Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous Basins

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Classification Basin scale: 9 medium and 7 large (less and greater than 1000 km 2 ); Seasonality: warm and cold period (May-Aug and Sep-Nov); Severity: low to moderate and high flow rate (below and above the 90 th percentile of gauge-simulated runoff). Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous Basins Yiwen Mei 1 , Efthymios I. Nikolopoulos 2 , Emmanouil N. Anagnostou 1 and Marco Borga 2 1 Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA 2 Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Padova, Italy This study investigates the error characteristics of six quasi-global satellite precipitation products and associated error propagation in flow simulations for 16 mountainous basin scales (areas ranging from 255 to 6967 km 2 ) and two different periods (May-Aug & Sep-Nov) in northeast Italy. The satellite products used in this study are 3B42-CCA, 3B42-V7, CMORPH and PERSIANN with their respect gauge-adjusted products. To evaluate the error propagation in flood simulations satellite precipitation datasets were used to force a gauge-calibrated hydrologic model to simulate runoff for the 16 basins, and comparing them to the gauge-driven simulated hydrographs for a range of moderate to high flood events spanning a nine- year period (2002 to 2009). Statistics describing the systematic and random error, the temporal similarity and error ratios between precipitation and runoff are presented. Upper Adige River Basin (6967 km 2 ); 104 rain gauges and 143 temperature stations; Integrated Catchment Hydrological Model (ICHYMOD): snow routine, soil moisture routine, flow routine Introduction Study Area Methods Error metrics Mean Relative Error (MRE); Centered Root Mean Square Error (CRMSE); Correlation Coefficient (CC); Ratio between Error Metric (γ). Role of elevation on systematic error Effects of basin scale, seasonality and flow severity Error Propagation May-Aug Sep-Nov 3B42 CMORPH PERSIANN 3B42 CMORPH PERSIANN Mean Basin Elevation (m a.s.l.) MRE in Flow MRE in Rainfall MRE Above 90 th Percentile Below 90 th Percentile CRMSE CC γ MRE γ CRMSE γ CC Satellite Precipitation Products Satellite Precipitation Products Conclusions Systematic error ranged from underestimation to overestimation with the mean basin elevation; Low to moderate flow rate group (below the 90 th percentile threshold) yield higher consistency compared to the high flow rate one; Random errors are reducing and converging with basin scale and from cold to warm period; Gauge-adjusted products outperform their near-real- time counterparts; Lower degree of variability from ratios of random error and temporal similarity metrics for larger basins and warm period cases; Significant dampening effect in random error compared to the other metrics.

Transcript of Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous Basins

Page 1: Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous Basins

Classification

• Basin scale: 9 medium and 7 large (less and greater than 1000 km2);

• Seasonality: warm and cold period (May-Aug and Sep-Nov);

• Severity: low to moderate and high flow rate (below and above the 90th percentile

of gauge-simulated runoff).

Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous BasinsYiwen Mei

1, Efthymios I. Nikolopoulos

2, Emmanouil N. Anagnostou

1and Marco Borga

2

1 Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA

2 Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Padova, Italy

This study investigates the error characteristics of six quasi-global satellite

precipitation products and associated error propagation in flow simulations for 16

mountainous basin scales (areas ranging from 255 to 6967 km2) and two different

periods (May-Aug & Sep-Nov) in northeast Italy. The satellite products used in this

study are 3B42-CCA, 3B42-V7, CMORPH and PERSIANN with their respect

gauge-adjusted products. To evaluate the error propagation in flood simulations

satellite precipitation datasets were used to force a gauge-calibrated hydrologic

model to simulate runoff for the 16 basins, and comparing them to the gauge-driven

simulated hydrographs for a range of moderate to high flood events spanning a nine-

year period (2002 to 2009). Statistics describing the systematic and random error, the

temporal similarity and error ratios between precipitation and runoff are presented.

• Upper Adige River Basin (6967 km2);

• 104 rain gauges and 143 temperature stations;

• Integrated Catchment Hydrological Model (ICHYMOD):

snow routine, soil moisture routine, flow routine

Introduction

Study Area

Methods

Error metrics

• Mean Relative Error (MRE);

• Centered Root Mean Square Error (CRMSE);

• Correlation Coefficient (CC);

• Ratio between Error Metric (γ).

Role of elevation on systematic error

Effects of basin scale, seasonality and flow severity

Error Propagation

May

-Au

gS

ep-N

ov

3B42 CMORPH PERSIANN 3B42 CMORPH PERSIANN

Mean Basin Elevation (m a.s.l.)

MR

E i

n F

low

MR

E i

n R

ain

fall

MRE

Ab

ove

90

th

Per

cen

tile

Bel

ow

90

th

Per

cen

tile

CRMSE CC

γMRE γCRMSE γCC

Satellite Precipitation Products

Satellite Precipitation Products

Conclusions

• Systematic error ranged from underestimation to

overestimation with the mean basin elevation;

• Low to moderate flow rate group (below the 90th

percentile threshold) yield higher consistency

compared to the high flow rate one;

• Random errors are reducing and converging with

basin scale and from cold to warm period;

• Gauge-adjusted products outperform their near-real-

time counterparts;

• Lower degree of variability from ratios of random

error and temporal similarity metrics for larger basins

and warm period cases;

• Significant dampening effect in random error

compared to the other metrics.