Evaluation and Transferability of the Noah Land Surface Model in Semiarid Environments

Abstract

This paper investigates the performance of the National Centers for Environmental Prediction (NCEP) Noah land surface model at two semiarid sites in southern Arizona. The goal is to evaluate the transferability of calibrated parameters (i.e., direct application of a parameter set to a "similar" site) between the sites and to analyze model performance under the various climatic conditions that can occur in this region. A multicriteria, systematic evaluation scheme is developed to meet these goals. Results indicate that the Noah model is able to simulate sensible heat, ground heat, and ground temperature observations with a high degree of accuracy, using the optimized parameter sets. However, there is a large influx of moist air into Arizona during the monsoon period, and significant latent heat flux errors are observed in model simulations during these periods. The use of proxy site parameters (transferred parameter set), as well as traditional default parameters, results in diminished model performance when compared to a set of parameters calibrated specifically to the flux sites. Also, using a parameter set obtained from a longer-time-frame calibration (i.e., a 4-yr period) results in decreased model performance during nonstationary, short-term climatic events, such as a monsoon or El Niño. Although these results are specific to the sites in Arizona, it is hypothesized that these results may hold true for other case studies. In general, there is still the opportunity for improvement in the representation of physical processes in land surface models for semiarid regions. The hope is that rigorous model evaluation, such as that put forth in this analysis, and studies such as the Project for the Intercomparison of Land-Surface Processes (PILPS) San Pedro-Sevilleta, will lead to advances in model development, as well as parameter estimation and transferability, for use in long-term climate and regional environmental studies.

Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

“…Folbwing this work, Liu et al (2004) perfomed con&olled paameter estimation studies of offline and p&iaIIy-eoupled models and examhed the effects of hcl&ing atmospheSe fin addition to soil a;nd vegetation} paameters in the optimization. Hogue et al (2005) were entirely based on qualitative image-differencing, they were able to isolate the impst of soil properties on the image differemes versus &at of initial soil moistwe, land cover, and raSall distri-btttion The impact of antecedent precipitation is suggested for &her study, as it geatly impacts the stage of soil dxying being m i t w e d by $he PBMR.…”

Land Surface Models Evaluation for Two Different Land-Cover Types: Cropland and Forest

“…The model contains four soil layers measuring 10, 20, 60, and 100 cm, and its root zone depth can be changed spatially according to the vegetation class (De Haan et al 2007). The model can further be implemented for 13 vegetation covers and 9 soil types (Hogue et al 2005). The Noah LSM considers the soil surface resistance according to the relationship between the evaporation efficiency and soil water content (Yang et al 2009).…”

“…In most cases, the root-mean-square error (RMSE) was 20 -25%. Hogue et al (2005) identified that the Noah LSM accurately generated energy budget components when compared with in situ data at two semiarid sites in southern Arizona.…”

“…Over the past few decades the focus of LSMs research studies has been land-surface developments and parameterizations (Avissar and Pielke 1989;Li and Avissar 1994;Oleson et al 1997;Liang and Guo 2003;Kahan et al 2006;Han et al 2008;Patil et al 2011). A number of experiments have been conducted to develop and evaluate LSMs with the goal of facilitating their advancement (Hogue et al 2005).…”

Impact of Land Model Calibration on Coupled Land–Atmosphere Prediction

“…In order to combat these limitations, numerous attempts have been made to calibrate (or 195 'optimize') LSM parameters using observations of state variables such as soil moisture and 196 surface temperature as constraints (Gupta et al 1999;Hess 2001;Hogue et al 2005;Liu et al 197 2003Liu et al 197 , 2004Liu et al 197 , 2005Santanello et al 2007;Peters-Lidard et al 2008;Harrison et al 2012 calibration, and highlight the computational difficulty in calibrating large parameter sets in 220 coupled models (which has precluded the calibration of a full 3-D mesoscale model to date). As 221 a result, they suggest a stepwise procedure of offline before coupled calibration as an alternative.…”