Calculation Of Standardized Precipitation Index Research Articles

PENGUKURAN KINERJA DATA HUJAN CHIRPS DALAM PENILAIAN KEKERINGAN DI LOMBOK TENGAH

PENGUKURAN KINERJA DATA HUJAN CHIRPS DALAM PENILAIAN KEKERINGAN DI LOMBOK TENGAH

Spatially Consistent Drought Hazard Modeling Approach Applied to West Africa

A critical stage in drought risk assessment is the measurement of drought hazard, the probability of occurrence of a potentially damaging event. The standard approach to assess drought hazard is based on the standardized precipitation index (SPI) and a drought intensity classification established according to a fixed set of SPI values. We show that this method does not allow for the assessment of region-specific hazards, and we propose an alternative method based on the extreme value theory. We model precipitation using an extreme value mixture model, with a normal distribution for the bulk, and a generalized Pareto distribution for the upper and lower tails. The model estimation allows us to identify the threshold value below which precipitation can be qualified as extreme. The quantile function is used to measure the intensity of each category of droughts and calculate the drought hazard index (DHI). By construction, the DHI value varies according to the specific characteristics of the left tail of the precipitation distribution. To test the relevance of our approach, we estimate the DHI over a gridded set of rainfall data covering West Africa, a large and climatically heterogeneous region. The results show that our mixture model fits the data better than the model used for SPI calculation. In particular, our model performs better to identify extreme precipitation in the left tail of the distribution. The DHI map highlights clusters of high drought hazard located in the central part of the region under study.

Changes in rainfall patterns in southern Brazil over 1961–2020 period detected by rain gauge data

AbstractLong‐term instrumental climate records are a major source of data for historical trends and extreme event analysis. Time series analysis of several rain gauges shows that the mid‐latitudes of Brazil experienced an increase in both annual and extreme rainfall over the last 60 years (1961–2020). The annual time series shows a significant upward trend, while changes in the frequency and intensity of heavy rainfall indicate a shortening of the recurrence interval for extreme precipitation events. In support of these results, the standardized precipitation index (SPI) also shows a positive trend for rainfall anomalies. Thus, the flood risk is higher at present than a few decades ago, while drought intensity is lower for both short and long monthly intervals used for the SPI calculation. The 60‐year instrumental rainfall data for southern Brazil shows that the amount of rainfall and changes in intensity could be higher than climate projections from models based on a broader spatial scale.

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.

Sensitivity analysis of standardized precipitation index to climate state selection in China

In the calculation of the standardized precipitation index (SPI) index, it is necessary to select a certain period of precipitation samples as the reference climate state, and the SPI obtained by different reference climate states have different size. Therefore, the influence of different reference climate states on the accuracy of SPI calculation is worth analyzing. Based on the monthly precipitation data of 1184 stations in China from 1961 to 2010, the influence of the selection of the reference climatic state in the calculation of SPI was analyzed. Using 30 consecutive years as the duration of the reference climatic state, 1961–2010 is divided into three periods 1961–1990, 1971–2000, 1981–2010. Taking the SPI obtained from the entire period as the standard value, the spatial distribution of SPI error and the accuracy of SPI classification based on each reference period were analyzed. Then, the resampling method was used to analyze the influence of time-continuous precipitation samples on the size of SPI. The results show that the SPI error of most sites is less than 0.2, and the accuracy of SPI classification is more than 80%. Although the errors of SPI mostly come from extreme drought and extremely wet, this does not affect the accuracy of the recognition of extreme drought and extremely wet. In most regions, it is reliable to calculate SPI based on the precipitation data of continuous 30 years, but the reliability of SPI is relatively low in areas with frequent drought. The results of the resampling analysis and 30-year sliding analysis show that the distribution parameters have noticeable turning characteristics, and the precipitation distribution parameters of nearly 85% stations had noticeable turning point before 1985, which led to the precipitation data of continuous 30 years easily overestimate the dry/wet.

Bivariate Frequency of Meteorological Drought in the Upper Minjiang River Based on Copula Function

Based on the Standardized Precipitation Index (SPI) and copula function, this study analyzed the meteorological drought in the upper Minjiang River basin. The Tyson polygon method is used to divide the research area into four regions based on four meteorological stations. The monthly precipitation data of four meteorological stations from 1966 to 2016 were used for the calculation of SPI. The change trend of SPI1, SPI3 and SPI12 showed the historical dry-wet evolution phenomenon of short-term humidification and long-term aridification in the study area. The major drought events in each region are counted based on SPI3. The results show that the drought lasted the longest in Maoxian region, the occurrence of minor drought events was more frequent than the other regions. Nine distribution functions are used to fit the marginal distribution of drought duration (D), severity (S) and peak (P) estimated based on SPI3, the best marginal distribution is obtained by chi-square test. Five copula functions are used to create a bivariate joint probability distribution, the best copula function is selected through AIC, the univariate and bivariate return periods were calculated. The results of this paper will help the study area to assess the drought risk.

Evaluation of Remotely Sensed Precipitation Estimates from the NASA POWER Project for Drought Detection Over Jordan

Droughts can cause devastating impacts on water and land resources and therefore monitoring these events forms an integral part of planning. The most common approach for detecting drought events and assessing their intensity is use of the Standardized Precipitation Index (SPI), which requires abundant precipitation records at good spatial distribution. This may restrict SPI usage in many regions around the world, particularly in areas with limited numbers of ground meteorological stations. Therefore, the use of remotely sensed derived data of precipitation can contribute to drought monitoring. In this study, remotely sensed precipitation estimates from the POWER/Agroclimatology archive of NASA and their derived SPI for different time intervals were evaluated against gauged observations of precipitation from 13 different stations in arid and semiarid locations in Jordan. Results showed significant correlations between remotely sensed and ground data with relatively high R values (0.67–0.91), particularly where seasonal precipitation exceeded 50 mm/year. For evaluation of remotely sensed data in SPI calculation, several objective functions were used; the results showed that SPI based on satellite estimates (SAT-SPI) showed good performance in detecting extreme droughts and indicating wet/dry conditions. However, SAT-SPI showed high tendency to overestimate drought intensity. Based on these findings, remotely sensed precipitation from the POWER/Agroclimatology archive showed good potential for use in detecting extreme meteorological drought with the provision of careful interpretation of the data. These types of studies are essential for evaluating the applicability of new drought monitoring information and tools to support decision-making at relevant scales.

Assessment of Drought Hazard: A Case Study in Sampean Baru Watershed, Bondowoso Regency

Abstract. The Sampean Baru watershed is categorized as an area with a very high level of vulnerability to drought. The purpose of this study was to assess the hydrological drought in the Sampean Baru watershed. NCEP / NCAR Reanalysis climate change data is used to obtain synthetic rainfall models of the past. This climate change data has crude resolution and is global in scale. The NCEP / NCAR Reanalysis data was processed through a downscaling process to obtain local scale climate data in the form of past synthetic rains. Artificial Neural Network (ANN) is one of the downscaling models used in this study. The ANN downscaling output was processed through discharge modeling using SWAT. Hydrological drought assessment used the Standardized Precipitation Index (SRI) method. The SRI calculation was based on the accumulated discharge over a period of time. The results indicated that the ANN downscaling process can bridge global scale climate data to local scale climate data. SWAT modeling gave excellent results. SRI-6 can describe past droughts. It can be seen from the compatibility between the results of the drought assessment and the drought data belonging to the relevant authorities.

Uncertainty in SPI Calculation and its Impact on Drought Assessment in Different Climate Regions over China

AbstractUncertainty in the calculation of a Standardized Precipitation Index (SPI) attracted growing concerns in the hydrometeorology research community in the last decade. This issue is addressed in the present study from the perspective of candidate probability distributions, the data record length, the cumulative timescale and the selection of a reference period with the bootstrap and Monte Carlo methods using daily precipitation data observed in four climate regions across China. The impacts of the uncertainty in an SPI calculation on drought assessment are also investigated. Results show that the Gamma distribution is optimal in describing the cumulative precipitation in China; among the four timescales investigated in the present study, the minimal timescale appropriate for SPI calculation is 20 days for the humid region, 30 days for the semi-humid/semi-arid region and Tibetan Plateau (mostly its eastern part), and 90 days for the arid region. The uncertainty in SPI calculation decreases with the increase of timescale and record length, essentially as a consequence of the decrease of the confidence interval width of Gamma distribution parameters with the increase of timescale and record length. But there is little improvement for the parameter estimation with record length longer than 70 years. There is greater uncertainty for high absolute SPI values than for small ones, consequently there is greater uncertainty in assessing extreme droughts than moderate droughts. Reference period selection has large impacts on drought assessment, especially in the context of climate change. The uncertainty of the SPI calculation has large impacts on categorizing droughts, but no impact on assessing the temporal features of drought variation.

Drought Monitoring Using Rainfall, Evapotranspiration and Streamflow Data: A Case Study of Kaduna River Catchment Area (Nigeria)

Drought is defined as the lack of adequate precipitation, either rain or snow that causes reduced soil moisture or groundwater, diminished streamflow, crop damage and a general water shortage. The objective of this study focuses on meteorological and hydrological drought monitoring in river Kaduna catchment area. Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) drought indices were used to characterize meteorological drought while Streamflow Drought Index (SDI) was used for hydrological drought monitoring for a period of 34 years (1967 – 2001). DrinC software, a drought indices calculator, was used for the calculation of SPI, RDI, and SDI respectively. The drought severity classification based on meteorological and hydrological drought indices gave 33% and 37% drought conditions period with the year 1967 – 2001. Based on these indexes, the drought characteristics of the catchment area were investigated by analyzing meteorological data from 1967 to 2001. The results of this analysis show that more non-drought/normal conditions were predominant than drought conditions. During the period under study (34 years), only one-year return period of extreme drought condition.

Uncertainty Analysis of Standardized Precipitation Index Due to the Effects of Probability Distributions and Parameter Errors

The standardized precipitation index (SPI) is widely used in drought assessments to monitor drought conditions due to its simple data requirement and multi-scale characteristics. However, there are some uncertainties existing in the process of its calculation cannot be ignored for accurate drought assessments. This study, taking the Heihe River basin in northwest of China as the study area, mainly focuses on investigated the uncertainty issuesies both in SPI calculation and in drought characteristics associated with the probability distributions and parameter estimation errors. based on the precipitation data at 4 9 meteorological stations covering a period of 1960-2015 in Heihe River basin(Northwest China). 10Ten probability distributions (two- and three-parameter Log-logistic and Lognormal, Generalized Extreme Value, Pearson-III, Burr, Gamma, Inverse Gaussian and Weibull) are employed were selected to estimate the SPI.and Maximum Likelihood Estimation isMaximum Likelihood EstimationMLE was used to estimate distribution parameters. Randomly generating parameters based on the normality assumption was is applied to quantify the uncertainty of parameter estimations and their effects toeffects on drought index. Results showed that, Log-Logistic logistic type distribution presents quite close performance with the benchmark Gamma distribution, and thus was is recommended as an alternatives in SPI calculation in fitting the precipitation data over the study area., and resulting in large uncertainty in SPI values although it passed the Kolmogorov-SmirnovK-S and Anderson-DarlingA-D tests. Effects of both uncertainty sources grades are more reflected on extreme droughts (extremely dry or wet). The more extreme the SPI value, the greater uncertainties caused bythese two both sources. Furthermore, the drought characteristics vary a lot from different distributions and parameter errors. These findings highlight the importance of uncertainty analysis of drought assessments, given that most studies in climatology focus on extreme values for drought analysis.

Effects of Gamma-Distribution Variations on SPI-Based Stationary and Nonstationary Drought Analyses

This study aims to analytically explore the effects of changing rainfall distributions in terms of variations in the mean and variance of gamma distributions on the drought analysis based on standardized precipitation index (SPI). Traditional SPI calculation involves the fitting of observed rainfall series to a time-invariant probability distribution; the gamma distribution is commonly used. Fitting a time-varying gamma distribution to a trending rainfall series leads to nonstationary SPI (NSPI) series. The effects of changing gamma distributions on the SPI and NSPI can be systematically summarized by the proposed nine-category distributional-change scheme in terms of variations in the mean and variance of the gamma distributions. The annual wet-season rainfall series at Taipei (1897–2017) and Dawu (1940–2017), which exhibit significantly increasing and insignificantly decreasing trends, respectively, were selected for demonstration. A clearly increasing rainfall trend at Taipei over the last four decades corresponds to less severe droughts in the SPI series and more frequent and more severe droughts in the NSPI series. These contradictory results are attributed to the time-invariant gamma distribution, which causes the trending SPI series to be identical to the rainfall series, and the time-varying gamma distribution, which results in the trend-free NSPI series. The modeling of nonstationarity in rainfall series in the proposed calculation framework depends on the purposes of the analysis since different information is revealed for drought assessments.

Modified Standardized Precipitation Index and Evaluation of its Effectiveness using Past Extreme Drought Cases

The standardized precipitation index (SPI) was developed based on the fact that if precipitation decreases, a relative shortage of water would occur compared to the required water demand, resulting in drought. However, similar to other drought indexes, the relative standard distribution of the time series of precipitation or runoff is calculated. The analysis suggests that relatively high-rainfall areas occur with no actual occurrence. Hence, modified drought analysis techniques are required to mitigate this phenomenon. Therefore, in this paper, a gamma distribution, calculated for the time series of points in the SPI calculation process, was expanded nationwide, and the drought index was calculated by applying it to the standard regular distribution after the calculation. In addition, the modified SPI (M-SPI) was verified for its effectiveness using the extreme drought and drought duration in areas where there was limited water supply in the past. Consequently, the SPI recognized drought only in 1995, 2001, and 2014, while the drought duration in 2010 and 2014 remained underestimated as a result of the comparison with the year of limited water supply and the results of the SPI and M-SPI. However, the M-SPI determined droughts in 1995, 1996, 2001, and 2010. In the case of Yeosu, although the SPI did not recognize two extreme droughts, M-SPI successfully recognized one extreme drought. In the case of Goheung, both the drought indexes recognized extreme drought. Keywords: Extreme Drought, M-SPI, SPI, Space Time Series, Gamma Distribution

Sensitivity of SPI to Distribution Functions and Correlation Between its Values at Different Time Scales in Central Africa

This paper studies sensitivity of Standardized Precipitation Index (SPI) to statistical distribution functions used in SPI computation procedure, in order to find out which are more appropriate and to assess SPI shift if using inappropriate distribution functions. Results may explain one of the reasons why spatial SPI computed with unique distribution function as usually, sometimes does not give better drought description. Central Africa is chosen as the study area because of its importance in climate change perspective and the necessity to use improved tools for drought quantification in this region. Monthly precipitation data for the period 1951–2016, both from the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) were used. They were first aggregated at various time scales and four statistical distribution functions (gamma, weibull, exponential and lognormal) were tested to select the best-fit. Next, SPI was calculated for various time scales using the best fit function at each grid point and results were compared to those computed assuming a same distribution function at all grid points. Results show that, from 1- to 9-month time scales, observed spatial patterns of distribution functions were more homogeneous and the weibull function had the highest extended spatial rate, followed successively by gamma, lognormal and exponential. From 12-month time scale, spatial patterns were inhomogeneous, and no gridded precipitation followed the exponential function. The study of cross-correlations showed significant resemblance between SPIs at different time scales, leading to reduce the studies from 1- to 15-month. SPI values were affected if inappropriate distribution functions were used and the shift increases in correlation with the increase of time scale. The two datasets CRU and GPCC showed similar results, but GPCC’s SPIs were wetter and distribution functions somewhat more dispersed spatially from 12-month time scale. For SPI calculation in Central Africa, the weibull and gamma functions if used lead to good SPI results at short time scales (not more than 9-month) compared to SPI calculated using exponential or lognormal function. From 12-month time scale, it is recommended to choose the best fit distribution function at each grid point in order to expect good SPI results and then better drought description.

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.

Drought severity based on the SPI index and its relation to the ENSO and PDO climatic variability modes in the regions North and Northwest of the State of Rio de Janeiro - Brazil

Drought severity in regions North and Northwest of Rio de Janeiro state (SRJ) was evaluated using the Standardized Precipitation Index (SPI) for the monthly (SPI-1) and annual (SPI-12) scales, as well as its relationship with El Niño - Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Data of 14 rainfall stations from a time series considering the period of 1967–2013 was used. The SPI calculation was based on the SCI package available at R software version 3.4.2 library. SPI categories used are Very Dry, Moderately Dry and Extremely Dry associated with the hot and cold phases of the PDO, followed by ENSO events according to the Oceanic Niño index (ONI) in El Niño region 3.4 using an interaction matrix. The matrix was evaluated by the coefficients of Kendall (τ), Sperman (ρ), Pearson (r) and R2 determination, followed by Willmott's concordance index (d), Standard Error of Estimation (SEE) and Student's t-test. The intense activity of La Niña in the cold 1st phase of the PDO increased rainfall with greater intensity in the Northwest region compared to the North. In the 2nd cold phase of the PDO, the events of El Niño, La Niña and Neutral years of the ENSO mutually contribute to the changes in rainfall regime of both regions. The most frequent SPI categories are Moderately Dry and Extremely Dry. SPI-1 was efficient in perceiving extreme droughts, while SPI-12 results were not significant in the revealing of extreme droughts. In addition, there was no relational pattern through statistical indexes between droughts detected by SPI, ENSO and PDO. Nevertheless, when ENSO cycles were persistent within both phases of the PDO, droughts occurred, mainly on the monthly scale.

Assessing the threat of future megadrought in Iberia

ABSTRACTWater scarcity is a critical issue in Iberia but few studies have investigated future drought over the region. The few that do are continental or global studies and do not report the range of possible future projections. In this paper, historical drought and future projections of drought were examined, using observed and downscaled rainfall data from climate models, for the main Iberian international basins (Douro, Tagus and Guadiana). Two drought indices were used, the standardized precipitation index (SPI) and the drought severity index (DSI). However, problems were found in the distribution fitting required for the calculation of SPI which render this index inadequate for assessing droughts in Iberia. The skill of CMIP5 climate models in representing historical drought for the Douro, Tagus and Guadiana is very variable with some not showing enough persistence of dry conditions, and others simulating droughts that are too long and too severe. Nevertheless, the observed range of drought conditions for these basins was encompassed by the ensemble members. Furthermore, we found no relationship between performance in simulating mean large‐scale circulation and model ability to reproduce the historical drought characteristics for the region. All models project an intensification of drought conditions for the three basins. However, some only project small increases, while most project extreme multi‐year droughts by the end of the century. We found that climate model future projections are neither related to their performance in simulating historical drought nor to their large‐scale circulation patterns. This result emphasises the need for climate change impact studies to take into account the whole range of climate model projections in order to provide the best information for robust adaptation. Choosing one or a subset of climate models based on historical performance would not lead to a credible and justified reduction in uncertainty.

Drought monitoring and reliability evaluation of the latest TMPA precipitation data in the Weihe River Basin, Northwest China

The high resolution satellite precipitation products bear great potential for large-scale drought monitoring, especially for those regions with sparsely or even without gauge coverage. This study focuses on utilizing the latest Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA 3B42V7) data for drought condition monitoring in the Weihe River Basin (0.135×106 km2). The accuracy of the monthly TMPA 3B42V7 satellite precipitation data was firstly evaluated against the ground rain gauge observations. The statistical characteristics between a short period data series (1998–2013) and a long period data series (1961–2013) were then compared. The TMPA 3B42V7-based SPI (Standardized Precipitation Index) sequences were finally validated and analyzed at various temporal scales for assessing the drought conditions. The results indicate that the monthly TMPA 3B42V7 precipitation is in a high agreement with the rain gauge observations and can accurately capture the temporal and spatial characteristics of rainfall within the Weihe River Basin. The short period data can present the characteristics of long period record, and it is thus acceptable to use the short period data series to estimate the cumulative probability function in the SPI calculation. The TMPA 3B42V7-based SPI matches well with that based on the rain gauge observations at multiple time scales (i.e., 1-, 3-, 6-, 9-, and 12-month) and can give an acceptable temporal distribution of drought conditions. It suggests that the TMPA 3B42V7 precipitation data can be used for monitoring the occurrence of drought in the Weihe River Basin.

Evaluation of the Standardized Precipitation Index as an early predictor of seasonal vegetation production anomalies in the Sahel

ABSTRACTWe analysed the performance and timeliness of the Standardized Precipitation Index (SPI) in anticipating deviations from mean seasonal vegetation productivity in the Sahel. Gridded rainfall estimates are used to compute the SPI for 1–6-month timescales, whereas the Z-score of the cumulative value of the Fraction of Absorbed Photosynthetically Active Radiation over the growing season (zCFAPAR) is used as a proxy of seasonal productivity. Results show that the strength of the link varies in space as a function of both the SPI timescale and the timing of the SPI calculation with respect to the vegetative season’s progress. For productivity forecasting, we propose an operational strategy to select per grid cell the SPI timescale and computation time with the highest correlation with zCFAPAR at different moments of the season. The linear relationship between SPI and zCFAPAR is significant for 32–66% of the study area, depending on the timing at which SPI is considered (at 0% and 75% of the seasonal progress, respectively). For these areas, the selected SPI explains on average about 40% of the variance of zCFAPAR and may thus assist in the earlier identification of agricultural drought.

STANDARDIZED PRECIPITATION INDEX (SPI) CALCULATED WITH THE USE OF LOG-NORMAL DISTRIBUTION

SUMMARY The problem analyzed in this paper is the continuation of research conducted on data from Wroclaw-Swojec agro- and hydrometeorology observatory in 1964–2009 period and published in “Infrastruktura i Ekologia Terenow Wiejskich” nr 3/III/2012, pp. 197–208. The paper concerns two methods of calculation of standardized precipitation index (SPI). The first one extracts SPI directly from gamma distribution, since monthly precipitation sums in the 1964–2009 period in Wroclaw are gamma distributed. The second method is based on the transformations of data leading to normal distribution. The authors calculate SPI with the use of log-normal distribution and confront it with values obtained by gamma and normal distributions. The aim of this paper is to comparatively assess the SPI values obtained with those three different methods.