Presentación de PowerPoint

Presentación de PowerPoint

3rd Workshop of the International Precipitation Working Group Melbourne, Australia, 23-27 October 2006 Modeling storm hydrographs in a midsize basin using satellite rainfall estimates Daniel Barrera, Viviana Zucarelli and Mara V. Morresi Universidad Nacional del Litoral CONICET / Universidad de Buenos Aires ARGENTINA Rainfall-runoff models of any type need rainfall areal estimates as input. Characteristics of operational rain-gauge networks: 1. Low spatial density of stations 1. Irregular spatial distribution Intrinsic characteristic of rainfall: High spatial variability Consequence: Important errors in areal rainfall estimates over sub-basins or other model input areas. Location of the Feliciano River Basin

in the Province of Entre Ros (Argentina) 26 South latitude (degrees) 28 30 32 34 36 38 64 62 60 58

56 West longitude (degrees) 54 Feliciano river basin. Sub-basins and drainage system. 30 30.0 Location of Feliciano river basin 30 LATITUD SUR (GRADOS) 31 31.0 31 31 31

31 31 60 60 59 59 59 59 59 59 59 59 59

59 58 32.0 A division in 17 sub-basins and basin segments was made for hydrologic modelling purposes 33.0 Lag time for this mid-size basin (5500 km2): ~ 4-5 days 34.0 60.0 59.0 WEST LONGITUDE (degrees) 58.0

On the other hand: Satellite-based methods of rainfall estimation at a pixel unit are affected by significant errors at present, which depend on the type of rain among other factors. Advantage over point observations: They provide full spatial coverage (precipitation estimations can be performed at all pixels) and account for spatial variability of rainfall. Therefore, they are more adequate to get the area-average rainfall over a sub-basin containing several to many pixels. Location of pixel centers and Paso Medina station (outlet) over the watershed S o u th latit ud e ( d e g re e s ) 30.4 30.6 30.8 31.0

59.6 59.4 59.2 59.0 58.8 West longitude (degrees) 58.6 58.4 Techniques to obtain accumulated precipitation estimates at a given pixel consist on two steps: 1. Estimation of mean spatial rain rate at every pixel 2. Time integration over a lapse assigned to the analyzed image

The Auto-estimator techique (Vicente, Scofield and Menzel, 1998) with further modifications is called Hydro-estimator (HE). A version of this technique was developed at the University of Buenos Aires and is run operationally at the National Meteorological Service of Argentina. Auto-estimator technique: Designed to estimate convective rainfall in an operational way, accumulated on lapses lesser than 1 day, with a spatial resolution of 1 pixel (4km x 4km). A power-law empirical function relates the brightness temperature at the cloud top with the rain rate at the cloud base Rc = ko exp (- Tb1.2) Vicente, Scofield and Menzel, 1998 The relationship applies to unicellular

and multicell convective clouds (cumuloninmbus) at mature stage. A further modification introduced the atmospheric precipitable water as a parameter family of curves Rc = ko exp (- Tb1.2) ko , functions of PW (Scofield and Kuligowski, 2003) Satellite GOES East: 4 sectors of images generation Scanning frequency CONUS: 1 every 15 minutes SOUTHERN HEMISPHERE: 1 every 30 minutes over 70% of the time during the day. 1 every 90 minutes over the lasting 30% of the time. Source: NOAA / NESDIS

To better describe the displacement of cloud systems and minimize the deformation in the obtained rainfall fields, it was proposed the generation of synthetic brightness temperature images interpolated between two consecutive observed images. The image was sectorized in boxes of 4x4 degrees. In order to determine the mean vector displacement of clouds on each box, two criteria were proposed, applied and tested. The vector difference (in pixel units) between pixels with cloud tops at the same tropospheric layer in both the analyzed and the precedent images was searched, which complies with one of the following conditions: The maximum cross-correlation coefficient of temperature values at pixels in two consecutive images; and The highest joint frequency of the same interval class of temperature (or same Storm of April 10, 2002 28S

30S Isoyetal map obtained from GOES plus synthetic images (procedure 2) (same period) 54W 0 56W 20 58W 40 60W

60 62W 100 32S 80 Isoyetal map obtained from GOES images (procedure 1) (6hr period) 28S 30S 32S 62W

60W 58W 56W 54W Estimated area-averaged rainfall over sub-basin 13 Left bars: Procedure 1 Right bars: Procedure 2 60 50 Rainfall (mm) 40 30 20 10

0 1 2 3 4 5 6 7 8 9 10 3-hour lapses starting on Apr/10/02 at 0 UTC

11 12 13 14 6-hour lapses evolution of area-averaged rainfall. Procedure 1 and Procedure 2 90 80 70 PP (mm) 60 50 40 30 20 10 0 1

2 3 4 5 6 Time in 6hr lapses Procedure 1 Procedure 2 7 8 9 OCINE2 Rainfall-Runoff Model

It is a conceptual, pseudo-distributed parameters model developed by the project team, which computes both overland flows and stream flows by applying the kinematic wave theory. Basic equations Q A q x t Continuity for stream segment Q s * A ms Momentum for stream segment y q p f t x Continuity for catchment segment

q c * y mc Momentum for catchment segment x: distance along the stream segment (m); Q: runoff in the stream segment (m3/sec) ; A: area of the wetted stream cross-section (m2) ; q: discharge per unit width in the catchment segment (m2/sec); y: average depth of flow (m); p: rainfall rate (mm/hr); f: infiltration rate (mm/h); c, mc, s, ms, are calibration parameters for the catchment and stream segments, respectively. APPLICATION TO THE FELICIANO RIVER BASIN WITH OUTLET IN PASO MEDINA STATION As part of the calibration process, a segmentation in 17 sub-basins was performed after an analysis of the dynamics of hydrological responses to rainfall inputs. A Topologic framework with 51 segments

(34 for overland flow and 17 for stream flow) was utilized. Storm hydrographs modelled. Due to a lack of information about soil characteristics and soil moisture conditions previous to the storm to be modelled, it is not possible at this stage to predict the runoff component of the rainfall. Therefore, for each of the cases modelled, the volume of the simulated hydrograph has been adjusted to match the volume of the observed hydrograph in order to evaluate the model skill in predicting the distribution of that runoff in time, that is, the shape of the storm hydrograph. Observed and simulated hydrographs Storm starting on April 9, 2002 250 Q (m3/s) 200

150 100 50 0 1 2 3 4 5 6 7 observed

8 9 10 simulated 11 12 13 14 15 Observed and simulated hydrographs Storm starting on April 22, 2002 800 700 Q(m3/sec)

600 500 400 300 200 100 0 1 3 5 7 9 observed 11 13

15 simulated 17 19 Observed and simulated hydrographs Storm starting on December 28, 2002 300 250 Q (m3/sec) 200 150 100 50 0 2 2 3 3

3 3 3 3 3 3 2 3 3 3 3 /0 2 /0 2 /0 1 /0 1 /0 1 /0 1 /0 1 /0 1 /0 1 /0 1 /0 1 /0 1 /0 1 /0 1 /0 2 /1 /1 /0 /0 /0 /0 /0 /0 /1 /0 /0

/0 /0 /0 /0 27 29 31 02 04 06 08 10 12 14 16 18 20 22 24 observed modeled Observed and simulated hydrographs Storm starting on March 9, 2003 250 Q (m3/sec) 200 150 100 50

0 1 2 3 4 5 6 7 8 9 10 11 12

observed 13 14 15 16 simulated 17 18 19 20 21 22 23

Observed and simulated hydrographs Storm starting on April 23, 2003 2500 2000 1500 1000 500 0 1 2 3 4 5

observed 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 sim. f rom HE estimates sim. f rom raingauges CONCLUSI ONS HE estimates lead to compute area-averaged rainf all over large sub-basins which are as good as the ones obtained f rom conventional raingauge networks to be used as input in rainf all-runoff models. The present state-of -the-art permits to implement operational hydrological f orecast systems in ungauged basins of developing countries, based on satellite rainf all estimates as model inputs.

Thank you

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