Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such an extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programmes or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions by combining in a probabilistic framework two consolidated drought indices: the standardized precipitation index (SPI) and the vegetation health index (VHI). The new index, called the probabilistic precipitation vegetation index (PPVI), is scalable, transferable all over the globe and can be updated in near real time. Furthermore, it is a remote-sensing product, since precipitation is retrieved from satellite data and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of the PPVI has been evaluated by means of a receiver operating characteristic curve and compared to that of the SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early-warning systems.
Every year droughts affect an increasing number of people. In the years from 2014 to 2018, more than 70 drought events were reported all over the
world and about 450 million people suffered because of drought-related impacts
In addition, the quantification of drought effects is a complicated task, since drought impacts are non-structural, widespread over large areas, and
of different types and magnitudes within the drought-affected area; they also depend on economic, social and environmental system vulnerabilities
Drought identification through an objective and automatic determination of drought onset, termination and severity allows for the timely adoption of
appropriate risk management strategies, such as weather index insurance programmes
Drought features are usually determined through the use of two instruments: indicators, which are variables and parameters used to assess drought
conditions (such as precipitation, temperature and others), and indices, which are numerically computed values from meteorological or hydrological
inputs
In recent years various remote-sensing indices have been developed and can be employed in agricultural drought monitoring. The most widespread is the
normalized difference vegetation index (NDVI), which uses NOAA AVHRR satellite data to monitor vegetation greenness
An overview of indices used in agricultural drought monitoring; Met is meteorological, Hydro is hydrological, Ag is agricultural.
Since drought is a complex phenomenon, a single index or indicator can be insufficient to fully characterize drought severity and extent. The
combination of more than one indicator can be invaluable in the evaluation of all the variables involved in drought monitoring, such as precipitation, soil
moisture and streamflow. Over the past 20 years many composite indicators, relying on two or more drought indices or indicators, have been proposed
to overcome the issues related to the use of a single variable. Table
An overview of aggregate and composite drought indices useful for agricultural drought monitoring; Met is meteorological, Hydro is hydrological, Ag is agricultural. Input abbreviations not defined in the text are as follows: fraction of absorbed photosynthetically active radiation (fAPAR), soil moisture anomaly (SMA), evapotranspiration (ET) and land surface temperature (LST).
Multiple methods for taking into account the multivariate behaviour of drought have been explored
In the last few years multiple studies have focused the attention on modelling the joint behaviour of two drought characteristics or indices applying bivariate
or multivariate statistical approaches. In various cases bivariate distributions are developed by means of copulas as in
The use of copulas to quantify the joint behaviour of drought indices is gaining popularity too. Many drought indices derived by multivariate
distributions have been proposed. For example the multivariate standardized drought index (MSDI;
Both single and composite indices for agricultural drought monitoring showed some limitations, highlighted in Tables
In this paper, we propose the following:
a new drought index, the probabilistic precipitation vegetation index (PPVI), that takes advantage of well-consolidated indices, the
standardized precipitation index (SPI; a framework to identify a drought event using the new index, i.e. a set of rules for the definition of a drought event; when the set of
conditions is verified, a drought event is identified based on the new index; otherwise, no drought event is identified.
With respect to the indices already available in the literature, we will show in this paper that the new index has some interesting features:
It is able to identify drought-driven events of vegetation stress. It is parsimonious in terms of number of inputs required. It is a remote-sensing product with high spatial and temporal resolution. It is based on quasi-near-real-time datasets, with a relatively short latency time (less than 1 week). More than 30 years of records are available at global scale for its calibration.
The paper is structured as follows: Sect.
Two remote-sensing datasets were used: one for precipitation and the other for the VHI. Precipitation was retrieved from the satellite-only Climate
Hazards Group Infrared Precipitation (CHIRP) dataset. CHIRP has a quasi-global coverage (50
The vegetation health index was retrieved from the global vegetation health products (global VHP) of the National Oceanic and Atmospheric
Administration (NOAA) Center for Satellite Applications and Research
As previously mentioned, two consolidate drought indices were combined: the SPI and the VHI. The SPI was selected because it is a commonly used index
to detect meteorological drought; it is standardized, and therefore SPI values can be compared even in different climate regimes; and it is recommended by
the WMO
SPI computation is based on a long-term precipitation record for a desired period. The precipitation record is then fitted to a probability
distribution (in this work a gamma distribution was used), which is then transformed into a normal distribution. Traditionally monthly precipitation
records are employed, and the SPI is computed aggregating precipitation at a predefined time step
In the present work, weekly precipitation records were used. The SPI aggregation period was then selected, and the index, computed over one of the
traditional aggregation periods, was updated every week. The SPI is normally distributed by definition. Conventionally drought starts when the SPI is lower
than
Drought classification based on SPI according to
The VHI is a remote-sensing index developed to include the effects of temperature on vegetation; in fact, it combines the VCI with the temperature
condition index (TCI;
Drought classification based on VHI according to
The VHI is standardized to make comparisons with the SPI easier. As mentioned by
The probabilistic precipitation vegetation index (PPVI) is a composite index that takes into account both meteorological drought through the SPI and agricultural drought conditions by including the VHI.
In order to combine the two consolidated indices, the following preparatory steps are performed:
extraction of the area under study from both the datasets; regridding of both precipitation and the VHI to bring them to the same spatial resolution (0.05 aggregation of precipitation at a weekly timescale (CHIRP has daily temporal resolution); computation and weekly update of the SPI according to the methodology proposed in standardization of the VHI, as previously described.
The combination of the SPI and VHI is performed using a bivariate normal distribution function, as defined by
To check the assumption of normality for the joint distribution, the joint probability values, retrieved from Eq. (
PPVI validation: empirical copula versus bivariate joint probability function. The red line corresponds to the 45
Since the data lie on the 45
By keeping the same probability intervals of the SPI, we can compute the PPVI values for the drought classification as shown in
Table
Drought classification according to PPVI.
Once the index is defined, the set of rules to establish when a grid cell is in a drought should be identified. In particular, two parameters have to be
identified:
a threshold a threshold
According to the model proposed here, a drought in a grid cell starts when the index is lower than
Observations of drought are compared with the model outputs for various combinations of thresholds
Contingency table for the deterministic estimates of a series of binary events
The optimal threshold for a ROC curve is the one for which the distance from the 45
The case study region is Haiti. The country, which has an extent of 27 750
Haiti is divided administratively into 10 departments (Fig.
Map of Haiti departments.
Haiti produces over half of the world's vetiver oil (used in cosmetics), and mangos and cocoa are the most important export crops. Two-fifths of all
Haitians depend on the agriculture sector, mainly small-scale subsistence farming. The country is prone to all types of natural hazards. Earthquakes,
storms, hurricanes, landslides and droughts have caused huge damage and losses in recent years. Haiti was ranked as the third country most affected
by extreme weather events in terms of lives lost and economic damage in the period from 1994 to 2013
Number of people affected by natural disasters in Haiti (1900–2018). Source:
Droughts threaten the livelihoods of Haitians in many different ways. The scarcity of crops production means a rise in food prices that brings
widespread food insecurity since the majority of people cannot afford the increase. Unavailability of drinking water leads to cholera outbreaks among
the population. Water is also an issue for breeders, who lose livestock on which they rely for milk production and meat consumption. In the period
from 1980 to the present, more than 10 drought events have been reported by the government or the humanitarian organizations working in Haiti
(Table
Reported drought events in Haiti from 1980 to the present.
Effective drought management is crucial for Haiti, but at present, a reliable early-warning system for drought is still lacking. Weather stations on the ground are few and data records are often very short and therefore not useful for drought monitoring of the entire country. Satellite images can be an effective and inexpensive tool to improve drought management and preparedness in the country.
Haiti has been divided into 987 grid cells, accounting for 90 % of the country's area. A total of 1941 weeks were considered, starting from week 35 of 1981 and ending with week 52 of 2018. The release date of a new VHI image was considered as the starting date of a week. In the present study, four precipitation aggregation periods were considered (1, 2, 3 and 6 months), and the corresponding values of SPI (SPI1, SPI2, SPI3 and SPI6) were computed in order to select the SPI aggregation timescale to be used to create the PPVI.
To evaluate the strength of the statistical relationship between the SPI at various timescales and the VHI, a correlation analysis was then
performed. Various studies have already evaluated the correlation among drought indices or between drought indices and exogenous variables; for
example
Number of significant correlations (Pearson correlation coefficient) between VHI and various SPI aggregation timescales. Values are expressed as percentages evaluated with respect to the total number of grid cells (987).
Before computing PPVI as described in the previous sections, a test on the normality of the SPI3 and
The PPVI was computed as described in Sect.
The ROC curves were computed according to the following methodology: at first a combination of the thresholds
The analysis was repeated by varying another threshold among
ROC curves for the set of thresholds reported in Table
Example of set of thresholds used to draw ROC curves for model calibration. Thresholds
The aim of this section is not to validate in absolute terms the proposed methodology since the data record is too short to serve both for calibration and for validation. In the present section, instead, we provide a validation by comparing the performance of the PPVI in identifying observed drought events with those of widely recognized and used indices such as the SPI and VHI.
The performance of the PPVI was then compared to those of the SPI3 and VHI considered separately. Thresholds analogous to
Comparison between the performances of the SPI3, VHI and PPVI in identifying reported drought events; thresholds
It is clear from Fig.
Best configuration parameters for the model when applied with the PPVI, SPI3 and VHI.
Comparison between observed drought events and drought events identified by the PPVI, SPI3 and VHI when calibrated with the best-performing
parameters shown in Table
Comparison of the performance of the SPI3, VHI, and PPVI in identifying the areas hit by drought in week 45 of 1995. Departments highlighted in red
are the ones in drought according to observations (Table
Same as Fig.
The ability of the model in identifying the country area hit by the drought was also assessed. A visual comparison of the areas under drought
identified by the three indices was performed, as was performed by
Here some significant weeks are shown. At first, week 45 of 1995 was considered. No drought events were reported in that period according to the
information available in the analysed documents (see Table
Same as Fig.
Performance of the PPVI, SPI3 and VHI in identifying departments hit by drought during week 8 of 2012 and comparison with
observations. Observations are retrieved from the text-based documents reported in Table
Severity, duration and mean areal extent of the drought events identified by the PPVI were computed. Severity was computed as the sum of all the values
identified by the condition that a grid cell is in a drought condition when the PPVI is lower than
Drought events in Haiti according to the PPVI, duration, severity and mean areal extent.
The PPVI showed overall a better capacity in identifying drought events with respect to the SPI3 and VHI considered separately. However, some false alarms still remain. This can be linked to the uncertainty in information on past drought events for the analysed area. Short-term droughts are often not reported in text-based documents, and information on drought start and end dates was retrieved from documents that mainly described the impacts related to drought. The PPVI showed a good agreement with reported information in identifying the areas of the country hit by droughts.
The timely identification of drought events is of great importance in agricultural areas, especially when rainfed agriculture is practised. At the same time, the evaluation of the damage caused by drought is a key point to select appropriate risk management strategies, such as weather index insurance programmes, agricultural index insurance, disaster financing and early action planning. The new composite index proposed in this paper, the probabilistic precipitation vegetation index, PPVI, is a powerful tool since it can identify events of vegetation stress and, at the same time, select from among those the ones actually due to drought, thanks to the use of both the VHI and the SPI. As such it can be helpful in agricultural drought monitoring and can be used to identify drought events affecting a region, their severity and their duration as was shown in the case of Haiti. In particular, the PPVI can be invaluable in those areas where rainfed agriculture is of vital importance since people rely on it for food production for personal consumption.
Among the interesting aspects of the PPVI, there is the fact that few data are required for its computation: only precipitation and the VHI. This aspect is crucial, since many composite indicators able to identify agricultural droughts already exist, but large quantities of data are required to compute them. For example, the United States Drought Monitor combines more than 40–50 inputs, while other indices specific to agricultural drought monitoring, such as the VegDRI and the VegOut, require the use of temperature and oceanic indices. The number of parameters required to compute the PPVI is low even with respect to the OBDI, SWS, CDI or CDSI.
A second important advantage is that, since the SPI was computed starting from satellite precipitation (CHIRP dataset) and that the VHI is a remote-sensing drought index, the PPVI is also a remote-sensing product. The use of datasets with global coverage means that the PPVI is easily transferable and scalable over the entire globe. In addition, the PPVI can be a very useful tool in areas with scarce gauge coverage such as the Caribbean islands. Both precipitation and the VHI have a very high spatial and temporal resolution, thus allowing drought monitoring via satellite even in small areas. The PPVI can be computed even in those regions with short data records, since the VHI has more than 30 years of records (data collection began in August 1981) and CHIRP precipitation is available from January 1981.
Both the SPI and the VHI are updated with a weekly time step since every week a new VHI image is released, and the CHIRP precipitation dataset has a daily temporal resolution; therefore the PPVI can be updated more frequently than other composite indices, such as the CDI, which is updated every 10 d. In addition, due to the relatively short latency time (less than 1 week) of both the datasets employed to create the PPVI, the index is available in near real time, therefore allowing for the timely implementation of drought mitigation strategies. This last feature is of particular interest when the PPVI is used to implement measures to reduce drought risk in agriculture, where a timely identification of drought is crucial to prevent damage to the sector.
Many advantages are also related to the adoption of the set of rules here proposed to identify drought events. First of all, these rules enable an objective and standardized identification of drought events from the mathematical point of view. Additionally, they can be adjusted according to the needs and the objectives of various possible end users of the model, such as farmers, governments or insurance companies.
The performances of the PPVI in identifying drought events were tested in a specific case study (Haiti) and compared to the ones of the SPI and VHI considered separately. The PPVI performed better than the single indices considered separately in reproducing past drought events. The PPVI identified drought areas in Haiti better than the SPI and VHI even from the spatial point of view; thus it is more reliable than a single index. A comparison of PPVI performances with respect to the ones of other composite indices was not performed in the present study due to the unavailability of composite indices with the same characteristics of the PPVI. In fact previous composite indices do not include both the meteorological and the agricultural aspect of drought, are not available globally, or cannot be computed with only remote-sensing datasets.
Both CHIRP and the NOOA VHI dataset are freely available at the links cited in the references.
This research is part of the PhD thesis of BM. Both BB and MM were the thesis supervisors.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Recent advances in drought and water scarcity monitoring, modelling, and forecasting (EGU2019, session HS4.1.1/NH1.31)”. It is a result of the European Geosciences Union General Assembly 2019, Vienna, Austria, 7–12 April 2019.
The research leading to these results has received funding from the Disaster Risk Financing Challenge Fund of the World Bank Group in the context of the SMART (a statistical, machine learning framework for parametric risk transfer) project. The research has been developed within the framework of the project Dipartimenti di eccellenza, funded by the Italian Ministry of Education, University and Research at IUSS Pavia.
This paper was edited by Athanasios Loukas and reviewed by two anonymous referees.