The RDI as a composite climatic index

Water Resources Management, 2007

Regional drought assessment is conventionally based on drought indices for the identification of drought intensity, duration and areal extent. In this study, a new index, the Reconnaissance Drought Index (RDI) is proposed together with the well known Standardized Precipitation Index (SPI) and the method of deciles. The new index exhibits significant advantages over the other indices by including apart from precipitation, an additional meteorological parameter, the potential evapotranspiration. The drought assessment is achieved using the above indices in two river basins, namely Mornos and Nestos basins in Greece. It is concluded that although the RDI generally responds in a similar fashion to the SPI (and to a lesser extent to the deciles), it is more sensitive and suitable in cases of a changing environment. Key words meteorological drought. drought indices. Reconnaissance Drought Index (RDI). deciles. SPI. Nestos river basin. Mornos river basin

Water Resources Management, 2018

Traditionally, drought indices are calculated under stationary condition, the assumption that is not true in a changing environment. Under non-stationary conditions, it is assumed the probability distribution parameters vary linearly/non-linearly with time or other covariates. In this study, using the GAMLSS algorithm, a time-varying location parameter of lognormal distribution fitted to the initial values (α 0) of the traditional Reconnaissance Drought Index (RDI) was developed to establish a new index called the Non-Stationary RDI (NRDI), simplifying drought monitoring under non-stationarity. The fifteen meteorological stations having the longest records (1951-2014) in Iran were chose to evaluate the NRDI performances for drought monitoring. Trend analysis of the α 0 series at multiple time windows was tested by using the Mann-Kendall statistics. Although all stations detected decreasing trend in the α 0 series, eight of them were significant at the 5% probability level. The results showed that the time-dependent relationship is adequate to model the location parameter at the stations with the significant temporal trend. There were remarkable differences between the NRDI and the RDI, especially for the time windows larger than 6 months, implying monitoring droughts using the NRDI under non-stationarity. The study suggests using the NRDI where the significant time trend appears in the initial values of RDI due to changing climate.

Based on a literature study the suitability of several common drought indices is evaluated for application in regional drought forecasting in climates with seasonal frost influence. In these regions hydrological droughts caused by a deficit in precipitation (summer droughts) and frost (winter droughts) have to be distinguished. Suitable drought indices should be objective, spatially and temporally robust, sensitive, interpretable and practical. They also need to enable drought forecasting, which is in this study considered to be based on a statistical model linking the drought indices to large-scale ocean and atmosphere patterns. None of the studied indices were found to be optimal for regional forecasting mainly due to a lack of robustness. It is suggested to use a set of three or four indices, including the Standardized Precipitation Index for meteorological drought, a streamflow drought index based on the threshold level method with a constant seasonal threshold, and a flow anomaly index to detect deviations from normal hydrological conditions.

Arabian Journal of Geosciences, 2019

There are numerous drought indicators used by decision makers all around the globe which have been developed to fulfill specific needs. By far, risks associated with drought and related consequences have become a bold topic for scientists in which debates still taking place everywhere. No global drought indices could provide universally accepted results since almost all of these indices are based on observed data as key performance indicators. In this respect, researchers spend a lot of effort on this issue for a better understanding on the various indices which are proposed until now. It is crucial to get a better sense on how drought can develop and how it can be monitored. It is also important to understand that, recent global challenges like climate change also amplifies the obligation on continues effort toward developing better indicators and methods to monitor droughts. As climate patterns change or a seasonal shift occurs, predefined drought indicators become useless. In this review, the concepts of drought indices and indicators are revisited and evaluated. Pros and cons of frequently used indices are addressed and the major differences between them are bolded. It is concluded that each index is applicable to fulfill expectations of a specific drought type while pre-knowledge about each case is very crucial. However, there is a need to develop a composite drought index to integrate all relevant data and drought definitions, with respect to the dominant types of monthly droughts in time and space together with climate change scenarios.

Journal of Hydrologic Engineering, 2018

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2016

The objective of this study was to determine suitable drought monitoring indexes using the approach of systematic simulation and evaluation of interactions between water resources and historical drought in a region and to develop a mixed method. One of the important and efficient tools in drought monitoring systems is monitoring indexes, which in the case of compatibility with the conditions of application environment has a considerable effect on monitoring, pre-warning and often predicting this phenomenon. This is only possible if the mentioned indexes are selected based on the need and conditions of the application location. In this research, the studied region was Ardabil province in north-west of Iran and the objective was to determine severity and range of drought in the region. Three indexes were utilized for monitoring drought: PNPI (Percent of Normal Precipitation Index), DPI (Deciles Precipitation Index) and SPI (Standardized Precipitation Index). Information of 10 stations...

Journal of Water and Land Development

Assessing drought severity is an element of the drought monitoring. The assessment of severity and frequency of dry periods in a given severity class depends on the assumed criteria of drought. Using the series of precipitation records in the growing periods of 1954-1998 from 6 meteorological stations located in Wielkopolska and Kujawy, the relative precipitation index RPI and the standardized precipitation index SPI have been calculated. In view of the lack of statistically significant differences in mean precipitation between selected stations, it has been assumed that the region is homogenous in that aspect. According to RPI and the Kaczorowska's criteria, dry months constituted 40 % of the growing periods. According to SPI, dry months amounted only 15 %. In the paper the values of both indices and their correlations are analysed. The reasons of significant differences in the frequency of occurrence of dry months are indicated. The indices are evaluated with regard to their a...

A new general drought index is proposed for the assessment of meteorological drought severity. The new index called Reconnaissance Drought Index, RDI, is based on cumulative values of precipitation and potential evapotranspiration. Three expressions of RDI are given: the initial, the normalised and the standardised. The standardised RDI can be directly compared to the Standardised Precipitation Index (SPI) which is widely used. The new index has certain advantages when compared to SPI since it is more representative of the deficient water balance conditions than an index based only on precipitation.

Water Resources Management, 2014

One major characteristic of the Standardized Precipitation Index (SPI) is its flexibility to be calculated in a variety of time scales and hence being aware of different types of droughts. However, various time scales may result in confusion of the water resources' researchers, decision makers and users in identifying and specifying drought periods in a certain region. To solve this problem in this article, a multivariate approach has been utilized having the ability to aggregate a variety of the SPI time series into a new time series called the Multivariate Standardized Precipitation Index (MSPI). The MSPI is based on the principal components analysis (PCA) of the SPI time series in a certain location. Having specified the first principal component's (PC 1 's) scores, the MSPI would be simply attained by dividing PC 1 's values by the monthly standard deviation. In this article, MSPI's capability in depicting the variability of the SPI time series (in five ranges of time scales, including 3-6, 6-12, 3-12, 12-24, and 24-48 months) was studied at four weather stations representing the four different climates in Iran from the driest to the wettest climates. The results showed that the PC 1 is able to justify more than 74 % of the variability for the selected sets of the SPI time scales in the studied climates. Also, it became clear that the drought and wet severity classes monitored by MSPIs matched very satisfyingly to those of the five sets of the SPI time scales. Therefore, in cases where the aggregation time scales for calculating the SPI are not previously known, this study recommends the researchers use the MSPI.

Water Resources Management, 2011

Comparability analyses are performed to investigate similarities/differences of the standard precipitation index (SPI) and the reconnaissance drought index (RDI), respectively, utilizing precipitation and ratio of precipitation over potential evapotranspiration (ET 0 ). Data are from stations with different climatic conditions in Iran. Drought characteristics of the 3-month, 6-month and annual SPI and RDI time series are developed and Markov chain order dependencies are investigated by the Log-likelihood, AIC and BIC tests. Steady state probabilities and Markov chain characteristics, i.e., expected residence time in different drought classes and time to reach "Near Normal" class are investigated. According to results, both indices exhibit an overall similar behaviour; particularly, they follow the first order Markov chain dependency. However, climatic variability may produce some differences. In several cases, the "Extremely Dry" class has received a more critical value by RDI. Furthermore, the expected residence time of "Near Normal" class and expected time to reach "Near Normal" class are quite different in a number of cases. The results show that the RDI by utilizing the ET 0 can be very sensitive to climatic variability. This is rather important, since if the drought analyses are for agricultural applications, utilization of the RDI would seem to serve a better purpose.