precipitation, although individual events may be under‐ or over‐estimated. The reconstructions show more SRW extremes from 1580 to 1700 and 1850 to 1915, a dearth of extremes prior to 1550, and a 2–8 year oscillation after 1550. Using tree‐ring proxies beyond the SRW often dampens the reconstructed extremes, but these data occasionally help to identify known extreme events. Overall, reconstructions of SRW extreme precipitation can help to understand better the historic variability of these events.

Development and Forecasting Drought Indices Using SPI (Standardized Precipitation Index) for Local Level Agricultural Water Management

Drought is primarily an agricultural phenomenon that refers to conditions where plants are responsive to certain levels of moisture stress that affect both the vegetative growth and yield of crops. It occurs when supply of moisture stored in the soil is insufficient to meet the optimum need of a particular type of crop. Causes of drought in Bangladesh are related to climate variability and non-availability of surface water resources. While it may be possible to indicate the immediate cause of a drought in a particular location, it often is not possible to identify an underlying cause. Therefore, to improve all these services in favour of enhancing agricultural production and reducing food insecurity in Bangladesh, it is mandatory to develop an effective way for disseminating the SPI data indicating drought indices to farmers, and enhance drought and climate resilience. To develop future plan and policy in agricultural sector of Bangladesh, it is vital to understand the previous droughts events with accurate indicators. Since this study will contribute to the agricultural development of Bangladesh therefore there is an obvious need to understand the change of drought frequency all over Bangladesh using a standardized drought index. The main intention of this project is to prepare a proper baseline for forecasting drought indices using SPI data. So, the final outcome of this project would be a knowledge base where a proper forecasting tools and dissemination networks can be updated/developed for farmers.

Long-Term, Gridded Standardized Precipitation Index for Hawai‘i

Spatially explicit, wall-to-wall rainfall data provide foundational climatic information but alone are inadequate for characterizing meteorological, hydrological, agricultural, or ecological drought. The Standardized Precipitation Index (SPI) is one of the most widely used indicators of drought and defines localized conditions of both drought and excess rainfall based on period-specific (e.g., 1-month, 6-month, 12-month) accumulated precipitation relative to multi-year averages. A 93-year (1920–2012), high-resolution (250 m) gridded dataset of monthly rainfall available for the State of Hawai‘i was used to derive gridded, monthly SPI values for 1-, 3-, 6-, 9-, 12-, 24-, 36-, 48-, and 60-month intervals. Gridded SPI data were validated against independent, station-based calculations of SPI provided by the National Weather Service. The gridded SPI product was also compared with the U.S. Drought Monitor during the overlapping period. This SPI product provides several advantages over currently available drought indices for Hawai‘i in that it has statewide coverage over a long historical period at high spatial resolution to capture fine-scale climatic gradients and monitor changes in local drought severity.

Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach

This study presents a steady-state Markov chain model to predict the long-term probability of drought conditions. The research aims to propose a rigorous framework for statistical analysis of drought characteristics and its trends over time for a large area of aquifers and plains in Iran. For this purpose, two meteorological indicators called the Standardized Precipitation Index (SPI), and the Groundwater Resource Index (GRI) are examined. The groundwater drought study includes more than 26,000 wells in about 600 meteorological stations over 20 years being surveyed daily. This study discusses the spatial interpolation of drought steady-state probabilities based on recorded SPI and GRI data at three intervals, i.e., 1994 to 2004, 2005–2015, and 1994 to 2015. The final zoning of the system results in an average increase in the steady-state constant of the SPI index in the first half of the whole study period to approximately 62%. While in the second period of study, the average percentage of the steady-state climatic drought was calculated to be 75%. The average amount of drought in the extended study area of the country was found to be up to 46%.

A Hybrid Wavelet-Arima Model for Standardized Precipitation Index Drought Forecasting

ABSTRACTThis paper proposes A Hybrid Wavelet-Auto-Regressive Integrated MovingAverage (W-ARIMA) model to explore the ability of the hybrid model over an ARIMAmodel. It combines two methods, a Discrete Wavelet Transform (DWT) and ARIMAmodel using the Standardized Precipitation Index (SPI) drought data for forecastingdrought modeling development. SPI data from January 1954 to December 2008 used wasdivided into two - (80%/20% for training/testing respectively). The results were comparedwith the conventional ARIMA model with Mean Square Error (MSE) and Mean AverageError (MAE) as an error measure. The results of the proposed method achieved the bestforecasting performance.

Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index

This paper proposes Wavelet-Group Methods of Data Handling (W-GMDH) model to explore its ability of drought forecasting. The W-GMDH model was developed by combining Discrete Wavelet Transform (DWT) and GMDH model using the Standardized Precipitation Index (SPI) drought data for forecasting to assess the effectiveness of the new (W-GMDH) model. These methods were used on four SPI data sets (SPI3, SPI6, SPI9 and SPI12). To achieve this, a 624 month of SPI data from January 1956 to December 2008 was used and divided into two parts (80% for training and 20% for testing). The results of the W-GMDH model were then compared with the conventional GMDH model using Root Mean Square Error (RMSE), Mean Average Error (MAE) and coefficient of correlation as the performance evaluation measures. Both results of the proposed W-GMDH model and the GMDH showed very clearly that the propose method can achieve the best forecasting performance in terms of accuracy for each of the SPI data series. The key role played by the DWT is to smooth the analysis of SPI data obtained after the wavelet decomposition which is also used to decompose the SPI data into different number of component series to minimize the forecasting error. In all the results computed, the proposed model has a minimum error indicating its superiority over the GMDH model. This indicates that W-GMDH model’s performance has outweighed that of the conventional GMDH model in SPI drought forecasting. The research contributes to the discovering of viable forecasting of drought and demonstrates that the established model is good and appropriate for drought. In all the analysis W-GMDH model has the minimum error. The overall results showed that SPI12 has the minimum error among all the SPI data considered.

Integrated TRMM Data and Standardized Precipitation Index to Monitor the Meteorological Drought

Droughts are a major problem in Iraq especially in the Arid and Semi-Arid Lands where they are frequent and causes a great deal of suffering and loss. Drought monitoring and forecasting requires extensive climate and meteorological data which is usually largely missing in developing countries or not available in the required spatial and temporal resolutions. In this study, the drought categories were defined for the years 2000, 2005, 2010, 2015 and 2017 using the TRMM data to map the spatiotemporal meteorological drought, and the Standardized Precipitation Index (SPI) to analyze the meteorological drought at 11 stations located in Western Iraq. The SPI analyses were performed on 12-month datasets for five years. The results showed that the northeast region has the higher rainfall indices and the southwest region has the lowest rainfall. An analysis of the drought and rain conditions showed that the quantity of extreme drought events was higher than that expected in the study area, especially in the south and southwest areas. Therefore, an alternate classification is proposed to describe the drought, which spatially classifies the drought type as mild, moderate, severe and extreme. In conclusion, the integration between TRMM data SPI data proved to be an effective tool to map the spatial distribution and drought assessment in the study area.

FORECASTING DROUGHT WITH ARIMA MODEL AND STANDARDIZED PRECIPITATION INDEX (SPI)

Drought forecasting is an important forecasting procedure for preparing and managing water resources for all creatures. Natural disasters across the regions such as flooding, earthquakes, droughts etc. have caused damages to life as a result of which numerous researches have been conducted to assist in reducing the phenomenon. Consequently, therefore, this study considered using Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting drought using Standardized Precipitation Index (SPI) as a forecasting tool which was used to measure and classify drought. The models are developed to forecast the SPI series. Results indicated the forecasting ability of the ARIMA models which increases as the timescales. The study is aimed at using ARIMA method for modeling SPI data series. The studies used data set made up of 624 months, obtained from 1954 to 2008. In the analysis only SPI3 series was non-seasonal while others have seasonality and Seasonal ARIMA was carried out, SPI12 was significant compared with the forecasting accuracy alongside the diagnostic checking having a minimum error of RMSE and MAE in both testing and training phases. The research contributes to the discovering of feasible forecasting of drought and demonstrates that the established model is good and appropriate for forecasting drought.

Relationship of Drought and Teleconnection Patterns; Case Study of Qara-Qom Basin

Aims & Backgrounds: Drought is a natural feature of an area and in every region that occurs, it leads to economic, social and environmental losses. In this research, the Teleconnection patterns in drought occurrence in Qara-Qom basin has been investigated. Methodology: In this regard, precipitation data of 30 rain gauge and synoptic stations as well as data on 32 numerical indexes of teleconnection from NOAA site during the 1987-2013 period were obtained. Initially, the standardized precipitation index data in the 9 to 48-month scale were classified by Factor Analysis and stations with similar behavior were identified in the study area. Then, the relationship between the average drought index of each area with each of the Teleconnection patterns was evaluated simultaneously and with a delay of 6 and 9 months through the correlation statistical method. Findings: The effects of Teleconnection patterns on drought vary in different zones. The Multivariate ENSO Index, Pacific Decadal Oscillation, Southern Oscillation Index, Nino 3/4, 3 has the most significant correlation with the standardized precipitation index scales. Also, the correlation with 6-month delayed drought index indicated that four factors (total basin), drought index with nino 4 indexes, 3/4 and 3, multivariate ENSO, Madden Julian oscillation in regions 20, 70, 80, 100 degrees East direct relationship, and With East Pacific- North Pacific index, the southern oscillation and Madden Julian in the regions of 160 ° East, 120 ° and 40 ° West has a significant negative relationship. Four factors (total basin stations): The 9-month delayed drought index showed a significant negative correlation with the East Pacific- North Pacific index, southern oscillation and Mandden Julian fluctuations in the regions 120 and 40 degrees west, and with the Nino 3 indicator, the multivariate ENSO And the fluctuations of Madden Julian in the regions of 70, 80 and 100 degrees east has a significant direct relationship. Conclusion: The results of correlation analysis showed that there is a significant relationship between Teleconnection patterns and droughts in Qara-Qom basin.

Drought characterization for the state of Rio de Janeiro based on the annual SPI index: trends, statistical tests and its relation with ENSO

Drought indices allow for the characterization of drought events, being the most widely used the Standardized Precipitation Index (SPI), which is recommended by the World Meteorological Organization (WMO) as the standard drought index. The objectives of this study are: i) to characterize the variation of annual SPI index in the state of Rio de Janeiro from 1979 to 2009; ii) to relate ENSO events with the variation of the annual SPI; iii) to identify possible significant trends of rainfall increase and reduction based on the annual SPI; and iv) characterize drought evolution in the SRJ in the 1979–2009 period (Appendix A). For the SPI calculation, a monthly time series with 31 consecutive years was used, based on rainfall data from 99 rainfall stations distributed in eight government regions. Annual SPI data was presented using maps created with ArcGIS software version 10.2.2 and all statistical procedures were calculated in the R environment software. The Shapiro-Wilk (SW) and Bartlett (B) tests characterized rainfall data as being mostly normal and non-homogeneous. The annual SPI showed that the Center South region experienced the highest continuous dry period between 1999 and 2000 and 2002–2003. The annual SPI was assessed with the Mann-Kendall (MK), Curvature Sen (magnitude) and Pettitt (abrupt inflection) tests. The MK test indicated significant trends of annual rainfall increase in the North and Mountainous regions, and significant reduction trends in the Mountainous and Center South regions. Pettitt's test indicated significant abrupt changes in the North, Center South and Mountainous regions. The Curvature Sen test revealed that stations Dois Rios (North) and Cordeiro (Mountainous) registered a greater magnitude in the increase and reduction of the annual SPI, respectively.

Modified analogue forecasting in the hidden Markov framework for meteorological droughts

An analogue method (AM) is a nonparametric approach that has been applied to predict the future states of a dynamic system by following the evolution of the analogues in the historical archive. In this study, we proposed a hidden Markov model (HMM) framework for a modified analogue forecasting (MAF) approach for meteorological droughts in Korea. The unobservable (hidden) state process in the framework aims to model the underlying drought state, while the observation process was formed from the time series of the standardized precipitation index (SPI) as a drought index. Within the framework, the likelihood estimator was used as the measure of similarity between past SPI analogues and current data. The MAF approach was conducted on the selected analogues to make forecasts at lead times of one and three months. The proposed model was applied to five selected stations in Korea using the SPI data from 1973 to 2016. The forecasting performance of the proposed model was tested during the validation period (2003–2016) using several statistical criteria and it was compared to a persistence-based benchmark model. The results showed significant improvement in the forecasting capacity, and satisfactory performance for numerical SPI forecasting and categorical drought forecasting. The results also suggested that the proposed model was able to provide useful information for determining future drought categories for early drought warning with a lead time of up to three months.

A 1286‐year hydro‐climate reconstruction for the Balkan Peninsula

We present a June–July drought reconstruction based on the standardized precipitation index (SPI) for the Balkan Peninsula over the period 730–2015 CE. The reconstruction is developed using a composite Pinus heldreichii tree‐ring width chronology, from a high‐elevation network of eight sites in the Pindus Mountains in northwest Greece, composed of living trees and relict wood. The dataset includes the ring width series of Europe's currently oldest known living tree, dendrochronologically dated to be more than 1075 years old. The spatial coverage of the reconstruction is improved by using an averaged gridded SPI data target derived from a response field that is located north of the study region. Justification for this approach includes the remoteness of instrumental data, the spatial variability of precipitation and synoptic scale circulation patterns. Over the past 1286 years, there have been 51 dry and 43 pluvial events. The driest year during the 1286‐year‐long period was 1660 and the wettest year was 1482. Comparison with shorter reconstructions and documentary evidence validates the new reconstruction, and provides additional insight into socioeconomic impacts and spatial patterns of extreme events. Fifty‐nine of 72 previously undescribed extremes occurred prior to the 17th century. The new reconstruction reveals long‐term changes in the number of extremes, including substantially fewer drought and pluvial events in the 20th century. Additional tests on the long‐term effects of age structure, replication and covariance changes support the heteroscedastic nature of the reconstructed hydro‐climatic extremes.

Drought prediction over the East Asian monsoon region using the adaptive neuro‐fuzzy inference system and the global sea surface temperature anomalies

ABSTRACTPrediction 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.

Integrating TRMM and MODIS satellite with socio-economic vulnerability for monitoring drought risk over a tropical region of India

Drought is a recurring feature of the climate, responsible for social and economic losses in India. In the present work, attempts were made to estimate the drought hazard and risk using spatial and temporal datasets of Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) in integration with socio-economic vulnerability. The TRMM rainfall was taken into account for trend analysis and Standardized Precipitation Index (SPI) estimation, with aim to investigate the changes in rainfall and deducing its pattern over the area. The SPI and average rainfall data derived from TRMM were interpolated to obtain the spatial and temporal pattern over the entire South Bihar of India, while the MODIS datasets were used to derive the Normalized Difference Vegetation Index (NDVI) deviation in the area. The Geographical Information System (GIS) is taken into account to integrate the drought vulnerability and hazard, in order to estimate the drought risk over entire South Bihar. The results indicated that approximately 36.90% area is facing high to very high drought risk over north-eastern and western part of South Bihar and need conservation measurements to combat this disaster.