HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-20-5049-2016Using crowdsourced web content for informing water systems operations in snow-dominated catchmentsGiulianiMatteomatteo.giuliani@polimi.ithttps://orcid.org/0000-0002-4780-9347CastellettiAndreahttps://orcid.org/0000-0002-7923-1498FedorovRomanFraternaliPieroDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza L. da Vinci, 32, 20133 Milano, ItalyInstitute of Environmental Engineering, ETH, Wolfgang-Pauli-Str. 15, 8093 Zurich, SwitzerlandMatteo Giuliani (matteo.giuliani@polimi.it)21December20162012504950625August201623August201618November201623November2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/20/5049/2016/hess-20-5049-2016.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/20/5049/2016/hess-20-5049-2016.pdf
Snow is a key component of the hydrologic cycle in many regions of the world.
Despite recent advances in environmental monitoring that are making a wide
range of data available, continuous snow monitoring systems that can collect data at high spatial and temporal resolution are not well established yet, especially in inaccessible high-latitude or mountainous regions. The unprecedented
availability of user-generated data on the web is opening new opportunities
for enhancing real-time monitoring and modeling of environmental systems
based on data that are public, low-cost, and spatiotemporally dense. In this
paper, we contribute a novel crowdsourcing procedure for extracting
snow-related information from public web images, either produced by users or
generated by touristic webcams. A fully automated process fetches mountain
images from multiple sources, identifies the peaks present therein, and
estimates virtual snow indexes representing a proxy of the snow-covered area.
Our procedure has the potential for complementing traditional snow-related
information, minimizing costs and efforts for obtaining the virtual snow
indexes and, at the same time, maximizing the portability of the procedure to
several locations where such public images are available. The operational
value of the obtained virtual snow indexes is assessed for a real-world water-management problem, the regulation of Lake Como, where we use these indexes
for informing the daily operations of the lake. Numerical results show that
such information is effective in extending the anticipation capacity of the
lake operations, ultimately improving the system performance.
Introduction
Snow accumulation and melting are fundamental components of the hydrological
cycle in many watersheds across the world
e.g.,. Approximately 40–50 % of the
Northern Hemisphere is covered by snow and snow plays a
key role in mountain areas, which, in Europe, account for 40 % of the total
surface .
In such contexts, an accurate characterization of snow availability and its
evolution in time can be extremely valuable for a variety of operational
purposes, from avalanche prediction
e.g.,, water-systems
operations through medium to long-term streamflow forecast
e.g.,, or drought risk management
e.g.,. The projected temperature increase
induced by climate change, with consequent reductions of large volumes of
snowpacks and acceleration of the water cycle in many mountainous areas, will
further amplify the importance of better understanding snow dynamics
.
Snow processes are generally monitored through both ground monitoring
networks e.g., and
remote sensing for a review, seeand references
therein. Yet both sources have serious
limitations in alpine contexts, mainly related to the high spatial
e.g., and temporal variability of snow-related processes .
Ground stations are generally very unevenly distributed distributed. Satellite products
provide data on a denser grid but are diversely constrained depending on the
sensors installed . High spatial- and temporal-resolution
imagery (i.e., daily maps with spatial resolution of about 500 m) can be
derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products,
which are, however, strongly affected by the weather because optical sensors
cannot see the earth surface when clouds are present
. Space-board passive microwave radiometers (e.g.,
AMSR-E) penetrate clouds but have coarse spatial resolution (25 km).
Finally, the use of active microwave systems (e.g., RADARSAT) is so far
limited to the detection of liquid water content.
The last few years have seen a rising interest in complementing traditional
observations by using cameras and short-range visual content analysis
techniques , which allow improvement of the temporal and
spatial resolutions for specific applications. Many case studies showed that
the use of one or several time-lapse cameras allows mapping of both the spatial
and temporal patterns of a variety of snow characteristics, including glacier
velocity, snow-cover changes, or detailed monitoring of snowfall interception
seeand references therein. However, most of
these systems generally rely on cameras designed and positioned ad hoc
e.g.,, possibly including in the camera view
some specific objects, such as flags or sticks, which simplifies the
calibration of geometry and colors
e.g.,.
In addition, the use of these cameras is generally very expensive and often
requires intensive manual efforts in the image-processing phase. This latter
includes a variety of crucial, time-consuming operations, such as the
selection of photographs with good meteorological and visibility conditions,
the photo-to-terrain alignment and orientation, and the labeling of snow-covered pixels for estimating the total snow cover
e.g.,.
The availability on the web of large volumes of public, low-cost, and
spatiotemporally dense data raises the question of whether it is possible to
use such data as a supplement, or at least as a complement, to traditional
monitoring systems in operational contexts. The main advantage of such public
data, albeit collected for completely different purposes and with much lower
quality standards, is that they can significantly increase the spatial and
temporal coverage at little-to-no cost
. This idea is part of a growing
application of so-called “citizen science” approaches to water resources
systems operation and, more generally, to diverse
environmental problems . Crowdsourced
observations may act as low-cost virtual sensors in a variety of
environmental contexts , for example,
contributing to monitoring the dynamics of forests
e.g.,, storms e.g.,, or
streamflow e.g.,, with potential benefit in
terms of the prediction of flood events and of the timely delivery of alarms
e.g.,.
However, despite this interest in environmental public web- and user-generated
data , most works focus on data collection and
analysis, with limited assessment of the practical value of such crowdsourced
information.
In this paper, we explore the potential for web- and crowdsourced data to
retrieve relevant information on snow availability and dynamics in a river
basin, and assess the utility of such information in informing a real-world
decision-making problem. More precisely, we contribute a novel crowdsourcing
procedure for extracting snow-related information from public web images,
either produced by users or generated by touristic webcams, and we quantify
the operational value of this information compared to other more traditional
snow information, such as ground observations and a hybrid mix of satellite-retrieved information, ground data, and model outputs. Our procedure employs
an articulated architecture , which
automatically crawls content from multiple web data sources with a content-acquisition pipeline integrating public webcams and user-generated
photographs posted on Flickr. Next, the procedure retains only geo-tagged
images containing a mountain skyline with high probability and identifies the
visible mountain peaks in each image, using a digital elevation model (DEM).
Then, a supervised learning classifier extracts a snow mask from each image,
which distinguishes the image pixels as snow or not-snow. Finally, the
resulting snow masks are post-processed to derive time series of virtual snow
indexes (VSIs) representing a proxy of the snow-covered area.
The extracted VSIs are used to inform water system operations. The evaluation
is performed in the snow-dominated catchment of Lake Como, a regulated lake
in northern Italy, where snow melt is the most important contribution to the
seasonal storage. The VSI operational value is quantified by comparing, via
simulation, the performance of the lake operating policies designed using
crowdsourced and traditional snow information, with the performance of the
baseline policy obtained by regulating the lake without snow information
. This form of assessment provides an indirect
validation of the utility of web- and crowdsourced information as the VSIs
extracted from general-purpose mountain images and the traditional
observational data collected with dedicated tools are not directly comparable
due to the difference in their physical interpretation and spatiotemporal
resolution (e.g., geo-located photos allow estimating the presence of snow,
but not the physical measures usually employed in snow process models, such
as the snow water equivalent).
The paper is organized as follows: in the next section, we introduce our
methodology for the computation of VSIs based on public web content and the
assessment of their operational value. Section describes
the Lake Como study site, followed by the discussion of the numerical
results. The last section concludes with final remarks and directions for
further research.
Methods and tools
This section describes the methodology adopted in this work, which is
illustrated in Fig. . Details about each phase of the
procedure are provided in the following sub-sections. A detailed technical
analysis of the outputs of the image processing architecture is reported in
.
Flowchart of the methodology adopted in this study.
Web content crawling and pre-processing
Two types of public web content are considered, namely touristic webcams and
mountain photographs from Flickr. In particular, webcams produce a
temporally dense series of images of the same view, while crowdsourced photos
have better spatial distributions but lower time coverage.
Public webcams
A webcam is a standalone camera positioned at a fixed known location, usually
with a fixed orientation, which captures frames with a certain frequency and
exposes them via a web service. In contrast to surveillance webcams, which
can provide real time updates (several frames per second, resulting basically
in a video stream), public webcams deployed for touristic, meteorological,
and publicity reasons update the current frame with lower frequency,
typically from one minute to one hour. The public webcam processing phase
consists of three main steps:
Webcam image crawling: public webcams most often expose a single
fixed URL for the current frame, and change the image itself over time. This
method simplifies the crawling, which amounts to checking the URL of the
webcam periodically and downloading the image, when it changes with respect
to the last acquisition. We collected the address of more than 3500 webcams
in the European Alps and manually inspected them, discarding those that do
not frame a significant mountain profile, retaining nearly 2000 webcams.
Since December 2014, a crawler acquires all the images of these webcams,
checking for frame updates every minute, thus obtaining from 10 to 1500
images per webcam per day, depending on the update frequency.
Skyline Visibility Filtering: webcams are crawled independently
of the weather conditions. As a consequence, although the temporal density of
webcam images guarantees a high number of input frames, filtering must be
applied to discard unsuitable images that may bias the VSI computation (e.g.,
an image of a mountain covered by fog can be considered as completely covered
by snow in the next steps). A random sampling of 1000 images from four webcams
in our data set revealed that 67 % of the images were not suitable for snow-cover analysis due to adverse weather conditions (e.g., fog, heavy snowfall,
or rain), insufficient visibility, or the presence of mobile obstacles such as
cars or persons. Therefore, the implemented filter automatically discards
unsuitable images, identified by checking for occlusions of the mountain
skyline. In practice, for each webcam, the pixels that belong to the skyline
L are first identified manually on a sample frame through a
crowdsourcing experiment. Then, the binary skyline neighborhood mask L,
which identifies pixels p=(x,y) close to the skyline, is determined as
follows:L(p)=1if ∃p′∈L:‖p-p′‖≤τ0otherwise,where ‖⋅‖ is the Euclidean norm and τ is a distance threshold.
In other words, L is a binary mask of the same dimension as the webcam
image containing a dilated skyline profile.
Then, for each webcam image, its binary edge map E is computed by the
Compass algorithm , where a pixel is marked as an edge
when it corresponds to an abrupt color variation. The binary matrix E⊙L, where ⊙ denotes the pixel-wise product between two images of the
same size, represents the edges of the image that belong to the skyline. To
check for occlusions, we compute a skyline visibility score v defined asv=f(E⊙L)/f(L),where f(⋅) is a function that, given an image, returns the number of
columns containing at least one non-zero entry. The value of v ranges
between 0 and 1, and can be intuitively seen as the percentage of the
skyline which is visible in the given image. After setup trials, we discard
images with v<v¯, where v¯ is a fixed threshold equal to
0.75. The experimental validation of the filtering algorithm on 1000 manually
annotated images (i.e., frames manually classified as having “good visibility” or
“bad visibility”) showed that the algorithm achieves a true positive rate
(TPR) equal to 87.4 %, while having a false positive rate (FPR) equal to
3.5 %.
Daily Image Aggregation: the images selected by the skyline visibility
filter can still present several undesirable features due to shadows, solar
glare, or temporary obstacles below the skyline (e.g., people standing in
front of the camera). To attenuate such biases, assuming that the snow cover
does not vary during a day significantly, we produce a single image for each
webcam per day by aggregating all the images acquired in the same day. Such a
daily median image (DMI) is obtained as the median of every pixel across all
the daily images accepted by the filter. Given a daily sample of N images
I1,…,IN, the DMI is formally defined asDMI(x,y)=med{I1(x,y),I2(x,y),…,IN(x,y)},where med{⋅} is the median operator applied to the image pixel
values. Figure shows a DMI obtained from 11 daily
images: it attenuates transient light conditions and removes the people
standing in front of the webcam.
A second challenging aspect of DMI creation is the presence of webcam trembling
. The webcam orientation is not perfectly constant
in time but may change slightly, especially in windy regions and when webcams are
fixed to poles. To overcome this problem, we extract edge maps of all daily images
and calculate their cross-correlation to quantify not only the similarity between
the two edge maps, but the entire set of similarities at every possible position.
The value of cross-correlation is then used to derive the best offset of every image
with respect to the reference represented by the first image of the day. We consider
a maximum offset of 10 pixels to reduce the computation time and avoid possible correction errors.
Finally, the DMI is determined from images normalized with such an offset. Intuitively,
this procedure can be seen as applying a small displacement to each image in order to
obtain the best possible overlap between its edges and the edges of the first image of the day.
Example of Daily Median Image obtained from 11 images acquired in
the same day.
User-generated photographs
The second source of mountain images are the photographs generated by common
people and publicly available on social networks and photo sharing platforms.
Although the volume of user-generated photographs can not obviously reach the
number of webcam images, user-generated photographs present higher spatial
density. The webcams are located in a few fixed locations, whereas the
user photographs can be potentially acquired in any place.
We selected Flickr as the content source because it contains a high number of
public photographs with associated geo-tags
e.g.,. Furthermore, Flickr does not remove
the EXIF (exchangeable image file format) information present in the original images ; the
EXIF container specifies several photo-related details, in particular the GPS
location, the camera model and manufacturer, and optical information, such as
the focal length used during the shot. This information is fundamental for
the peak-detection algorithm (see Sect. ). A
continuous search system was set up for querying images within a 300×160 km region in the Alps. At present, the Flickr search pipeline examined
around 600 000 candidate photos. Differently from registered webcams, which
produce thousands of images of the same view, the user-generated photographs
are taken at unknown locations and may have irrelevant content, and thus
must be classified as relevant one-by-one. To this end, a supervised
content-based classifier was developed to perform mountain-image detection.
The classifier was trained on a set of 6940 images randomly sampled from the
very large crawled data set; the ground-truth images were classified manually
through a crowdsourcing experiment. For each image, three users were asked to
reply to the following question: “Does the image contain a significant
mountain profile?”. A web interface proposed a tutorial on how to annotate
an image as positive (mountain image) or negative (non-mountain image). The
experiment was conducted using an internal (non-paid) crowd, collecting a
total of more than 20 000 image classification labels. The aggregated
ground-truth label of each image was then derived via majority voting.
Approximately 23 % of the original 6940 images were classified as positive.
The automatic classification was performed with a support vector machine
(SVM) classifier fed with dense SIFT (scale invariant feature transform) and ”bag-of-visual-words” (BoVW) feature
selectors . This technique relies on the idea that
every image is composed of small patches (i.e., image portions), which
somehow share common features with the images in the same class (i.e., images
that do contain or do not contain mountains). Since the number of possible
patches to observe is very large, the patches are split into a finite number
of clusters. Each patch represents a visual word, which contributes to
defining the content of the image. All the visual words of the image are
aggregated into a histogram, which is then used as a feature vector for the SVM
classifier. To create a balanced data set, we retained all the positive
samples and randomly selected the same number of negative samples. Then, we
used around 70 % of these images for training and validation, and the
remaining 30 % for testing. The performance attained by the classifier on
the test data set is: 95.1 % accuracy, 94.0 % precision, and 96.3 %
recall.
Orientation and mountain-peak identification
The orientation and mountain-peak identification procedure (see
Fig. ) is applied to the user-generated
photographs classified as positive and to the median daily images of webcams.
In fact, although webcams are geo-located, the information regarding the
orientation of the webcam, and consequently the corresponding mountain peaks
observed, is not available. In both cases, image orientation is estimated
through the alignment with respect to a 360∘ virtual panorama
generated using a digital elevation model (DEM) that specifies the position
of the visible mountain peaks in the panorama.
Example of the orientation and mountain peak identification
procedure: (a) input image (top) and corresponding panorama
(bottom); (b) edge maps; (c) skyline detection;
(d) global alignment; (e) local alignment.
Example of an image (top) and the computed snow mask (bottom), where
white stands for snow, black for non-snow, and gray for sky.
The automatic alignment of an image to the virtual panorama requires scaling
the image to achieve the same angular and/or pixel dimension. This step is performed
by computing the image field of view (FOV), namely the size of the angle
comprising the view. The FOV can be estimated from the image EXIF
information, such as focal length, camera model, and manufacturer. The
procedure extracts the edge maps for both the scaled image and the virtual
panorama. In particular, a modified version of the multi-stage graph
algorithm by was used for extracting the skyline from
the edge maps and to eliminate all the edges above the skyline (clouds and
obstacles) by gradually reducing the strength of the edges below the skyline.
This step was not applied to the webcams as a single annotation was
sufficient for obtaining a precise skyline extraction. Then, the best
overlapping position between the image and the virtual panorama is identified
with a vector cross-correlation (VCC) procedure .
The VCC finds the best horizontal overlap position of the image with respect
to the panorama by maximizing the cross-correlation score of the edges, while also
considering the estimated image orientation. The identified overlap position
allows projection of the peak positions from the panorama to the image, to
estimate which peaks are visible, and what their coordinates are, in the image. When the
image does not contain the EXIF information, the automatic orientation and
mountain-peak identification procedure can not be applied, and the image
requires a manual alignment with respect to the panorama. Finally, a local
refinement of the alignment is obtained by repeating the VCC procedure with a
maximum radius equal to 50 pixels. This is done for each mountain peak independently to
adjust its position through the identification of the best match in its
neighborhood region.
The orientation and peak identification algorithm was tested on a data set of
162 images randomly sampled from the web and manually aligned to the
corresponding virtual panoramas to create the ground-truth data. Considering
a tolerance of 3∘, 75 % of the
image orientations were correctly estimated. The accuracy grows to 77.6 %
for photos with no clouds and to 81.6 % for photos with no mountain slopes
in the very short range (the effect of GPS errors is more relevant if
mountains are close to the shooting location). The average peak positioning
error was 0.78∘.
Snow-mask extraction and computation of virtual snow indexes
The third step of the procedure is the conversion of the snow information
contained in the aligned image into one or more VSIs associated with the
mountain viewpoint portrayed in the photo. This phase requires estimating a
snow mask representing the portion of the terrain that is covered with snow.
Formally, let I denote an image and M a binary mask having the same size
as I, where M(x,y)=1 indicates that the pixel p(x,y) of the image
belongs to the mountain area, or M(x,y)=0 otherwise. The binary mask M
is derived from the alignment of the image with the virtual panorama (see the
previous section), which allows for differentiation of pixels that correspond to
terrain or sky.
The pair (I,M) is processed by a pixel-level binary classifier, which
extracts the snow mask S by assigning to each pixel a label denoting the
presence of snow (S(x,y)=K1), non-snow (S(x,y)=K2), and sky
(S(x,y)=K3), as shown in Fig. . We computed snow masks
using the Random Forest supervised learning classifier with spatiotemporal
median smoothing of the output . Such a
classifier discriminates the presence of snow in a pixel based on its color
and on the color of the neighbor pixels. Moreover, it applies a
spatiotemporal median filter to smooth the snow variation and attenuate the
errors. Smoothing implements the assumption that pixels close to each other
in the same image and pixels in the same position in images close in time
should belong to the same class (i.e., snow or non-snow). The training and
testing of the supervised classifier was performed on a data set including 59
images annotated in snow or non-snow areas, containing more than 7 million
single pixel ground-truth labels. The accuracy attained by the classifier is
93.5 %, outperforming other existing methods for pixel-level classification
of snow presence .
Finally, different VSIs are computed from the snow masks S, potentially also
considering the altitude associated with each pixel, which can be
determined from the image to virtual panorama alignment. In this work, we
report the results obtained with a virtual snow index σ representing a
proxy of the snow-cover area, defined as follows:
σ=∑p(x,y)∈IΦ(p(x,y)),whereΦ(p(x,y))=1 if S(x,y)=K10 otherwise .
Assessment of the operational value of virtual snow indexes
The operational value of the extracted VSIs is assessed as the difference in
system performance between an operating policy based upon the VSIs and a
policy relying on more traditional information, including water availability
in the lake and day of the year. In particular, the operating policies are
computed by solving a multi-objective optimal control problem
formulated as follows:
p∗=argminpJ=|J1,…,Jq|,
where the policy p is defined as a closed-loop control policy that
determines the release decision ut=p(dt,xt,It)
at each time step t as dependent on the day of the year dt, the current
state of the system is xt (i.e., the level of the lake at time t),
and a vector of exogenous information is It (i.e., variables that
are observed but are not endogenous in the problem formulation and hence are
not modeled). Note that the resolution of Problem ()
does not yield a unique optimal solution but a set of Pareto optimal
solutions.
The most common technique to solve Problem () is
dynamic programming (DP) . However, DP is severely limited by
the curse of modeling in designing operating policies conditioned on
exogenous information and by the curse of multiple
objectives in exploring multidimensional tradeoffs . We
therefore solve Problem () by means of an evolutionary
multi-objective direct policy search (EMODPS) , an
approximate dynamic programming approach that combines direct policy search,
nonlinear approximating networks, and multi-objective evolutionary
algorithms. EMODPS allows the direct use of exogenous information through a
partially data-driven controller tuning approach . The
operating policy, defined as a nonlinear approximating network, is directly
conditioned on observations of exogenous information, which cannot be
accurately modeled and would produce detrimental effects on the performance
of an operating policy conditioned on approximate model's outputs
. The selected policy parameterization strongly
influences the selection of the optimization approach, as the number of
parameters necessary to obtain a good approximation for the unknown optimal
control policy grows with the increasing dimension of the policy's argument
. Since the optimization of the policy parameters requires
searching high dimensional spaces that map to stochastic and multimodal
objective function values, global optimization methods such as evolutionary
algorithms are preferred to gradient-based methods
.
Given the Pareto optimal solutions of Problem (), the
operational value of the estimated VSI is quantified by means of two metrics
. The first metric is a measure of the proximity
between a pre-defined target solution JT and the closest
alternative in the Pareto front of the policy under examination, i.e.,
Dmin=mini=1,…,N‖JT-Ji‖,
where ‖⋅‖ stands for the (normalized) Euclidean norm, N is the
number of solutions in the Pareto front under exam, and Ji is
the performance of the ith solution in the Pareto front. The lower the
value of Dmin, the closer the performance is to the target.
A more informative assessment can be done by evaluating not only how close a
given policy can get to a pre-defined target solution but, more generally,
how the Pareto approximate solutions distribute in the objective space.
Among the commonly used metrics adopted in the literature (see
, and references therein), we adopt the hypervolume indicator
(HV), which captures both the convergence of the Pareto front under
examination F to the optimal one F∗ as well as
the representation of the full extent of tradeoffs in the objective space.
The hypervolume measures the volume of objective space dominated (⪯)
by the considered set of solutions. This metric allows set-to-set
evaluations, where the Pareto Front with higher HV is considered to be better. HV
is calculated as the hypervolume ratio between F and
F∗, formally defined as:
HV(F,F∗)=∫αF(x)dx∫αF∗(x)dx,whereαF(x)=1 if ∃x′∈F such that x′⪯x0 otherwise .
Lake Como study siteSystem description
Lake Como is a regulated lake in the Adda River basin, Italy
(Fig. ). The lake has an active storage capacity of 254 Mm3
and is fed by a 3500 km2 alpine catchment that reaches altitudes over
4000 m a.s.l. Downstream from the lake, the Adda River serves a dense
network of irrigation canals belonging to four agricultural districts for a
total irrigated area of 1400 km2 (the green area in Fig. ).
Major cultivated crops are maize and temporary grasslands, while minor crops
include rice, soybean, wheat, tomato, and barley. The hydro-meteorological
regime in the catchment is the typical sub-alpine one, with scarce discharge
in winter and summer, and peaks in late spring and autumn due to snowmelt and
rainfall, respectively. In particular, snowmelt from May to July is the most
important contribution to the formation of the seasonal storage
(Fig. ).
Adda River basin: Lake Como, Adda River, downstream agricultural districts,
ground stations, and public webcams.
The alpine orography constrains the accurate monitoring of snow dynamics. The
existing ground stations (46 over the 10 500 km2 alpine area in the
Lombardy region) provide a very coarse coverage of the region and are not
sufficient to reliably monitor the snow coverage and the associated water
content. This is instead estimated by the Regional Agency for Environmental
Protection (Agenzia Regionale per la Protezione dell'Ambiente – ARPA), which
produces estimates of snow water equivalent (SWE) through a hybrid procedure
combining snow height and temperature data from ground stations, measures of
snow density in few specific locations, satellite retrieved data of snow
cover from MODIS, and model outputs for spatially interpolating these data.
As a result of this complex procedure, ARPA elaborates a weekly estimate of
SWE. Such reports are delivered only weekly due to the well known limitations
of snow products derived from optical sensors associated with the frequent
satellite occlusion by cloud coverage. This limitation is particularly
restrictive in the alpine region, where previous studies observed an average
cloud occlusion of 63 % over a five-year monitoring period
, with critical episodes of cloud coverage
lasting for more than 25 days per month in wintertime. In contrast,
webcams are less affected by cloud coverage and can provide observations
during cloudy days as shown illustratively in Fig. . In this
study, we contrast the operational value in informing the lake operation of
three different snow-related data sources: (i) daily observations of snow
height from coarsely distributed ground stations; (ii) weekly SWE estimate
provided by ARPA; (iii) daily values of the VSI σ extracted from
public web images.
Hydro-meteorological regime of Lake Como.
Comparison of MODIS daily snow-cover map (left panel) with the
images acquired by a webcam (right panel) on 9 January 2014 at the location
denoted by the asterisk in the map.
The existing regulation of the lake is driven by two primary, competing
objectives: water supply, mainly for irrigation, and flood control in the
city of Como, which is the lowest point of the lake shoreline. In particular,
the agricultural districts downstream would like to store the snowmelt volume
for the summer water-demand peak, when the natural inflow is not sufficient
to satisfy the irrigation requirements (see the magenta area in
Fig. ). Yet, storing such water increases the lake level
and, consequently, the flood risk, which would be instead minimized by
keeping the lake level as low as possible. On the basis of previous works
e.g.,,
these two objectives are formulated as follows:
Flood control: the average annual number of flooding days in the
evaluation horizon H, defined as days when the lake level ht is higher
than the flooding threshold (h¯=1.24 m):
Jflood=1H/365∑t=1HΛ(ht),whereΛ(ht)=1 if ht>h¯0 otherwise .
Irrigation supply: the daily average quadratic water deficit between
the lake release rt+1 and the daily water demand wt of the downstream
system, subject to the minimum environmental flow constraint qMEF
to ensure adequate environmental conditions in the Adda River:Jirr=1H∑t=1Hmaxwt-max(rt+1-qMEF,0),02.This quadratic formulation aims to penalize severe deficits in a single time
step, while allowing for more frequent, small shortages
.
Comparison of the trajectories in 2013 of the Virtual Snow Index
σ with the snow height measured at Oga San Colombano (left panel) and
with the freezing level (right panel).
Experiment setting
Our assessment of the operational value of the VSIs relies on the comparison
of the performance attained by informing the operating policies of Lake Como
with alternative snow-related information: (i) policies P1 informed by snow
height observations from ground stations; (ii) policies P2 informed by SWE
estimates provided by ARPA; (iii) policies P3 informed by the virtual snow
index σ. Performance is evaluated against an upper-bound solution,
designed assuming perfect foresight of future inflows, and a baseline
solution, corresponding to a traditional regulation conditioned on the day of
the year and the lake level. The experimental setting is structured as
follows:
Observational data: we consider the time horizon 2013–2014
over which time series of snow height, SWE estimate, and VSIs are available.
In particular, snow height data are measured at the Truzzo ground station, while the VSI
derives from the images of a webcam in Livigno (see Fig. );
both sources have time series covering the selected time horizon.
Informed solutions: the operating policies P1, P2, and P3 are
designed via EMODPS by parameterizing the policies as Gaussian radial basis functions,
which have been demonstrated to be effective in solving these types of multi-objective
policy design problems , particularly when
exogenous information is used for conditioning the operations .
To perform the optimization, we use the self-adaptive Borg MOEA ,
which has been shown to be highly robust in solving multi-objective optimal control problems,
where it met or exceeded the performance of other state-of-the-art MOEAs .
Each optimization was run for 2 million function evaluations. To improve solution diversity
and avoid dependence on randomness, the solution set from each formulation is the result of
30 random optimization trials. The final set of Pareto optimal policies for each experiment
is defined as the set of non-dominated solutions from the results of all the optimization trials.
Upper-bound solution: this ideal set of operating policies, which assume
perfect foresight of future inflows, were designed via deterministic dynamic
programming over 2 years (2013–2014). The weighting method is used to
aggregate the two operating objectives (i.e., flood control and irrigation)
into a single objective, via convex combination.
Baseline solution: the traditional regulation of the lake is represented in
terms of a set of operating policies conditioned on the day of the year dt and on the
lake level ht. Also these policies were designed via EMODPS.
Results and discussion
A first qualitative analysis of the virtual snow index σ defined in
Eq. () can be performed by comparatively analyzing the
trajectory of this VSI with respect to the snow height observations in the
closest ground station (i.e., Oga San Colombano, located around 15 km
from the webcam) or with respect to some physical variables closely related
to the snow dynamics. Figure contrasts the historical
trajectory of σ in 2013 with the trajectories of snow height
observations at Oga San Colombano station (left panel) and of the freezing
level (right panel). Despite some differences due to the different locations
of the webcam and the ground station, the first comparison shows similar
temporal patterns: most of the snowmelt occurs between April and first half
of May, followed by a late snowfall at the end of May; no snow is present in
the summertime from late June, with the first snowfall of the next winter observed in early
October. The comparison between σ and the freezing level shows a
negative correlation between low values of freezing level from January to
March as well as in November and December, which are associated with high
values of σ. On the contrary, the freezing level increases in summertime in correspondence to low and zero values of σ. Moreover, it is
worth noting the consistency in the oscillations of the two trajectories
especially in wintertime, when the snow accumulation is captured by
increasing values of σ associated with decreasing freezing levels and,
vice versa, the snow melting corresponds to decreasing values of σ and
increasing freezing levels.
To further demonstrate the value of σ, we then quantified its
operational value for informing the Lake Como operations (see
Sect. ). The performance of this set of informed
operating policies (P3) is contrasted with the baseline solution, namely the
traditional lake regulation conditioned on the day of the year and the lake
level, and the upper-bound solution, namely an ideal set of policies designed
under the assumption of perfect foresight of future inflows. The same
experiment is repeated using either ground observations of snow height (P1)
or SWE data provided by the ARPA (P2) in order to validate the value of the
VSI information with respect to traditional data sources.
Performance obtained by different Lake Como operating policies
informed with ground observations (P1 – green circles), SWE estimated by
ARPA (P2 – cyan circles), or virtual snow indexes (P3 – red circles). The
performance of these solutions is contrasted with the upper bound of the
system performance (black squares) and the baseline operating policies (blue
circles).
Figure illustrates the performance of the different
set of solutions in terms of flood control (Jflood) and
irrigation supply (Jirr), evaluated over the horizon 2013–2014.
The arrows indicate the direction of increasing preference, with the best
solution located in the bottom-left corner of the figure. Visual comparison
of the baseline (blue circles) and upper-bound solutions (black squares)
shows the potential room for improvement generated by the ideal perfect
information of the future inflow trajectories. A quantitative measure of
this space is provided by the values of the two metrics Dmin and HV
introduced in Sect. . Table
shows that the normalized distance between the closest baseline solution to
the target upper-bound solution is 0.342, with this gap also confirmed for
the entire set of solutions by the 0.292 difference in terms of hypervolume
indicator. Valuable snow-related information is hence expected to fill the
gap between the baseline and upper-bound solutions. It is interesting to
observe that, besides improving the performance of the operating policies with
respect to both the objectives, the use of perfect information reduces the
conflict between flood control and water supply, and discovers a number of
solutions close to the independent optima of the two objectives, including
the selected target solution JT=(4.5;250.6).
Given the references provided by the baseline and upper-bound solutions, we
can assess the operational value of different snow-related information by
looking at the performance of informed operating policies, represented by the
green, cyan, and red circles in Fig. . Not
surprisingly, numerical results show that enlarging the information used in
the lake operations by accounting for the snow dynamics in the upstream
catchment is producing an improvement of the system performance. In fact, the
baseline solutions are completely dominated by the sets P1, P2, and P3. These
informed operating policies successfully exploit the available snow data to
implicitly obtain a medium- to long-term forecast of the future water
availability due to snow melt, which supports the daily operations of the
lake, balancing flood protection on the short term and water supply on the
long one. Overall, the three sets of Pareto optimal solutions, obtained using
different snow information, attain similar performance, thus suggesting that
the VSI can be considered equivalent to the other two physically based
indexes. Figure also shows that policies P1 are the
best for very low values of Jflood but high values of
Jirr, while policies P3 result to be the best in the compromise
region of the objectives space (i.e., Jflood<10 days and
Jirr<275 (m3/s)2), which is likely including the most interesting solutions for the
lake operator as they successfully balance the system tradeoffs.
Operational value of the VSIs quantified by the two metrics
introduced in Sect. .
Finally, the values of the metrics reported in Table
confirm this visual evaluation. The three sets P1, P2, and P3 attain similar
values of hypervolume indicator, which assesses the quality of the entire set
of solutions. Interestingly, the policies P3 relying on the VSI outperform
the other informed solutions both in terms of proximity to the target
solution (i.e., lowest value of Dmin) as well as quality of the entire
Pareto front (i.e., highest value of HV). Although the differences in terms
of hypervolume are limited, the operational value of σ in terms of
Dmin is relevant and improves the performance of the baseline solutions
by 30 %, doubling the improvement achievable by using either snow height or
SWE data.
Conclusions
In this paper, we present a web content processing architecture for
extracting snow-related information from public web images, either produced
by users or generated by touristic webcams. The images, crawled from multiple
web data sources, are automatically processed to derive time series of
virtual snow indexes representing a proxy of the snow-covered area. We then
quantify the operational value of such data for informing the operations of
Lake Como.
Numerical analysis shows that the time series of the virtual snow index
extracted from a representative webcam is positively correlated with the snow
height observations from ground stations and negatively correlated with the
freezing level's dynamics. Moreover, our results demonstrate that the
operational value of the virtual snow index meets or exceeds the one of
traditional snow information. While the use of any snow information allows
attainment of a 10 % increase in the hypervolume indicator with respect to the
baseline system operations, the operating policies that use the virtual snow
index are the closest to the target solution, selected as a good compromise
between flood control and irrigation supply.
It is worth noting that our results require a large computing effort for
crawling and processing webcams and user-generated photos for the selected
study site. For example, the generation of a 1500×12 000 pixel
panoramic view requires approximately 1000 ms with a GeForce GTX 850M
graphic card. The alignment of an image to the virtual panorama requires
approximately 30 000 ms on an OpenStack virtual instance with 4 2.5 GHz
VCPUs and 8 Gb of RAM. Conversely, the requirements in terms of human
intervention are very low. Human intervention is indeed required only for the
skyline annotation and for setting up the experiment on the Lake Como basin
(e.g., select the webcam to use, ensuring it has enough information).
Finally, the availability of public information, either in terms of webcams
or photos, represents a key point for implementing our approach. In our case,
we split the 300×160 km region of the Italian and Switzerland Alps
using a 5×5 km grid. We analyzed all the images acquired in the
specified region over a 6 months period (from 1 December 2014 to 31 May
2015) obtaining a spatial coverage (i.e., the fraction of grid cells
containing at least one image over the considered observation period) of 38
and 10 % for photographs and webcams, respectively.
Future research efforts will focus on consolidating this approach by
extending the evaluation horizon and by using, at the same time, multiple
webcams and photographs to better understand the system dynamics in terms of
snow accumulation and melting as well as of the informed lake operations. In
parallel, the amount of web content is expected to increase, potentially
improving the spatial and temporal resolution of the generated snow-related
information and its operational value. We have indeed developed a gamified
web portal (http://snowwatch.polimi.it/) where users can cooperatively
access and enrich the data set of alpine mountain images, possibly allowing a
direct comparison between the information extracted from a webcam and from a
user-generated photo in the same location, which is currently unfeasible
because we do not have overlapping data. The gamified portal is also expected to
facilitate the users' engagement, fostering a more active participation in
our image collection effort. Furthermore, our intent is to transform the web
platform into a unique mountain-related media repository for testing novel
methods and tools. Finally, we are going to release our algorithms as an open-source implementation in order to maximize the portability of our
architecture in other snow-dominated catchments where public webcams and
user-generated photos are available. This will also allow for exploration of its potential in
different environmental problems that may benefit from using public web
content sources as low-cost virtual sensors, including sediment monitoring in
river beds or vegetation monitoring in remote mountain regions.
Data availability
The hydrologic data used in this study were provided by Consorzio dell'Adda
(http://www.addaconsorzio.it/) and Agenzia Regionale per la Protezione
dell'Ambiente (http://ita.arpalombardia.it). The crowdsourced data are
available on the SNOW WATCH portal (http://snowwatch.polimi.it).
Acknowledgements
The authors would like to thank Agenzia Regionale per la Protezione
dell'Ambiente, especially Dario Bellingeri and Enrico Zini, and Luigi Bertoli
from Consorzio dell'Adda for providing the data used in this study. The work
has been partially funded by the Proactive FESR project of Region Lombardy
(grant n. 2760/2013), the IMPREX project funded by the European Commission
under the Horizon 2020 framework programme (grant n. 641811), and the SOWATCH
project funded by Fondazione Cariplo. Edited
by: T. Bogaard Reviewed by: S. Gascoin and one anonymous
referee
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