Implications of A Priori Parameters on Calibration in Conditions of Varying Terrain Characteristics: Case Study of the SAC-SMA Model in Eastern United States

This study seeks to advance the knowledge about the effect of the Sacramento Soil Moisture counting Model (SAC-SMA) a priori parameters on calibration. We investigated the catchment characteristics where calibration is most affected by the limitations in the a priori parameters and we studied the effect on the modeled processes. The a priori SAC-SMA model parameters were determined from soil-derived physical expressions that make use of the soil’s physical properties. The study employed 63 catchments from the eastern United States (US). The model calibration employed the Shuffle-Complex algorithm (SCE-UA) and used the a priori parameters as default allowing for ±35% as a range of deviation. The model efficiency after calibration was sensitive to the catchment landscape properties, particularly the soil texture and topography. The highest efficiency was obtained in conditions of well-drained soils and flat topography where the saturation excess overland flow is predominant. Most of the catchments with smaller efficiency had poorly drained soils where mountainous and forested catchments of predominant subsurface stormflow had the lowest efficiency. The current regional study shows that improvements of SAC-SMA a priori parameters are crucial to foster their operational use for calibration and prediction at ungauged catchments.

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Quantification of the effect of modeled lightning NO<sub>2</sub> on UV–visible air mass factors

Atmospheric Measurement Techniques ◽

10.5194/amt-10-4403-2017 ◽

2017 ◽

Vol 10 (11) ◽

pp. 4403-4419 ◽

Cited By ~ 10

Author(s):

Joshua L. Laughner ◽

Ronald C. Cohen

Keyword(s):

United States ◽

A Priori ◽

Early Summer ◽

Lightning Flash ◽

Eastern United States ◽

Ozone Monitoring Instrument ◽

Reanalysis Dataset ◽

Near Surface ◽

Air Mass ◽

Convective Clouds

Abstract. Space-borne measurements of tropospheric nitrogen dioxide (NO2) columns are up to 10x more sensitive to upper tropospheric (UT) NO2 than near-surface NO2 over low-reflectivity surfaces. Here, we quantify the effect of adding simulated lightning NO2 to the a priori profiles for NO2 observations from the Ozone Monitoring Instrument (OMI) using modeled NO2 profiles from the Weather Research and Forecasting–Chemistry (WRF-Chem) model. With observed NO2 profiles from the Deep Convective Clouds and Chemistry (DC3) aircraft campaign as observational truth, we quantify the bias in the NO2 column that occurs when lightning NO2 is not accounted for in the a priori profiles. Focusing on late spring and early summer in the central and eastern United States, we find that a simulation without lightning NO2 underestimates the air mass factor (AMF) by 25 % on average for common summer OMI viewing geometry and 35 % for viewing geometries that will be encountered by geostationary satellites. Using a simulation with 500 to 665 mol NO flash−1 produces good agreement with observed NO2 profiles and reduces the bias in the AMF to <  ±4 % for OMI viewing geometries. The bias is regionally dependent, with the strongest effects in the southeast United States (up to 80 %) and negligible effects in the central US. We also find that constraining WRF meteorology to a reanalysis dataset reduces lightning flash counts by a factor of 2 compared to an unconstrained run, most likely due to changes in the simulated water vapor profile.

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Estimating a-priori kinematic wave model parameters based on regionalization for flash flood forecasting in the Conterminous United States

Journal of Hydrology ◽

10.1016/j.jhydrol.2016.06.011 ◽

2016 ◽

Vol 541 ◽

pp. 421-433 ◽

Cited By ~ 20

Author(s):

Humberto Vergara ◽

Pierre-Emmanuel Kirstetter ◽

Jonathan J. Gourley ◽

Zachary L. Flamig ◽

Yang Hong ◽

...

Keyword(s):

United States ◽

Flash Flood ◽

A Priori ◽

Wave Model ◽

Flood Forecasting ◽

Model Parameters ◽

Kinematic Wave ◽

Kinematic Wave Model ◽

Flash Flood Forecasting

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A comparison of regionalisation methods for catchment model parameters

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-2-509-2005 ◽

2005 ◽

Vol 2 (2) ◽

pp. 509-542 ◽

Cited By ~ 37

Author(s):

J. Parajka ◽

R. Merz ◽

G. Blöschl

Keyword(s):

Snow Cover ◽

Predictive Accuracy ◽

Model Parameters ◽

Stream Network ◽

Ungauged Catchments ◽

Verification Period ◽

Model Efficiency ◽

Complete Set ◽

Rainfall Runoff Model ◽

Catchment Model

Abstract. In this study we examine the relative performance of a range of methods for transposing catchment model parameters to ungauged catchments. We calibrate 11 parameters of a semi-distributed conceptual rainfall-runoff model to daily runoff and snow cover data of 320 Austrian catchments in the period 1987-1997 and verify the model for the period 1976-1986. We evaluate the predictive accuracy of the regionalisation methods by jack-knife cross-validation against daily runoff and snow cover data. The results indicate that two methods perform best. The first is a kriging approach where the model parameters are regionalised independently from each other based on their spatial correlation. The second is a similarity approach where the complete set of model parameters is transposed from a donor catchment that is most similar in terms of its physiographic attributes (mean catchment elevation, stream network density, lake index, areal proportion of porous aquifers, land use, soils and geology). For the calibration period, the median Nash-Sutcliffe model efficiency ME of daily runoff is 0.67 for both methods as compared to ME=0.72 for the at-site simulations. For the verification period, the corresponding efficiencies are 0.62 and 0.66. All regionalisation methods perform similar in terms of simulating snow cover.

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