=Paper=
{{Paper
|id=Vol-1152/paper36
|storemode=property
|title=Integrated Modeling of Hydrological Processes Through OpenMI and Web Services
|pdfUrl=https://ceur-ws.org/Vol-1152/paper36.pdf
|volume=Vol-1152
|dblpUrl=https://dblp.org/rec/conf/haicta/KokkinosLSI11
}}
==Integrated Modeling of Hydrological Processes Through OpenMI and Web Services==
https://ceur-ws.org/Vol-1152/paper36.pdf
Integrated Modeling of Hydrological Processes through
OpenMI and Web Services
Konstantinos Kokkinos1, Athanasios Loukas2, Nicholas Samaras1 and Omiros
Iatrellis1
1
Department of Information Technology and Telecommunications, Technological
Educational Institute of Larissa, Greece, e-mail: k_kokkinos, nsamaras, iatrellis@.teilar.gr
2
Civil Engineering Department, University of Thessaly, Volos, Greece, e-mail:
aloukas@civ.uth.gr
Abstract. Difficulty in linking data and models across organizations is one of
the barriers to be overcome in developing integrated decision-making systems
since not all models exist in the same location. OpenMI is a popular standard
for coupling spatially and temporarily hydrological models but it requires that
all involved models exist on the same machine. In this paper we present a Web
Services based collaborative framework to couple hydrological models. This is
achieved by converting the interface of the OpenMI configuration to be web-
based and to remotely invoke the computational engines of models. Our case
study shows the remote linking of a water balance model and a reservoir model
applied for the reservoir of the restored Lake Karla in Thessaly, Greece. The
results show that the collaboration process is not affected by the
communication overhead introduced and it is bounded by the time, space and
optimization characteristics of the coupled models.
Keywords: Environmental, web, services, coupling, OpenMI.
1 Introduction
Integrated environmental management systems have arisen, because managing
environmental processes independently does not always produce sensible decisions
when the wider view is taken. However, a major deficiency in integrated
management is the complexity of the involved processes we attempt to manage.
Managers are therefore turning to decision support systems which in this context
comprise one or more models, their associated data and a user interface. These
models predict outcomes of various scenarios of interest and therefore, they help the
managers in decision making for given scenarios and thus help the manager chose the
most appropriate option (Moore and Tindall, 2005; Browning-Aiken et al., 2004,
2006; Matthies et al., 2007). The most important step in building such systems is the
accurate transformation of the conceptual models that map the physical processes
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409
into computational models to produce a numerical simulation (Gregersen et al.,
2007).
Recently, the research in collaborative decision making in Hydrology and other
sciences has been elaborated through the introduction of the Open Modeling
Interface, (OpenMI) standard ((Gregersen, 2005; Gregersen et al., 2007). OpenMI
offers a means under which compatible models can be spatially and temporarily
synchronized, interlinked and interchange data. Prior to linking, all models must be
migrated into the OpenMI-standard to become compliant for coupling. The migration
process enriches the model computational engines with a migration wrapper which
must be present to interact with other external models by just promoting the standard.
In that way, models are marked by a two-way link to define mutual dependence and
they may run asynchronously. The above methodology however can be applied only
by implementing the necessary infrastructure in order to achieve compliance with
OpenMI and only if the models involved in the interlinking process are all installed
in the same machine on which OpenMI is installed.
The motivation for this research work was to design a web service for the remote
model interlinking of models. To the best of our knowledge, such a standard does
not currently exist, yet is needed to achieve an end-to-end integration for modeling
water resource systems. While this approach has obvious benefits this methodology
for integrated modeling has also a collection of challenges to overcome that are either
due to model specifics or due to the technology limitations that exist. The need for
model calibration prior to model coupling is apparent and thus restricts the
collaborative environment to approach the linking process holistically. On the other
hand, the need for modeler authenticated access in a web based framework, the
apparent existence of state variables to synchronize both ends of the system and the
simultaneous model simulations force for boundary communication conditions that
need to be optimized in order to avoid the introduction of overheads.
In this paper, we propose and experimentally verify with a case study a semi-web
services approach to overcome the above challenges. Assuming that, the OpenMI
standard is installed on the server side then, modeler-clients of the system need to
upload their models in the server repository and need a web-interface of the OpenMI
configuration editor for setting up the coupling process. The simulation runs are
triggered by the modeler remotely however, the whole computation process is done
on the server. A simulation monitoring tool (in a form of logs) must also be provided
for modelers to control the coupling and initiate interrupts, if needed. Such an
approach is limited compared to a fully web-service methodology which needs
implementation of service registries, proxy servers, service orchestration and
extensions to the SOAP protocol to accommodate the spatial and temporal
synchronization of the models. However, it is faster and open-ended on the number
of models that exist on the repository.
In the following sections of the paper we illustrate a case study of the migration
through OpenMI using the semi-web service approach discussed above of two water
resources models: The first model, called UTHBAL is a conceptual monthly water
balance hydrological model. The model has been developed by (Loukas et al., 2003)
and updated in its present form for simulation of hydrologic cycle components in
(Loukas et al., 2007). The second one is a reservoir model, called UTHRL which
simulates the natural water runoff inflows from a specific watershed area, any water
410
transfers from nearby water resources, the water withdrawals, and the net water
losses (Loukas et al., 2007). Prior to this work we have implemented a stand-alone
framework called UTH-MODELER under which the above models have been
plugged and interchange data.
Section 2 provides an in depth yet compact description of the UTHBAL and
UTHRL models. In section 3, we explain the UTH-MODELLER framework from a
software engineering point-of view summarizing its functional requirements and
providing all the additional extensions to become a web service. That is, we excluded
from the framework all added models and provided model portability via an XML-
schema incorporating all time-series data along with the geometries of the nodes of
where the data were observed. Section 4 shows the coupling mechanism between the
models in the web service, the uploading of models and provides details of the
simulation runs, studies and results produced. Finally, in the last section we elaborate
on the future challenges of our methodology.
2 Model Description
2.1 The Hydrological Water Balance Model
The hydrologic model used for this research is a monthly conceptual water balance
model, called UTHBAL which was developed and optimized by (Loukas et al., 2003;
Loukas et al., 2007). The model observes three input variables: precipitation,
temperature and potential evapotranspiration. The precipitation is divided into
rainfall and snowfall. The estimations of the rainfall and snowfall percentages are
done using a logistic relationship based on the mean monthly air temperature with a
simple degree-day method which is described in (Semadeni-Davies, 1997; Knight et
al., 2001). More specifically, let %S be the snow percentage then, according to the
model:
(1)
0
%S = 0 if T ³ 12.22 C
1
%S = if - 10 0 C £ T £ 12.22 0 C
(1.35T *1.61) + 1
%S = 1 if T £ -10 0 C
The snowmelt of the jth month, SM(j), is calculated to be
SM ( j ) = Cm .T ( j ) . (2)
where, T(j) is the jth mean monthly temperature and Cm is the monthly melt rate factor.
The snow water equivalent of the accumulated snowpack is another variable under
interest here. It is denoted as SWESP and it is estimated to be equal to
411
SWESP ( j ) = SWESP ( j - 1) + S ( j ) - SM ( j ) . (3)
where, S(j) is the snowfall during the jth month, which is equal to S(j)=P(j)x%S(j) and
P(j) is the total precipitation of that month.
Fig. 1. A logical diagram of the hydrologic balancing model UTHBAL.
According to the model, the total watershed runoff of the jth month is divided into
three separate components: the surface runoff, SR(j), the medium runoff, MR(j) and
the base flow runoff using a soil moisture mechanism. The first priority of the model
is to fulfill the actual evapotranspiration. The monthly actual evapotranspiration Ea of
the jth month depends on the available soil moisture and the average surface potential
evapotranspiration Ep of that month. The monthly actual evapotranspiration is
estimated using a relationship proposed by (Loukas et al., 2007):
{
E a (J ) = min E p (J ) ( 1 - a
Smoist( J ) E p ( J )
}
), Smoist(J ) . (4)
As depicted above in Fig.1, the model depends on five empirical factors namely α, β,
Κ, CN, Cm that must be optimized in order for the model to produce optimal output.
For that reason a calibration process is applied. The time-series subset used for the
calibration can be arbitrary large. The calibration of the model is based on non-linear
optimization techniques. More specifically the Reduced Gradient Decent is applied to
the above time-series data to optimize the factors.
2.2 The Water Reservoir Model
To manage the renewable surface water resources in a study area we use a reservoir
model, called UTHRL which is an optimized version of the reservoir model given in
412
(Loukas et al., 2007). This is a simple monthly conceptual model using a general
equation to describe the operation of the reservoir in a monthly time step:
V ( j ) = V ( j - 1) + Q( j ) - E( j ) - A( j ) - Y ( j ) . (5)
where V(j) and V(j-1) correspond to the stored water volumes in the reservoir on the
months j and j-1 respectively, Q(j) is the inflow to the reservoir on the month j, E(j)
is the net water loss from the reservoir for the month j, A(j) is the real withdrawal for
the month j, and Y(j) is the real overflow during that month. The reservoir storage
and overflow are calculated at each monthly time step using (5). The monthly net
water loses of the reservoir are estimated from the equation:
E ( j ) = E0 ( j ) - P0 ( j ) + L( j ) + Q( j ) . (6)
where, E(j) are the net water loses of month j, Eo(j) is the evaporation from the
reservoir water surface of month j, Po(j) is the direct precipitation on the reservoir
during month j, L(j) are the estimated deep percolation loses to groundwater and Q(j)
is the natural surface runoff that would have been generated from the area of the
reservoir if the reservoir does not exist. The deep percolation loses are usually
estimated by field geological measurements before the development of the reservoir.
In case that such measurements do not exist, estimates of the deep percolation loses
are used or the deep percolation loses are taken equal to zero. All the above
quantities are expressed in millimeters. The above quantities are expressed to volume
units (hm3) by multiplying them with the reservoir surface area. The water level and
the reservoir surface area are estimated using the reservoir storage-water level and
surface area-water level curves. Using these curves, an expression is developed
relating the reservoir water surface area, F, to reservoir storage, V:
F = a + bV c . (7)
3 The UTH-MODELER Framework as a Web Service
The UTH-MODELER is an integrated simulation framework that was introduced in
(Kokkinos et al., 2010) for the coupling of hydrological models as a stand-alone
application. The framework has the open-ended inclusion of models property. It
accepts models carrying their time-series data along with their geometry
idiosyncrasies. The UTH-MODELLER incorporates the extensibility feature that
allows the introduction of other hydrological and/or water resources models.
Furthermore, this framework provides: a) a visualization component for creating
time-series data histograms of the simulated variables, b) a calibration and
optimization component for the conceptual models and c) an automated migration
into OpenMI component for the incorporated models in this stand-alone version. This
stand-alone migration methodology via OpenMI has been presented in (Loukas et al.,
2008; Kokkinos et al., 2010). Incorporating the OpenMI-standard as a primary
413
coupling mechanism, it solves the problem of model spatial variance. Each model
carries its own data acquisition nodes as mapping points of a specific location as a
composite data structure with a unique ID for each node or as grid cells (i.e. XYZ-
polygons). Furthermore, it accommodates simultaneous simulations of models in
lumped mode (treating the overall geographical area of study as a whole) or in the
opposite extreme in fully distributed mode achieving spatial desegregation of the data
acquisition nodes. The framework is implemented as a set of various components
including: a) a storage component of the time-series data accepting Excel
spreadsheets, DEM input files or GIS-shape input files, b) a calibration and
optimization component for the included models, c) a visualization component for
the graphical representation of the input and all output variables of the simulations,
and d) an automatic migration and export component that adds to the models the
property of OpenMI-compliance.
Fig. 2. The Web OpenMI-Configurator Interface.
We added to the framework the functionality of automatic storage of model data
in XML format where each node of the model carries out its own spatial and
temporal characteristics along with its data. In this way models that currently the
UTH-MODELER supports may be loaded to the framework. For this to happen, we
also changed the model interoperability functionality of the framework to become a
web service. Furthermore, we extended the framework by implementing a web
interface for the OpenMI-Configuration Editor so that modelers can now couple
models into UTH-MODELER after loading them to the service. At the same time,
the new framework allows the permanent deposition of models into the service
414
repository, thus prompting future reusability. For the experimental verification of the
service, we added models that were incorporated in the stand-alone version of the
framework.
The web application of the OpenMI configurator includes all the operations that
the stand-alone version included. Fig. 2 illustrates the model inclusion process into
the web interface. It implements a web service designed to serialize all the attributes
of the model coupling composition. We added to the functional requirements of the
framework the ability of the user to select which model variables should be available
through the model data exchange. For our already programmed models (UTHBAL,
UTHRL) this automation is practical because it helps to show all intermediate output
data variables. Furthermore event listeners for the monitoring of the simulation
process are added as Fig. 3 depicts.
Fig. 3. Variability of input/output exchange items in the linked simulation and monitoring
of process interrupts.
The web application is based on the composition class which implements all the
web methods relating to the coupling composition consisting namely: adding models,
initiating triggers, setting up connections, indicating input and output exchanged
items and monitoring output properties. A session variable is used to store the
serialized composition class every time when an update or an interrupt occurs.
The framework repository saves all coupled models that have been implemented
within the UTH-MODELER in a binary file (named UTHMODELER.ubm) which
must exist in the same directory with the manifest files (.OMI extension) that
accompany the models for coupling. This file needs to be present so that, the engines
of the models, (namely UthBalModelOMIEngine and UthRLModelOMIEngine)
415
know which data series can be exchanged. The OpenMI SDK provides a template
class called IEngine that is used as an interface of each model engine making it ready
to interchange data. The web Configurator instantiates a separate IEngine object for
each model engine for the simulation. These components discover the directory path
of the corresponding .ubm file so that they can instantiate the OpenMI-compliant
model accordingly. It is worth noting that, we cannot create just the web engine
components for the models without the corresponding .ubm file since, the simulation
cannot run without data.
4 Collaborative simulation case study through the Web
Configurator.
In this section we describe the coupling of the hydrological model UTHBAL with the
reservoir operation model UTHRL so that, we will have data exchange between the
models under simultaneous simulation. This operation is handled via the web service
described previously. We set up a case study of a specific region in Lake Karla,
Thessaly, Greece in order to experimentally verify our models in terms of surface
water inflow assessment into to the reservoir of the lake at a monthly time scale. The
area under consideration is depicted in Fig. 4 which contains a digital elevation
model of the Lake Karla watershed in the location of the reservoir. Furthermore in
the same figure we illustrate how the spatial characteristics of the models are
visualized in the Web Configurator.
Fig. 4. A) Digital Elevation Model of Lake Karla watershed in the location of the
reservoir and B) Visual representation of the model grid cells in the Web Configurator.
416
The study area is an intensely cultivated agricultural region. The over-exploitation
of groundwater and the unsustainable water resources management has led to a
remarkable drawdown of the water table. However, the necessary functions have
been initiated for the lake restoration recently. The partial restoration of the former
Lake Karla is expected to reverse this situation and the relevant environmental
problems caused by the lake drainage and provide new irrigation water resources for
the local farmers.
The available data for the estimation of the water resources were precipitation
data, temperature data, and discharge data. These data span for a period of time of
over 50 years and they are on a monthly basis. The original meteorological and
discharge measurements were collected by regional and prefecture water resources
agencies and the competent authorities. The measurements have been checked for
errors, homogenized and processed according to the World Meteorological
Organization techniques and standards. The UTHBAL model runs on three spatial
modes: a) fully distributed mode, b) semi-distributed mode and lumped simulation
mode.
Fig. 5. Illustrative representation of the simulation time step between the UTHBAL and the
UTHRL model. (The joining variable is the surface runoff)
The spatially lumped mode was chosen for the experiment. In lumped mode, and
for each monthly time step, the UTHBAL model feeds the UTHRL model with a
single value for the total surface runoff. This event triggers the UTHRL model to
include as inflow the value Q(j), in order to execute its time step compotation and to
wait for the next feeding. The initiation of the simulation is invoked by the UTHRL
417
through a call to the GetValues() OpenMI-function. For that reason, we explicitly
connect to it an OpenMI-trigger in the composition design phase. This process
becomes then repetitive for the overlapping time period of the two models. Fig. 5
shows such a simulation step where in fig. 6 we illustrate the web log of the whole
simulation produced. The inclusion of data of the two models in our experiment
makes then partially synchronized. However, for an overlapping period of 10 years
(i.e. 120 time steps) the system succeeds to interchange values.
Fig. 6. A screen shot of the simulation web log produced.
Apart from the fact that, the user can set parameters in visualizing exchange
quantities in the OpenMI configuration editor, our modeler also produces output in
text files, EXCEL files and ascii-DEM files which can be seamlessly read by a GIS
system. More specifically and for the case of the UTHBAL model, it is also our
intention to provide data acquisition methodologies for real time input coming from
wireless sensor networks or networks of meteorological stations for the regions under
study.
Finally, it is worth noting that comparing the simulation of simultaneous runs for
the models in stand-alone mode with the remote simulation over the web service
implemented we did not find any significant delays. Any communication overhead
produced is meaningless since, the simulation starts after both models are loaded
onto the server. Thus the set of all HTTP-requests is limited.
5 Conclusions and Future Challenges.
We implemented a web-based hydrological decision support system to meet the
challenges of current desktop-based implementations. Such a system is based on the
OpenMI-coupling standard and has achieved to convert the OpenMI-Configurator in
to a web service that can be remotely accessed and configured to couple our prior
implemented models UTHBAL and UTHRL. Our prototype avoided the step-by-step
data interchange of models over the SOAP protocol by introducing a mechanism to
upload all models of interest in the server repository. The benefits of such an
418
infrastructure are essential and they span into: a) provision of remote access to large
time series databases, b) deployment of simulations on one machine while the
simulation runs are migrated to dedicated servers, c) model linking of multiple
providers without the need for moving the data and d) effective usage of simulation
licenses. In this way modelers need only a simple web interface that: a) uploads
model engines and data series assuming that are migrated to the OpenMI-paradigm,
b) designs a coupling composition c) triggers the simulation start and d) provides an
observation/monitoring tool for the simulation.
Our future research challenges span in two directions. Primary concern is given to
achieving the conversion of the UTH-MODELER framework into a fully functional
web service. For this to happen, we need to provide a functionality named “Any-to-
UBM” that can accept arbitrary models and automatically convert their mode and
data specifications into our XML schema structure our service implements.
Secondly and arguably more difficult task is the creation of a web mapping
service that detects and automatically resolves any model spatial and temporal
heterogeneities. Unfortunately the intricacies of the model mathematics involved
along with the special visualization tools needed for the modelers to control such
simulations indicates the computational complexity of such implementation.
Acknowledgments. Part of this research has been funded by EC under the LIFE
Framework Program and contributing to the implementation of the thematic
component LIFE-Environment under the policy area "Sustainable management of
ground water and surface water management". Contract no: LIFE06
ENV/UK/000409.
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