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Using PyWPS for Water Resources Quantification in Mountain Basins

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Susanna Grasso at Regional Environmental Protection Agency, Piedmont, Italy
Susanna Grasso
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Abstract

This work presents a WPS service, built up using PYWPS and GRASS GIS as backend, for geoprocessing operations to estimate Flow Duration Curves (FDCs) in ungauged basins in Northwestern Italy.
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GeoPython Conference
Basel - Switzerland, May 7-9, 2018
Using PyWPS for Water Resources
Quantification in Mountain Basins
Susanna Grasso
Polytechnic of Torino
Department of Environment, Land and Infrastructure Engineering
GeoPython Conference 2018
What is a Flow Duration Curve?
This work presents a WPS service, built up using PYWPS and GRASS as backend, for geoprocessing operations to
estimate Flow Duration Curves (FDCs) in ungauged basins in North-West Italy.
A FDC represents the relationship between the magnitude and frequency of daily, weekly, mounthly streamflow for a particular
river basin, proving an estimate of the percentage of time a given streamflow was equaled or exceeded over a historical period.
Estimation of the FDC for a gauged site
80 days
21,86 %
Introduction
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What are FDC curves used for?
High flowsFlood flows Intermediate flows Low flows
Hydropower
Ecosystem preservation
Water quality assessment
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Estimation of the FDC for a ungauged site
In an ungauged site, the FDC can be represented with an analytical formulation through a regional statistical model. In particular
there are several examples in the literature of regional models for estimating the FDC from relationships based on physical
characteristics of a catchment.
Location of gauging stations used in the analysis
OBSERVED FDC
L-Moments
Ungauged
site
descriptors
Estimated
L-Moments
100+ Basin
descriptors
L-CA
L1 L-CV
Choice of
FDC
analytical
form
Multiple
regression
models
Method adapted from flood freq. analysis
•Laio et al. 2011, J. Hydrology
•Ganora & Laio 2015, WRR
Spatially Smooth Estimation method (SSEM)
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Gli L-momenti permettono di
scegliere la distribuzione adatta
a = f(Y,b,c)
b= f(LCA, LCV)
c= f(LCA, LCV)
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Logical framework
DELIMITATION
OF THE BASIN
EXTRACTION OF THE
GEOMORPHOLOGICAL
AND CLIMATIC
CHARACTERISTICS
CHOICE OF
DISTRIBUTION
(Burr, Weibull, Pareto)
CALCULATION OF THE
PARAMETERS
APPLICATION OF
REGRESSIONS FOR L-
MOMENTS (Y, LCV,LCA)
ESTIMATION
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A first application developed
To WPS services
Insert
geomorphological
and climatic
desciptor
Manual estimation of
parameters distribution
based on L-moments
values
QGis Processing Toolbox
➢Provide access to GIS data and functionality over the internet
➢Allows users to access calculations independently of the underlying software (procedure accesible by
web browser)
➢Data does not need to be housed locally (client side) but are maintained by the hosting entity
➢Server processing times faster than client side scripting
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What is WPS?
WPS (Web Processing Service) is one of the OGC specifications to provide access to GIS data
or functionality over the internet in a standardized way.
OGC Standard services:
• WMS - Web Map Service
• WFS - Web Feature Service
• WCS - Web Coverage Service
• WPS - Web Processing Service
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CLIENT
Web Server
(Apache)
Database
PostgreSQL
Mapserver
GisClient3
Author
Client
Application Server
Mapping application:
Javascript,
Openlayers, CSS…
SERVER
Browser
Plugin
WPS
QGIS, ArcGIS
WMS
WFS
WCSC
WPS
WPS
Extension PyWPS
GRASS
PyWPS enables integration, publishing and
execution of Python processes via the WPS
standard.
The platform developed
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class Process(WPSProcess):
"""Main process class"""
def __init__(self):
"""Process initialization"""
# PROCESS DESCRIPTION
WPSProcess.__init__(self,
identifier = "Renerfor_delimitazione",
title="Renerfor_delimitazione",
version = "0.1",
storeSupported = "true",
statusSupported = "true",
abstract="RENERFOR delimitazione di bacino",
grassLocation = "WGS84-UTM32N_32632")
# PROCESS INPUT/OUTPUT DECLARATION
# Input
self.Inputx = (self.addLiteralInput(identifier="Inputx",
title="Inserire la coordinata est della sezione di chiusura del bacino", type=FloatType)
self.Inputy= (self.addLiteralInput(identifier="Inputy",
title="Inserire la coordinata nord della sezione di chiusura del bacino", type=FloatType)
# Output
self.vectorout = (self.addComplexOutput(identifier="shapefilebacino",
title="shapefilebacino",
formats = [{"mimeType":"text/xml", "encoding":"utf8","schema":"http://schemas.opengis.net/gml/3.2.1/gml.xsd"}],
)
# # EXECUTION METHOD DEFINITION
def execute(self):
#estrazione bacino
grass.run_command('r.water.outlet', drainage='piemonte_drain_r100@PERMANENT', basin='BACINO', easting='%f’
%(self.Inputx.getValue()), northing=‘%f’ %(self.Inputy.getValue()),overwrite=True)
grass.run_command('g.region', zoom='BACINO')
grass.run_command('r.mask',input='BACINO', maskcats='1',quiet=True)
……..
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Two WPS procedures are developed:
➢Delimitation of a basin
➢Extraction of basin descriptors and estimation of regional FDC curve:
The WPS services proposed
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GRASS
r.water.outlet
Creates watershed basins from
the drainage direction map
DELIMITATION OF A BASIN
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DELIMITATION OF A BASIN
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DELIMITATION OF A BASIN
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ESTIMATION OF REGIONAL FDC CURVE
GRASS
r.univar
r.stats
Python
Estimate regional
L-moments
Calculate
distribution
parameters
Plotting FDC
curve
openpyxl
Matplotlib
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ESTIMATION OF REGIONAL FDC CURVE
GeoPython Conference 2018
ESTIMATION OF REGIONAL FDC CURVE
Thank you for
your attention
References
•Gallo, E., Ganora, D., Laio, F., Masoero, A., and Claps, P.: Atlante dei bacini imbriferi piemontesi ISBN:978-88-96046-06-7,
available at: http://www.idrologia.polito.it/web2/open-data/Renerfor/atlante_bacini_piemontesi_LR.pdf (last access:
05/05/2018), 2013 (in Italian).
•Ganora D., Laio F., Masoero A. and Claps P.,Spatially-Smooth regionalization of Flow Duration Curves in non- pristine basins,
IAHS-ICWRS Conference, Bochum, Proceeding IAHS 373, pp.73-80, 2016.
•Ganora, D. and Laio, F.: Hydrological Applications of the Burr Distribution: Practical Method for Parameter Estimation, J. Hydrol.
Eng., 20, 11, doi:10.1061/(ASCE)HE.1943-5584.0001203, 2015.
•Ganora, D., Gallo, E., Laio, F., Masoero, A., and Claps, P.: Analisi idrologiche e valutazioni del potenziale idroelettrico dei bacini
piemontesi ISBN:978-88-96046-07-4, available at: http://www.idrologia.polito.it/web2/open-
data/Renerfor/analisi_idrologiche_LR.pdf (last access: 05/05/2018), 2013 (in Italian).
•Laio, F., Ganora, D., Claps, P., and Galeati, G.: Spatially smooth regional estimation of the flood frequency curve (with
uncertainty), J. Hydrol., 408, 67–77, doi:10.1016/j.jhydrol.2011.07.022, 2011.
•K Nruthya, VV Srinivas, Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A
Case Study, Aquatic Procedia 4, 641-648
•RM Vogel, NM Fennessey, Flow-duration curves. I: New interpretation and confidence intervals, Journal of Water Resources
Planning and Management 120 (4), 485-504
Contact:
susanna.grasso@gmail.com
ResearchGate has not been able to resolve any citations for this publication.
Hydrological Applications of the Burr Distribution: Practical Method for Parameter Estimation
Article
  • Mar 2015
The three-parameter Burr XII distribution has been seldom used in hydrological appli- cations, although it is particularly appealing because its range covers positive values only, which is convenient when modeling streamflows or rainfall data. Moreover, it has two shape parameters, thus allowing it to be quite adaptable to different samples as it covers a wide range of skewness and kurtosis values. Parameter estimation methods currently available in the literature require the numerical solution of two joint non-linear equations to estimate the shape parameters of the distribution. Here we propose a simplified, although accurate, method to analytically compute the two shape parameters starting from the dimensionless L-moments ratios representing the variability (L-CV) and the skewness (L-skewness) of the distribution. The obtained param- eters can be directly used in practical applications, or otherwise may be useful to properly initialize the algorithms to obtain a numerical solution for the shape parameters. A detailed analysis of the accuracy of the approximated solution is performed, showing that the er- rors in the estimation of the distribution quantiles are negligible compared to the sample variability typically affecting hydrological samples. An extensive dataset of empirical flow duration curves from stations located in Northwestern Italy is considered to demonstrate the suitability of the extended Burr XII distribution to represent flow duration curves in a wide range of situations.
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Flow‐Duration Curves. I: New Interpretation and Confidence Intervals
Article
  • Jul 1994
A flow-duration curve (FDC) is simply the complement of the cu-mulative distribution function of daily, weekly, monthly (or some other time interval of) streamflow. Applications of FDCs include, but are not limmited to, hydropower planning, water-quality management, river and reservoir sedimentation studies, habitat suitability, and low-flow augmentation. Although FDCs have a long and rich history in the field of hydrology, they are sometimes criticized because, tra-ditionally, their interpretation depends on the particular period of record on which they are based. If one considers n individual FDCs, each corresponding to one of the individual n years of record, then one may treat those n annual FDCs in much the same way one treats a sequence of annual maximum or annual minimum streamflows. This new annual-based interpretation enables confidence intervals and recurrence intervals to be associated with FDCs in a nonparametric framework.
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Spatially smooth regional estimation of the flood frequency curve (with uncertainty)
Article
  • Sep 2011
  • J HYDROL
Identification of the flood frequency curve in ungauged basins is usually performed by means of regional models based on the grouping of data recorded at various gauging stations. The present work aims at implementing a regional procedure that overcomes some of the limitations of the standard approaches and adds a clearer representation of the uncertainty components of the estimation. The information in the sample records is summarized in a set of sample L-moments, that become the variables to be regionalized. To transfer the information to ungauged basins we adopt a regional model for each of the L-moments, based on a comprehensive multiple regression approach. The independent variables of the regression are selected among a large number of geomorpholoclimatic catchment descriptors. Each model is calibrated on the entire dataset of stations using non-standard least-squares techniques accounting for the sample variability of L-moments, without resorting to any grouping procedure to create sub-regions. In this way, L-moments are allowed to vary smoothly from site to site in the descriptor space, following the variation of the descriptors selected in the regression models. This approach overcomes the subjectivity affecting the techniques for the definition and verification of the homogeneous regions. In addition, the method provides accurate confidence bands for the frequency curves estimated in ungauged basins. The procedure has been applied to a vast region in North-Western Italy (about 30,000 km(2)). Cross-validation techniques are used to assess the efficiency of this approach in reconstructing the flood frequency curves, demonstrating the feasibility and the robustness of the approach.
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