Final Report of the World Bank Project "Assessing the Impacts of

Transcripción

Final Report of the World Bank Project
"Assessing the Impacts of Climate Change on Mountain Hydrology:
Development of a Methodology through a Case Study in Peru"
October, 2009
CONTENT
FINAL REPORT SUMMARY
PART 1 - DESCRIPTION OF THE TASKS ASSIGNED TO IRD
1.1 - Task 1: Elaboration of selection criteria for the representations
1.2 - Task 2: Evaluation and choice of the models
1.3 - Task 3: Data acquisition for the river basins
1.4 - Task 4: Evaluation of climate change impacts
1.5 - Task 5: Documentation and dissemination
PART 2 - COMPLETION OF THE ASSIGNED TASKS
2.1 - Task 1: Elaboration of selection criteria for the representations
2.2 - Task 2: Evaluation and choice of the models
2.3 - Task 3: Data acquisition for the river basins
2.4 - Task 4: Evaluation of climate change impacts
2.5 - Task 5: Documentation and dissemination
PART 3 – MODELLING THE RIMAC-MANTARO SYSTEM
PART 4 – CONCLUSION AND PROSPECTS
REFERENCES
IRD-WB Contract 7148343
1
Final Report, October, 2009
FINAL REPORT SUMMARY
This document constitutes the final report of Contract 7148343 between IRD and the
World Bank, corresponding to the project "Assessing the Impacts of Climate Change
on Mountain Hydrology: Development of a Methodology through a Case Study in
Peru". The IRD contributors are Jean-Christophe Pouget, Wilson Suarez, Thomas
Condom, Patrick Le Goulven. In this project between July 2008 and August 2009, IRD
worked in close collaboration with Stockholm Environment Institute (SEI). Due to the
existing IRD research network in Peru, IRD collaborated with several institutes
including the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI), the
Universidad Nacional Agraria La Molina (UNAM), and the glacier and hydrological unit
of ANA / INRENA.
The current report begins with a summary of the tasks requested by the World Bank
in IRD’s original scope of work. Part 2 continues with a description of the work
completed. Table 1 presents the work schedule, as envisaged initially, and as
performed. Due to delays encountered by the project partners charged with producing
future climate projections, IRD could not run the elaborated models for future
scenarios, in order to evaluate climate change impacts on Andean hydrology,
corresponding to Task 4. While collaborating with SEI, IRD led the development of the
models of the Rimac and Mantaro river basins. Part 3 presents this modelling of the
Rimac-Mantaro system, as it corresponds to the IRD work between April and August
2009.
The report concludes with a section on prospects for future activity and includes the
following documents:
Appendix 1 -
Technical Report on glacier and high elevation wetlands model
selection and parameterization - Condom T., Suarez W., Pouget J.C.,
Le Goulven P. – June 2009
Appendix 2 -
Technical Report on data acquisition and pre-processing for the Rio
Santa, Rimac and Mantaro river basins in Peru - Suarez W., Pouget
J.C., Condom T., Le Goulven P. – September 2009
Appendix 3 -
Manuscript: Modelling the Hydrologic Role of Glaciers within a Water
Evaluation and Planning System (WEAP): A case study in the Rio
Santa watershed (Peru) – Condom T., Escobar M., Purkey D., Pouget
J.C., Suarez W., Ramos C., Apaestegui J., Zapata M., Gomez J.,
Vergara W. - Submitted to Journal of Hydrology, July 2009
8/08 9/08 10/08 11/08 12/08 1/09 2/09 3/09 4/09 5/09 6/09 7/09 8/09 9/09
Task 1
Task 2
Task 3
Task 4
Rimac-Mantaro
Task 5 p.5.1
p.5.2
p.5.3
a.5.4
Table 1 – Work Schedule
envisaged initially
2
performed
PART 1 - DESCRIPTION OF THE TASKS ASSIGNED TO IRD
As the IRD’s primary scientific responsibility on the project consists of choosing and
calibrating appropriate models of tropical glaciers and high elevation Andean
wetlands, the IRD research network was used to validate models on reference cases in
the Rio Santa system and to extend these insights to two additional study basins
(Figure 1.1). Although the IRD team in charge of project management operated out of
Quito, Ecuador, IRD has proposed collaboration with a Peruvian colleague who
recently completed a PhD on the Rio Santa system with the support of IRD (Suarez,
2007) and with an IRD researcher who works in Peru in high sites hydrology. The
tasks initially assigned to IRD are presented below.
Figure 1.1. Map of study river basins location in Peru
1.1 - Task 1: Elaboration of selection criteria for representations of tropical
glaciers and high elevation wetlands
As there is a range of approaches available to represent tropical glaciers and high
elevation wetlands, including statistical models, conceptual models, quasi-physical
models, and process-based models, some criteria need to be established to assess
which approach is the most accurate in the current project. One critical consideration
will be the level of correspondence between the results expected, the various
representations and the data availability in the first investigation basin which is the
Rio Santa.
1.2 - Task 2: Evaluation and choice of glacier and high elevation wetlands
models
In order to select modelling approaches to simulate the evolution of glaciers and high
elevation wetlands, we assess the performance of the various models on one well
measured system (the Artesónraju glacier in the Rio Santa basin). This task will be
3
concluded with the drafting of a technical report describing the model chosen, the
range of appropriate parameterizations, and on the data needed to implement the
selected model in the WEAP application (Water Evaluation and Panning System).
1.3 - Task 3: Data acquisition for the Rio Santa, Rimac and Mantaro river
basins in Peru
In addition to glaciers and páramos, the river basins in Peru include other land units
which influence the overall hydrologic response. In order to develop a tool to assess
the hydrologic implications of climate change, the land units must also be
characterized. As the investigated basins are not pristine, we have selected two other
watersheds based on their importance to the Peruvian hydropower system. Data also
needs to be gathered on the actual and potential infrastructure. Finally, as
hydropower is not the only important use of water in Peruvian river basins, relevant
data on other sectors could also be collected.
In this task, the strategy will be to develop the most complete database for the Rio
Santa, including spatial data on land units and water management infrastructure. A
first attempt will be made to assess water demand in the various sectors in this basin
so that the eventual utility of the tool could be to explore water management
implications under a climate change and to propose potential adaptations in a
preliminary fashion. However, the primary focus of this phase of work in the Rio Santa
system will be on hydrologic change and its potential hydropower impacts.
For the Rio Rimac and Rio Mantaro systems, database development will be more
difficult. Local institutions, such as Sedapal and the hydropower utilities, will be
requested to provide most of the data following the topology and guide provided by
SEI for such tasks. The application of WEAP with glacier and páramos modules will
serve to test and verify the main hydrologic and hydropower utility of the glacier and
páramos models developed and parameterized for the Rio Santa.
1.4 - Task 4: Evaluation of climate change impacts on Andean hydrology
This task will really begin the process of integrating WEAP into the Peruvian water
management community by running the model under the climate change scenarios
developed in Task 8. The major output of this task will be to characterize the
hydrologic implications of climate change in the investigated basins. The implications
of these changes on hydropower potential will also be assessed in the three basins
with a first assessment of the broader water management implications of climate
change being conducted in the Rio Santa system.
1.5 - Task 5: Documentation and Dissemination
This task will include the organization of briefings on project activities and outputs for
the World Bank in Washington, D.C. and for appropriate Peruvian institutions in Lima.
A technical report on the application of the tool in the three basins and an assessment
of the model results will also be produced and disseminated. This report will note the
potential climate change impacts on the hydropower sector in Peru and will include
recommendations on how the hydrologic model could be used and expanded to other
sectors, in other basins in Peru and the wider Andean region.
p.5.1 - Technical report on glacier and páramos model selection and parameterization
p.5.2 - Reports on the application of the hydrologic model to the selected pilot
watersheds
p.5.3 - Final report
a.5.4 - Presentations of the project and the results.
4
PART 2 - COMPLETION OF THE ASSIGNED TASKS
2.1 - Task 1: Elaboration of selection criteria for representations of tropical
glaciers and high elevation wetlands
This task was performed as envisaged initially (see Table 1). During the meeting in
Lima at the university La Molina from September 25 to 27, 2008, Wilson Suarez and
Thomas Condom presented various approaches for glaciers modelling. Appendix 1
Technical Report on glacier and high elevation wetlands model selection and
parameterization begins to present in detail several kinds of glacier modelling. Table
2.1 presents the principal selection criteria of glacier modelling. According to the basin
data availability, we choose the degree-day model for the glacier representation.
Characteristics
Energy Balance
Degree_day, Index
Hybrid (Balance+
degree-day)
Short description
Model based on the
study of the exchange
of energy between the
surface glacier and the
atmosphere
Starts by a similar
energy balance concept,
but considers that all
the physical processes
are summarized in the
temperature (the T° is a
consequence and not a
cause)
Similar to the degreeday, but to improve its
efficiency it uses the
albedo, radiation, etc.
These variables are
added one by one.
Complexity
High
Simple
Intermediate
Represents physical
processes
Yes
No
Partially
Efficiency of the model
High
Intermediate - high
Intermediate - high
Number of parameters
6 to 9
2 or 3
2 to 5
Input variables for the
whole model
More than 6:
- Incident radiation
- Diffuse radiation
- Liquid and solid
precipitation
- Humidity
- Long wave radiation
(incident and reflected)
- Short wave radiation
(incident and reflected)
etc.
3:
- Precipitation
- Evaporation
- Temperature
Depending on the
complexity:
- Precipitation
- Evaporation
- Temperature
- Albedo
- Radiation
Level of spacialization
Complex (generally
grid)
Global or semi
distributed
Semi distributed, global
or grid
Advantages
Its efficiency : physical
process representation
Few parameters
Few input variables
Few parameters
Few input variables
Disadvantages
Needs too much
information (sometimes
non- inexistent)
Does not explain
physical processes
Explains the physical
processes partially
Possible application
Probably Santa
Santa, Rimac, Mantaro
Santa
Recommended
Bibliography
Hook, 2005;
Favier, 2004;
Juen, 2006
Hook, 2005;
Schaefli and all, 2005;
Martinec and Rango,
1986
Hook, 2005;
Klok and all, 2001;
Lang, 1990;
Zhang and all, 2007
Table 2.1. Principal selection criteria of glacier modelling.
5
2.2 - Task 2: Evaluation and choice of glacier and high elevation wetlands
models
Since the meeting with Marisa Escobar and David Purkey in September 2008 at Lima,
we worked in close collaboration with SEI to propose and evaluate a conceptual
modelling of mountain basins partially covered with glaciers. Also, we produced
several versions of a working paper titled “An Approach for Modelling the Hydrologic
Role of Glaciers in WEAP”. The first proposal was sent to the World Bank on October
30, 2008. The last proposal from January 30, 2009 is presented as an appendix to the
first report (Appendix 1).
The document “Construcción del Modelo WEAP del Río Santa” from November, 2008
presents, for the Santa Basin, the following: (1) data collection; (2) river basin
characterization; (3) recognition visit (September 21-24, 2008); (4) climate data
process; (5) demands estimation; (6) first model calibration for sub basins without
glaciers (cf. “www.mpl.ird.fr/divha/aguandes/peru/doc/Avance_RioSanta_WEAP-200811.pdf”).
From January to February 2009, we took an active part in the equations checking of
the glaciers model within WEAP and in the calibration of this model for the Artesón
glacier in the Rio Santa basin. From February to April 2009, we worked closely with
SEI-US, leader on this task, in order to calibrate and validate a complete model for
the Santa river basin. This accurate modelling strategy caused an increase of the Task
2 duration.
Results from the final calibration-validation of the Rio Santa model are presented in
detail in Appendix 3. Given the importance of the simulated flows at La Balsa in terms
of assessing potential climate change impacts to the hydroelectric power station of
Cañon del Pato, the performance of the Rio Santa WEAP application at that point on
the river is particularly satisfactory (Figure 2.1).
Figure 2.1. Correspondence between simulated (continuous think line) and observed (discontinuous thick
line) stream flow at Balsa gauge station between Sep 1969 – Aug 1997.
As simulated streamflow represents the combined contribution of runoff from both
glaciated and non-glaciated portions of a watershed, the simulated glacier area
evolution results were also evaluated and found satisfactory. Observations suggest
that the initial glaciated area in this watershed, 507 km2 in 1970, was reduced to 387
km2 in 1999 (Table 2.2). This trend was well captured by the model. Comparison to
observed glacier areas from 1987 and 1999 indicates a good correspondence with
simulated areas (Figure 2.2; Table 2.2). In the Rio Santa watershed, the calibration of
larger glaciers is better than the calibration of smaller glaciers, likely because small
glaciers are more likely to be dominated by unique conditions that are not well
captured by either the glacier module itself or the regional parameterization that was
developed for the Rio Santa watershed (Appendix 3).
6
Table 2.2. Simulated and observed data of glaciers evolution between 1970 and 1999.
Figure 2.2. Scatter plot graph with observed versus simulated glacier areas
for the two periods (1987 and 1998).
It should be pointed out that the results in Figures 2.1 and 2.2 were achieved using a
single set of parameter values for both the rainfall-runoff and glacier routines in
WEAP. A more refined calibration could be achieved if an effort was made to calibrate
each glacier and sub-watershed in the Rio Santa separately, although care would need
to be taken to develop a spatially reasonable set of parameters (SEI final report, sep.
2009).
From April to August 2009, IRD led the effort to develop the Rio Mantaro and Rio
Rimac WEAP applications. As there is a major water transfer from the Rio Mantaro to
the Rio Rimac watershed, it was decided that both rivers should be implemented in a
single WEAP application. The Rimac River was modelled to the point of diversion to
the Lima water system, and the Mantaro River was modelled down to the proposed La
Guitarra hydropower facility. We worked closely with SEI in order to calibrate and
validate this complex application. We present this work in PART 3 – MODELLING THE
RIMAC-MANTARO SYSTEM. Note, the results of the glacier area evolution simulation in
the Rio Mantaro/Rio Rimac system were satisfactory.
Although extensive páramos landscapes are not present in the three pilot watersheds
in Peru, IRD attempted to parameterize existing rainfall-runoff models in a manner
that could capture the unique nature of hydrologic processes in watersheds dominated
by páramos. We used data of a little watershed near Quito in Ecuador, with 90% of
páramos. The existing WEAP rainfall-runoff model (Soil Moisture Model) could be
calibrated. We also calibrated the GR2M, monthly two parameter rainfall-runoff model
of Génie Rural (Mouelhi et al., 2006). We used these calibrations and the GR2M
codification within WEAP as examples during the courses “WEAP and Climate Change”
(VIII Encuentro Internacional GTNH PHI-UNESCO, Sep. 18-23, 2009, Quito).
7
2.3 - Task 3: Data acquisition for the Rio Santa, Rimac and Mantaro river
basins in Peru
The data acquisition is a very sensitive point of our work because the necessary data
is dispersed across different institutions and is submitted to some restrictions and
limited access (public and private institutions). We have established with SEI-US a list
of the required information for the WEAP modelling (see Appendix 1.,
“www.mpl.ird.fr/divha/aguandes/peru/doc/Avance_RioSanta_WEAP-2008-11.pdf”)
and for the glaciers models calibration.
Initially we asked for support from the ministry of energy and mines of Peru (MINEM).
We obtained recommendation letters to Duke Energy, Electroperú, EDEGEL and the
Electric Operation Committee (COES). We have contacted the following persons:
- ANA / INRENA
Ing. Carlos Pagador, Intendente de aguas
Ing. Aldrin Contreras, Responsable del area hidrológica
Ing. Marco Zapata, Director de la unidad de glaciología
- SENAMHI
Ing.
Ing.
Ing.
Ing.
- IRD
Dr. Robert Gallaire, Responsable GREATICE Perú
- Duke Energy
Ing. Carlos Gálvez, Ing. de producción
Ing. Julio Velásquez, Sub-gerente comercial
Ing. Abel Rodríguez, Jefe hidrológica de la Central Huallanca
- EDEGEL
Ing.
Ing.
Ing.
Ing.
Carlos Rosas, Sub gerente de Comercialización
Miguel Suarez, Centro de control y operaciones
Eduardo Ibarra, Coordinador
Jhony Huaman, Coordinador
- Electroperu
Ing.
Ing.
Ing.
Ing.
Guillermo Romero, Gerente de proyectos
José Barbe, Gerente de producción
Jaime Huaman, Responsable del área hidrológica
Juan Villegas, responsable del área de Lagunas
Julio Ordoñez, Director de Hidrología
Héctor Vera, Hidrología
Oscar Felipe, Dirección de hidrología
Waldo Lavado, Dirección de hidrología
In addition to the data acquisition, an important treatment work was carried out to
constitute time operational databases for the hydro-meteorological data (Hydraccess).
Furthermore, an important project to compile geographic information was completed,
including the processing of satellite images to reconstitute glaciers extensions and the
spatialization of climate data. This work is presented in detail within the Appendix 2 Technical report on data acquisition and pre-processing for the Rio Santa, Rimac and
Mantaro river basins in Peru - Suarez W., Pouget J.C., Condom T., Le Goulven P. –
September 2009.
Tables 2.3, 2.4, and 2.5 present the collected and processed data for Santa, Rimac
and Mantaro river basins.
8
Datos Requeridos para Alimentar el Modelo
Prioridad
Santa
Fuente
Datos de Entrada – Demandas
o
Cobertura de vegetación
1
UNALM
o
Precipitación (series de datos históricas, i.e. promedio mensual
en cada año del periodo de modelación)
1
32 estaciones
IRD, INRENA,
DUKE
o
Temperatura (series de datos históricas, i.e. promedio mensual
en cada año del periodo de modelación)
1
7 estaciones
IRD, INRENA,
DUKE
o
Humedad
modelación)
1
6 estaciones
IRD, INRENA,
DUKE
Relativa
(promedio
mensual
del
periodo
de
o
Viento (promedio mensual del periodo de modelación)
1
5 estaciones
IRD, INRENA,
DUKE
o
Numero de usuarios
1
Población
Urb./rural
INEI (pagina
WEB)
1
4 lagunas
DUKE
Datos de Entrada – Suministro y Recursos
-
Reservorios/represas
Datos físicos:
o
Capacidad de almacenamiento
4 lagunas
DUKE
o
Volumen inicial
4 lagunas
DUKE
o
Curva de volumen/elevación
4 lagunas
DUKE
o
Evaporación
o
Perdidas a agua subterránea
Datos de operación
o
Máximo nivel de conservación
Huallanca
COES
o
Máximo nivel de seguridad
Huallanca
COES
o
Máximo nivel inactivo
Huallanca
COES
Huallanca
COES
-
Capacidad hidroeléctrica
1
o
Mínimo caudal de turbina
Huallanca
COES
o
Máximo cauda de turbina
Huallanca
COES
o
Cabeza hidráulica
Huallanca
COES
o
Factor de Planta
Huallanca
COES
o
Eficiencia
Huallanca
COES
2
Huallanca
COES
1
25 estaciones
IRD, INRENA,
DUKE
-
Requerimientos de caudales mínimos
Datos para Calibración del Modelo
o
Ríos
Series de tiempo de caudales
SIG
O
IRD
Imágenes glaciares
2006-Aster
SENAMHI
2003-SPOT5
IRD
2000Landsat5
SENAMHI
1987Landsat5
SENAMHI
1970-Carta
Nacional
SENAMHI
Table 2.3. Collected and processed data for the Santa river basin.
9
Prioridad
Rímac
Fuente
1
1 carta
INADE
o
Precipitación (series de datos históricas, i.e. promedio mensual en cada
año del periodo de modelación)
1
24
estaciones
SENAMHI,
EDEGEL
o
Temperatura (series de datos históricas, i.e. promedio mensual en cada
año del periodo de modelación)
1
5
estaciones
SENAMHI,
EDEGEL
o
Humedad Relativa (promedio mensual del periodo de modelación)
1
5
estaciones
o
1
por
determinar
SENAMHI,
EDEGEL
o
Numero de usuarios
1
Población
Urb./rural
Censo
nacional.
1
15 lagunas
y presas
EDEGEL
o
-
Datos físicos:
o
15 lagunas
EDEGEL
o
Volumen inicial
15 lagunas
EDEGEL
o
o
Evaporación
1 laguna
EDEGEL
o
1 laguna
EDEGEL
EDEGEL
Datos de operación
o
6 centrales
COES
o
6 centrales
COES
o
6 centrales
COES
6 centrales
COES
-
1
o
6 centrales
COES
o
6 centrales
COES
o
Cabeza hidráulica
6 centrales
COES
o
Factor de Planta
6 centrales
COES
o
Eficiencia
6 centrales
COES
2
6 centrales
COES
1
8 estac.
-
o
Ríos
SIG
IRD
Imágenes glaciares
2008Landsat5
INPE
1998Landsat5
INPE
1988Landsat5
INPEMaryland
1980Landsat2
INPE
1970-Carta
Nacional
Table 2.4. Collected and processed data for the Rimac river basin
10
Prioridad
Mantaro
Fuente
o
1
o
Precipitación (series de datos históricas, i.e. promedio mensual en
cada año del periodo de modelación)
1
172
estaciones
SENAMHI,
ELECTROPERU
o
Temperatura (series de datos históricas, i.e. promedio mensual en
cada año del periodo de modelación)
1
Evaluacion
SENAMHI,
ELECTROPERU
o
Humedad Relativa (promedio mensual del periodo de modelación)
1
Evaluacion
SENAMHI,
ELECTROPERU
o
1
Evaluacion
SENAMHI,
ELECTROPERU
o
Numero de usuarios
1
Población
Urb./rural
Senso
nacional
1
21 constr.28 proyecto
Electroperu
COES
19 lagunas
y presas
19 lagunas
y presas
19 lagunas
y presas
Electroperu
COES
Electroperu
COES
Electroperu
COES
-
Datos físicos:
o
o
Volumen inicial
o
o
Evaporación
o
Datos de operación
o
3 centrales
COES
o
3 centrales
COES
o
3 centrales
COES
3 centrales
COES
-
1
o
3 centrales
COES
o
3 centrales
COES
o
Cabeza hidráulica
3 centrales
COES
o
Factor de Planta
3 centrales
COES
o
Eficiencia
3 centrales
COES
2
3 centrales
COES
1
20
estaciones
-
o
SIG
IRD
Imagenes glaciares
2008Landsat5
INPE
1998Landsat5
INPE
1988Landsat5
INPEMarilland
1980Landsat2
INPE
1970-Carta
Nacional
Table 2.5. Collected and processed data for the Mantaro river basin
11
2.4 - Task 4: Evaluation of climate change impacts on Andean hydrology
Due to delays encountered by the project partners charged with producing future
climate projections, we could not run the elaborated models for future simulations. In
connection with SEI-US, we took the lead in developing the models of the Rimac and
Mantaro river basins.
Although it was not possible to perform detailed climate change analysis during the
period of project implementation, SEI performed a preliminary exercise using stylized
climate projections derived from available datasets. Section 3 of the SEI final report
describes the results of this preliminary assessment which was carried out using the
Rio Santa WEAP application and provided insight into how the types of analysis of the
potential impacts of climate change in the region can be supported using the WEAP
software enhanced to represent unique features of Andean hydrology.
2.5 - Task 5: Documentation and Dissemination
We organized several presentations: projects management unit of Electroperu (July,
2008), management unit of EDEGEL (July, 2008), production unit of Duke Energy
(July, 2008), production unit of Electroperu (Enero, 2008), etc.
Important steps of the dissemination were:
- The mission organized by IRD in the Santa river basin, September 21-24, 2008, with
the participation of Adriana Valencia, World Bank; Marisa Escobar, SEI-US; Cayo
Ramos, Universidad La Molina; Thomas Condom, Jean-Christophe Pouget, and
Wilson Suarez, IRD, as well as with visits to the water collecting unit of the
CHAVIMOCHIC irrigation project; hydro power unit of Cañon del Pato with the Duke
Energy staff; and several characteristic parts of the river basin with notably
regulated
lakes
downstream
of
glaciers
(see
www.mpl.ird.fr/divha/aguandes/peru/santa/mision-2008-09/);
- The training course “Sistema de Evaluación y Planeación de Agua - Una Herramienta
para el Análisis de Sostenibilidad del Agua”, Universidad La Molina Lima, September
25-27, 2008, co-organized by IRD, SEI-US and Universidad Nacional Agraria La
Molina, with 45 participants from public and private institutions related to water
resources management in Peru, notably from SENAMHI (two persons), Electroperu
(three persons), INRENA (four persons), DUKE Energy (one person), EDEGEL (one
person), CHAVIMOCHIC (one person), Ministerio del Medio Ambiente (one person).
The use of the WEAP Santa river basin model has been used for several presentations:
•
Apoyo a la Gestión de los Recursos Hídricos – Introducción a la Herramienta
WEAP., J.C. Pouget, Universidad Nacional de San Agustín de Arequipa, 28 y 29
Septiembre, 2008 (ver www.mpl.ird.fr/divha/aguandes/peru/arequipa/misionJCP-UNSA-Arequipa-2008-09-29.pdf)
•
Curso-Taller IRD, EPN, FONAG, Sistema de Apoyo a la Planificación de los
Recursos Hídricos - Capacitación Basica a la Herramienta WEAP, J.C. Pouget,
Escuela Politécnica Nacional de Quito, Febrero 19 y 20, 2009
•
Presentación al 1er Congreso Nacional del Agua de Peru – Variaciones glaciares
y disponibilidad del agua en la Cordillera Blanca del Perú desde hace 40 años -,
T. Condom, Universidad Nacional La Molina, Lima, Marzo 19 al 21, 2009
•
VIII Encuentro Internacional de Investigadores del Grupo de Trabajo Nieves y
Hielos (GTNH) de América Latina del PHI-UNESCO, Curso taller de Hidro
glaciología - WEAP y el Cambio Climático, Organizadores: B. Cáceres,
12
M. Villacis, B. Francou, J.C. Pouget, E. Ramirez, 18-23 septiembre 2009, Hotel
Mercure, Quito – Ecuador - Modelación hidroglaciológica orientada a la gestión
de cuencas hidrográficas con cobertura parcialmente glaciar. Expositores:
Thomas Condom (IRD, Peru), Edson Ramírez (UMSA, Bolivia) - Ejemplos y
ejercicios de calibración en Ecuador y Perú - Adaptación y Construcción de
nuevos módulos en WEAP, Expositores: J.C. Pouget (IRD, Ecuador), David
Purkey (SEI, USA).
•
Taller internacional sobre cambio climático en los Andes - Estado del
conocimiento y enfoques para el futuro - Lima 24 - 26 septiembre 2009 –
MINAM – CAN - IRD - Cooperación regional Francia - Lugar: Sede CAN, Lima Cambios climáticos y recursos agua de origen glaciar: ejemplos tomados en la
Cordillera Real de Bolivia y en la Cordillera Blanca del Perú. Expositores: Edson
Ramírez (UMSA, Bolivia), Thomas Condom (IRD, Peru) - El Cambio climático, la
regresión de los glaciares y la definición de un nuevo manejo del agua en las
cuencas. Expositor: David Purkey (SEI, USA)
Beyond this final report, the most important documentation produced during this
study is the manuscript for scientific publication entitled Modelling the Hydrologic Role
of Glaciers within a Water Evaluation and Planning System (WEAP): A case study in
the Rio Santa watershed (Peru), which was submitted to peer-reviewed Journal of
Hydrology (see Appendix 3). SEI and IRD intend to revise this article, as needed, until
it is published in a peer-reviewed journal. The article does not include, however, any
statement on potential climate change impacts on the hydropower sector in Peru due
to delays encountered by the project partners charged with producing future climate
projections. While it was anticipated that these projections would be available prior to
the end of the current project in July 2009, this did not happen. Such analysis would
certainly constitute suitable material for drafting a subsequent journal article.
We can also note the creation of the Santa, Rimac and Mantaro river basins
presentation with interactive maps from the site www.mpl.ird.fr/divha/aguandes/.
These applications were developed with Google Maps technology and optimized with
Firefox version 3. Figure 2.3 presents the interactive map of the Santa river basin,
which permits access to data on the following: glaciers extensions, rainfall and
hydrometric stations, irrigation areas, hydro power units, etc.
Figure 2.3. Santa river basin – www.mpl.ird.fr/divha/aguandes/
13
PART 3 - MODELLING THE RIMAC-MANTARO SYSTEM
In close collaboration with SEI, IRD took the lead in developing the models of the
Rimac and Mantaro river basins. As there is a major water transfer from the Rio
Mantaro to the Rio Rimac watershed, it was decided that both rivers should be
implemented in a single WEAP application. The Rimac basin river was modelled to the
point of diversion to the Lima water system and the Mantaro basin river was modelled
down to the proposed La Guitarra hydropower facility.
3.1 – Study area data
The description of the study area and the pre-processing data of this complex system
were presented within the Technical Report on data acquisition and pre-processing for
the Rio Santa, Rimac and Mantaro river basins in Peru (Appendix 2). Figure 3.1
presents the rainfall areas and the location of data stations.
Figure 3.1. Map of rainfall areas and location of data stations of the Rimac and Mantaro river basins.
The optimal period of simulation is between September 1966 and August 1996
(Appendix 2). We considered: (1) a calibration period between September 1970 and
August 1981; 4 years, between 1966 and 1970, used to stabilize the model;
(2) a validation period between September 1981 and August 1996.
14
As for the temperature and humidity data, only one good quality set of long and
continuous time-series data exists, collected from the Cercapuquio station (12.422°S,
75.417°W). Continuous temperature data for each catchment was obtained using a
temperature gradient of 0.6°C/100m applied to the temperature observed at
Cercapuquio. For the humidity and wind speed, we assumed that the long-term
monthly time series at Cercapuquio applied to all catchments.
3.2 – Proposed modelling and parameters
The combined Rimac-Mantaro WEAP application (Figure 3.2) includes 38 WEAP
reservoir objects, suggesting a much more significant level of hydraulic manipulation
than that which exists in the Rio Santa system where extensive glaciers provide much
of the water storage service. Twenty-two WEAP demand sites represent the urban and
rural water demands in individual provinces, along with 276 (102 for Rimac and 174
for Mantaro) WEAP catchment objects that are used to simulate rainfall-runoff
process. Five WEAP diversion objects and nine WEAP run of river hydropower objects
are used to represent the hydropower production system and 28 WEAP streamflow
gauge objects were available for calibration-validation of the hydrologic routines.
Figure 3.2. Schematic of the Rimac and Mantaro system within WEAP.
The Rio Mantaro and Rio Rimac watersheds are more complex than the Rio Santa,
with many more subwatersheds and a higher level of hydraulic manipulation
accomplished via reservoir storage and release (see Section 3.5). As such, the final
calibration-validation of the Rio Mantaro/Rio Rimac model focused on obtaining a set
of parameters to reasonably represent the hydrology of the mainstream Mantaro and
Rimac rivers. Given that the rivers are located in dissimilar watersheds (the Mantaro is
in the Amazon Basin, the Rimac drains to the Pacific), the project team, after a
hundred simulations, did not attempt to define a uniform set of parameters. Instead,
parameter values for each watershed were adjusted separately to represent the
different physical processes of each basin, although each basin arrived at an internally
uniform set of parameters, presented in Table 3.1.
15
Land use parameters (part without glacier)
Parameter
unit
Crop coefficient
Root zone capacity
mm
Root zone conductivity Cultivos
mm/month
others
mm/month
Deep water capacity
mm
Deep water conductivity
mm/month
Runoff resistance factor Cultivos
Matorral
Planicie costera
Tundra
Flow direction
% horizontal
Initial storage fractions
z1
%
z2
%
Glacier parameters
Parameter
unit
T0
°C
asnow
mm.month-1.°C-1
aice
mm.month-1.°C-1
Rimac
0.9
425
840
600
7500
800
1.2
0.9
0.6
0.6
0.2
30
10
Mantaro
1.2
425
420
300
300
300
1.2
0.9
0.6
0.6
0.2
30
30
Rimac
1.7
300
600
Mantaro
1.7
300
600
Table 3.1. Land use parameter values for the non glacial part
and parameter values for the glacier module
Although the Rio Mantaro modelling domain extends to the location of the projected
hydropower facility at La Guitarra, as there are no historical streamflow records at
that site model calibration could not be attained at this most downstream point.
Similarly, while the Rio Rimac modelling domain extends to the point of water
diversion to the city of Lima, the most downstream gauge in the system was located
upstream, once again limiting the ability to calibrate the model at this key point of
management interest.
Detailed in Section 3.3, the assessment of model performance for the calibration
period 1970-1981 and validation period 1981-1996 was done at several gauge
stations (19 stations for Mantaro, six stations for Rimac). But to retain the parameters
presented in Table 3.1, the focus was notably on the bigger downstream stations. For
the Rio Mantaro, working upstream from La Guitarra, these stations include Pongor;
Mejorada; Moya; Puente Stuart; Puente Chulec; and Upamayo. For the Rio Rimac,
working upstream from the Lima diversion, the main gauging stations include Chosica
and Surco.
Table 3.1 shows that the Runoff resistance factor and the Root zone conductivity
parameters were defined considering the land cover (Cultivos, Matorral, Planicie
costera, Tundra).
3.3 – Calibration and validation streamflows results
Efficiency criterions
In order to test the validity of the different simulations scenarios and to calibrate the
different parameters values, we use three statistics: (a) the Root Mean Square Error
(RMSE); (b) the BIAS; and (c) the Nash-Sutcliffe parameter (Nash and Sutcliffe,
1970).
100
RMSE =
Qo
∑
n
i =1
(Qs ,i − Qo ,i ) 2
n
(a)
16
BIAS = 100[(Q s − Q o ) / Q o ]
n
E f = 1−
∑ (Q
i =1
n
i =1
− Qo ,i ) 2
s ,i
∑ (Q
(b)
o ,i
− Qo ) 2
(c)
Where Qs,i and Qo,i are simulated and observed outflow data for each time step i.
For the calibration period 1970-1981, n corresponds to a maximum value of 144.
For the validation period 1981-1996, n corresponds to a maximum value of 196.
Calibration and validation results
Calibration and validation statistics for all stations are presented in Table 3.2 for the
Rio Mantaro and Table 3.4 for the Rio Rimac, indicating a satisfactory performance of
the model for the bigger downstream stations. The stations are classified from the
biggest watershed to the smallest one. For the calibration and the validation period
and for each station, the following information is presented:
-
RMSE, BIAS, Ef: the efficiency criterions;
-
n: the time steps number of monthly observed flows;
-
Qo m3/s: the corresponding observed flow average.
Calibration period 1970-1981
3
station
n
Qo m /s
Pongor
90
310.2
Validation period 1981-1996
BIAS
Ef
n
Qo m3/s
53.76
-14%
0.74
39
375.2
RMSE
RMSE
BIAS
Ef
44.57
5%
0.68
Mejorada
90
176.0
37.91
16%
0.81
158
170.6
48.14
36%
0.65
Puente Stuart
107
91.8
37.16
10%
0.81
142
81.9
72.33
50%
-0.25
Puente Chulec
91
57.5
37.40
5%
0.73
113
50.4
51.67
30%
-0.03
Moya
119
24.5
57.52
14%
0.62
115
23.7
65.82
40%
0.31
Upamayo
124
25.9
45.72
14%
0.65
161
23.5
84.65
41%
0.06
Chinchi
130
15.7
61.03
-5%
0.68
151
17.7
86.15
-8%
0.55
Angasmayo
132
17.5
52.95
10%
0.77
159
12.2
84.62
67%
0.46
Quillon
122
11.3
76.59
53%
0.36
99
8.2
128.62
98%
-0.55
Yanacocha
128
6.9
6.1
216.42
189%
-1.16
Pachacayo
131
10.7
61.60
107.50
82%
-0.26
149.65
130% -0.53 173
-3%
0.60
174
9.4
Huapa
131
10.7
59.35
8%
0.68
164
11.0
54.47
3%
0.72
Santa Elena
126
8.4
78.52
-17%
0.59
174
10.7
115.89
-39%
0.33
Huari
131
6.8
54.37
-11%
0.70
174
6.1
131.36
97%
-0.69
Cochas Tunel
132
6.1
73.52
11%
0.36
156
2.3
79.91
33%
0.52
Yulapuquio
127
5.8
83.14
-13%
0.48
172
4.9
82.41
24%
0.47
Canipaco
119
3.8
88.02
-13%
0.55
159
4.2
163.14
-10%
0.31
Piñascocha
132
1.8
95.94
62%
0.17
177
1.5
225.38
181%
-2.94
Casaracra
122
1.8
82.99
49%
0.07
156
2.3
79.91
33%
0.52
Table 3.2. Criteria for the calibration and validation periods for the Mantaro sub-watersheds.
17
Table 3.2 shows that, for the Mantaro calibration, almost 70% of the stations (13
stations / 19) have a BIAS less than 15% and with an efficiency (Ef Nash-Sutcliffe)
greater than 0.5; for the validation, almost 60% of the stations (11 stations / 19)
have a BIAS less than 40% and with an efficiency greater than 0.3.
Figure 3.3 presents the seasonal fluctuations during the calibration period of observed
and simulated stream flows for the bigger downstream stations of Mantaro.
Figure 3.3. Monthly averages during the calibration period (1970-1981) of observed and simulated
stream flows for the Mantaro river basin.
Figure 3.4. Observed and simulated stream flows at Pongor station in Mantaro River system.
18
Figures 3.4 and 3.5 present the observed and simulated stream flows of the Pongor
and Mejorada stations.
Figure 3.5. Observed and simulated stream flows at Mejorada station in Mantaro River system.
Table 3.3 shows that, for the Rimac calibration, 50% of the stations (3 stations / 6)
have a BIAS less than 25% and with an efficiency (Ef Nash-Sutcliffe) greater than 0.6;
for the validation, four stations have a BIAS less than 20%, but only two stations
present an efficiency greater than 0.4.
Calibration period 1970-1981
Validation period 1981-1996
station
n
Qo m3/s
RMSE
BIAS
Ef
n
Qo m3/s
RMSE
BIAS
Ef
Chosica
132
28.6
36.26
-3%
0.62
180
27.7
61.18
17%
-0.16
Surco
132
17.0
41.29
-24%
0.66
124
16.0
42.16
-11%
0.60
Tamboraque
110
14.3
49.93
-34%
0.46
180
14.0
51.29
-11%
0.44
San Mateo
130
12.7
51.37
-42% -0.10
99
13.1
48.06
-35%
-0.02
Sheque
132
11.5
45.43
-32% -0.04 180
11.2
51.74
-16%
-0.54
Rio Blanco
131
3.3
61.87
21%
3.0
91.77
61%
0.10
0.63
64
Table 3.3. Criteria for the calibration and validation periods for the Rimac sub-watersheds.
Figure 3.6 presents the seasonal fluctuations during the calibration period of observed
and simulated stream flows at Chosica and Surco stations. All observed and simulated
stream flows are presented in Figures 3.7 and 3.8 for the Chosica and Surco stations.
19
Figure 3.6. Monthly averages during the calibration period (1970-1981) of observed and simulated
stream flows for the Rimac river basin.
Figure 3.7. Observed and simulated stream flows at Chosica station in Rimac River basin.
Figure 3.8. Observed and simulated stream flows at Surco station in Rimac River basin.
20
3.4 – Simulation of the glacier area evolution since the 70’s
The results of the glacier area evolution simulation in the Rimac-Mantaro system were
satisfactory (see glacier parameters in Table 3.1). The observed initial glacier area of
these watersheds in 1970 was 113 km2, which decreased to roughly 40 km2 in 1997
(Table 3.4). This trend was well captured by the model when comparing simulated
and observed glaciated areas at discrete times during the calibration-validation period
(Table 3.4 and Figure 5). In contrast to the results obtained in the Rio Santa, in the
Rimac-Mantaro system there is no apparent performance trend of the glacier module
as a function of the extent of individual glacier extent. Note, however, that the
glaciers in the Rio Mantaro and Rio Rimac watersheds are much smaller than those of
the Rio Santa.
Azu11
Carh10
Hua09
Huap08
Huay10
Mej07
Pte.St08
Pte.chu09
Sma08
Tem10
Upa10
Yurac10
Medido (Km²)
1970
1988
14.42
11.49
4.32
2.77
19.73
7.58
3.66
0
3.13
1.53
12.84
6.56
17.55
5.74
12.16
2.84
6.25
0.88
4.79
3.75
4.63
0
9.67
4.38
1997
10.16
2.2
6.38
0
1.21
5.25
4.33
1.9
0.46
3.04
0
3.87
Simulado (KM2)
1988
1996
9.1
7.7
0.0
0.0
11.3
9.7
0.0
0.0
1.8
1.8
6.2
5.3
7.5
5.8
0.0
0.0
3.2
2.3
3.5
3.3
0.0
0.0
5.0
3.7
Table 3.4. Simulated and observed data of glaciers evolution between 1970 and 1997.
for the two periods (1987 and 1996).
21
3.5 – Conclusions
A double validation of the model was done by comparing (1) observed and simulated
streamflows at 25 control points in the Rimac-Mantaro system; (2) the glacier area
calculated by the model and that observed with Landsat images for two periods (1988
and 1997). This validation gave reasonable results. But the Rimac-Mantaro system
future simulations have to be able to reproduce the reservoirs operation management,
16 reservoirs operational since 1995-2000, and 28 reservoirs planned for the future
(Table 3.5).
nombre
Upamayo
Tablachaca
Malpaso
Huaylacancha
Carhuacocha
Tembladera
Azulcocha
Yuraccocha
Vichecocha
Nahuincocha
Yanacoha-Palcán
Huacracocha
Hueghue
Chilicocha
Nahuincocha
Balsacocha
Yurajcocha
Hulchicocha
Coyllorcocha
Antacocha
Tunshu
Norma
Paucara
Llacsa
Parlona I
Calzada
Caullau
Huarmicocha
Luquina
Habascocha
Tipicocha
Tranca Grande
Paccha
Lacsacocha
Huacracocha-Huari
Abascocha
Inticojasa
Ampacocha
Tutayac II
Aclacocha
Huascacocha
Yanacoha-Pampahua
Tanserococha
Tipicocha
Turmanya
Pajaco
tipo
represa
represa
represa
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
laguna regulada
empresa
Electroperu
Electroperu
Electroandes
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Electroperu
Vol (MMC)
441
7
2.76
22.4
23.0
5.0
6.0
2.2
10.6
1.4
7.6
4.9
18.4
42.8
7.0
3.0
16.0
19.0
11.0
2.4
3.5
3.0
4.4
3.3
2.5
2.3
5.6
1.5
11.1
3.1
3.5
14.5
11.6
4.6
11.0
2.4
1.3
4.7
1.0
5.2
26.0
6.1
13.1
10.1
46.9
61.1
Inicio operación
1973
1995
1995
1997
1997
1995
1995
1995
2000
2000
2000
1999
1999
1999
1999
1999
1999
estudio definitivo
estudio definitivo
estudio definitivo
estudio factibilidad definitivo
estudio definitivo
estudio definitivo
estudio definitivo
estudio definitivo
estudio definitivo
estudio factibilidad
Table 3.5. Existing and projected reservoirs of the Mantaro river basin.
22
CONCLUSION AND PROSPECTS
Understanding and modelling hydrology are crucial in Andean tropical mountains as
part of efforts to plan and manage water resources.
One of the main challenges in this region is to be able to simulate the hydrology with
scarce availability of meteorological and hydrological data which has high spatial
variability similar to the temperature and precipitation gradients observed in Andean
mountains watersheds. Several assumptions need to be made and interpolation
methods need to be implemented in order to obtain continuous climate time-series
that can feed hydrologic models. The historical studies of IRD (Pouyaud et al., 2005;
Suarez, 2007; Suarez et al., 2008) facilitated the data processing on the Santa river
basin. For the Rimac and Mantaro river basins, database development was more
difficult due to the system complexity and the lack of reference studies. For instance,
we needed to process satellite images to reconstitute glacier extensions and land
covers.
One of the difficulties in constituting databases came from the data dispersion across
the various institutions. It is worth reiterating that (1)
certain institutions (unidad
de glaciología del ANA - ex- INRENA) and companies (Electroperú) supporting this
project have a data confidentiality commitment, in which the IRD guarantees data use
only for the project and that this data cannot be transferred to another institution
without their authorization; (2) within this cooperation framework, the different
institutions supporting the project have access to the final technical report.
The originality of this work rests on the successful linkage of a glacier evolution
module based on the degree-day method to a WEAP’s integrated rainfall-runoff/water
resource systems modelling framework. A double validation of the model was done by
comparing the glacier area calculated by the model and that observed with Landsat
images for two periods (1987 and 1998) and observed and simulated streamflows at
16 control points in the Rio Santa watershed. Modelling the Rimac-Mantaro system,
presented in this report, confirmed the effectiveness of the glacier evolution
simulation (1988, 1996) and gave reasonable flow results for the downstream gauge
stations.
Although extensive páramos landscapes are not present in the three pilot watersheds
in Peru, IRD and SEI attempted to parameterize existing rainfall-runoff models in a
manner that could capture the unique nature of hydrologic processes in watersheds
dominated by páramos. IRD used data of Antisana upstream watersheds near Quito in
Ecuador, with more than 70% of páramos land cover (collaboration GreatIce IRD unit,
EMAAP-Q, INAMHI). The existing WEAP rainfall-runoff model (Soil Moisture Model) and
the GR2M, monthly two parameter rainfall-runoff model of Génie Rural (Mouelhi et al.,
2006) could be calibrated. As the World Bank supported Proyecto Regional Adaptación
Andina (PRAA) in Ecuador used Antisana watersheds as the pilot area, it seems
reasonable to envisage an active collaboration.
Due to delays encountered by the project partners charged with producing future
climate projections, IRD could not run the elaborated models for future scenarios, in
order to evaluate climate change impacts on Andean hydrology, corresponding to Task
4. Clearly the preliminary climate change analysis carried out by SEI based on the
development of two stylized future climate projections is not sufficient. The WEAP
applications developed under this project should be run using scientifically rigorous
climate projections like the ones that are being developed by NCAR and PNNL.
Developing the ability to simulate the impacts of a set of assumed future climate
projections is an important step in creating the capacity for Andean water managers
to plan for climate change.
23
This is critical, for in addition to the hydrologic dimension of the model, the WEAP
software provides the ability to represent and simulate different water uses and water
system elements. Further steps into this modelling exercise should focus on detailing
the implications of hydrologic change on water demands including (1) hydropower, (2)
agriculture, and (3) ecosystem flow and the consequent economic implications.
(1) For hydropower generation, in cooperation with Duke Energy, the operators of
the Cañon del Pato hydropower project, it was possible to assess the
performance of the final WEAP Rio Santa application in terms of its utility in
simulating the hydropower system. The production simulation for 1997-1998
gave a reasonable result. As such, no substantial effort could be made to verify
the veracity of simulated hydropower operations in the Rimac-Mantaro system
beyond a simple assessment that the numbers were realistic. As the World Bank
supported Proyecto Regional Adaptación Andina (PRAA) in Peru and uses the
Mantaro river basin as one of its pilot areas, it seems reasonable to envisage the
performance evaluation of the hydropower routines at some point in the future,
depending on a collaborative relationship with the system operators. It is worth
reiterating that the Mantaro system future simulations have to be able to
reproduce the reservoirs operation management, 16 reservoirs operational since
1995-2000, and 28 reservoirs planned for the future.
(2) For agriculture, it would be beneficial to refine the representation of agricultural
water demands in these systems under different scenarios regarding the
evolution of irrigated areas and the climate driven water demand associated with
these changes. An IRD research project in Quito focuses on the competition
between demands for drinking water and irrigation leading to significant transfers
from Amazonian high-altitude watersheds with páramos. We propose an
irrigation representation combining water rights practices and crops-soils water
budget (www.mpl.ird.fr/divha/aguandes/ecuador/hoya-quito).
(3) For ecosystem flow, it may be useful to begin to introduce this consideration into
the analysis and to see how these targets might constrain system operations
under alternative future climate projections. WEAP is being used extensively in
this manner in numerous locations around the world. An IRD research project in
Quito aims to define ecosystem flows for Andean mountains watersheds
(CAUdales ECOlógicos, www.mpl.ird.fr/divha/aguandes/ecuador/papallacta/).
24
REFERENCES
Bahr, D. B., M. F. Meier, and S. D. Peckham. 1997. The physical basis for glacier
volume-area scaling. Journal of Geophysical Research 102:20355-20362.
Hock, R., 2005. "Glacier melt: a review of processes and their modelling." Progress in
Physical Geography 29: 362-391.
Mouelhi, S., Michel C., Perrin C., Andréassian V., 2006. Stepwise development of a
two-parameter monthly water balance model, J. Hydrol., 318, 200-214,
doi:10.1016/j.jhydrol.2005.1006.1014
Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models.
Part 1 – A discussion of principles. Journal of hydrology, 27 (3).
Pouyaud, B., M. Zapata, J. Yerren, J. Gomez, G. Rosas, W. Suarez and P. Ribstein,
2005. "Devenir des ressources en eau glaciaire de la Cordillère Blanche." Hydrological
Sciences Journal 50: 999-1022.
Suarez, W., 2007. "Le bassin versant du fleuve Santa (Andes du Pérou) : dynamique
des écoulements en contexte glacio-pluvio-nival", Thèse Université Montpellier 2,
(2007), 290 p.
Suarez, W., P. Chevallier, B. Pouyaud, and P. Lopez. 2008. Modelling the water
balance in the glacierized Paron Lake basin (White Cordillera, Peru). Hydrological
Sciences 53.
Yates, D., J. Sieber, D. Purkey, and A. Huber-Lee. 2005. WEAP21 - A demand-,
priority-, and preference-driven water planning model Part 1: Model characteristics.
Water International 30:487-500.
25
LIST OF TABLES
Table 1 Work schedule, envisaged initially and performed..................................... 2
Table 2.1 Principal selection criteria of glacier modelling....................................... 5
Table 2.2. Simulated and observed data of glaciers evolution
between 1970 and 1999.................................................................... 7
Table 2.3. Collected and processed data for the Santa river basin.......................... 9
Table 2.4. Collected and processed data for the Rimac river basin. .......................10
Table 2.5. Collected and processed data for the Mantaro river basin. ....................11
Table 3.1. Land uses parameters values for the non glacial part
and parameters values for the glacier module .....................................16
Table 3.2. Criterions for the calibration and validation periods
for the Mantaro and Rimac sub-watersheds ........................................17
Table 3.3. Criterions for the calibration and validation periods
for the Rimac sub-watersheds ...........................................................19
Table 3.4. Simulated and observed data of glaciers evolution
between 1970 and 1997...................................................................21
Table 3.5. Existing and projected reservoirs of the Mantaro river basin .................22
LIST OF FIGURES
Figure 1.1. Map of study river basins location in Peru ........................................... 3
Figure 2.1. Correspondence between, simulated and observed stream flow
at Balsa gauge station between Sep 1969 – Aug 1997. ....................... 6
for the two periods (1987 and 1998) ............................................................7
Figure 2.3. Santa river basin – www.mpl.ird.fr/divha/aguandes/ ..........................13
Figure 3.1. Map of rainfall areas and location of data stations of the Rimac
and Mantaro river basins...............................................................14
Figure 3.2. Schematic of the Rimac and Mantaro system within WEAP...................15
Figure 3.3. Monthly averages during the calibration period (1970-1981)
of observed and simulated stream flows for the Mantaro river basin ....18
Figure 3.4. Observed and simulated stream flows at Pongor station
in Mantaro River system ................................................................18
Figure 3.5. Observed and simulated stream flows at Mejorada station
in Mantaro River system ................................................................19
Figure 3.6. Monthly averages during the calibration period (1970-1981)
of observed and simulated stream flows for the Rimac river basin .......20
Figure 3.7. Observed and simulated stream flows at Chosica station
in Rimac River basin......................................................................20
Figure 3.8. Observed and simulated stream flows at Surco station
in Rimac River basin......................................................................20
for the two periods (1987 and 1996). ..............................................21
26

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