Open Journal of Modern Hydrology, 2013, 3, 79-88 Published Online April 2013 (
Weather Radar Data and Distributed Hydrological
Modelling: An Application for Mexico Valley
Baldemar Méndez-Antonio1, Ernesto Caetano2, Gabriel Soto-Cortés3, Fabián G. Rivera-Trejo4,
Ricardo A. Carvajal Rodríguez1, Christopher Watts1
1Sonora University, Hermosillo, Mexico; 2National Autonomous University of Mexico, Mexico City, Mexico; 3Metropolitan Auto-
nomous University, Mexico City, Mexico; 4Tabasco Autonomous Juárez University, Villahermosa, Mexico.
Received January 28th, 2013; revised March 2nd, 2013; accepted March 13th, 2013
Copyright © 2013 Baldemar Méndez-Antonio et al. This is an open access article distributed under the Creative Commons Attribu-
tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
The frequent occurrence of exceptionally very heavy rainfall in Mexico during the summer causes flash floods in many
areas and major economic losses. As a consequence, a significant part of the annual government budget is diverted to
the reconstruction of the disasters caused by floods every year, resulting hold up in the country development. A key
element to mitigate the flash flood hazards is the implementation of an early warning system with the ability to process
the necessary information in the shortest possible time, in order to increase structural and non-structural resilience in
flood prone regions. The real-time estimation of rainfall is essential for the implementation of such systems and the use
of remote sensing instruments that feed the operational rainfall-runoff hydrological models is becoming of increasing
importance worldwide. However, in some countries such as Mexico, the application of such technology for operational
purposes is still in its infancy. Here the implementation of an operational hydrological model is described for the Mix-
coac river basin as part of the non-structural measures that can be applied for intense precipitation events. The main
goal is to examine the feasibility of the use of remote sensing instruments and establish a methodology to predict the
runoff in real time in urban river basins with complex topography, to increase the resilience of the areas affected by
annual floods. The study takes data from weather radar operated by the National Meteorological Service of Mexico, as
input to a distributed hydrological model. The distributed unit hydrograph model methodology is used in order to assess
its feasibility in urban experimental basin. The basic concepts underlying the model, as well as calibration and valida-
tion are discussed. The results demonstrate the feasibility of using weather radar data for modeling rainfall-runoff proc-
ess with distributed parameter models for urban watersheds. A product resulting from this study was the development of
software Runoff Forecast Model (ASM), for application in distributed hydrological models with rainfall data in real
time in watersheds with complex terrain, which are usually found in Mexico.
Keywords: Radar; Distributed Hydrologic Modeling; Resilience
1. Introduction
Soussan and Bourton [1] defined adaptation as the ability
to respond and adapt to real or potential impacts under
changing weather conditions in order to moderate dam-
age. In the face of extreme hydrometeorological condi-
tions the greater or lesser resilience or strength, defines
vulnerability which a community is exposed. Lack of re-
silience is manifested at the structural, physical, eco-
nomic, social, political, and institutional level. Thus, the
human vulnerability and lack of resilience explain many
of the large-scale disasters and y to decrease the risk of
disaster, measures should be taken to modify those fac-
tors [2]. In order to build resilience in areas vulnerable to
flooding, structural and nonstructural measures must be
taken. Early warning systems (EWS) supported by op-
erational hydrological runoff prediction models are a fun-
damental part of the non-structural measures. The disas-
ter risk reduction and increased preparedness to natural
hazards in different development sectors have multiplier
effects and accelerate the achievement of the Millennium
Development Goals [3].
The use of distributed hydrological models has in-
creased in the last three decades [4,5]. These models are
developed in order to physically represent the hydrologi-
Copyright © 2013 SciRes. OJMH
Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley
cal processes occurring in a watershed through analogies
and mathematical simplifications. Early attempts to de-
velop distributed models were limited by computing ca-
pacity required for processing and storage of information,
but the digital revolution, responsible for the huge growth
in the quantity of geospatial data available today, has
allowed the development of more robust distributed hy-
drological models [6]. It is noteworthy that the spatial
and temporal variability of precipitation has a strong im-
pact on the outcome of distributed hydrological modeling,
while the density of the rain gauge network also plays an
important role [7-9].
Additionally, EWS have been greatly improved with
the development of geographic information systems (GIS)
and the Internet [10]. Remote sensing instruments, such
as radar and weather satellites, due to their ability to es-
timate the spatial variability of precipitation in real-time,
are ideal for use in distributed hydrological modeling.
The combination of remote sensing, GIS and the Internet,
allows the collection and transmission of real-time data
more efficiently. The data generated by these systems
can be used as input to operational hydrological models
and so estimate the basin response in the presence of
extreme precipitation events. In Mexico, although efforts
have been made to use this technology by both federal
local governments and academic institutions, the results
are still only at the diagnosis level [11].
This paper examines the use of weather radar data for
operational hydrological modeling in an experimental
catchment located in the metropolitan area of Mexico
City. The methodology proposed here could easily be
applied to others basins anywhere that radar data is avail-
able. Additionally, EWS can be integrated into contin-
gency plans allowing countries to build resilience and
prevent large losses from annual flooding, thus improv-
ing regional and national development.
2. Study Area
The selected case study area is the Mixcoac River expe-
rimental basin, located in Mexico City, (Figure 1). This
Figure 1. Basin study location and Mixcoac River Basin
is a mountain river, with low runoff in dry period and
sudden and intense floods in the rainy periods. Intense
precipitation occurs often due to topographically enhan-
ced convection over this area [12]. This basin was se-
lected for this study because of the availability of input
data from: 1) the Cerro Catedral weather radar, located
35 km from the study basin (Figure 1); 2) runoff and
precipitation estimates from 78 rain gauges network with-
in the radar coverage area. This data set allows us to es-
tablish a robust relationship between rainfall and runoff
that eventually will help calibrate the hydrological model.
3. Hydrological Modeling
In traditional hydrological models, the runoff is produced
by the fraction of precipitation not absorbed by the soil,
this flow component is referred as direct or surface run-
off, and the volume portion of precipitation which has
produced it, is called excess or effective precipitation
[13]. This type of runoff is known as Horton excess infil-
tration mechanism and usually occurs in rivers with steep
slopes or low permeability soils. Another mechanism
(Dunne) occurs when soil moisture is saturated, known
as over-saturation, is more common in flat areas with
permeable soils or near wetlands [14]. The river Mixcoac
is a typical mountain stream, so the Horton mechanism is
employed for obtain the infiltration.
By using a transfer function, the precipitation sur-
pluses are converted into direct runoff, and are added to
the base flow to obtain the total runoff hydrograph. This
scheme corresponds to lumped parameter hydrologic
models, which use spatial averages for physiographic
features and precipitation and reproduce the temporal
variability of the output basin response [15]. Moreover,
the distributed hydrological modeling considers the spa-
tial variability of the physical properties and precipitation
dividing the basin into sub-basin or cells. Naturally, the
development in remote sensing and geographic informa-
tion systems has facilitated spatially distributed informa-
tion management. An advantage of distributed models is
that they allow the analysis of different elements that
influence the hydrological response and can be modified
by human intervention, such as the vegetation and land
use, and with appropriate calibration. Distributed models
can estimate changes in the hydrological response of the
basin to extreme precipitation events, caused by these
Distributed models obtain the flows in each of the
sub-basins simultaneously, which can indicate the state
of the system at any point of the drainage network and
improve flood risk assessment. Namely in distributed
models the spatial variation of the characteristics and
processes are explicitly considered, while in aggregate
models, spatial variations are averaged or ignored.
Although it is believed that implementing Geographic
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Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley 81
Information Systems (GIS), the problem is solved, this is
just a tool to facilitate the determination of watershed
hydrologic parameters, but no improvements due to spa-
tial data scarcity and/or temporal components of the hy-
drological cycle. If the input precipitation data is ob-
tained from a sparse density/spatial distribution rain
gauge network, these data cannot characterize adequately
the rain fields at the same level of spatial detail as the
terrain described by the models. This occurs because the
rain gauge network does not necessarily detect the most
intense storm so often interpolated data or extrapolated
are used, resulting in misrepresentation of observed rain
fields. In this sense, modelers have great interest in esti-
mating precipitation from data from remote sensing in-
struments, such as weather radar, which potentially pro-
vide estimated rainfall at the spatial detail required by
distributed hydrological models. In this regard, a variety
of models such as HEC-HMS, SWAT, CEQUEAU,
consider the use of such data [16].
It is also important to note that distributed models give
good results without an excessive amount of parameters.
From the operational point of view, the model must be
simple and of fast execution, without losing the physical
representation of phenomena. The simplicity and agility
of the model operation are key factors for applications in
operational forecasting, since otherwise there may be too
little time for decision making and taking action to miti-
gate the effects of flash floods. Models with many pa-
rameters are only used in experimental watersheds in-
strumented for this purpose [17]; for this reason, they are
not attractive for distributed modeling in countries like
Mexico where measurements are scarce.
4. Methodology
4.1. Data Grid Generation
In distributed hydrological modeling, the Mixcoac river
basin is represented by a set of square cells, where each
cell is considered as the basic runoff production unit
(Figure 2), with a spatial resolution of 1 × 1 km. The
georeferenced grid containing the basin physiographic
Figure 2. Weather radar grid data area.
parameters, such as: the land use, soil type and curve
number (CN) obtained from the digital elevation model
(DEM). The digital precipitation arrays were estimated
from a calibration equation for this radar survey area
Two storms were selected where radar rainfall (every
15 minutes) and runoff data were available. These storms
occurred on July 28 and August 23 1998.
4.2. Distributed Hydrological Model
The distributed hydrological model comprises two con-
ceptual sub-models: one for the runoff production in each
of the components of the distributed system, and another,
which represents travel runoff to be added, from each
cell to reach the basin outlet. The following describes
each of the sub-models.
4.3. Runoff Production Sub-Model
The runoff production in each hydrological unit (cell) is
obtained from an infiltration or loss model. These losses
consist of initial abstraction and the ground infiltrated
water during the storm. Initial losses include water inter-
cepted by the vegetation, the water stored in the depres-
sions of the surface (puddles) and water infiltrated into
the ground until it is saturated.
The Natural Resources Soil Conservation Service
(SCS), or Curve Number (CN) was used to determine
runoff because of its simplicity [19]. This is one of most
widely used methods for estimating runoff volumes es-
timates from a single parameter, the main physical char-
acteristics of the watershed such as slope and use and soil
type are considered to produce runoff. This method has
the advantage of high predictability and stability; in addi-
tion, it is a conceptual method which estimates runoff
directly from precipitation [20].
Once the basin has been divided into rectangular cells,
the value of the curve number (CN) is set to each of the
soil properties (type and land use) and then the direct
runoff in each of the cells is estimated. The water volume
not converted into runoff is infiltrated into the ground,
where part is stored as soil humidity and the rest passes
to form deeper underground storage.
The SCS method does not explicitly include any infil-
tration scheme, so this was estimated directly from ac-
cumulated runoff, accumulated precipitation, soil storage
capacity and initial losses. The conversion of rainfall to
runoff, essential to surface hydrological modeling, is
based on the conservation of mass or water balance;
 (1)
where P = total precipitation (cm); Pe = effective precipi-
tation; Ia = initial infiltration i (cm); Fa = accumulated
infiltration (cm). Ia and Fa represent losses and their
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Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley
quantification is based on two fundamental assumptions
[19]. The first states that the relationship between the
effective volume of precipitation (Pe), or direct runoff,
and the maximum potential runoff (P – Ia), correspondent
to an impervious surface, is equal to the ratio between the
real infiltration Fa and the maximum potential infiltration
S (Equation (2)). The second hypothesis assumes that the
initial infiltration is directly proportional to the potential
retention (Equation (3)):
Emerging Equations (1) and (2):
 (4)
From Equation (3), assuming λ = 0.2,
S (cm) is given:
2540 25.4CN
And Pe (cm) is obtained [21] as follows:
508 5.08
2032 20.32
The curve number (CN) is determined from the land
use and soil type defined by the US Soil Conservation
Service, and the values of P and Pe are expressed in cm.
Equations (6) and (7) are valid for P Ia. The parameter
Ia depends on regional geological and climatic factors.
The main hydrological interest in land use maps lies in
the infiltration modeling as a function of soil properties,
thereby capturing spatial variability. To determine infil-
tration parameters from soil properties require some re-
classification of representative soil parameters units for
the hydrological model. The SCS proposed a criterion for
to estimate the effective precipitation in terms of total
precipitation and soil characteristics from a table of val-
ues for the curve number according to soil type [22]. For
the particular case of Mexico, the classification hydro-
logical soil texture and curve number given by [23] were
used (Tables 1 and 2). Soil properties (Table 1), de-
pending on their permeability, is used to define the hy-
drological soil type. Table 2 describes the use of soil,
which together with the soil type defined in Table 1, and
the slope, enables allocation of the curve numbers.
Geo-referenced soils, vegetation and topography maps
Table 1. Hydrological soil type classification (source [23]).
Soil Group Properties Permeability
A Sands with little slime and
clay (minimum runoff) Very High
B Fine sands and slime Good
C Very fine sands, slime and
quite clay Medium
Clays in large quantities,
shallow soils almost
(maximum runoff)
(DEM) facilitate the processing and spatial allocation of
curve numbers. Once curve numbers maps were obtained
(CN), then Equation (7) is applied to obtain the runoff
generated by each storm.
4.4. Runoff Routing Sub-Model
The routing of effective precipitation (Pe) at the water-
shed outlet is an interdependent component in the hydro-
logical cycle, while a proportion of the rainfall is lost to
infiltration, excess rain generates runoff, which accumu-
lates and drains through the stream network to the basin
outlet. The widely used transfer hydrological method the
unit hydrograph [24]is applied to this experimental
The basin was divided into cells, which represent a set
of elements where the continuity equation is applied. The
change in the volume Vs stored in the drain network ele-
ment during a time interval expresses the difference be-
tween the stored volume at the end of the previous period
and the stored volume in the end of the next period. That
is, the change in storage Vs is equal to the difference be-
tween the volume of water entering Vi and leaving the
soil volume Vo during the time interval Δt:
iot t
 (8)
where t
and t
O are the mean input and output flood
discharges, respectively, during the time interval Δt. The
previous equation can also be represented as:
dt (9)
or in finite differences as:
010 1
ttt t
The stream flow routing from any point to the basin
outlet can be modeled by a simple aggregation through
the distributed unit hydrograph or the Clark modified
unit hydrograph [25] also called isochrones distributed
unit hydrograph [26], (Figure 3).
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Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley 83
Table 2. Soilcover (source [23]).
Hydrologic soil group
Cover type Slope % A B C D
No cultiveted - 77 86 91 94
Row crops
Straight row >1 72 81 88 91
Straight row <1 67 78 85 89
Contoured >1 70 79 84 88
Contoured <1 65 75 82 86
Terraced >1 66 74 70 82
Terraced <1 62 71 78 81
Small grain
Straight row >1 65 76 84 88
Straight row <1 63 75 83 87
Contoured >1 63 74 82 85
Contoured <1 61 73 81 84
Terraced >1 61 72 79 82
Terraced <1 59 70 78 81
Rotation meadow
Straight row >1 66 77 85 89
Straight row <1 58 72 81 85
Contoured >1 64 75 83 85
Contoured <1 55 69 78 83
Terraced >1 63 73 80 83
Terraced <1 51 67 76 80
- >1 68 79 86 89
- <1 39 61 74 80
Contoured >1
47 67 81 88
Contoured <1 6 35 70 79
grassland 30 58 71 78
Very sparse - 56 75 86 91
Sparse - 46 68 78 84
Normal - 36 60 70 77
Dense - 26 52 62 69
Very dense - 15 44 54 61
Dirt - 72 82 87 89
Paved - 74 84 90 92
Residential area - 77 85 90 92
Cemeteries - 68 79 86 89
Brush - 48 67 77 83
Parks - 53 72 83 88
(a) (b)
Figure 3. (a) Isochrones; and (b) Time-area histogram for a
watershed (adapted from [27]).
The runoff routing to the basin outlet was performed
using the Muskingum method. This method employs the
continuity equation (Equation (10)) and a relationship
between storage V and inputs and outputs of the analysis
section (Equation (11)).
VKxI IxO
where I is the inflow, O is outflow, K is the attenuation
coefficient and x storage weight factor that relates the
input and output storages in the current segment.
4.5. Distributed Unit Hydrograph
In order to use weather rainfall data, the method of Clark
unit hydrograph must be modified to apply in distributed
hydrological models and hydrological forecasting [25].
The conceptual model of this approach to distributed
models is shown in Figure 4.
This type of unit hydrograph is interpreted as the result
of the combination of a pure translation process, fol-
lowed by a routing in a linear storage. According to this
scheme, the actual travel time of a water particle is given
by the time-area diagram plus the retaining time in the
linear reservoir [17].
This method requires the estimation of four parameters
for determining the hydrograph of the basin: the time of
concentration tc; the Muskingum storage attenuation co-
efficient K; the basic flow recession constant R and a
time-area histogram that is used to obtain the initial infil-
tration Ia and infiltration potential maximum S. The con-
centration time tc is defined as the time which the pre-
cipitation takes to reach the basin outlet from most re-
mote point. This is a measure of pure delay, regardless of
the effect of storage. In the literature there are several
equations to calculate the concentration time tc [28], in
this study the Kirpich equation was used:
tcconcentration time (hours),
L—river bed length (m),
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Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley
S—basin average slope (dimensionless).
The storage attenuation coefficient K is the second pa-
rameter and is a measure of the delay caused by natural
storage. For calibration of this parameter an initial value
of K = 0.6tc is assumed [23]. The recession constant R is
a measure of the flow rate decrease in the recession curve
between Qi and interval. That is,
The fourth parameter (the time-area histogram) repre-
sents the watershed area contributing to runoff at the ba-
sin outlet in a given time and transforms the effective
rainfall hyetograph into a runoff hydrograph, regardless
of time storage. This area is obtained by constructing a
map of isochrones, defined with travel time from each
cell to the basin outlet (Equation (12)). By relating the
areas between isochrones with corresponding time inter-
val, the time-area histogram of the basin is obtained. This
parameter is very important in this methodology because,
together with the storage constant K, it determines the
runoff response of the basin to its outlet (Figure 4).
5. Hydrological Model Calibration
Hydrologic model calibration was performed with the
storms of July 28 and August 23, 1998 with precipitation
mapped every 15 minutes. The study period was from
18:00 to 00:00 h (local time) on 28 July and from 16:45
at 18:45 h (local time) on 23 August.
The rain period matrices for storm August 23, 1998
are shown in Figure 5. They show the evolution of the
storm approaching the basin until it finally dissipates.
This allowed obtaining the rainfall-runoff model to ex-
perimental river basin Mixcoac.
The grid parameters represent the cells as sub-basins,
in this way, from the current length and slope of each cell
the travel time to the basin outlet is estimated to create
Figure 4. Conceptual model of clark method for distributed
parameters (adapted from [25]).
the isochrones (Figure 6). The spatial variation of the
curve number (CN) was determined using a Geographic
Information System (GIS) in raster format, ensuring that
the study area and the data format matching the radar
rainfall grid. For this grid, land use and soil type maps
(Figure 7) of the study area were used. This format al-
lows the CN of each of the cells in the runoff generation
model to be included as input (Figure 8).
Finally using the modified Clark method, this uses the
Histogram Time-Area (HTA) defined with subareas built
between consecutive isochrones from the remote areas to
the basin outlet. This HTA is the basis to transform rain-
fall into runoff and is determined from the convolution
equation. The time interval used in the hydrograph basin
response defines the travel time between two adjacent
isochrones defined by [27] as:
where: j is the number of time intervals, Q is the basin
outlet runoff, E is the excess rainfall intensity and A is
the area enclosed between isochrones. This method cali-
brates the model until the hydrograph estimate is compa-
rable to the observed hydrograph of selected storms. The
rainfall and runoff data were used as observed data to
calibrate the hydrological model.
With the proposed methodology, the hydrological mod-
eling was performed and the results obtained are shown
in the Figures 9 and 10. The parameters obtained for the
two storms are shown in Table 3.
Where Ia is the initial infiltration, S is the retention
potential, tc is the concentration time of the watershed, K
is the Muskingum storage coefficient, Qbi is the starting
base runoff, R is the recession constant and Qu is the
threshold base runoff.
Figures 9 and 10 show, for the event on July 28, the
difference in volume between the measured and the ob-
served hydrograph is 5%, and for the event of August 23
the difference is 1%, while the peak occurs in the first
case with a delay of 30 minutes, and the second with an
advance of 30 minutes. These differences in volumes and
the time in the peak flow could be associated to the lim-
ited information available to calibrate the model.
Table 3 lists the seven parameters required for model
Table 3. Parameters resulting for each storm.
Basin Model Parameters
Date Ia S tc K Qbi R Qu
July 28 0.60.1250.25 1.0 0.6 0.8 0.1
August 230.60.410.25 1.0 0.6 0.8 0.1
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Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley
Copyright © 2013 SciRes. OJMH
16:45 17:00 17:15
17:30 17:45 18:00
18:15 18:30 18:45
Figure 5. Precipitation matrix every 15 minutes for 23 August 1998 (16:45 - 18:45, local time).
calibration: initial infiltration (Ia), and potential retention
(S) are influenced by the antecedent soil moisture. The
time of concentration (tc) and storage coefficient (K) af-
fect the shape of the hydrograph. Meanwhile, initial base
runoff (Qbi), the recession constant (R) and threshold
based runoff (Qu) are affected by the observed historical
base flow parameters.
The physical parameters of the basin, which can be
considered constant, were obtained with the software
HEC-GEOHMS. However, thinking of a future opera-
tional hydrological model and with the intent to encour-
age the use of radars and distributed hydrological models,
as an additional product of this study, the software Run-
off Forecast Model (RFM) was created, for calculation of
variable parameters required by the model's hydrological
basin Mixcoac [29]. This software is available com-
Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley
Figure 6. Mixcoac basin isochrones maps.
Figure 7. Map of hydrologic soil ty pe (a) and use land (b).
pletely free of all hydrologists interested in distributed
hydrological models. The RFM software, along with its
manual (in Spanish) and application example can be
Figure 8. Soils curve number in the Mixcoac Basin.
Figure 9. Outflow hydrograph for the storm of July 28,
Figure 10. Outflow hydrograph for the storm of August 23,
Copyright © 2013 SciRes. OJMH
Weather Radar Data and Distributed Hydrological Modelling: An Application for Mexico Valley 87
downloaded from the Engineering Institute of the Na-
tional Autonomous University of Mexico [30].
The results show that radar data input in operational
hydrological models can be of great help for flood fore-
casting in real time and useful to the Meteorological Ser-
vice of any country. Currently, the weather radar is not
seen as the only solution to flood problems, but as part of
a whole observation system that includes automatic wea-
ther stations, radar, satellite and also regional weather
forecast models. The estimation of rainfall from satellite
data has been developed extensively in recent years and
the proposed Global Precipitation Measurement (GPM)
is giving great support to researchers, developers and
operational hydrologists. Moreover, mesoscale forecast
models, such as WRF provide a great advantage in urban
hydrology and other fast response watershed, as the fore-
cast window is important in helping to anticipate sites of
high flood risk and issue warning notices for this popula-
tion. Additionally, radars and satellites can be used to
generate immediate forecasts (nowcasting), a very useful
product in urban hydrology and mountain river basins.
All of these rainfall product can be used as input into the
operational hydrological models developed here.
6. Conclusions
The major physical significance of distributed models, as
used here, is to consider temporal and spatial variability
of storms and the spatial variability of soil characteristics
of the basin in order to accurately reproduce the hydro-
logical processes inside the watershed, thereby generat-
ing more realistic and accurate hydrographs. In this sense,
the weather radar is an excellent option to estimate the
spatial variability of precipitation affecting hydrological
processes within a watershed. In addition, as the response
of the basin is non linear, the distributed model allows
more basin accurate integration.
Concerning model parameters considered, the initial
infiltration and potential retention of soil moisture were
calibrated considering runoff volumes shown in observed
hydrographs and it was seen that only in the case of the
second parameter there is a variation from one storm to
another, mainly due to changes in the humidity. In the
case of the two selected storms, infiltration parameters
showed that, for the storm of July 28, the infiltration is
less than in the case of the August 23 storm. This is an
indication that the second storm occurs when the soil
moisture is greater than the first one.
The difference between measured and estimated vol-
umes are 5% and 1% for storms in July and August, re-
spectively, providing an accurate estimation of the peak
discharge volume, although its timing is less accurate.
However, if a larger number of events are available for
calibration, the results can be considerably improved.
Moreover, of the seven parameters used in the calibration
model, six remain constant and only one is variable—the
antecedent moisture. This is a problem not yet solved by
hydrologists, since it is difficult to estimate the soil
moisture conditions when a rainfall event occurs. Except
for this problem, the model is operationally simple and
produces valuable information for decision-making in
high-risk area. In addition, of the seven parameters re-
quired by the model, three of them, relating to base flow,
can be obtained directly from the historical analysis,
leaving only four for calibration.
This effort also aims to establish the basis for real time
storm monitoring systems, in order to integrate them into
an early warning system in Mexico river basins with
major flooding risks, as part of the non-structural meas-
ures which should be implemented to increase resilience
in flood prone areas. The investment in this type of meas-
ure is far less than the economic losses which occur each
year in these areas of the country [31]. The methodology
shown can be used in other basins. Additionally, this type
of hydrological modeling is also useful to assess the un-
certainty in runoff forecasting models in real time.
In an attempt to encourage the use of remote sensing
instruments in operational distributed hydrological mod-
els, the Runoff Forecast Model (RFM) was developed.
This model can be downloaded from the website of the
Institute of Engineering of UNAM.
7. Acknowledgements
The authors would like thank the National Weather Ser-
vice of Mexico and the Water System of the City of Me-
xico, for the data provided and PAPIIT, under contract
IT100712, and CONACYT-SEMANART, under contract
107997, for the financial support in the development of
the distributed hydrological model using weather radar
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