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Many studies have shown a growing trend in terms of frequency and severity of extreme events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s surface has become a key point for the identification of areas potentially affected by floods. In order to guarantee an almost global spatial coverage, NASA Global Precipitation Measurement (GPM) IMERG products proved to be the most appropriate source of information for precipitation retrievement by satellite. This study is aimed at defining the IMERG accuracy in representing extreme rainfall events for varying time aggregation intervals. This is performed by comparing the IMERG data with the rain gauge ones. The outcomes demonstrate that precipitation satellite data guarantee good results when the rainfall aggregation interval is equal to or greater than 12 h. More specifically, a 24-h aggregation interval ensures a probability of detection (defined as the number of hits divided by the total number of observed events) greater than 80%. The outcomes of this analysis supported the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS: erds.ithacaweb.org). This system is now able to provide near real-time alerts about extreme rainfall events using a threshold methodology based on the mean annual precipitation.
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remote sensing
Article
Improving an Extreme Rainfall Detection System
with GPM IMERG data
Paola Mazzoglio 1, *, Francesco Laio 2, Simone Balbo 1, Piero Boccardo 3and Franca Disabato 1
1ITHACA—Information Technology for Humanitarian Assistance, Cooperation and Action,
10138 Torino, Italy; simone.balbo@ithaca.polito.it (S.B.); franca.disabato@ithaca.polito.it (F.D.)
2Politecnico di Torino, Dipartimento di Ingegneria dell’Ambiente, del Territorio e delle Infrastrutture,
10129 Torino, Italy; francesco.laio@polito.it
3Politecnico di Torino, Dipartimento Interateneo di Scienze, Progetto e Politiche del Territorio,
10125 Torino, Italy; piero.boccardo@polito.it
*Correspondence: paola.mazzoglio@ithaca.polito.it or mazzoglio.paola@gmail.com
Received: 11 January 2019; Accepted: 19 March 2019; Published: 21 March 2019


Abstract:
Many studies have shown a growing trend in terms of frequency and severity of extreme
events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s
surface has become a key point for the identification of areas potentially affected by floods. In order to
guarantee an almost global spatial coverage, NASA Global Precipitation Measurement (GPM) IMERG
products proved to be the most appropriate source of information for precipitation retrievement
by satellite. This study is aimed at defining the IMERG accuracy in representing extreme rainfall
events for varying time aggregation intervals. This is performed by comparing the IMERG data
with the rain gauge ones. The outcomes demonstrate that precipitation satellite data guarantee good
results when the rainfall aggregation interval is equal to or greater than 12 h. More specifically, a 24-h
aggregation interval ensures a probability of detection (defined as the number of hits divided by
the total number of observed events) greater than 80%. The outcomes of this analysis supported
the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS:
erds.ithacaweb.org). This system is now able to provide near real-time alerts about extreme rainfall
events using a threshold methodology based on the mean annual precipitation.
Keywords: early warning system; extreme events; flood monitoring; GPM; hydrology; rainfall
1. Introduction
According to the IPCC (Intergovernmental Panel on Climate Change), “an extreme weather event
is an event that is rare at a particular place and time of year. Definitions of rare vary, but an extreme
weather event would normally be as rare as or rarer than the 10th or 90th percentile of a probability
density function estimated from observations. By definition, the characteristics of what is called
extreme weather may vary from place to place in an absolute sense” [1].
Although the definition of these phenomena is not univocal (extreme conditions in one location can be
normal in another one), their effect and their consequences, unfortunately, are clear and easily identifiable.
As never before, having tools capable of monitoring the amount of rain that affects the Earth’s surface has
become a key point for the timely identification of areas potentially affected by floods [2].
In order to face this problem on a global scale, it is necessary to work with data acquired on a
continuous basis and made available in real time. Satellites play an important role in the measurement
field on a global scale. The main feature concerns the possibility of carrying out a measurement
on huge areas of the Earth’s surface (satellites have a more extensive coverage compared to other
instruments). For this reason, in the last decades, a great effort has been made to improve the
precipitation measurement by satellite data.
Remote Sens. 2019,11, 677; doi:10.3390/rs11060677 www.mdpi.com/journal/remotesensing
Remote Sens. 2019,11, 677 2 of 24
Nowadays, a number of early warning systems are freely available: European Flood Awareness
System (EFAS) [
3
], Global Flood Monitoring System (GFMS) [
4
] and Dartmouth Flood Observatory
(DFO) [
5
] are some examples. They are operating at large scales, with updating cycles in the range of
hours to days [
6
]. Their performances highlight that some gaps (in particular, when we focus on local
scales) will remain until new data become available and better approaches are established [7].
The Extreme Rainfall Detection System (ERDS: erds.ithacaweb.org), developed and implemented
by ITHACA [
8
], is a demo service for the monitoring and forecasting of exceptional rainfall events, with
a nearly global spatial coverage. ERDS is also an alert system designed to identify hydrometeorological
events (such as hurricanes, tropical storms, convective storm, flash flood and heavy rainfall that
could lead to flood events or landslides). The information is accessible through a WebGIS application,
developed in a complete Open Source environment.
The original version of this system was using NASA TRMM (Tropical Rainfall Measuring Mission)
TMPA (TRMM Multi-satellite Precipitation Analysis) 3-hourly data as input. Due to the TRMM
instrument shutdown that happened during 8 April 2015 [
9
], an alternative rainfall source had to be
investigated. Currently available satellite rainfall measurements (NASA GPM IMERG, PERSIANN,
PERSIANN-CCS and GSMaP) were evaluated, taking into consideration the importance of working
with a source of information with a high spatial coverage, a high spatial resolution, a frequent update
and a low latency. NASA GPM (Global Precipitation Measurement) data have been chosen for this
purpose. This paper describes the analysis performed to adapt ERDS to this newly available satellite
data and to increase its accuracy.
The GPM Mission, designed and managed by NASA and JAXA, uses a Core satellite launched on
27 February 2014 and a constellation of international satellites [
10
]. Thanks also to the other satellites,
it is possible to guarantee a covering region between 65N and 65S.
The Core satellite has multiple sensors on board (it carries an active and a passive precipitation
sensor). These two sensors are a Dual-frequency Precipitation Radar (DPR) and a GPM Microwave
Imager (GMI) [
10
]. While TRMM was designed to measure moderate to heavy rainfall in tropical and
subtropical areas, GPM can measure from light rain to heavy rain. Unlike TRMM, GPM Core satellite
can also measure snow thanks to GMI high frequency.
The GPM Mission provides different IMERG (Integrated Multi-satellite Retrievals for GPM)
products since 12 March 2014. The IMERG algorithm was developed to intercalibrate, merge
and interpolate satellite microwave precipitation measurements, microwave-calibrated infrared (IR)
satellite measurements, rain gauge analyses and, potentially, other ancillary precipitation estimators.
These gridded and georeferenced products are characterized by a spatial coverage between 60
N–60
S
and a spatial resolution of 0.1
×
0.1
[
11
]. The products are provided at several temporal resolutions:
30 min, 3 h, 1 day, 3 days, 7 days and 1 month [
12
]. The data that were chosen for a near real-time
extreme rainfall detection are the ones available in a grid format at a temporal resolution of 30 min.
The other aggregation intervals would have led to a longer latency in the provision of alerts.
IMERG products are available in three different versions [
11
]: early run [
13
] (with a 4 h latency),
late run [
14
] (with a 12 h latency) and final run (with a 2.5 months latency). The early and late products
are multi-satellite data. The final run is, instead, obtained taking advantage of a combination of
information acquired from satellite and monthly rain gauges data. Considering the short delay in their
availability, IMERG early and late half-hourly data can be used for near real-time flood risk monitoring
applications. Final data are, instead, interesting for research purposes because of their longer latency.
Every half-hourly early and late file contains different data fields, including different versions
of the precipitation measurement (precipitationCal, precipitationUncal, HQprecipitation and
IRprecipitation) and some additional information [
10
]. IRprecipitation was discarded due to a reduced
accuracy. IR sensors can provide rainfall estimation at a high temporal resolution on large areas.
However, this measurement is not good enough due to the indirect linkage between precipitation
and IR signal [
15
]. HQprecipitation, instead, was rejected due to the high number of missing data in
every file. Contrary to precipitationUncal, precipitationCal early and late run data are subject to a
Remote Sens. 2019,11, 677 3 of 24
month/location-varying climatological calibration [
10
]. After a brief analysis performed comparing
the rain gauge historical data with these measurements, precipitationCal proved to be the most
appropriate source of rainfall information. Several studies conducted at the local scale confirmed the
better performance of precipitationCal compared to precipitationUncal [1618].
A considerable amount of studies was performed in order to investigate the GPM IMERG accuracy
on small areas (generally, every study focused on a specific country). Guo et al. [
16
] analyzed the GPM
IMERG performance over the seven Chinese subregions (characterized by different elevations, mountain
ranges and mean annual precipitation distributions). The GPM data demonstrated a good performance,
except during winter time. The study indicated that GPM data underestimated the rainfall measurement
almost everywhere, except for some arid and semiarid zones, where GPM overestimated light rain events.
The GPM data proved to be more accurate than the TRMM TMPA ones. Khodadoust Siuki et al. [
15
]
highlighted an underestimation over the northeast of Iran. However, the GPM IMERG data proved to have
better performance than the TRMM TMPA data. Sharifi et al. [
19
] examined the GPM IMERG performance
over Iran, confirming the slight underestimation on a daily scale. Good results were obtained with rainfall
amounts greater than 15 mm/day during rainy periods or in humid regions. Local underestimation
or overestimation over mountainous regions indicates that an accurate satellite-based precipitation
measurement remains a challenge in some places of Earth’s surface. An underestimation of the GPM
IMERG early run data was also identified by Gaona et al. [
17
] over the Netherlands and by Sahlu et al. [
18
]
over the Upper Blue Nile basin. Kim et al. [
20
] validated the GPM IMERG data in Far-East Asia during the
pre-monsoon and monsoon seasons, reporting several drawbacks induced by topographical factors in the
coastal regions. The study highlighted the higher performance of GPM (compared to TRMM) both in light
rainfall and in convective rainfall measurement.
Recent evidence suggested that GPM is also able to provide more accurate information about
extreme rainfall events compared to TRMM. Prakash et al. [
21
] compared both the TRMM TMPA
research-quality product (V7) and GPM IMERG final run (V03) with the rain gauge measurements over
India. Even though the TMPA data showed a larger probability of detection of heavy rainfall events
over most parts of the country, it showed a large false alarm ratio and a relatively small critical success
index. Moreover, the IMERG data showed notable improvements over TMPA during the comparison
with the rain gauge measurements, especially in mountainous areas. Chen et al. [
22
] compared both
the GPM IMERG V05 data and the TRMM 3B42 V7 data with the rain gauge measurements over the
Huaihe River basin (China). This work revealed that both products tend to overestimate rainfall events
characterized by rainfall rate between 0.5 to 25 mm/day. Conversely, they tend to underestimate light
(0–0.5 mm/day) and heavy (>25 mm/day) rainfall. The underestimation is greater for intensities
greater than 100 mm/day. The IMERG data proved to have a better performance in temporal and
spatial rainfall detection compared to TRMM.
Although a big effort has been made to investigate the performances at a country/basin scale of the
rainfall measurements provided by satellite in the spatial and temporal identification of rainfall events,
a small number of studies was conducted in order to increase its accuracy. Moreover, a considerable
amount of work is still required to increase the overall accuracy of early warning systems working
with remotely sensed measurements as input data.
The research work summarized in this article was conducted keeping in mind this lack and trying
to introduce a new methodology and new results in the previously mentioned field. The first objective
of this study is the assessment of the IMERG accuracy in the extreme rainfall detection at the global scale
for different aggregation intervals. The obtained results (summarized in
Sections 3.1 and 3.2
) allowed
the definition of the proper aggregation intervals to be used in the second part of the work. The second
objective is to propose a new extreme rainfall detection methodology based on event-identification
thresholds, different for every place of the world, calibrated taking into account the mean annual
rainfall. The methodology is described in Section 2.2 while the results are reported in Section 3.3
and discussed in Section 4. The results of this research were implemented in a freely accessible early
warning system.
Remote Sens. 2019,11, 677 4 of 24
2. Materials and Methods
2.1. Analysis of the Accuracy of the Input Data
An evaluation of the difference in the accuracy between the early and late products (specifically,
IMERG V04A precipitationCal data) was performed in order to quantify whether the gain in accuracy in
the late product could be considered negligible with respect to the more promptly available early products
considering the final use of the data. This study covered the period from 12 March 2014 to 30 April 2017.
The first step for the evaluation of the GPM IMERG products accuracy was the analysis of the
presence of cells with a missing precipitation measurement within the area normally covered by the
precipitationCal band (with a spatial extent from 60
N to 60
S for a total of 1200
×
3600 cells) for
early and late half-hourly data. The two 30
extended bands present at the poles were neglected since,
in these areas, only a few measurements are available. The spatial arrangement of pixels characterized
by the absence of data was then analyzed to identify areas possibly subject to a reduction in accuracy
due to a systematic absence of measure.
The outcomes of the second part of the analysis allowed the assessment of the most appropriate
aggregation intervals usable to provide information regarding the rainfall amount and to evaluate the
presence of places potentially affected by hydrometeorological disasters. The IMERG half-hourly early
and late products were compared with the rain gauge measurements in order to evaluate their relative
accuracy. The present study covered different climatic zones with the aim to carry out a valid analysis
on a global scale. Fifty different locations were analyzed (Figure 1). The location choice was the result
of a preliminary analysis aimed at identifying institutions [
23
,
24
] that collect and publish free rain
gauge data with a good temporal resolution. In order to investigate the accuracy even at the sub-daily
scale, the rain gauge measurements with a temporal resolution of at least one hour were chosen.
Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 24
2. Materials and Methods
2.1. Analysis of the Accuracy of the Input Data
An evaluation of the difference in the accuracy between the early and late products (specifically,
IMERG V04A precipitationCal data) was performed in order to quantify whether the gain in accuracy
in the late product could be considered negligible with respect to the more promptly available early
products considering the final use of the data. This study covered the period from 12 March 2014 to
30 April 2017.
The first step for the evaluation of the GPM IMERG products accuracy was the analysis of the
presence of cells with a missing precipitation measurement within the area normally covered by the
precipitationCal band (with a spatial extent from 60° N to 60° S for a total of 1200 × 3600 cells) for
early and late half-hourly data. The two 30° extended bands present at the poles were neglected since,
in these areas, only a few measurements are available. The spatial arrangement of pixels characterized
by the absence of data was then analyzed to identify areas possibly subject to a reduction in accuracy
due to a systematic absence of measure.
The outcomes of the second part of the analysis allowed the assessment of the most appropriate
aggregation intervals usable to provide information regarding the rainfall amount and to evaluate
the presence of places potentially affected by hydrometeorological disasters. The IMERG half-hourly
early and late products were compared with the rain gauge measurements in order to evaluate their
relative accuracy. The present study covered different climatic zones with the aim to carry out a valid
analysis on a global scale. Fifty different locations were analyzed (Figure 1). The location choice was
the result of a preliminary analysis aimed at identifying institutions [23,24] that collect and publish
free rain gauge data with a good temporal resolution. In order to investigate the accuracy even at the
sub-daily scale, the rain gauge measurements with a temporal resolution of at least one hour were
chosen.
Figure 1. The spatial distribution of the rain gauges used for the evaluation of the accuracy of GPM
(Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) data. The
reference system is WGS84.
The major limitation of this approach is linked to the biased rain gauge distribution. During the
work, the difficulty of finding appropriate reference data emerged. A reduced number of
organizations, in fact, provide freely available rain gauge measurements characterized by a good
temporal resolution, an almost zero percentage of missing data and a historical series that covers
several years.
The following aggregation intervals were considered in order to evaluate the cumulated rainfall
and the relative intensity: 1, 2, 3, 6, 12, 24, 48, 72, 96, 120, 144 and 168 h.
Figure 1.
The spatial distribution of the rain gauges used for the evaluation of the accuracy of
GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) data.
The reference system is WGS84.
The major limitation of this approach is linked to the biased rain gauge distribution. During the
work, the difficulty of finding appropriate reference data emerged. A reduced number of organizations,
in fact, provide freely available rain gauge measurements characterized by a good temporal resolution,
an almost zero percentage of missing data and a historical series that covers several years.
The following aggregation intervals were considered in order to evaluate the cumulated rainfall
and the relative intensity: 1, 2, 3, 6, 12, 24, 48, 72, 96, 120, 144 and 168 h.
For the purpose of performing an assessment of the error that characterizes satellite measures,
the bias and mean absolute error were calculated for each location and for the previously mentioned
set of aggregation intervals. The bias and mean absolute error are obtained as
Remote Sens. 2019,11, 677 5 of 24
BIAS =1
n
n
t=1
[RSATELLITE (t)RGAUGE (t)] (1)
MAE =1
n
n
t=1
|[RSATELLITE (t)RGAUGE (t)]|(2)
where
n is the total number of time instants;
t is time;
R
SATELLITE
is the average rainfall intensity measured by satellite in the time interval t (expressed
in mm/h);
R
GAUGE
is the average rainfall intensity measured by rain gauge in the same time interval t
(expressed in mm/h).
While the bias defines the average difference between the satellite and rain gauge data (and can
be either positive or negative), the mean absolute error provides the average magnitude of the error.
Both equations are valid only for nonzero rainfall measurements. These parameters, in fact, quantify
the differences between the rainfall estimation and the “true” rainfall (i.e., the rainfall measurement
provided by rain gauges). A further study was conducted, taking into consideration only a rainfall
rate greater than the 99th percentile of the intensities distribution, with the aim of analyzing the GPM
IMERG performance in heavy rainfall detection.
The third part of the analysis consisted of the comparison of the observed and estimated events.
For every location and for every aggregation interval previously mentioned, a contingency table (Table 1)
was generated. In the contingency table, the occurrences of the following four conditions were reported:
both rain gauge and satellite data are null (case A, correct negatives);
nonzero rain gauge data and zero satellite data (case B, misses);
zero rain gauge data and non-null satellite data (case C, false alarms);
both rain gauge and satellite data are non-null (case D, hits).
Table 1. An example of a contingency table usable to evaluate the quality of the predictions.
Rain Gauge
= 0 mm/h >0 mm/h
Satellite
=
0 mm/h
Correct Negatives
(A)
Misses
(B)
Estimated
Non events
>0 mm/h False Alarms
(C)
Hits
(D)
Estimated
Events
Observed
Non events
Observed
Events
Three indices were derived using these tables as a basis: the false alarm ratio, the probability of
detection and the critical success index [25].
The false alarm ratio (FAR) was defined on the basis of elements contained in false alarms and
hits cells. The ideal situation is characterized by approximately zero FAR.
FAR =FALSE ALARMS
HITS +FALSE ALARMS (3)
Remote Sens. 2019,11, 677 6 of 24
The probability of detection (POD) was instead evaluated by combining elements contained in
the misses and hits cells. The ideal situation is characterized by a unitary POD value.
POD =HITS
HITS +MISSES (4)
The critical success index (CSI) can be expressed in terms of POD and FAR. This parameter, unlike
POD and FAR, combines the characteristics of hits, false alarms and misses. Thanks to this link, CSI
can help in the identification of the most relevant component. The ideal situation is characterized by a
unitary CSI.
CSI =HITS
HITS +FALSE ALARMS +MISSES (5)
2.2. Development of an Extreme Rainfall Detection Methodology
The main purpose of this section is to describe some tests conducted in order to develop a new
methodology for detecting extreme rainfall and providing alerts in near real-time and with an almost
global spatial coverage. The whole study was conducted using GPM IMERG half-hourly early data as
the input.
The methodology is based on the concept of activation threshold: an event is identified when
the rainfall exceeds a given threshold value. An “event-identification threshold” (EIT) represents the
amount of rainfall needed to trigger a flood event induced by extreme rainfall. EITs are used to define
near real-time alerts about extreme rainfall. Specifically, an alert is provided if for a selected time
interval the accumulated rainfall exceeds the EIT.
The definition of these thresholds was performed using an empirical approach, analyzing rainfall
events that have led to a hydrometeorological disaster. These threshold values, obviously, vary from
place to place. Moreover, it is impossible to define a threshold if the aggregation interval is not defined.
A longer time interval has, in fact, a higher EIT.
To develop this extreme rainfall detection methodology, the first step was to search databases
of natural disasters with a global spatial coverage (in particular, we focused on hydrometeorological
ones) to be used as truth data. The main adopted database was EM-DAT (The Emergency Events
Database) [
26
]. EM-DAT has also been integrated using information derived from Reliefweb [
27
] and
Floodlist [
28
], which allowed us to take into consideration also some flood and flash flood events not
present in the previous one. In these three databases, the information is provided at the country-scale
(the databases indicate for almost all records the country in which the disaster occurred but not the
precise coordinates of the affected areas).
The methodology consisted of the identification of the optimal EIT for different aggregation
intervals (12 h, 24 h, 48 h, 72 h and 96 h) and for every place of the world. Each temporal interval was
studied separately. The study covered 85 different countries, from 12 March 2014 to 30 April 2017.
In 63 of these countries, at least 1 hydrological disaster happened. Further, 22 countries in which no
disasters occurred were inserted at a later moment for validation purposes. Globally, 211 different
disasters were analyzed.
For every temporal interval and for every country, a series of simulations was performed by
varying the threshold from 0 mm to 600 mm with a 5 mm step. For each simulation, the occurrences of
the following three conditions were reported:
the number of “false alarms” (a false alarm is a condition that occurs if, at least in one cell of the
examined country, the accumulated rainfall exceeds the threshold but, in that day, no disaster is
reported in the database for the examined country);
the number of “missed alarms” (a missed alarm is a condition that happens if, on the day a
disaster has occurred in the examined country, in any cell of the country, the amount of rainfall
does not exceed the threshold);
Remote Sens. 2019,11, 677 7 of 24
the number of “hits events” (a hit event is a condition that happens if, on the day a disaster has
occurred, the accumulated rainfall exceeds the threshold in at least one cell of the examined country).
In this study, only disasters identified in a properly selected time window are considered as “hits
events”. According to the duration of the aggregation interval, different time windows were selected.
Specifically, the considered useful days are
for the aggregation of 12, 24 and 48 h, an interval ranging from 4 days before the start date of the
disaster to the following 2 days;
for the aggregation of 72 h, an interval ranging from 4 days before the start date of the disaster to
the following 3 days;
for the aggregation of 96 h, an interval ranging from 4 days before the start date of the disaster to
the following 4 days.
Events identified by ERDS before these 4 days have not been counted as “hits events” because an
alert given too early may not be useful. The user, in fact, might think that this is a false alarm, seeing
the days go by and not observing the occurrence of any disaster. This false sense of confidence could
be as dangerous as a missed alarm. Events identified after the selected time windows were instead
considered as “missed events”. ERDS, in fact, is a near real-time alert system. In other words, it is
essential to adopt thresholds able to identify extreme events in time.
It was, therefore, necessary to define a criterion for the identification of the optimal EITs. One of
the possible approaches takes into account the four conditions reported in the standard contingency
table (Table 1). While conditions like correct negatives or hits do not influence in a negative way
the reliability of the system, misses and false alarms heavily impact it. In order to evaluate the
performances of the early warning system, it is crucial to take into account these two situations (a
perfectly working system is, in fact, able to minimize both of them). It was consequently possible to
evaluate an indicator based on a weighted sum of both conditions. This led to defining, for every
threshold, a cost. This cost is made by two different components: false alarms cost and missed alarms
cost. While the false alarm cost is relatively simple to be evaluated (it is mainly influenced by the
extra work that local authorities and Civil Protection have to face to put in security the potentially
affected area and to monitor the situation), the quantification of the missed alarm cost is a challenging
problem. It mainly depends on the losses of human lives, the damage grading reported at buildings
and infrastructures, temporary interruption to services, etc. This parameter is, therefore, site-specific
and presents a higher value in areas with a high degree of anthropization and population density.
Taking into consideration these aspects, it was, therefore, possible to calculate, for each threshold
and for each aggregation interval, the total cost.
TOTAL COSTT(i)=nF.A. (T(i)) ·CF.A. +nM.A. (T(i)) ·CM.A. =CF.A. ·nF.A. (T(i)) +nM.A. (T(i)) ·CM.A.
CF.A. (6)
where
TOTAL COSTT(i) is the total cost of threshold T related to the aggregation interval i;
nF.A. (T(i)) is the number of false alarms related to thresholds T and aggregation interval i;
nM.A. (T(i)) is the number of missed alarms related to threshold T and aggregation interval i;
CF.A. is the false alarm cost;
CM.A. is the missed alarm cost.
Note that C
F.A.
acts as a scaling factor in Equation (6), and as such, it does not influence the position
of the threshold minimizing the total cost. It was, in contrast, necessary to define the ratio between
the missed alarm and false alarm costs. Several tests were performed using different ratios (1/2, 1/5,
1/10 and 1/20) in order to investigate how the final results vary as the missed alarm cost increases.
After having assessed the proper value, it was possible to determine, for every aggregation interval,
the total cost corresponding to every threshold value. From this operation, for every aggregation
Remote Sens. 2019,11, 677 8 of 24
interval, a curve is obtained. The minimum of the total cost curve is the optimal threshold (i.e., the EIT).
The goal is to assess a threshold that allows one to balance missed alarms and false alarms (trying to
minimize both missed and false alarms) and to maximize the number of identified events. A high
percentage of error (both in terms of missed alarms and in terms of false alarms) is in fact expected to
reduce the confidence in the reliability of the early warning system and the value of a warning [29].
It was, therefore, decided to analyze the possible relationship between optimal threshold and
mean annual rainfall of the country object of study, with the aim to calibrate the methodology with a
site-specific parameter. This total rainfall amount (Figure A2 reported in Appendix A) was calculated
using 10 years of GPCC (Global Precipitation Climatology Centre) “Monitoring product” [
30
] with a
1
resolution. The “Monitoring product” is a monthly global data which is available about 2 months
after the end of the month which it refers to. About 7000–9000 stations contribute to this dataset.
The “Monitoring product” is recommended by GPCC to be used for applications that need high-quality
gridded measures of rainfall.
Unfortunately, the accuracy in the extreme rainfall detection could be affected by the adoption of
a single EIT for the entire country. There are, in fact, countries were the mean annual rainfall varies
in a significant way. The possibility of adopting thresholds, different for each aggregation interval
depending on the average annual rainfall, was therefore explored.
As before, the different aggregation intervals were studied separately. For each of these, a series
of simulations using this 1
×
1
total rainfall amount was performed. In every simulation, thresholds
equal to a percentage of the mean annual rainfall have been adopted. Specifically, the thresholds were
calculated using the equation
T=T.R. ·pT.R. (7)
where
T represents the threshold;
T.R. represents the total rainfall (i.e., the mean annual rainfall calculated using 10 years of
GPCC data);
pT.R.
is a parameter representing the fraction of the total rainfall leading to the extreme
event identification.
The application of an upper bound and a lower bound proved to be necessary. This constraint was
included for two reasons. There are places where the recorded average annual rainfall amount is very
low (below 100 mm in a year), which would lead to very low threshold values, comparable with the
satellite measurement accuracy, with
pT.R.
values around 0.1–0.2. Analogously, in places where the total
annual rainfall is very high (above 2000 mm in a year), the EIT could be unrealistically high because in
these places, rainfall events tend to occur in the form of low-intensity/high-frequency events.
Several tests were performed. For every aggregation interval, in order to choose the proper
thresholds, both the criterion of minimization of the total cost and the maximization of the number
of identified events were taken into account. The results obtained with the previous threshold
methodology adopted in the Extreme Rainfall Detection System were also considered for comparison
with the purpose of increasing the system performances. The methodology adopted in the previous
version of ERDS is briefly summarized in Appendix B.
3. Results
3.1. Analysis of the Temporal and Spatial Influence of Missing Data
The temporal series of no data cells contained in every half-hourly GPM IMERG product was
analyzed. The presence of missing data resulted as acceptable for both the late and early products
since the registered averages of the percentage of no data values were equal to 0.026% for the early
product dataset and 0.019% for the late product.
Remote Sens. 2019,11, 677 9 of 24
Figure A1 reported in Appendix Ashows the spatial distribution of the pixels in which the
precipitation value is absent. The most affected areas are the southern part of Patagonia region (South
America), the northern part of North America (in particular Quebec province and Newfoundland and
Labrador province) and the northeast part of Russia. No significant differences were found between these
two versions.
3.2. GPM IMERG Accuracy Evaluation
The bias and mean absolute error were calculated for every aggregation interval previously
mentioned. The results are reported in Figure 2, taking advantages of the peculiarity of a boxplot.
In these figures, the first quantile, the median value of the distribution and the third quantile are
identifiable. The mean values are instead summarized in Table 2. On the whole, a negative value of
the bias emerged from the analysis. As a general rule, therefore, the satellite-derived data tend to
underestimate rainfall with respect to the rain gauge. Several studies [
15
19
] reported in Section 1
agreed with this statement. A good accuracy for a near real-time application was obtained with
aggregation intervals greater or equal to 12 h. For longer aggregation intervals, the bias tends to have
a null value, allowing more accurate information. The early and late data performed similarly.
Figure 2.
The bias (
a
) and mean absolute error (MAE) (
b
) evaluated for nonzero rainfall intensities:
The orange boxplot refers to the GPM IMERG half-hourly early data while the red boxplot refers to the
GPM IMERG half-hourly late data.
Table 2.
The bias and MAE values calculated taking into account only the nonzero rainfall intensities
both for the early (E) and for the late (L) products.
1 h 2 h 3 h 6 h 12 h 24 h 48 h 72 h 96 h 120 h 144 h 168 h
BIAS E
0.51 0.39 0.33 0.24 0.17 0.13 0.09 0.08 0.07 0.07 0.07 0.07
L
0.53 0.41 0.35 0.26 0.19 0.13 0.10 0.09 0.08 0.07 0.07 0.07
MAE E
2.19 1.58 1.28 0.87 0.59 0.40 0.28 0.23 0.20 0.19 0.18 0.17
L
2.11 1.55 1.26 0.87 0.59 0.40 0.28 0.23 0.20 0.19 0.18 0.17
The results (in terms of false alarm ratio) reported in Figure 3and Table 3demonstrate that the
GPM IMERG half-hourly data guarantee good performances for a near real-time application with
aggregation intervals equal to or greater than 12 h. Smaller time intervals have an unsatisfactory value
Remote Sens. 2019,11, 677 10 of 24
of false alarm ratio (approximately equal to or greater than 0.5). As expected, FAR showed a decreasing
trend for both products. The outcomes demonstrate also that a 24-h aggregation interval ensures
a probability of detection greater than 80% and a critical success index greater than 50%. With an
aggregation interval of 72 h, a probability of detection greater than 90% was reached.
Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 24
ensures a probability of detection greater than 80% and a critical success index greater than 50%. With
an aggregation interval of 72 h, a probability of detection greater than 90% was reached.
Figure 3. The false alarm ratio (a), the probability of detection (b) and the critical success index (c):
The orange boxplot refers to the GPM IMERG half-hourly early data while the red boxplot refers to
the GPM IMERG half-hourly late data.
Table 3. False alarm ratio (FAR), probability of detection (POD) and critical success index (CSI) mean
values calculated both for the early (E) and for the late (L) products.
1 h
2 h
3 h
6 h
12 h
24 h
48 h
72 h
96 h
120 h
144 h
168 h
FAR
E
0.58
0.56
0.55
0.51
0.46
0.40
0.32
0.27
0.23
0.19
0.17
0.14
L
0.56
0.54
0.52
0.48
0.44
0.37
0.30
0.25
0.22
0.18
0.16
0.14
POD
E
0.47
0.53
0.57
0.64
0.71
0.78
0.85
0.88
0.91
0.93
0.94
0.95
L
0.51
0.56
0.59
0.65
0.71
0.78
0.84
0.88
0.90
0.92
0.93
0.94
CSI
E
0.28
0.31
0.33
0.38
0.43
0.51
0.60
0.67
0.72
0.76
0.79
0.82
L
0.30
0.33
0.35
0.40
0.45
0.53
0.61
0.67
0.72
0.76
0.80
0.82
A further study was conducted taking into account only rainfall rate greater than the 99th
percentile of the intensities distribution. The obtained results allowed us to understand the GPM
IMERG performances in heavy rainfall detection. Figure 4 and Table 4 show the modest
underestimation of GPM data.
Figure 3.
The false alarm ratio (
a
), the probability of detection (
b
) and the critical success index (
c
): The
orange boxplot refers to the GPM IMERG half-hourly early data while the red boxplot refers to the
GPM IMERG half-hourly late data.
Table 3.
False alarm ratio (FAR), probability of detection (POD) and critical success index (CSI) mean
values calculated both for the early (E) and for the late (L) products.
1 h 2 h 3 h 6 h 12 h 24 h 48 h 72 h 96 h 120 h 144 h 168 h
FAR
E
0.58 0.56 0.55 0.51 0.46 0.40 0.32 0.27 0.23 0.19 0.17 0.14
L
0.56 0.54 0.52 0.48 0.44 0.37 0.30 0.25 0.22 0.18 0.16 0.14
POD
E
0.47 0.53 0.57 0.64 0.71 0.78 0.85 0.88 0.91 0.93 0.94 0.95
L
0.51 0.56 0.59 0.65 0.71 0.78 0.84 0.88 0.90 0.92 0.93 0.94
CSI
E
0.28 0.31 0.33 0.38 0.43 0.51 0.60 0.67 0.72 0.76 0.79 0.82
L
0.30 0.33 0.35 0.40 0.45 0.53 0.61 0.67 0.72 0.76 0.80 0.82
A further study was conducted taking into account only rainfall rate greater than the 99th
percentile of the intensities distribution. The obtained results allowed us to understand the
GPM IMERG performances in heavy rainfall detection. Figure 4and Table 4show the modest
underestimation of GPM data.
Remote Sens. 2019,11, 677 11 of 24
Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 24
Figure 4. The bias (a) and mean absolute error (b) related to the heavy rainfall events: The orange
boxplot refers to the GPM IMERG half-hourly early data while the red boxplot refers to the GPM
IMERG half-hourly late data.
Table 4. The bias and MAE values calculated both for the early (E) and for the late (L) products, taking
into account only heavy rainfall conditions.
1 h
2 h
3 h
6 h
12 h
24 h
48 h
72 h
96 h
120 h
144 h
168 h
BIAS
E
10.35
9.01
8.02
6.21
4.59
3.18
2.16
1.72
1.50
1.31
1.19
1.08
L
10.02
8.71
7.76
6.03
4.48
3.10
2.11
1.68
1.47
1.29
1.17
1.06
MAE
E
11.88
10.31
9.18
7.10
5.17
3.57
2.41
1.89
1.63
1.41
1.26
1.13
L
11.58
10.07
8.97
6.96
5.07
3.49
2.36
1.84
1.60
1.39
1.24
1.11
3.3. Development and Test of a New Extreme Rainfall Detection Methodology
A scatter diagram (Figure 5) was used to investigate the relationship between the optimal
thresholds and the total rainfall for a set of different conditions, taking into account every country.
Every scatter plot refers to a different ratio between the false alarm cost and the missed alarm cost.
Figure 4.
The bias (
a
) and mean absolute error (
b
) related to the heavy rainfall events: The orange
boxplot refers to the GPM IMERG half-hourly early data while the red boxplot refers to the GPM
IMERG half-hourly late data.
Table 4.
The bias and MAE values calculated both for the early (E) and for the late (L) products, taking
into account only heavy rainfall conditions.
1 h 2 h 3 h 6 h 12 h 24 h 48 h 72 h 96 h 120 h 144 h 168 h
BIAS E
10.35
9.01 8.02 6.21 4.59 3.18 2.16 1.72 1.50 1.31 1.19 1.08
L
10.02
8.71 7.76 6.03 4.48 3.10 2.11 1.68 1.47 1.29 1.17 1.06
MAE E
11.88 10.31 9.18 7.10 5.17 3.57 2.41 1.89 1.63 1.41 1.26 1.13
L
11.58 10.07 8.97 6.96 5.07 3.49 2.36 1.84 1.60 1.39 1.24 1.11
3.3. Development and Test of a New Extreme Rainfall Detection Methodology
A scatter diagram (Figure 5) was used to investigate the relationship between the optimal
thresholds and the total rainfall for a set of different conditions, taking into account every country.
Every scatter plot refers to a different ratio between the false alarm cost and the missed alarm cost.
Similar results were obtained also for the other aggregation intervals. Overall, these graphs
outline that a ratio equal to 1/5 represents the most appropriate value to be used for the next steps of
the study. Even if, for some aggregation intervals, the best results were obtained with a ratio equal to
1/2, this value was discarded because it does not allow us to take into account the greater severities of
a missed alarm, especially in highly populated areas. A ratio equal to 1/5 represents a more realistic
condition and obtains good results in terms of correlation coefficient (the value is, in fact, very similar
to the one related to a ratio equal to 1/2). Moreover, several studies confirmed the results gathered
using this approach. A study conducted by the European Civil Protection and Humanitarian Aid
Operations outlined that “every euro spent for disaster risk reduction and preparedness saves between
four and seven euros in disaster response” [
31
]. Schröter et al. [
32
] found that early warning systems
are characterized by a benefit/cost ratio between 1/2.6 and 1/9. Hugenbusch et al. [
33
] pointed out
that the World Bank estimated that every dollar spent on risk reduction saves seven dollars in relief
and repairs.
Figure 6provides an overview of the results obtained for a 24-h aggregation interval using a
ratio between the false alarm cost and the missed alarm cost equal to 1/5. The red dots represent the
minimum of each total cost curve for every analyzed country (i.e., they represent the EITs).
Remote Sens. 2019,11, 677 12 of 24
Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 24
Figure 4. The bias (a) and mean absolute error (b) related to the heavy rainfall events: The orange
boxplot refers to the GPM IMERG half-hourly early data while the red boxplot refers to the GPM
IMERG half-hourly late data.
Table 4. The bias and MAE values calculated both for the early (E) and for the late (L) products, taking
into account only heavy rainfall conditions.
1 h
2 h
3 h
6 h
12 h
24 h
48 h
72 h
96 h
120 h
144 h
168 h
BIAS
E
10.35
9.01
8.02
6.21
4.59
3.18
2.16
1.72
1.50
1.31
1.19
1.08
L
10.02
8.71
7.76
6.03
4.48
3.10
2.11
1.68
1.47
1.29
1.17
1.06
MAE
E
11.88
10.31
9.18
7.10
5.17
3.57
2.41
1.89
1.63
1.41
1.26
1.13
L
11.58
10.07
8.97
6.96
5.07
3.49
2.36
1.84
1.60
1.39
1.24
1.11
3.3. Development and Test of a New Extreme Rainfall Detection Methodology
A scatter diagram (Figure 5) was used to investigate the relationship between the optimal
thresholds and the total rainfall for a set of different conditions, taking into account every country.
Every scatter plot refers to a different ratio between the false alarm cost and the missed alarm cost.
Figure 5.
The optimal thresholds for every country object of study obtained for four different conditions:
The results refer to a 24-h aggregation interval and were obtained with a false alarm cost equal to 1.
Regarding the missed alarm cost, the following costs were used: 2 (a), 5 (b), 10 (c) and 20 (d).
Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 24
Figure 5. The optimal thresholds for every country object of study obtained for four different
conditions: The results refer to a 24-h aggregation interval and were obtained with a false alarm cost
equal to 1. Regarding the missed alarm cost, the following costs were used: 2 (a), 5 (b), 10 (c) and 20
(d).
Similar results were obtained also for the other aggregation intervals. Overall, these graphs
outline that a ratio equal to 1/5 represents the most appropriate value to be used for the next steps of
the study. Even if, for some aggregation intervals, the best results were obtained with a ratio equal to
1/2, this value was discarded because it does not allow us to take into account the greater severities
of a missed alarm, especially in highly populated areas. A ratio equal to 1/5 represents a more realistic
condition and obtains good results in terms of correlation coefficient (the value is, in fact, very similar
to the one related to a ratio equal to 1/2). Moreover, several studies confirmed the results gathered
using this approach. A study conducted by the European Civil Protection and Humanitarian Aid
Operations outlined that “every euro spent for disaster risk reduction and preparedness saves
between four and seven euros in disaster response” [31]. Schröter et al. [32] found that early warning
systems are characterized by a benefit/cost ratio between 1/2.6 and 1/9. Hugenbusch et al. [33] pointed
out that the World Bank estimated that every dollar spent on risk reduction saves seven dollars in
relief and repairs.
Figure 6 provides an overview of the results obtained for a 24-h aggregation interval using a
ratio between the false alarm cost and the missed alarm cost equal to 1/5. The red dots represent the
minimum of each total cost curve for every analyzed country (i.e., they represent the EITs).
Figure 6. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5: The results refer to a 24-h aggregation interval. The
countries naming follow ISO 3166.
The outcomes summarized in Figure 6 show that using a single threshold for every location of
the Earth’s surface is not advisable. The range of obtained optimal values is, in fact, very large. Using
an average value globally would result in
the introduction of too many false alarms in areas with a higher value of optimal threshold and
a high number of missed events in areas with a lower value of optimal threshold.
Figure 7 shows the relation between optimal threshold (obtained using Equation (6)) and the
total rainfall for a 24-h aggregation interval using a missed alarm costs equal to 5. A good correlation
between the optimal threshold and the total rainfall emerges from this figure. The results related to
the other aggregation intervals are instead reported in Appendix A (from Figures A3–A6).
Figure 6.
The optimal thresholds for every country object of study obtained using a false alarm
cost equal to 1 and a missed alarm cost equal to 5: The results refer to a 24-h aggregation interval.
The countries naming follow ISO 3166.
The outcomes summarized in Figure 6show that using a single threshold for every location of the
Earth’s surface is not advisable. The range of obtained optimal values is, in fact, very large. Using an
average value globally would result in
the introduction of too many false alarms in areas with a higher value of optimal threshold and
a high number of missed events in areas with a lower value of optimal threshold.
Figure 7shows the relation between optimal threshold (obtained using Equation (6)) and the
total rainfall for a 24-h aggregation interval using a missed alarm costs equal to 5. A good correlation
between the optimal threshold and the total rainfall emerges from this figure. The results related to the
other aggregation intervals are instead reported in Appendix A(from Figures A3A6).
Figure 8shows the results (in terms of total cost and identified events) achieved for a 24-h
aggregation interval applying Equation (7), considering a missed alarm cost five times greater than
the false alarm one. In the graph, better results (compared to the one obtained using the threshold
Remote Sens. 2019,11, 677 13 of 24
methodology adopted in the previous version of ERDS) are highlighted with a red dot. The optimal
simulation (and its related total rainfall percentage and lower/upper boundaries) corresponds to the
one characterized by a low total cost and a high percentage of identified events.
Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 24
Figure 7. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 24-h aggregation interval. The countries naming follow ISO 3166.
Figure 8 shows the results (in terms of total cost and identified events) achieved for a 24-h
aggregation interval applying Equation (7), considering a missed alarm cost five times greater than
the false alarm one. In the graph, better results (compared to the one obtained using the threshold
methodology adopted in the previous version of ERDS) are highlighted with a red dot. The optimal
simulation (and its related total rainfall percentage and lower/upper boundaries) corresponds to the
one characterized by a low total cost and a high percentage of identified events.
Figure 8. A comparison between the results obtained for a 24-h aggregation interval: Both the total
cost (a) and the percentage of identified events (b) were derived using a false alarm cost equal to 1
and a missed alarm cost equal to 5. The horizontal axis was labelled with the parameters used in the
simulations performed during the development of the new extreme rainfall detection methodology:
Figure 7.
The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 24-h aggregation interval. The countries naming follow ISO 3166.
Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 24
Figure 7. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 24-h aggregation interval. The countries naming follow ISO 3166.
Figure 8 shows the results (in terms of total cost and identified events) achieved for a 24-h
aggregation interval applying Equation (7), considering a missed alarm cost five times greater than
the false alarm one. In the graph, better results (compared to the one obtained using the threshold
methodology adopted in the previous version of ERDS) are highlighted with a red dot. The optimal
simulation (and its related total rainfall percentage and lower/upper boundaries) corresponds to the
one characterized by a low total cost and a high percentage of identified events.
Figure 8. A comparison between the results obtained for a 24-h aggregation interval: Both the total
cost (a) and the percentage of identified events (b) were derived using a false alarm cost equal to 1
and a missed alarm cost equal to 5. The horizontal axis was labelled with the parameters used in the
simulations performed during the development of the new extreme rainfall detection methodology:
Figure 8.
A comparison between the results obtained for a 24-h aggregation interval: Both the total
cost (
a
) and the percentage of identified events (
b
) were derived using a false alarm cost equal to
1 and a missed alarm cost equal to 5. The horizontal axis was labelled with the parameters used in the
simulations performed during the development of the new extreme rainfall detection methodology: the
first value represents the percentage of the total rainfall while the values reported between the square
brackets correspond to the lower and the upper boundaries. When no lower or upper boundaries were
used, the
symbol was reported. Better results (compared to the one obtained using the threshold
methodology adopted in the previous version of ERDS) are highlighted with a red dot.
Remote Sens. 2019,11, 677 14 of 24
The best results for every aggregation interval were achieved with the parameters summarized in
Table 5. The threshold masks obtained with the parameters summarized in Table 5are instead reported
in Figure A7 of Appendix A.
Table 5. The threshold values used in the current extreme rainfall detection methodology.
Aggregation Interval
(hours)
pT.R.
(%)
Lower Bound
(mm)
Upper Bound
(mm)
12 6 100 150
24 8 120 210
48 12 140 240
72 15 170 260
96 16 190 280
3.4. Implementation
The Extreme Rainfall Detection System, implemented with the methodology described in this
article, is hosted in a specific cloud server and set up with all the necessary software. Moreover, ad hoc
software modules were developed because no existing software satisfied the requirements of the
methodology. The software modules are available on GitHub [
34
,
35
]. The main components of this
system are needed to perform four main steps: mirroring the source GPM site, accumulating rainfall
data, generating alerts and publishing alerts.
A local repository of GPM IMERG data is hosted in the cloud server and updated regularly, as soon
as a new measure is published by NASA. This behaviour is implemented in a software module that is
able to mirror a subset of the GPM official data repository by checking on it during scheduled times of
the day. From the point of view of the technology, the official GPM repository is constituted by a set
of gridded files, stored in HDF5 format and published by means of the ftp protocol in Reference [
36
].
Each HDF5 file contains the rainfall measurement over a period of 30 min. Despite the mentioned
temporal resolution of the source data, new files are published and grouped two by two, obtaining a
temporal resolution equal to 1 h. In conclusion, the software module establishes a connection with
the official GPM IMERG repository every hour and downloads locally the two latest files available.
Files that are older than necessary are deleted from the local repository.
A second software module is devoted to perform rain-data accumulation over several periods of
time. In particular, this module is triggered every time a new GPM IMERG file is available locally, as a
result of the previous step. This module is tuned to perform rain data accumulation on the following
periods: 12, 24, 48, 72 and 96 h. The resulting values, expressed in mm of rainfall, are stored locally as
grid files into a georeferenced TIFF file. Each GeoTIFF file contains results related to a single period
of accumulation.
A third software module is dedicated to perform the generation of the alerts. It is triggered
as soon as new accumulation data are produced from the previous step. The concept behind this
task is basic: if the accumulated rain values exceed the event-identification threshold defined with
the methodology described before for the respective accumulation period, an alert is produced.
This analysis is performed on a pixel-by-pixel basis. As a result, the pixel locations characterized by an
alert are marked with a value equal to 1, while the non-alerted pixel locations are marked with a value
equal to 0. These values are stored locally into a GeoTIFF file. Before publishing, the alerts produced
on areas entirely occupied by sea or ocean were discarded. In order to complete this operation, a mask
containing, in each cell, the water coverage of the area was used. This mask is freely available on
NASA’s website [
37
]. Alerts provided on cells characterized by a water coverage equal to 100% were
discarded. ERDS, in fact, is a tool developed in order to provide alerts on populated areas.
Both the accumulated rainfall data and the related alert data are published on the web.
The publication is two-fold: on the one hand, the data are published in a georeferenced grid format
(i.e., GeoTIFF) that can be used for generating maps in a Desktop GIS environment or for performing
Remote Sens. 2019,11, 677 15 of 24
further analysis; on the other hand, a map representation of the data is published with the aim of
allowing a quick check of the rain distribution and of the alerts.
The first level of publication is performed by taking advantage of a web server software installed
on the local server, the scope of which is to make the GeoTIFF files available for download through the
http protocol. In order to guide the download and make it easier for the end users, a specific homepage
was designed as well (see http://erds.ithacaweb.org). This home page is also used to communicate
the latest update of the system in terms of the available rain data.
With regards to the second level of publication, the technology used is the Web Map Service
(WMS). A WMS produces maps of spatially referenced data dynamically from geographic information.
WMS-produced maps are generally rendered in a pictorial format such as PNG, GIF or JPEG. A specific
software (called GeoServer) is used for providing this service.
4. Discussion
The quantitative evaluation of the GPM IMERG accuracy was established using statistical
performance scores and time series analyses. The obtained results are quite revealing in several ways.
Firstly, there is no significant accuracy increase associated with the use of GPM IMERG half-hourly late
data (provided with a longer latency) in a near real-time Extreme Rainfall Detection System. The use of
the late product for an early warning would be appropriate only in the case of a noticeable improvement
in terms of accuracy. Secondly, in addition to a high value of bias, mean absolute error and false alarm
ratio, there is a probability of detection not acceptable for aggregation intervals lower than 12 h. It is,
therefore, convenient to set the minimum rainfall aggregation interval to 12 h to be able to detect events
with an acceptable accuracy. This choice takes into account several aspects, such as the requirements
of the system, the ideal latency in the provision of information or the final use of the output. As a
consequence, similar aggregation intervals are suitable for these purposes. It is, however, important
to highlight the limitations in terms of the accuracy of the outputs obtained using this aggregation
interval. The results reported in Table 3highlight that this aggregation interval is characterized by a
relatively high mean value of the false alarm ratio (equal to 0.46). Moreover, the mean value of the
probability of detection is equal to 0.71. The ERDS outputs will be affected accordingly.
Summarizing, the current version of ERDS automatically downloads the most recent GPM
IMERG early run data and cumulates them according to specific time intervals. More importantly,
ERDS generates rainfall alerts where and when the rainfall amount is higher than a specific set of
event-identification thresholds.
The current version of ERDS is able to provide alerts every hour with a 0.1
×
0.1
resolution in
the latitude range between 60
N and 60
S. The ERDS data are uploaded every hour because GPM
IMERG data, despite the 30 min resolution, is made public in pairs of two. The latency is about 4 h.
The previous version of ERDS instead provided alerts every 3 h with a 0.25
×
0.25
resolution in
the latitude range between 50
N and 50
S. The latency was about 8 h. The new input data allowed
the improvement of the system in terms of spatial and temporal resolution, as well as the extent.
The greater temporal resolution allowed for the capture of short-term rainfall events that commonly
occurred in some areas. It is important to keep in mind that, in some cases, the alerts related to such
events can be produced with a modest delay due to the latency of the input data.
The results gathered from the study highlighted several advantages towards the previous ERDS
version. The different results obtained are summarized in Table 6. Only for the 12-h aggregation
interval, there is no comparison with the previous threshold methodology because, in the previous
system, this aggregation interval was not used.
Remote Sens. 2019,11, 677 16 of 24
Table 6.
The ERDS performance (a comparison between the results obtained with the previous
thresholds and with the current ones).
Previous Thresholds Current Thresholds
12-h Aggregation Interval - 150 *
24-h Aggregation Interval 131 * 137 *
48-h Aggregation Interval 85 * 120 *
72-h Aggregation Interval 48 * 112 *
96-h Aggregation Interval 39 * 118 *
Total Number of Identified Events
135 * 162 *
% of Identified Events 64% 76.8%
* On a total of 211 disasters analyzed.
The improvement in the performances of the new ERDS method is clear for all aggregation
intervals but becomes extremely relevant when aggregation intervals greater than 48 h are considered.
Note that many events are identified with several aggregation intervals.
A further study was performed in order to examine the ERDS performance in relation to the
disaster type. The left plot of Figure 9shows, for each analyzed subcategory, the number of identified
events compared to the total for this specific subcategory. In the right plot of Figure 9, instead, the same
results are shown in terms of percentages.
Remote Sens. 2019, 11, x FOR PEER REVIEW 16 of 24
Table 6. The ERDS performance (a comparison between the results obtained with the previous
thresholds and with the current ones).
Previous
Thresholds
Current
Thresholds
12-h Aggregation Interval - 150 *
24-h Aggregation Interval 131 * 137 *
48-h Aggregation Interval 85 * 120 *
72-h Aggregation Interval 48 * 112 *
96-h Aggregation Interval 39 * 118 *
Total Number of Identified Events 135 *
162 *
% of Identified Events 64% 76.8%
* On a total of 211 disasters analyzed.
The improvement in the performances of the new ERDS method is clear for all aggregation
intervals but becomes extremely relevant when aggregation intervals greater than 48 h are
considered. Note that many events are identified with several aggregation intervals.
A further study was performed in order to examine the ERDS performance in relation to the
disaster type. The left plot of Figure 9 shows, for each analyzed subcategory, the number of identified
events compared to the total for this specific subcategory. In the right plot of Figure 9, instead, the
same results are shown in terms of percentages.
(a) (b)
Figure 9. The ERDS performance compared to the disaster type: “A” represents “convective storms”,
“B” represents “flood events”, “C” represents “hurricanes and cyclones”, “D” represents “heavy rain-
induced landslides”, “E” represents “flash floods” and “F” represents “tropical storms”. (a) The
results in terms of the number of events and (b) the results in terms of percentage.
ERDS is an alert system designed to identify hydrometeorological events, such as hurricanes,
tropical storms, heavy rainfall that could lead to flood events, flash floods, convective storms, etc.
However, there are some types of phenomena that put a strain on this alert system. Specifically, ERDS
may fail in the identification of convective storms characterized by a high spatial and temporal
variability and discontinuity. They can indeed be transparent to the satellite (rainfall could affect an
area smaller than the size of a single cell of GPM data) or their intensity may be underestimated.
ERDS may also fail in the identification of heavy rain-induced landslides or in the provision of a
timely alert in the case of intense flash floods affecting very small basins. ERDS, in fact, is
characterized by a delay of about 4 h (due to original GPM data delay plus the time required by the
data download, processing and alert evaluation in the ERDS system). If the event is very short, very
intense and can cause a flood within 4 h, the alert will be provided too late. Conversely, the system
Figure 9.
The ERDS performance compared to the disaster type: “A” represents “convective storms”,
“B” represents “flood events”, “C” represents “hurricanes and cyclones”, “D” represents “heavy
rain-induced landslides”, “E” represents “flash floods” and “F” represents “tropical storms”. (
a
) The
results in terms of the number of events and (b) the results in terms of percentage.
ERDS is an alert system designed to identify hydrometeorological events, such as hurricanes,
tropical storms, heavy rainfall that could lead to flood events, flash floods, convective storms, etc.
However, there are some types of phenomena that put a strain on this alert system. Specifically, ERDS
may fail in the identification of convective storms characterized by a high spatial and temporal
variability and discontinuity. They can indeed be transparent to the satellite (rainfall could affect
an area smaller than the size of a single cell of GPM data) or their intensity may be underestimated.
ERDS may also fail in the identification of heavy rain-induced landslides or in the provision of a timely
alert in the case of intense flash floods affecting very small basins. ERDS, in fact, is characterized by
a delay of about 4 h (due to original GPM data delay plus the time required by the data download,
processing and alert evaluation in the ERDS system). If the event is very short, very intense and
can cause a flood within 4 h, the alert will be provided too late. Conversely, the system showed
a good performance regarding the identification of hydrometeorological disasters like hurricanes,
Remote Sens. 2019,11, 677 17 of 24
cyclones, tropical storms, heavy rainfall that might lead to flood events and flash floods characterized
by durations greater than the ERDS latency.
5. Conclusions
This work highlighted the potential of the NASA GPM IMERG half-hourly early run data (a
satellite precipitation measurement) as the input for a near real-time Extreme Rainfall Detection
System. The new input data allowed spatial and temporal resolution to increase. Moreover, the current
threshold methodology enabled system performance to increase, allowing the detection of 13% more
events. These findings suggest that, in general, this precipitation measurement provides satisfactory
results. However, a number of important limitations need to be considered.
The system performances are deeply influenced by the input data resolution. The system is
working at the global scale with a spatial resolution of 0.1
×
0.1
. These characteristics could lead to a
wrong picture of rainfall events that vary greatly on a small scale and over time. A local-scale validation
is advisable. Further studies aimed at developing a temporal/spatial downscaling of the GPM data
could help to provide more accurate and reliable outputs. Moreover, one of the major problems with
this kind of application is the error that may be present in the rainfall measurement. The system has
no control over any underestimation, overestimation or random errors. Furthermore, no remedy is in
place with reference to any kind of temporary interruptions in the provision of data.
With this type of application, the model calibration and performance evaluation continue to be
challenging problems in large data-scarce regions or in areas where only a few hydrometeorological
events were recorded. The currently adopted thresholds may be influenced by this problem.
Before concluding, it is important to highlight an additional information. One of the aspects to
be taken into consideration is the noncoincidence between the place where the alert was given and
the place where the flood may occur. ERDS is a tool developed for rainfall monitoring. The system
provides an alert where the amount of rainfall is higher than a specific threshold. The system, however,
does not take into account the morphology of the territory or information regarding basins. The flood,
therefore, may occur in the alerted cell or in nearby ones. Further studies could be done in order to
take into account this important aspect.
Author Contributions:
Formal analysis, P.M.; methodology, P.M. and F.L.; software, P.M. and S.B.;
writing—original draft, P.M.; writing—review and editing, P.M., F.L., S.B., P.B. and F.D.
Funding:
F.L. acknowledges the funding support provided by the European Research Council (ERC) through the
project “Coping with water scarcity in a globalized world” (ERC-2014-CoG, project 288 647473). F.D. acknowledges
the funding support provided by the DG ECHO through the project “TRIgger BUffers for inundaTion Events”
(TRIBUTE, ECHO/SUB/2016/742480/PREV08).
Acknowledgments:
We express our gratitude to Paolo Pasquali, who helped us in the development of the
web app.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Figure A1 shows the results obtained during the analysis of the presence of cells with
missing precipitation measurement within the area normally covered by the precipitationCal band.
The methodology is described in Section 2.1.
Remote Sens. 2019,11, 677 18 of 24
Remote Sens. 2019, 11, x FOR PEER REVIEW 18 of 24
Appendix A
Figure A1 shows the results obtained during the analysis of the presence of cells with missing
precipitation measurement within the area normally covered by the precipitationCal band. The
methodology is described in Section 2.1.
Figure A1. The spatial distribution of no data: The first graph (a) is related to the GPM IMERG half-
hourly early product, while the second graph (b) is related to the GPM IMERG half-hourly late
product. The colours refer to the number of half-hourly files in which the measure is absent in the
precipitationCal band (the analysis was performed from 12 March 2014 to 30 April 2017 on 55008
files). The reference system is WGS84.
Figure A2 shows the total rainfall amount (i.e., the mean annual rainfall) adopted during the
development of the new extreme rainfall detection methodology (described in Section 2.2).
Figure A2. The mean annual rainfall calculated using 10 years of GPCC monthly “Monitoring
product”. The white areas are places characterized by an absence of measurement. The reference
system is WGS84.
Figures A3–A6 show the relationship between the optimal thresholds and the total rainfall for a
set of different aggregation interval (12, 48, 72 and 96 h) using a missed alarm cost equal to 5 and a
Figure A1.
The spatial distribution of no data: The first graph (
a
) is related to the GPM IMERG
half-hourly early product, while the second graph (
b
) is related to the GPM IMERG half-hourly late
product. The colours refer to the number of half-hourly files in which the measure is absent in the
precipitationCal band (the analysis was performed from 12 March 2014 to 30 April 2017 on 55008 files).
The reference system is WGS84.
Figure A2 shows the total rainfall amount (i.e., the mean annual rainfall) adopted during the
development of the new extreme rainfall detection methodology (described in Section 2.2).
Remote Sens. 2019, 11, x FOR PEER REVIEW 18 of 24
Appendix A
Figure A1 shows the results obtained during the analysis of the presence of cells with missing
precipitation measurement within the area normally covered by the precipitationCal band. The
methodology is described in Section 2.1.
Figure A1. The spatial distribution of no data: The first graph (a) is related to the GPM IMERG half-
hourly early product, while the second graph (b) is related to the GPM IMERG half-hourly late
product. The colours refer to the number of half-hourly files in which the measure is absent in the
precipitationCal band (the analysis was performed from 12 March 2014 to 30 April 2017 on 55008
files). The reference system is WGS84.
Figure A2 shows the total rainfall amount (i.e., the mean annual rainfall) adopted during the
development of the new extreme rainfall detection methodology (described in Section 2.2).
Figure A2. The mean annual rainfall calculated using 10 years of GPCC monthly “Monitoring
product”. The white areas are places characterized by an absence of measurement. The reference
system is WGS84.
Figures A3–A6 show the relationship between the optimal thresholds and the total rainfall for a
set of different aggregation interval (12, 48, 72 and 96 h) using a missed alarm cost equal to 5 and a
Figure A2.
The mean annual rainfall calculated using 10 years of GPCC monthly “Monitoring product”.
The white areas are places characterized by an absence of measurement. The reference system is WGS84.
Figures A3A6 show the relationship between the optimal thresholds and the total rainfall for a
set of different aggregation interval (12, 48, 72 and 96 h) using a missed alarm cost equal to 5 and a
false alarm cost equal to 1. The results related to a 24-h aggregation interval were instead discussed in
detail in Section 3.3.
Remote Sens. 2019,11, 677 19 of 24
Remote Sens. 2019, 11, x FOR PEER REVIEW 19 of 24
false alarm cost equal to 1. The results related to a 24-h aggregation interval were instead discussed
in detail in Section 3.3.
Figure A3. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 12-h aggregation interval. The countries naming follow ISO 3166.
Figure A4. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 48-h aggregation interval. The countries naming follow ISO 3166.
Figure A3.
The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 12-h aggregation interval. The countries naming follow ISO 3166.
Remote Sens. 2019, 11, x FOR PEER REVIEW 19 of 24
false alarm cost equal to 1. The results related to a 24-h aggregation interval were instead discussed
in detail in Section 3.3.
Figure A3. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 12-h aggregation interval. The countries naming follow ISO 3166.
Figure A4. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 48-h aggregation interval. The countries naming follow ISO 3166.
Figure A4.
The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 48-h aggregation interval. The countries naming follow ISO 3166.
Remote Sens. 2019,11, 677 20 of 24
Remote Sens. 2019, 11, x FOR PEER REVIEW 20 of 24
Figure A5. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 72-h aggregation interval. The countries naming follow ISO 3166.
Figure A6. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 96-h aggregation interval. The countries naming follow ISO 3166.
Figure A7 shows the thresholds masks currently adopted in the updated version of the Extreme
Rainfall Detection System. The threshold values were calculated for a set of aggregation intervals (12,
24, 48, 72 and 96 h) using the methodology described in Section 2.2. Every thresholds mask has a ×
1° spatial resolution and was obtained applying the parameters summarized in Table 5 at the mean
annual rainfall reported in Figure A2.
Figure A5.
The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 72-h aggregation interval. The countries naming follow ISO 3166.
Remote Sens. 2019, 11, x FOR PEER REVIEW 20 of 24
Figure A5. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 72-h aggregation interval. The countries naming follow ISO 3166.
Figure A6. The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 96-h aggregation interval. The countries naming follow ISO 3166.
Figure A7 shows the thresholds masks currently adopted in the updated version of the Extreme
Rainfall Detection System. The threshold values were calculated for a set of aggregation intervals (12,
24, 48, 72 and 96 h) using the methodology described in Section 2.2. Every thresholds mask has a ×
1° spatial resolution and was obtained applying the parameters summarized in Table 5 at the mean
annual rainfall reported in Figure A2.
Figure A6.
The optimal thresholds for every country object of study obtained using a false alarm cost
equal to 1 and a missed alarm cost equal to 5 compared to the total rainfall of the country: The results
refer to a 96-h aggregation interval. The countries naming follow ISO 3166.
Figure A7 shows the thresholds masks currently adopted in the updated version of the Extreme
Rainfall Detection System. The threshold values were calculated for a set of aggregation intervals (12,
24, 48, 72 and 96 h) using the methodology described in Section 2.2. Every thresholds mask has a 1
×
1
spatial resolution and was obtained applying the parameters summarized in Table 5at the mean
annual rainfall reported in Figure A2.
Remote Sens. 2019,11, 677 21 of 24
Remote Sens. 2019, 11, x FOR PEER REVIEW 21 of 24
Figure A7. The event-identification threshold used for the extreme rainfall detection: The images are
related to the 12 (a), 24 (b), 48 (c), 72 (d) and 96 (e) hours aggregation intervals. The reference system
is WGS84.
Figure A7.
The event-identification threshold used for the extreme rainfall detection: The images are
related to the 12 (
a
), 24 (
b
), 48 (
c
), 72 (
d
) and 96 (
e
) hours aggregation intervals. The reference system
is WGS84.
Remote Sens. 2019,11, 677 22 of 24
Appendix B
The previous version of ERDS was using thresholds for the identification of extreme events
depending on a climatological classification [
38
]. Three different macro-areas were identified, using
Koppen–Geiger classification [39] as a basis. These macro-areas can be seen in Figure A8.
Remote Sens. 2019, 11, x FOR PEER REVIEW 22 of 24
Appendix B
The previous version of ERDS was using thresholds for the identification of extreme events
depending on a climatological classification [38]. Three different macro-areas were identified, using
Koppen–Geiger classification [39] as a basis. These macro-areas can be seen in Figure A8.
Figure A8. The ERDS macro-areas based on Koppen–Geiger climate classification: The different
colours correspond to different corrective coefficients. The reference system is WGS84.
Specifically, selected thresholds, shown in Table A1, were multiplied by a corrective coefficient
equal to
1 for classes A, D and E of the Koppen–Geiger classification (white areas shown in Figure A8);
0.80 for class C of the Koppen–Geiger classification (grey areas shown in Figure A8);
0.65 for class B of the Koppen–Geiger classification (black areas shown in Figure A8).
Where the accumulated rainfall was exceeding the corresponding threshold value, ERDS was
able to provide an alert. This alert could be of three entities: level 1 (low entity), level 2 (medium
entity) or level 3 (high entity).
Table A1. The threshold values used in the previous version of the Extreme Rainfall Detection System.
24 h 48 h 72 h 96 h 120 h 144 h
Threshold Alert Level 1 (mm) 150 230 310 390 470 500
Threshold Alert Level 2 (mm) 210 290 370 450 530 560
Threshold Alert Level 3 (mm) 270 350 430 510 590 620
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level 3 (high entity).
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2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... GPM data are used to provide information regarding the accumulated rainfall over the past 12, 24, 48, 72 and 96 h, while GFS data are used to provide the forecasts for the upcoming 12, 24, 48, 72 and 96 h. The system was tested and validated over several case studies, highlighting good alerting capabilities [7][8][9][10][11]. However, some types of rainfall events (such as short-duration, very localized convective events) can undermine its capacity to detect extreme rainfall events [7]. ...
... The system was tested and validated over several case studies, highlighting good alerting capabilities [7][8][9][10][11]. However, some types of rainfall events (such as short-duration, very localized convective events) can undermine its capacity to detect extreme rainfall events [7]. ...
... The extreme rainfall detection is performed using a pixel-by-pixel approach by comparing maps of rainfall amounts (evaluated separately on the basis of GPM, GFS and WRF data) with maps of pre-calculated rainfall thresholds: an alert is issued if the accumulated rainfall exceeds a specific threshold value [7]. The alerts issued with this procedure are then processed by applying a binary mask that distinguishes pixels covered by land from pixels covered by water (sea or ocean). ...
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In this work, we describe the integration of Weather and Research Forecasting (WRF) forecasts produced by CIMA Research Foundation within ITHACA Extreme Rainfall Detection System (ERDS) to increase the forecasting skills of the overall early warning system. The entire workflow is applied to the heavy rainfall event that affected the city of Palermo on 15 July 2020, causing urban flooding due to an exceptional rainfall amount of more than 130 mm recorded in about 2.5 h. This rainfall event was not properly forecasted by meteorological models operational at the time of the event, thus not allowing to issue an adequate alert over that area. The results highlight that the improvement in the quantitative precipitation scenario forecast skills, supported by the adoption of the H2020 LEXIS computing facilities and by the assimilation of in situ observations, allowed the ERDS system to improve the prediction of the peak rainfall depths, thus paving the way to the potential issuing of an alert over the Palermo area.
... Researchers from various parts of the world have conducted a preliminary assessment of GPM and TRMM products. The initial examination found that GPM products performed far better than TRMM (3B42V7), especially for detecting heavy rainfall events (Mazzoglio et al., 2019;Ning et al., 2016;Prakash et al., 2016;Retalis et al., 2020;Sharma et al., 2020;Tan and Duan, 2017). In the Indian context, the first-ever R-factor map for India was proposed by Babu et al. (1978) using only 44 rain gauge station data. ...
... However, it is sensitive to light rain and no rain condition. Mazzoglio et al. (2019) concluded that IMERG products were the most appropriate source of information for the rainfall aggregation interval greater than 12 h. For rainfall events greater than 24 h, the probability of detection is greater than 80%. ...
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The R-factor is a multi-annual average index that measures the climatological potential of rainfall and its kinetic energy. It is used to evaluate the effect of rainfall on sheet and rill-erosion. A continuous record of 30-minute resolution pluviograph data is required to compute rainfall erosivity. However, due to the limited availability of short interval precipitation data, rainfall erosivity mapping involves interpolation from point-rainfall erosivity values. This study attempts to prepare the first-ever rainfall erosivity map from high-resolution satellite precipitation data for India. We used the IMERG final run 30-minute precipitation data from 2001 to 2018 to compute spatially distributed rainfall erosivity for India. We also correlated rainfall erosivity with daily rainfall data using a power equation. We presented the calibrated parameter values of the developed power equation as maps at 0.1°×0.1° spatial resolution. The mean annual rainfall erosivity value ranges from 77 to 20,662 MJ mm ha⁻¹h⁻¹ yr⁻¹. The summer monsoon is the most erosive season, accounting for about 85% of the annual rainfall erosivity. We compared the rainfall erosivity map computed in this study with the previously available rainfall erosivity map produced from very few gauged point rainfall data. It is found that the spatial pattern of erosivity between the two maps matches very well. However, the erosivity map produced using satellite data exhibits slightly lower erosivity values in areas receiving very low and very high rainfall than the previously available erosivity map. We also computed erosivity density maps of India to identify the hotspot areas prone to erosion and landslide. Northeast and the Western Ghats of India receive high-intensity rainfall in the summer monsoon and have significantly high erosivity density. This study's outcome could serve as the primary source of information by the policymakers to take appropriate catchment management measures to reduce soil erosion.
... Therefore, the findings of comparing four SPPs at the sub-daily scale, as well as the conclusions drawn from those comparisons, may be somewhat different from those drawn at the daily scale. Moreover, the uncertainty of remote sensing data [23,54,55] may also affect the research results. ...
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Performance assessment of satellite-based precipitation products (SPPs) is critical for their application and development. This study assessed the accuracies of four satellite-based precipitation products (PERSIANN-CDR, PERSIANN-CCS, PERSIANN-DIR, and PERSIANN) using data of in situ weather stations installed over the Himalayan Mountains of Pakistan. All SPPs were evaluated on annual, seasonal, monthly, and daily bases from 2010 to 2017, over the whole spatial domain and at point-to-pixel scale. The assessment was conducted using widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI)). Results showed: (1) PERSIANN and PERSIANN-DIR products efficiently traced the spatio-temporal distribution of precipitation over the Himalayan Mountains. (2) On monthly scale, the estimates of all SPPs were more consistent with the reference data than on the daily scale. (3) On seasonal scale, PERSIANN and PERSIANN-DIR showed better performances than the PERSIANN-CDR and PERSIANN-CCS products. (4) All SPPs were less accurate in sensing daily light to medium intensity precipitation events. Subsequently, for future hydro-meteorological investigations in the Himalayan range, we advocate the use of monthly PERSIANN and PERSIANN-DIR products.
... Despite the fact that these most recent global SRPs are freely available at precise spatial and temporal resolutions, their performance differs from one location to the next across the globe [12,13]. The literature review revealed that the performance evaluation of SRPs is essential before their direct application in any region [7,[14][15][16]. ...
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This study compares the performance of four satellite-based rainfall products (SRPs) (PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0) in a semi-arid subtropical region. As a case study, Punjab Province of Pakistan was considered for this assessment. Using observations from in-situ meteorological stations, the uncertainty in daily, monthly, seasonal, and annual rainfall estimates of SRPs at pixel and regional scales during 2010-2018 were examined. Several evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias), as well as categorical indices (Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ration (FAR)) were used to assess the performance of the SRPs. The following findings were found: (1) CHIRPS-2.0 and SM2RAIN-ASCAT products were capable of tracking the spatiotemporal variability of observed rainfall, (2) all SRPs had higher overall performances in the northwestern parts of the province than the other parts, (3) all SRP estimates were in better agreement with ground-based monthly observations than daily records, and (4) on the seasonal scale, CHIRPS-2.0 and SM2RAIN-ASCAT were better than PERSIANN-CCS and PERSIANN. In all seasons, CHIRPS-2.0 and SM2RAIN-ASCAT outperformed PERSIANN-CCS and PERSIANN-CDR. Based on our findings, we recommend that hydrometeorological investigations in Pakistan's Punjab Province employ monthly estimates of CHIRPS-2.0 and SM2RAIN-ASCAT products.
... SREs tend to miss a significant volume of rainfall for extreme events. Mazzoglio et al. (2019) demonstrated that the SREs detect rain effectively when the rainfall aggregation interval is ≥ 12 h, meaning SREs could generally miss short-duration rainfall. In the northern Himalayas, the performance of SREs is affected by the dominance of orographic rain and the limited availability of GRG observations for calibration (Viviroli et al. 2011;Andermann et al. 2011). ...
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Spatial variability in catchment processes is crucial for hydrologic and water resources planning and management. The spatial density of ground-based rain gauge (GRG) observations is often limited. These limitations are more pronounced in the Himalayan region. The rainfall variability is one of the primary factors affecting the water-energy cycle and is often poorly captured by the GRG observations in mountain terrain. This study evaluates the applicability of four satellite-based products (i.e., CHIRPS, MSWEP, PERSIANN, and TMPA) in capturing the rainfall characteristics across a mountain river basin in the Himalayan region. We used rainfall observations from 44 GRG locations located at different physiographic and hydroclimatic areas as a reference for systematic comparison. The comparison was able to discriminate and highlight the benefits and pitfalls of selected satellite-based rainfall estimates (SREs) and rank them based on performance metrics for rugged topography. Monotonic trends based on both ground- and satellite-based products were computed. This study finds that SREs did not well capture short-duration rainfall extremes. Different SREs exhibit a different level of performance (under- to overestimation) for both rainfall frequency and amount. A general tendency of the south to north (S–N) decreasing rainfall amount and their temporal variations are well captured in the study area by the SREs. This study reinforces the idea that several SREs are applicable for water balance and hydrologic regime analysis with local bias correction for analyzing hydroclimatic extremes.
Chapter
The LEXIS (Large-scale EXecution for Industry & Society) H2020 project is building an advanced engineering platform taking advantage of HPC, Cloud solutions and Big Data, leveraging existing HPC infrastructures. In the framework of the LEXIS project, CIMA Research Foundation is running a three nested domain WRF Model with European coverage and radar data assimilation over Italy. WRF data is then processed by ITHACA Extreme Rainfall Detection System (ERDS), an early warning system developed for the monitoring of heavy rainfall events. The WRF-ERDS workflow has been applied to the heavy rainfall event that affected Southern Italy, in particular Calabria Region, at the end of November 2020. Rainfall depths obtained using global-scale rainfall datasets and WRF data have been compared both with rain gauge data and with the daily bulletins issued by the Italian Civil Protection Department. The data obtained by running the WRF-ERDS workflow shows as an advanced engineering platform based on HPC and cloud solutions can provide more detailed forecasts to an early warning system like ERDS.
Chapter
The Extreme Rainfall Detection System (ERDS) is an early warning system (EWS) developed for the monitoring and forecasting of rainfall events on a global scale. Within ERDS the near real-time rainfall monitoring is performed using the Global Precipitation Measurement data, while rainfall forecasts are provided by the Global Forecast System model. Rainfall depths determined on the basis of these data are then compared with a set of rainfall thresholds to evaluate the presence of heavy rainfall events: in places where the rainfall depth is higher than a rainfall threshold, an alert of a severe rainfall event is issued. The information provided by ERDS is accessible through a WebGIS application (http://erds.ithacaweb.org) in the form of maps of rainfall depths and related alerts to provide immediate and intuitive information also for nonspecialized users. This chapter is intended to describe the input data and the extreme rainfall detection methodology currently implemented in ERDS. Furthermore, several case studies (2019 Queensland flood event, 2017 Atlantic hurricane season, and 2017 Eastern Pacific hurricane season) are included to highlight the strengths and weaknesses of this EWS based on global-scale rainfall datasets.
Chapter
Precipitation is an important parameter of the essential climate variables in the Earth System and is a key variable in the global water cycle. However, surface observations of precipitation over oceans are relatively sparse. Satellite observations over oceans are the only viable means of monitoring the spatial distribution of precipitation. In an effort to improve global precipitation observations, the research community has developed a state-of-the-art precipitation dataset as part of the National Aeronautics and Space Administration/Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) program. One of the satellite-gridded products provides precipitation estimates using GPM and other satellite dataset is called Integrated Multi-satellitE Retrievals for GPM (IMERG). The highest resolution of IMERG products has a maximum spatial resolution of 0.1°×0.1° and temporal resolution of 30 min. Even with the recent advancements in satellite precipitation retrievals, there is a need to evaluate the uncertainty characteristics of IMERG precipitation estimates especially over oceans. To address this need, observations from Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN) project have been used to demonstrate the usefulness in evaluating IMERG precipitation products over the ocean. The OceanRAIN dataset was created using observations from the ODM-470 optical disdrometer that has been deployed on 12 research vessels worldwide with 6 long-term installations operating in all climatic regions, seasons, and ocean basins. More than 11.5 million 1-min observations have been collected during the OceanRAIN program. For this study, more than 5.6 million 1-min observations were used in the comparison period of March 20, 2014–December 31, 2018, which coincides with GPM IMERG products.
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Accurate, reliable, and high spatio-temporal resolution precipitation data are vital for many applications, including the study of extreme events, hydrological modeling, water resource management, and hydroclimatic research in general. In this study, we performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe. Asia, and in particular China, are the subject of the largest number of IMERG evaluation studies on the continental and country level. When compared to ground observational records, IMERG is found to vary with seasons, as well as precipitation type, structure, and intensity. It is shown to appropriately estimate and detect regional precipitation patterns, and their spatial mean, while its performance can be improved over mountainous regions characterized by orographic precipitation, complex terrains, and for winter precipitation. Furthermore, despite IMERG's better performance compared to other satellite products in reproducing spatio-temporal patterns and variability of extreme precipitation, some limitations were found regarding the precipitation intensity. At the temporal scales, IMERG performs better at monthly and annual time steps than the daily and sub-daily ones. Finally, in terms of hydrological application, the use of IMERG has resulted in significant discrepancies in streamflow simulation. However, and most importantly, we find that each new version that replaces the previous one, shows substantial improvement in almost every spatiotemporal scale and climatic condition. Thus, despite its limitations, IMERG evolution reveals a promising path for current and future applications.
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Morocco is blessed by a large reserve of freshwater resources that is being used in an unsustainable manner. In this study, we use a large suite of remote-sensing observations, land surface models, and field measurements to assess the sustainability of groundwater resources in Morocco and their responses to natural and anthropogenic interventions on both regional (entire Morocco) and local (watershed) scales. Results indicate the following: (1) Global Precipitation Measurement–derived monthly rainfall products (IMERG) could be used as alternatives and/or complements to rainfall gauges over Morocco, especially in areas with lower elevations and temperate climates; IMERG overestimates rainfall rates in higher elevation areas with temperate climate and underestimates rainfall rates in arid and semiarid climates, compared to rain gauges. (2) During the past 18 years (April 2002–April 2020), no significant variabilities were observed in terrestrial water storage or groundwater storage over northern or southern Morocco as a whole; groundwater in both regions is currently being used in a sustainable manner. (3) The groundwater levels in five Moroccan basins are declining (range: -13.32 to -145.8 cm/yr), one shows near-steady-state conditions, and two show increases in groundwater levels (range: +11.32 to +29.88 cm/yr); these trends are controlled mainly by groundwater extraction rates and rainfall variability. Our results signal an urgent need to enforce a more comprehensive groundwater policy that promotes sustainable use plans for surface and groundwater resources throughout Morocco. Our approach could be applied to develop sustainable use scenarios for groundwater resources in any hydrologic system across the globe.
Conference Paper
Full-text available
Many studies have shown a growing trend, in terms of number, frequency and severity of extreme events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s surface has become a focal point for the identification of areas potentially affected by floods. In order to guarantee an almost global spatial coverage, a precipitation evaluation provided by satellite products proved to be the most appropriate source of information. NASA GPM (Global Precipitation Measurement) mission provides since March 2014 different IMERG (Integrated Multi-satellite Retrievals for GPM) products with a spatial coverage of 60°N - 60°S and a spatial resolution of 0,1° x 0,1°. The first part of our study is aimed to compare at the global scale satellite IMERG early and late data and rain gauge precipitation data, in order to evaluate their relative accuracy. The outcomes demonstrate that satellite data guarantees good result when rainfall aggregation interval is equal or greater than 12 hours. More specifically a 24-hours aggregation interval ensures a probability of detection (defined as the number of hits events divided by the total number of observed events) greater than 80% and a bias of -0,1 mm/h. With an aggregation interval of 72 hours a probability of detection greater than 90% is reached. The outcomes of this analysis supported the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS - erds.ithacaweb.org). This system is now able to provide hourly near-real time alerts about extreme rainfall events. ERDS is a strategic tool, capable to provide, during the preparedness and response phases of the emergency cycle, immediate and intuitive information about potential flood events. The information is accessible through a WebGIS application, developed in a complete Open Source environment. Results are published on ERDS website by means of standard WMS services. Specifically, this system automatically downloads the most recent GPM IMERG early run half-hourly data and cumulates it according to specific periods (12hr, 24hr, 48hr, 72hr, 96hr). ERDS generates precipitation alerts where and when the precipitation amount is higher than a specific set of thresholds. This set of thresholds has been calculated for every aggregation interval on the basis of the average annual precipitation values evaluated on a 0,1° x 0,1° grid cell basis.
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The performance of the latest released Integrated Multi-satellitE Retrievals for GPM mission (IMERG) version 5 (IMERG v5) and the TRMM Multisatellite Precipitation Analysis 3B42 version 7 (3B42 v7) are evaluated and compared at multiple temporal scales over a semi-humid to humid climate transition area (Huaihe River basin) from 2015 to 2017. The impacts of rainfall rate, latitude and elevation on precipitation detection skills are also investigated. Results indicate that both satellite estimates showed a high Pearson correlation coefficient (r, above 0.89) with gauge observations, and an overestimation of precipitation at monthly and annual scales. Mean daily precipitation of IMERG v5 and 3B42 v7 display a consistent spatial pattern, and both characterize the observed precipitation distribution well, but 3B42 v7 tends to markedly overestimate precipitation over water bodies. Both satellite precipitation products overestimate rainfalls with intensity ranging from 0.5 to 25 mm/day, but tend to underestimate light (0-0.5 mm/day) and heavy ( > 25 mm/day) rainfalls, especially for torrential rains (above 100 mm/day). Regarding each gauge station, the IMERG v5 has larger mean r (0.36 for GPM, 0.33 for TRMM) and lower mean relative root mean square error (RRMSE, 1.73 for GPM, 1.88 for TRMM) than those of 3B42 v7. The higher probability of detection (POD), critical success index (CSI) and lower false alarm ratio (FAR) of IMERG v5 than those of 3B42 v7 at different rainfall rates indicates that IMERG v5 in general performs better in detecting the observed precipitations. This study provides a better understanding of the spatiotemporal distribution of accuracy of IMERG v5 and 3B42 v7 precipitation and the influencing factors, which is of great significance to hydrological applications.
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Every year riverine flooding affects millions of people in developing countries, due to the large population exposure in the floodplains and the lack of adequate flood protection measures. Preparedness and monitoring are effective ways to reduce flood risk. State-of-the-art technologies relying on satellite remote sensing as well as numerical hydrological and weather predictions that can detect and monitor severe flood events at a global scale. This paper describes the emerging role of the Global Flood Partnership (GFP), a global network of scientists, users, private and public organizations active in global flood risk management. Currently, a number of GFP member institutes regularly share results from their experimental products, developed to predict and monitor where and when flooding is taking place in near real-time. GFP flood products have already been used on several occasions by national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socio-economic impacts of disasters. This paper describes a range of global flood products developed by GFP partners, and how these provide complementary information to support and improve current global flood risk management for large scale catastrophes. We also discuss existing challenges and ways forward to turn current experimental products into an integrated flood risk management platform to improve rapid access to flood information and increase resilience to flood events at global scale.
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Accurate estimation of precipitation is crucial for crop yield assessment, flood and drought monitoring, and water structures management. Precipitation is subject to both temporal and spatial variability. While recording rain gauges support temporal resolution, they measure point rainfall and require dense network and application of interpolation techniques to provide spatial dimension. On the other hand, remote-sensing products cover regional and global spatial scales. Building upon the Tropical Rainfall Measuring Mission (TRMM) heritage, the Global Precipitation Measurement (GPM) mission is an international net of satellites that present the next-generation global observations of rain and snow at a spatial resolution of 0.1° × 0.1° with a half-hour temporal resolution. In this study, March–December 2014 3-hourly TRMM data (3B42V7) and half-hourly Integrated Multi-satellite Retrievals for GPM (IMERG) data are compared with the 3-hourly rain gauges data in Khorasan Razavi province, located in northwest of Iran. Coefficient of determination (R²), Bias, MBias, RBias, mean absolute error (MAE), root mean square error (RMSE) as well as probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics were measured for validation purposes. The result showed that correlation between IMERG data and rain gauge rainfall data is higher than those of 3B42V7 data. In addition, the values of MBias, Bias, and RBias confirmed that both of 3B42V7 and IMERG underestimated rainfall over the study area, whereas MBias of IMERG was higher than 3B42V7. Furthermore, MAE and RMSE values of 3B42V7 and IMERG were similar while IMERG evaluation turned out a better correlation coefficient (r) and POD than 3B42V7. This study showed that IMERG generally had reasonable agreement with the gauge data.
Article
The acquisition of accurate precipitation data is essential for analyzing various hydrological phenomena and climate change. Recently, the Global Precipitation Measurement (GPM) satellites were launched as a next-generation rainfall mission for observing global precipitation characteristics. The main objective in this study is to assess precipitation products from GPM, especially the Integrated Multi-satellitE Retrievals (GPM-3IMERGHH) and the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), using gauge-based precipitation data from Far-East Asia during the pre-monsoon and monsoon seasons. Evaluation was performed by focusing on three different factors: geographical aspects, seasonal factors, and spatial distributions. In both mountainous and coastal regions, the GPM-3IMERGHH product showed better performance than the TRMM 3B42 V7, although both rainfall products showed uncertainties caused by orographic convection and the land-ocean classification algorithm. GPM-3IMERGHH performed about 8% better than TRMM 3B42 V7 during the pre-monsoon and monsoon seasons due to the improvement of loaded sensor and reinforcement in capturing convective rainfall, respectively. In depicting the spatial distribution of precipitation, GPM-3IMERGHH was more accurate than TRMM 3B42 V7 because of its enhanced spatial and temporal resolutions of 10 km and 30 min, respectively. Based on these results, GPM-3IMERGHH would be helpful for not only understanding the characteristics of precipitation with high spatial and temporal resolution, but also for estimating near-real-time runoff patterns.
Article
The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.
Article
This work presents a first evaluation of the performance of the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product over the upper Blue Nile basin of Ethiopia. One of the unique features of this study is the availability of hourly rainfall measurements from an experimental rain gauge network in the area. Both the uncalibrated and calibrated versions of IMERG are evaluated, and their performance is contrasted against another high-resolution satellite product, which is the Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH). The analysis is performed for hourly and daily time scales and at spatial scales that correspond to the nominal resolution of satellite products, which is 0.1° spatial resolution. The period analyzed is focused on a single wet season (May–October 2014). Evaluation is performed using several statistical and categorical error metrics, as well as spatial correlation analysis to assess the ability of satellite products to repre...
Article
Two post-real time precipitation products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) are systematically evaluated over China with China daily Precipitation Analysis Product (CPAP) as reference. The IMERG products include the gauge-corrected IMERG product (IMERG_Cal) and the version of IMERG without direct gauge correction (IMERG_Uncal). The post-research TRMM Multisatellite Precipitation Analysis version 7 (TMPA-3B42V7) is also evaluated concurrently with IMERG for better perspective. In order to be consistent with CPAP, the evaluation and comparison of selected products are performed at 0.25° and daily resolutions from 12 March 2014 through 28 February 2015.