Daily hydrological modeling in the Peruvian Amazon basin using rainfall satellite estimates

Transcription

1 Daily hydrological modeling in the Peruvian Amazon basin using rainfall satellite estimates Eng. Ricardo Zubieta Barragán Espinoza JhanCarlo, Lavado Waldo, Saavedra Miguel 5th ORE-HYBAM Scientific Meeting

2 INTRODUCTION The Amazon basin extends and from the Andes to the Atlantic Ocean, covering approximately km2. Its fresh water contribution to the global ocean is 15% and its average discharge at the delta is m3/s (Molinier et al.,2009) 878,305 km2 (To the location station Tabatinga), average maximum flow49,000 m3/s( station Tamshiyacu) Fuente: El comercio

3 Predictions from rainfall runoff models are often unsatisfactory because spatial variability in rainfall is poorly represented in regions where data are scarce furthermorethecatch of conventional raingaugesis representative of only a small radius around the instrument.. satellite estimates can be an alternative source of data for rainfall-runoff simulation, by improving the spatial distribution of rainfall. One of the more promising applications of satellite-based rainfall estimates is the coupling of rainfall measured in real time, meteorological forecasts and rainfall runoff models for flow forecast, since there are very few real-time raingaugesin most of the Perú Rainfall regimes in eight stations in the Peruvian and Ecuadorian Amazon basin., (Espinoza et al., 2009).

4 Main Objective Evaluation of the usefulness of the estimates of rainfall, TRMM, CMORPH, PERSIANN and Reanalysis as an input variable from the modeling rainfall -runoff in the Peruvian Amazon basin using a distributed hydrological model. Source: SRTM

5 Modelo hidrológico concentrado ( SACRAMENTO, GR2M) distribuido (VIC, MGB) --- principal diferencia : The spatial variability of land by vegetation, soils. Distributed hydrological models unlike concentrates has been little studied and applied in Peru. On the other hand are also used semi distributed models (WEAP, SWAT, HEC-HMS). Figuras : Mantaro River Basin fuente proclim

6 MGB -IPH(Collischonn, 2001) Modelo de Grandes Bacias Daily hydrological modeling The law of conservation of mass, or principle of mass conservation, ET i,j PC i EI i,j Minicuencas Cell i Model grid cells Pixel and GRU discretization Collischonn 2007 Wm W i, j P i,j Dbas i,j Dsup i,j Dint i,j Minicuencas Cell aguas downstream of cell i abajo Qsup Qsub Qbas River channel

7 Data used METODOLOGY Caudales observados de la red ORE- HYBAM Reanalysis NCEP-NCAR (Humedad Relativa, Vel. Viento, Radicación solar, Temperatura del aire) SRTM (The Shuttle Radar Topography Mission). Mapa de cobertura vegetal del Perú (INRENA) Mapa de suelos del Perú (INRENA) Mapa de tipos de vegetación del Ecuador continental (INEFAN 1999) Mapa general de suelos del Ecuador (Sociedad Ecuatoriana de la Ciencia del Suelo 1986) Mapa de uso de suelo de Colombia (IGAC, 2002) Mapa de suelos de Colombia (IGAC, 2005). Adaptado de Collischonn 2007

8 Estimates of precipitation from remote sensing techniques

9 TMPA : (Huffmanet al., 2010) GPROF (Kumerow at al., 1996){ NESDIS (Zao and Weng 2002) IWP { Calib TMI Adj TCI (Miller 1972, Krajewskiand Smith (1991)) TCI (PR-TMI) GOES Precipitation Index(GPI; Arkin And Meisner 1987)

10 TMPA CMORPH PERSIANN Precipitación media multianual Elaboración propia

11 RESULTS Modelling Geographic Information Systems 1. Delimitación de cuencas semiautomatizada a diferentes detalles En Software GIS

12 Obtaining HRU BY GEOGRAPHIC INFORMATION SYSTEMS RESULTADOS Procesamiento de Mapas temáticos Uso de suelo Tipo de suelo Unidadde de Respuesta Hidrológica Imagen Landsat (a); Índice de área foliar (b) y albedo de superficie (c), estimado por satélite.

13 TRMM RESULTS RESULTADOS CMORPH PERSIANN REQUENA TRMM CMORPH PERSIANN SAN REGIS

14 TRMM CMORPH PERSIANN Efficiency coefficients Nash - Suttcliffe for estimated precipitation products.

15 Performance data from Reanalysis precipitation Number River Station Area NCEP NCAR NCEP DOE 1 Napo Nueva Loja 4331 < 0.1 < 0.1 < Napo San Sebastian 5311 < 0.1 < 0.1 < Ucayali Km < 0.1 < 0.1 < Marañon Santiago < 0.1 < 0.1 < Napo Francisco de Orellana < 0.1 < 0.1 < Napo Nuevo Rocafuerte < 0.1 < 0.1 < Marañon Nueva York < 0.1 < Marañon Chazuta < Marañon Borja < 0.1 < 0.1 < Napo Bellavista < 0.1 < 0.1 < 0.1 Santa Rosa de 11 Ucayali Ucayali Ucayali Pucallpa Ucayali Requena Marañon San Regis < 0.1 < Amazon Iquitos Enapu Amazon Tamshiyacu Amazon Nazareth < 0.1 < Amazon Tabatinga < ERA INTERIM

16 Conclusions In this work, the performance by MGB-IPH modeling of discharge values in the main tributaries of the Amazon Andes basin of Perú and Ecuador using six precipitation datasets at the daily time step over, as input to rainfall-runoff models was evaluated by comparing with observed discharge, focusing on the use of several high (TMPA, CMORPH, PERSIANN) and low (NCEP, NCEP-DOE, ERA INTERIM) The modeling using precipitation satellite is able to reproduce hydrographs for large river in the Peruvian Amazon, these results suggest that the product 3B42 v7 can be used as input to a hydrological model of rainfall - runoff in the Marañon and Ucayali basins. The results show a clear opposition in the performance of the model in the basins located between the north and south of the tropical regions of the Peruvian Amazon, and similar conditions for satellite precipitation products and Reanalysis used, this model shows the difficulty of hydrographs represent observed in regions closer to the equator, characterized by weak seasonal variability, achieving performance levels NS <0.4. Generally, results shows that not tend to be better when evaluated at gauging stations controlling large drainage areas. This is not clear and can be due to rainfall data quality difficult for the model to represent different streamflow regimes and the confluence of rivers upstream whose performance is much lower or higher than others.

17 GRACIAS THANK YOU OBRIGADO Agradecimientos :