Large-scale hydrological modelling of the Peruvian Amazon basin
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1 Large-scale hydrological modelling of the Peruvian Amazon basin Zed Zulkafli 1, Wouter Buytaert 1, Christian Onof 1, Waldo Lavado 2, Jean-Loup Guyot 3 1 Department of Civil and Environmental Engineering, Imperial College, London 2 Servicio Nacional de Meteorología e Higrología (SENAMHI), Lima, Peru 3 Institut de recherche pour le développement (IRD), Lima, Peru 17 December 2012
2 Outline Research objective Method and Data Results and conclusions
3 Research objective To assess the performance of JULES in large-scale humid tropical upland basins using 1.Simulation of streamflow 2.Simulation of observed surface fluxes
4 Method of evaluation 1. Simulation of daily streamflow - conventional performance scores 2. Representation of real systems simulated local surface fluxes versus observed magnitudes and variations from the literature CLIMATE REANALYSIS TRMM Surface Runoff Subsurface Runoff (Drainage) ROUTING constant celerity Throughfall Evapotranspiration Literature values Streamflow hydrograph Performance scores
5 Study area The Peruvian Amazon river basin > 360,000 km2 >
6 Upland grassland
7 Data Daily streamflow: San Regis mainstem, downstream
8 Data Daily streamflow: San Regis mainstem, downstream Borja mainstem, upstream
9 Data Daily streamflow: San Regis mainstem, downstream Borja mainstem, upstream Santiago Andean
10 Data Daily streamflow: San Regis mainstem, downstream Borja mainstem, upstream Santiago Andean Chazuta southern tributary
11 Data Land cover data: Digital ecological systems map of Bolivia and Peru (90m, Josse et al. 2009) IGBP classification (MODIS 1km, Loveland et al. 2000) Aggregated to model resolution of degree x degree Soil data: Harmonized World Soil Database ( 1km, FAO, 2009) Driving data: TRMM 3B42 3-hourly rainfall, 0.25 o resolution (Huffman, 2007) - Bias correction using the TRMM 2A25 climatology (Nesbit and Anders, 2009), 0.1 o resolution NCEP Climate reanalysis, post-processed by Sheffield et al o resolution, disaggregated to o by lapse rate interpolation
12 Model Parameters Most default parameters are retained PFT: Canopy heights for broadleaf trees from 10-38m Soil hydraulics: Brooks & Corey soil water retention parameters calculated using the pedotransfer functions of Tomasella & Hodnett, 1998.
13 RESULTS
14 Data uncertainty Modelling period: Q/P > 1 for most of the modelling period Evaluation focused on
15 Performance scores Q avg :16,600 Q avg : 4539 Q avg :1585 Q avg :3042 Trends follows the trends in the runoff ratios
16 Hydrological response Seasonality is well captured Good simulation of rising and recession limbs Inadequate simulation of floodplain regulation and baseflow contribution
17 Surface fluxes Blue plots are constructed from JULES simulated fluxes over the entire basin, separated by biomes Boxplots are constructed from published values in other lowland, flood, montane forests and Andean systems
18 Surface fluxes Underestimation of ET in the lowland and flood forests Underestimation of surface runoff in the montane forests and Andean systems
19 Conclusions JULES performance in humid tropical upland systems: Assessment is limited by data uncertainty Good simulation of hydrological response - except in baseflow-dominated and flood-regulated basins Tendencies to underestimate the region s high evapotranspiration rates
20 Conclusions JULES performance in humid tropical upland systems: Assessment is limited by data uncertainty Good simulation of hydrological response - except in baseflow-dominated and flood-regulated basins Tendencies to underestimate the region s high evapotranspiration rates Strategies to improve the representation of tropical systems: addressing errors in the data Improving the soil representation Incorporating floodplain storage
21 Conclusions JULES performance in humid tropical upland systems: Assessment is limited by data uncertainty Good simulation of hydrological response - except in baseflow-dominated and flood-regulated basins Tendencies to underestimate the region s high evapotranspiration rates Strategies to improve the representation of tropical systems: addressing errors in the data Improving the soil representation Incorporating floodplain storage Discussion paper: Zulkafli, Z., Buytaert, W., Onof, C., Lavado, W., and Guyot, J. L.: A critical assessment of the JULES land surface model hydrology for humid tropical environments, Hydrol. Earth Syst. Sci. Discuss., 9, , doi: /hessd , 2012
22 THANK YOU.
23 References Best et al. (2011), The Joint UK Land Environment Simulator (JULES), model description Part 1: Energy and water fluxes, Geoscientific Model Development, 4, , FAO/IIASA/ISRIC/ISS-CAS/JRC (2009) Harmonized World Soil Database, FAO Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, E.F. Stocker, D.B. Wolff, 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale. J. Hydrometeor., 8, Josse et al. (2007) Digital Ecological Systems Map of the Amazon Basin of Peru and Bolivia, NatureServe. Loveland et al. (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, International Journal of Remote Sensing, 21, Nesbitt, S. and Anders, A.: Very high resolution precipitation climatologies from the Tropical Rainfall Measuring Mission precipitation radar, Geophysical Research Letters, 36, L , org/ /2009gl038026, Sheffield et al. (2006) Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling, Journal of Climate, 19,
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