Hydrological Modeling in a Forested micro-catchment in Central Amazonia Luz Adriana Cuartas Pineda Javier Tomasella Carlos Nobre Antonio Donato Nobre Camilo Daleles Rennó Ralph Trancoso Maria Terezinha F Monteiro Manaus – AM November 2008

GOALS General: To improve the understanding of the complex interactions between the surface and the atmosphere in forested micro-catchment in Central Amazonia, in order to improve model representation of such processes at different spatial scales. Specific goals: To quantify different components of the hydrological cycle. To apply and test a micro-scale hydrological model. To assess the suitability of aggregation rules used in macro-scale hydrological models, in particularly in forested catchments.

Experimental site

Experimental site Nested micro-catchments Asu1 0,95 Asu2 6,46 Asu3 Área (km2) Asu1 0,95 Asu2 6,46 Asu3 12,43 Precipitation Interception Evaporation Soil moisture Groundwater levels Dicharge

Distributed Hydrological Modelling

DHSVM - Distributed Hydrology Soil Vegetation Model Physically based model, that explicitly estimates the spatial distribution of moisture, energy fluxes, and runoff generation by subdividing the model domain into small computational grid elements using the spatial resolution of an underlying digital elevation model (DEM). Spatial resolution: 5 - 30 m → up to 100 km2 100 m → between 100 – 104 km2 Time resolution: 1h – 1d. (Source: Wigmosta et al., 1994)

Data Inputs for DHSVM Digital Elevation Model - DEM Rock depth Soil Map Vegetation Drainage network Met data

Digital Elevation Model- DEM DHSVM Input Data Digital Elevation Model- DEM Shuttle Radar Topographic Mission - SRTM

Data Input for DHSVM Soil and Vegetation Maps (Source: Rennó et al., 1988) Imagem Landsat Soil and Vegetation Maps

General parameters Soil parameters

Vegetation parameters

DHSVM Results Soil Moisture Fitting and validation of DHSVM for the 2nd order catchment

DHSVM Results Groundwater Depth Evapotranspiration

DHSVM Results Discharge

Results for the 1st order catchment (without calibration)

Results for the 3rd order catchment (without calibration)

Lumped Hydrological Modelling

PDM - Probability Distributed Model (Source: Wooldridge et al., 2001) (Soruce: Moore e Bell (2002) e Moore (2007) (Source: Moore (2007) Storage capacity(c’): Random variable  f(c)

PDM results for the 2nd order Parameter Asu2 Units cmax 2703,30 mm cmin 58,02 b 1,62 – kd 1,88E-4 h mm-1 kg 0,066 h mm-2

PDM results for the 1st and 3rd order Parameter Asu1 Asu3 cmax 2703,30 cmin 58,02 b 1,86 1,02 kd 1,88E-4 kg 0,066

PDM Results

Variation of the b parameter of the PDM model across scales Pixel 10x10 km % of flooded areas Landscape position (%) Baixio Footslope Slope Plateau Min 0,09 14,71 19,28 14,68 22,73 Max 16,52 23,05 29,22 33,91 31,08 9 0,69 20,68 22,00 33.91 Landscape position Topographic index:

How to aggregated hydrological information at different scales? Using HAND for determine hydrological response units

CONCLUSIONS The overall performance of the DHSVM was satisfactory, indicating that the formulation is adequate for simulating tropical catchments. DHSVM could be potentially useful for the application on to other sites of Amazonia and for catchment with different land uses. The PDM model was validated at different scales and simulations were close to observation at all scales tested. PDM simulations produced more accurate results than the DHSVM model. Considering the PDM has a small number of parameters, it seems clear that the formulation has high potential for application of hydrological model at large scales. Results showed that PDM parameters are potentially related to topographic characteristics at diferent spatial scales. It should be noted, however, that the areas tested are hydrologically similar.

Obrigado to the following COLLABORATOR$$$ Obrigado to the following COLLABORATOR$$$! CTENERG CTHIDRO LBA Carbonsink LBA Fase II PPG7 Ecocarbon GEOMA WOTRO