Agrociencia
Uruguay
ISSN-e: 2730-5066
vol. 26, no.
2, e979, 2022
agrociencia@fagro.edu.uy
Natural and
environmental resources
SanAntonioApp: interactive visualization and repository of spatially distributed flow
duration curves of the San Antonio Creek - Uruguay
SanAntonioApp: visualización
interactiva y repositorio de curvas de duración de caudales espacialmente
distribuidas del arroyo San Antonio - Uruguay
SanAntonioApp: visualização interativa e repositório de curvas de duração de vazão
espacialmente distribuídas do arroio San Antonio -
Uruguai
Rafael Navas
Universidad de la República, Centro Universitario Regional Norte, Departamento del Agua, Salto, Uruguay
https://orcid.org/0000-0001-8559-9523
Vanessa Erasun
Ministerio de Ambiente, Dirección Nacional de Aguas, Montevideo, Uruguay
https://orcid.org/0000-0002-4361-2520
Rafael Banega
Universidad de la República, Centro Universitario Regional Norte, Departamento del Agua, Salto, Uruguay
https://orcid.org/0000-0003-2755-8082
Gonzalo Sapriza
Comisión Técnica Mixta de Salto Grande, Salto, Uruguay
https://orcid.org/0000-0002-6854-0172
Andrés Saracho
Universidad de la República, Centro Universitario Regional Norte, Departamento del Agua, Salto, Uruguay
https://orcid.org/0000-0002-7510-0757
Pablo Gamazo
Universidad de la República, Centro Universitario Regional Norte, Departamento del Agua, Salto, Uruguay
Received: 04 October 2021
Accepted: 19 August 2022
Published: 06 September 2022
Corresponding
author: rafaelnavas23@gmail.com
This
work is licensed under Creative Commons Attribution 4.0 International.
Resumen: Los proyectos de riego
necesitan información sobre la cantidad y la frecuencia del caudal de los ríos
para el diseño y el dimensionamiento del sistema de riego. Por un lado, esta
información se obtiene a través de estaciones de aforo o modelos hidrológicos.
Por otro lado, las estaciones de aforo son escasas y la implementación de
modelos hidrológicos es costosa, especialmente para proyectos de riego
pequeños. Este trabajo propone una metodología para estimar las curvas de
duración de caudales (FDC, por sus siglas en inglés) espacialmente distribuidas,
y describe la aplicación interactiva y el repositorio de acceso abierto SanAntonioApp, que es utilizado para compartir los
resultados de esta investigación. El marco propuesto utiliza tres años de
registros de una red hidrometeorológica densa para implementar, optimizar y
validar de forma cruzada el modelo hidrológico distribuido WFLOW-HBV en el
arroyo San Antonio (Salto, Uruguay). Luego, las FDC se generan extendiendo el
período de simulación con una estación agroclimatológica
de largo registro (30 años). Los resultados de este trabajo ayudan a evaluar la
disponibilidad de agua de la cuenca de San Antonio y brindan información sobre
la frecuencia con la que se garantiza esa disponibilidad. Además, la aplicación
permite estimar la probabilidad de excedencia del caudal diario para un mes y
el sitio determinado. Esta característica podría usarse para estimar el caudal
ambiental definido por la actual regulación de usos de aguas públicas de
Uruguay.
Palabras clave: curvas de duración de
caudal, modelos hidrológicos distribuidos, WFLOW-HBV, cuenca del San Antonio,
aplicación de acceso abierto.
Abstract:
Agricultural irrigation projects require
information on the quantity and frequency of streamflow to design irrigation
systems. On the one hand, this information is obtained from gauging stations or
hydrologic models. On the other hand, there are few gauging stations, and
hydrologic models are expensive to implement, especially for small irrigation
projects. This work proposes a method for estimating spatially distributed Flow
Duration Curves (FDC), and describes the SanAntonioApp
interactive application with open access and repository, which is used to share
the results of this work. The proposed framework uses three years of records of
a rich hydrometeorological network to implement, optimise
and cross-validate the WFLOW-HBV distributed hydrologic model in San Antonio
Creek (Salto, Uruguay). Then, FDC are generated by extending the simulation
period with the long records of an agro-climatological
station (30 years). The results of this work contribute to evaluate the water
availability of the San Antonio catchment and provide information on how often
this availability is guaranteed. In addition, the application allows estimating
the probability of exceedance of the daily streamflow for a given month and
location. This function could be used to estimate the environmental flow
established in the current water regulation in Uruguay.
Keywords:
flow duration curves, distributed
hydrological models, WFLOW-HBV, San Antonio catchment, open access application.
Resumo:
Os projetos de irrigação precisam de
informações sobre a quantidade e frequência da vazão do rio para o projeto e
dimensionamento do sistema de irrigação. Por um lado, essas informações são
obtidas por meio de estações hidrográficas ou modelos hidrológicos. Por outro
lado, as estações hidrográficas são escassas e a implementação de modelos
hidrológicos é cara, principalmente para pequenos projetos de irrigação. Este
trabalho propõe uma metodologia para estimar curvas de permanência de vazão
espacialmente distribuídas (FDC, por sua sigla em inglês) e descreve a
aplicação interativa e o repositório de acesso aberto SanAntonioApp,
que é utilizado para compartilhar os resultados desta pesquisa. A estrutura
proposta usa 3 anos de registros de uma rede hidrometeorológica
densa para implementar, otimizar e validar o modelo hidrológico distribuído
WFLOW-HBV no arroio San Antonio (Salto - Uruguai). Em
seguida, os FDCs são gerados estendendo o período de
simulação com uma estação agroclimatológica com uma
longa série de dados (30 anos). Os resultados deste trabalho ajudam a avaliar a
disponibilidade de água na bacia de San Antonio e
fornecem informações sobre a frequência com que essa disponibilidade é
garantida. Além disso, o aplicativo permite estimar a probabilidade de
superação da vazão diária para um determinado mês e local. Esta característica
poderia ser usada para estimar a vazão ambiental definida pela atual
regulamentação do uso público da água no Uruguai.
Palavras-chave:
curvas de permanência de vazão, modelo
hidrológico distribuído, WFLOW-HBV, bacia de San Antonio,
aplicativo de acesso aberto.
1. Introduction
The quantity,
quality, and timing of streamflow are crucial to sustain freshwater and
estuarine ecosystems, and to certain human activities (e. g., agriculture,
industry, electricity, domestic, recreation)(1). Agricultural irrigation is one of the
most water-intensive activities in the world, where the farmer must have prior
information of quantity and frequency of streamflows
to design irrigation systems and optimize water use without undesirable
environmental impacts. Flow Duration Curves (FDC) represent the relationship
between the magnitude and the frequency of streamflow and provide an estimate
of the percentage of time a given streamflow is equaled or exceeded over a
historical period(2). FDC are obtained from long-term
records of streamflow and are often used in hydropower, water-supply and
irrigation projects. In practice, it is rare to have gauging stations at the
project sites, for that reason hydrological simulation has become an attractive
alternative to estimate FDC.
Regionalization in
hydrologic models is applied to estimate streamflow at ungauged catchments
because they use effective parameters that are transferred from gauged
catchments to ungauged catchments(3). Narbondo and
others(4) applied the regionalization of the lumped GR4J model(5) in Uruguay, where the authors used the
concept of physical similarity to find relationships between model parameters
and physical attributes of the catchment at the country scale. However, this
type of approach can be strongly influenced by local conditions, especially in
small basins(6). An elegant solution is to account for
spatial variations of hydrologic processes within the catchment domain by using
spatially distributed models. In addition, this technique allows implementation
of novel approaches in optimization procedures through the use of soft data(7). However, these techniques are too complex to be implemented on-demand
for small irrigation projects, as they require great computational coding
development time and analysis.
A major drawback of
hydrologic modeling is that the results are generally only used for the
researchers who developed the model, because the format of the model outputs
are complex and difficult to share. In recent years, the open-source
programming language R(8) has emerged as a solution as it has
extensive benefits, such as: democratizing data science, improving reproducible
research, a wide range of computational tools, repositories, and support
provided by overseas contributors. The benefits of R have been welcomed by the
hydrologic sciences with a growing number of repositories, packages and applications(9). But despite the benefits of using R in
hydrology and the needs for information on water availability, there is no
hydrological R package or repository developed for Uruguay.
This work proposes a
methodology for estimating spatially distributed FDC and describe the SanAntonioApp(10),
the first application and repository developed for a catchment in Uruguay. The
repository contains visualization tools, the input dataset, the hydrologic
model, outputs, and FDC for any location of the San Antonio
river. The results are presented in a simple interactive interface,
where the user can visualize the results by simply clicking on the map.
2. Materials and methods
2.1 Study area and dataset
The San Antonio
Catchment (225 km2) is located in northwestern Uruguay in the
department of Salto (Figure
1). It has a humid subtropical climate (Cfa) according to Köppen climate classification(11).
The mean annual rainfall is 1430 mm with a slight seasonality, with lower
monthly rainfall in winter (southern hemisphere, June-July-August) (Figure 2a), and average daily temperatures of 10-15 ºC for the winter and 20-30
ºC in summer (Figure 2b). Precipitation (14 rain gauges) and streamflow are collected by a
rich network of gauges that became operational in 2018 as part of the Toward an Integrated Water Resources Management of Highly
Anthropized Hydrological Systems research project: San Antonio Creek - Salto/Arapey Aquifer (Figure 1).
Streamflow stations H1 (22.5 km2), H2 (33.3 km2) and H3
(106.8 km2) are strategically placed to capture the heterogeneity of
the hydrologic response within the catchment domain and to satisfy hydraulic
requirement of stream gauging(12).
Precipitation and streamflow were aggregated on a daily basis to match the
temporal resolution of the hydrologic model. In addition, the region has two
climate stations with more than 30 years of records (e. g. precipitation,
temperature, evaporation, humidity, radiation, wind speed, soil moisture),
supported by the National Agricultural Research Institute (INIA-SG) and the
School of Agronomy Experimental Station in Salto (EEFAS).
Figure 1
Hydrometeorological network and location
of the San Antonio Catchment (225 km2)
Figure 2
(a) Monthly precipitation and (b) mean
daily temperature at INIA-SG (1991-2020)
The relief of the San
Antonio Catchment is characterized by rolling landscapes and plains that favor
relatively slow runoff and drainage processes (Figure 3a).
The hydrogeology is described by sedimentary deposits and fissured effusive
rocks of Cretaceous-Tertiary periods which belong to the Salto-Arapey aquifer. The soils are Argiudolls
& Hapluderts predominated by silty clay-silty
clay loam(13).
The main field capacity (Figure
3c) of the soils ranges 60-170 mm(14). The land use and land cover have not
changed significantly in the last 10 years. The land use and land cover map of
2011(15) shows that the main land uses are row
crops (46.6%), native forest (19.4%), herbaceous (11.7%), urban areas (11.8%),
citrus trees (9.4%), and forestry (1.2%). This information forms the basis for
creating the static input maps of the model (section 2.2). In addition,
precipitation is preprocessed by interpolating daily precipitation using
inverse distance weighting. This technique accounts for the spatio-temporal
variability of precipitation and produces the dynamic input map of the model. Figure 3d shows an example of precipitation interpolation for a single
convective event that occurred on September 8, 2019.
Figure 3
(a) Hypsometry, (b) land uses, (c) field
capacity, and (d) 24 h precipitation field for a single event in the San
Antonio Catchment
2.2 Rainfall-runoff model
The Hydrologiska Byråns Vattenbalansavdelning (HBV) model, developed by Sten Bergström at the Swedish
Meteorological and Hydrological Institute(16)(17)(18),
is one of the most widely used hydrological models worldwide(19).
HBV is originally a daily lumped hydrologic model that uses precipitation and
potential evapotranspiration as meteorological forcing (optionally, temperature
is used to model snowmelt). Runoff, soil moisture, interception and
infiltration are calculated at the catchment scale with few parameters. There
are many versions of the model created over the years in different programming
languages, such as Python, Matlab, R, Fortran(20)(21)(22)(23).
The SanAntonioApp uses the distributed version of the HBV-96 model(18) within the hydrologic modelling
framework wflow (WFLOW-HBV) running in the Phyton
environment(24). WFLOW-HBV has 4 components: (1) snow,
(2) soil moisture, (3) sub-basin routing, and (4) river routing. The first
component is not considered because there is no snow precipitation in the
catchment (humid subtropical climate). The second component produces the soil
water balance at a daily time step and provides the direct runoff. This routine
also calculates infiltration, seepage and actual evaporation without time
delay. Three types of runoff are then considered:
quick runoff, interflow and slow runoff. The quick runoff and interflow are
represented by a single linear reservoir with two outputs governed by the water
content of the upper zone. Slow runoff is also represented by a linear
reservoir and simulates exfiltration from the lower zone. Finally, the direct
runoff is routed by the kinematic wave equation (river routing component)
rather than the triangular unit hydrograph of the original HBV-96(25).
In this approach, the hydraulic section of the river is partitioned by a main
channel and floodplains (assumed to be rectangular channels). This
configuration allows simulating fast flows in the mean channel and slow flow of
the flood plains; this characteristic is due to high roughness and shallower
depth of the flood plains with respect to the main channel. Equations and model
diagrams can be found in the openstreams/wflow site(24).
The spatial structure
of the San Antonio WFLOW-HBV model divides the catchment into 5491 grid cells
with a size of 0.002º (~200 m). The model runs for each cell at the daily scale
using the dynamic input dataset (precipitation and potential
evapotranspiration) and the static inputs maps (hypsometry, field capacity and
land use) (section 2.1). The dynamic input dataset is the climatological
forcing and the static input maps is used to parameterize each cell of the
model; e. g., field capacity is related with soil type, a parameter that
controls water storage in the soil. Then, the parameters of the model are
optimized to improve the model performance (section 2.3). The description of
the model parameters and the parameters ranges used in the sensitivity analysis
are listed in Table 1.
Table 1
Parameters
of the WFLOWHBV model and parameter ranges used in the sensitivity analysis
Parameter |
Description |
Range |
BetaSeepage |
Power coefficient for recharge and percolation [-] |
1-5 |
LP |
Evapotranspiration limitation factor [-] |
0-1 |
K4 |
Recession coefficient for the lower zone [1/day] |
0.001-0.3 |
KHQ |
Recession coefficient for the upper zone [1/day] |
0.006-10 |
AlphaNL |
Power coefficient for subsurface discharge [-] |
0.02-20 |
PERC |
Percolation threshold between upper and lower zones [mm/day] |
0.02-20 |
Cflux |
Maximum capillary flux [mm/day] |
0.005-10 |
Nriver |
Manning coefficient
for rivers [-] |
0.02-0.03 |
FCmult |
Maximum soil moisture storage capacity coefficient [-] |
0.1-3 |
HQ |
Outflow rate of the upper zone for which the recession rate is equal
to KHQ [mm/day] |
0.15-100 |
ICF |
Interception storage [mm] |
0.015-30 |
N |
Manning coefficient
for hillslopes [-] |
0.02-0.75 |
2.3 Optimization
of the model
parameters
Optimization of model
parameters (also known as model calibration) is performed to improve model
results with respect to observed time series of streamflow. The goodness of fit
of the model is quantified using the Kling-Gupta efficiency (KGE, Equation 1). This index accounts for correlation, bias and variability between
model simulations and observations, and improves on other classical efficiency
index such as Nash-Sutcliffe efficiency or mean squared error(26)(27).
[Equation 1]
Where φ is the Pearson product-moment correlation coefficient; σ is the standard deviation of simulations (sim) and observations (obs), and μ is the mean value of simulations and observations. KGE ranges between
-∞ and 1, values of KGE greater than -0.41 indicate that the model improves the
mean flow as a benchmark(28).
KGE is chosen instead of other objective functions that offer better fit at low
flow rates(29),
because we aim to obtain the best possible fit over the full range of flows.
Before the
optimization procedure, a General Sensitivity Analysis is performed by Latin
Hypercubes Sampling (GSA-LHS) with 1000 samples to simplify and reduce the
parameter space of the model. This technique is based on Markov Chain Monte
Carlo (MCMC) experiments and allows ranking the parameters according to their
importance(30)(31)(32), i. e., the
model parameters with minor effect on the model results can be considered as constant
values. Next, the optimization algorithm follows the Generalized Likelihood
Uncertainty Estimation (GLUE)(33), which is based on the concept of
equifinality and is associated with predictive uncertainty(34).
In this technique, two groups of parameters/simulations are defined: behavioral
denotes the group of parameters/simulations that are retained, and
non-behavioral denotes the parameters/simulations that are discarded because
they do not meet a minimum model performance criterion (defined by the
objective function). GLUE also employs an MCMC sampling algorithm. For this
purpose, a self-developed R script is coupled with the Python code WFLOW-HBV.
Model verification
(also known as model validation) is made by internal spatial cross-validation(35) instead of the classical technique
based on partitioning the time domain. In this technique, two of the three
streamflow stations are used to optimize the parameters and one streamflow
station is used to evaluate the model performance across ungauged sites.
Internal spatial cross-validation takes advantages of multiple streamflows stations and minimizes the impact of a relatively
short time period (July 2018 - February 2021).
2.4 Flow Duration Curves estimation
The Flow Duration
Curves (FDC), as mentioned earlier (Section 1), is a cumulative frequency curve
that indicates the percentage of time (or probability) that a given streamflow
is equaled or exceeded during a given period. This curve is calculated with long-records
of streamflow. In the absence of long-records of streamflow the FDC is produced
by hydrologic simulation that provides predictions for ungauged sites. In this
study, the distributed model WFLOW-HBV is used, which was spatially
cross-validated over a relatively short period of time (3 years), which is not
sufficient to produce a robust estimate of FDC. Considering this limitation,
the time domain of the model was extended to a 30-year period (1991-2020),
using the long-record of climate stations as the climatological forcing.
After the model
simulations are performed for the long period, the FDC for each river cell is
calculated with the complement of the cumulative distribution function of daily
streamflow. Then, the FDC is considered as a step function, which can be
calculated with equation 2, as follows:
[Equation 2]
Here FDC is the Flow
Duration Curve for a given streamflow qx
within a given period qt, the function
num (qx,qt)
counts the number of times were qt is
less than qx,
and n is the length of qt.
Then, the monthly FDCs are obtained by arranging the model simulations by
month.
3. Results
In this work,
WFLOW-HBV model was implemented in the San Antonio catchment during the period
July 2018 - February 2021 to estimate FDC in a distributed manner. The model
was simplified from 12 to 5 parameters by GSA-LHS and optimized according to
GLUE. In addition, the model was cross-validated in the inner spatial domain of
the catchment. The FDCs were obtained by extending the simulation period to
1991-2020 using the long records of the climate stations. Finally, the results
are stored in an R package SanAntonioApp and made
available on the Github platform(10).
3.1 WFLOW-HBV setup and performance
The GSA-LHS reveals
that KGE is sensitive only to 4 of the 12 parameters of the model. The
parameters ranked by the sensitivity index are: PERC (0.49), KHQ (0.29), LP
(0.24), ICF (0.23), N(0.16), HQ (0.16), BetaSeepage (0.10), K4 (0.09), Nriver
(0.07), FCmult (0.07), AlphaNL
(0.05), Cflux (0.04), where the sensitivity index is
the number between the parenthesis. The first 4 parameters are kept for
optimization, while the other 8 are considered as constant values. This
consideration is made to simplify the complexity of the optimization algorithm
and to speed up the convergence. The values used for the parameters considered
constant were obtained from the best 100 simulations of the GSA-LHS; these
values are listed in Table
2.
Table 2
Values
of parameters assumed as constant
BetaSeepage [-] |
K4 [1/day] |
AlphaNL [-] |
Cflux [mm/day] |
Nriver [-] |
FCmult [-] |
HQ [mm/day] |
N [-] |
3.2 |
0.16 |
9.76 |
5.17 |
0.03 |
5.17 |
40.9 |
0.38 |
Then, the PERC
parameter was regionalized for the so-called “upper-catchment” (PERC1) and
“lower-catchment” (PERC2). This regionalization was done to optimize the
parameter according to the soil type, since the Field Capacity map shows that
the upper-catchment differs from the lower-catchment (Figure 3c). The probability density functions of the behavioral parameters (Figure 4) show that PERC ranges from 0 to 10 mm (Figure 4a).
The mean value of PERC1 is 5.5, while PERC2 is 3.2. The standard deviation of
PERC1 and PERC2 is quite similar and is 2.4. KHQ has a behavioral response for
the entire range of the sample domain (Figure
4b), with the most frequent behavioral
values located around 7. ICF (Figure
4c) and LP (Figure 4d) are both slightly left skewed, with the mean of ICF being 22 mm and
of LP being 0.8 (with the median values close to the mean in both cases).
Figure 4
WFLOW-HBV optimized density functions
of: (a) Percolation threshold for the upper (PERC1) and lower (PERC2)
catchment; (b) Recession coefficient for the upper zone; (c) Interception
storage, and (d) Evapotranspiration limitation factor
The KGE values are
greater than 0.7 after the optimization/validation procedures, H1 has better
performance than H2, and H2 has better performance than H3 (Figure 5a). In other words, the performance of the model decreases as the area
of the sub-basins increases. The percentage of bias (Figure 5b) is 10% with under/over-estimate for the lower/upper zones of the
catchment. Internal spatial cross-validation is performed only for H2 and H3.
This consideration was made to avoid the singularities of the lower catchment;
for example, the lower catchment has field capacities below 100 mm, while the
upper catchment is in the range of 60-170 mm (Figure 3c).
The validation procedure shows that the performance of the model is retained
for the inner spatial domain with a small loss of prediction skill.
Figure 5
(a) Kling-Gupta Efficiency (KGE) and (b)
Percentage Bias of model simulations on H1, H2 and H3 streamflow stations
(subindex cal & val
mean calibration and validation, respectively)
Figure
6 shows a graphical verification of the
observed and simulated hydrographs for January 2019. This month was an
extraordinarily rainy month with multiple complex flood events that are
particularly difficult to simulate. The black lines represent the observed
runoff, the red line is the mean of the behavioral simulations, and the grey
shadow is the 95% prediction uncertainty. The ratios of January 2019 maximum streamflows to calibration period maximum streamflows are 0.56 (Chico), 0.42 (Cabecera) and 0.34 (Ruta 3). This graphical representation shows that the peaks
streamflows are well represented with a slight delay
for a few days. An interesting aspect for stations H2 and H3 is that the
observed values show 5 streamflow peaks rather than the 4 simulated peaks given
by WFLOW-HBV.
Figure 6
Observed and simulated hydrographs for
January 2019 with the 95% prediction uncertainty (95PPU) for (a) H1, (b) H2,
and (c) H3 streamflow stations
3.2 Flow Duration Curves
Monthly and annual
Flow Duration Curves have been estimated for the entire river domain. Figure 7 shows the FDC for August (Figure
7a), October (Figure 7b), and April (Figure
7c) for an arbitrary river section of the
river located at -57.829ºW -31.307ºS (blue lines) and the annual FDC (red
lines). This figure can be read according to two basic interpretations: (1)
Shifted curves, when one curve is shifted to the left relative to the other low
streamflow will be more frequent. This is the case for August and October,
where August (shifted to the left with respect to annual) is generally below
the annual streamflow and annual streamflow is below October (shifted to right
with respect to annual). (2) Looped curves, this is an interesting case that
shows differences in the variance of the two datasets. For example, April is
below annual on the left-tail, and above on the right-tail. This means that
both high and low streamflows are more common in
April. Also, the interception of the two curves implies equal probability for a
given streamflow. Another view is obtained by contrasting the probability
density distributions of the streamflow which show the probability within a
certain range (Figure 7d-f). This representation shows shifted to the left for August, shifted
to the right for October, and high dispersion and flattening for April, which
is helpful in interpreting the FDC.
Figure 7
(a-c) Monthly Flow Duration Curves and
(d-f) Monthly Density probability distribution of daily streamflow for an
arbitrary river section located in -57.829ºW -31.307ºS
3.3 SanAntonioApp
FDC were shared with
the SanAntonioApp R package(10).
The package is hosted on the Github platform and can
be installed via the R console by the following command:
devtools::install_github("rafaelnavas/SanAntonioApp")
The package has been
tested on Ubuntu and Windows operating systems. Version 1.0. contains the
folder “data”, with the FDCs and the function “SanAntonioFDC()”, which runs
the application into your local environment. In addition, to make the
application available from any browser, it is temporarily placed in the
following link: https://rafaelnavas23.shinyapps.io/SanAntonioApp/.
The SanAntonioApp allows the user to query the FDC by month and
location. The month is selected by the user with a slider input, and the
locations are selected by a simple click on the map. The input panel is located
on the left side under the tag “Aplicación”. The FDC
is then displayed on the right side of the screen. Figure 8 shows a simplified display of the application.
Figure 8
Display of the SanAntonioApp:
The bottom “Inicio” (1) is the home with credits.
Brief instructions are shown in “Modo de uso” (2).
The application page can be accessed by clicking “Aplicación”
(3). The month can be chosen in the slider input (4). Location of the target
site is assigned by clicking on the map. (6) Location of estimation site, (7)
Coordinates of the target site, (8) Coordinates of the estimation site, (9)
Distance between target and estimation sites, (10) Flow Duration Curve
4. Discussion
Spatially distributed
FDC were estimated for the San Antonio catchment. Estimation was performed by
distributed hydrological simulation using the WFLOW-HBV model. The conceptual
model of the catchment represents the actual physical properties shown in Figure 3. In other words, the model does not take into account land use changes,
farmer-led irrigation development or climate change; which are factors that
could have an effect in the runoff response of the catchment(36)(37)(38).
According to the land use map of 2000, and information given by the National
Water Authority (DINAGUA), there has been no significant change in land use or
surface water allocation in the catchment over the past 20 years, which gives
some confidence to the hypothesis taken in the model implementation.
Some applications of
the FDC can be summarized as: (1) estimating water quantity and frequency in
the interior of the catchment. This is useful for knowing whether a section of
river can meet a particular water demand. For example, a farmer on the riverbank
might know how much water is available and how often that amount is guaranteed.
(2) Environmental flow estimation. A variety of definitions of environmental
flow can be found in the literature. Basically, it is the flow regime required
to achieve the desired ecological objectives. Look-up tables, desktop analysis,
functional analysis and hydraulic habitat modelling are different approaches
used around the world(39).
In Uruguay, environmental flow is determined using look-up tables in the Decree
368/018 (provisional)(40),
which determines environmental flow based on the probability of exceeding the
daily streamflow for a given month, location in the river, and the type of
water intakes. The preliminary regulation establishes an exceedance probability
of 60% for reservoirs and 80% for direct water intake. These values of
streamflow can be easily determined at any location in the catchment using the SanAntonioApp.
An interesting
finding in the optimization of the distributed model was that the performance
of the model decreases as the area of the sub-basins increases. These results
contradict previous research that found that catchments with larger areas
generally performed better(30)(41).
However, this finding could be explained by the fact that the uncertainty in
the hydrologic simulation also depends on the uncertainty in the precipitation
input(42)(43)(44). The present study was conducted with a
very high density of rain gauges for the lower catchment (Figure 1). The rain gauge network design had two purposes: (1) to validate
rainfall estimation using microwave links(45),
which requires a large number of rain gauges in a very small area, and (2)
calibration/validation of the hydrologic model used in the present study. This
fact explains why the upper part of the basin may be subject to larger
uncertainties in the precipitation input. In addition, the model in the
lower/upper catchment shows a negative/positive bias. This result could be due
to the dynamics between the aquifer and surface waters, which is mainly
dominated by percolation in the upper catchment and exfiltration in the lower
catchment, which is not considered by the model. Another source of uncertainty
are the dynamics of vegetation, which affects actual evapotranspiration, and
the influence of climate variability(46)(47).
Neither factor was considered in this work. Future works should study the
implementation and performance of integrated surface-subsurface models(48) and the testing of information content
given the dependence of parameter to climate variability.
The FDC generated in
the present study allows to characterize the streamflow regime by month, with
August being the month with less streamflow, and October and May being the
months with more streamflow. In addition, April is more variable than the other
months. This hydrological regime is triggered by precipitation, which has a
similar shape. The streamflows regime in the San
Antonio catchment in northern Uruguay contrasts with the hydrological regime of
some basins in the south of the country. For example, the hydrologic regime of
the Santa Lucia catchment is mainly determined by
temperature/evapotranspiration, since precipitation is uniform throughout the year(37).
5. Conclusions
This paper presents
the development of the SanAntonioApp, which is part
of the project "Toward an Integrated Water Resources Management of Highly
Anthropized Hydrological Systems: San Antonio Creek - Salto/Arapey
Aquifer", funded by the National Agency for Investigation and Innovation.
The SanAntonioApp is an R package and application that contains
the WFLOW-HBV model of the San Antonio catchment, the input dataset, model
outputs and interactive visualization tools for Flow Duration Curves. The
application can be used to estimate the frequency of a given flow or the flow
for a given frequency at any location in the river network, which is useful for
estimating water availability as well as environmental flows for current water
regulation in Uruguay.
Acknowledgments
This publication was supported by the
National Agency for Investigation and Innovation, María Viñas
Fund – 2017 (FMV_1_2017_1_135656).
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Additional information
Editor: Mónica M. Barbazán https://orcid.org/0000-0002-5501-064X Universidad de la
República, Facultad de Agronomía, Montevideo, Uruguay
Author contribution statement: RN
designed the analysis and wrote the paper. VE and RB carried out the
experiments and helped to write the manuscript. GS conceived and planned the
experiments, and provided critical feedback to the manuscript. AS conducted
data analysis. PG supervised the project.