Abstract
Abstract. Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.
Highlights
Examples of human-induced land surface changes include urbanization, deforestation, and agriculture, all of which have significant impacts on local and regional water and energy budgets and hydrologic and biogeochemical processes
Though there have been a number of studies that rely on online estimation of biases (De Lannoy et al, 2007; Reichle et al, 2010), the common practice in land data assimilation studies is to remove the bias between the observations and the model and to use a bias-blind assimilation approach to correct only short-lived model errors
We examine the utility of satellite soil moisture retrievals to detect irrigated areas
Summary
Examples of human-induced land surface changes include urbanization, deforestation, and agriculture, all of which have significant impacts on local and regional water and energy budgets and hydrologic and biogeochemical processes. Though there have been a number of studies that rely on online estimation of biases (De Lannoy et al, 2007; Reichle et al, 2010), the common practice in land data assimilation studies is to remove the bias between the observations and the model and to use a bias-blind assimilation approach to correct only short-lived model errors This is typically achieved by rescaling the observations prior to assimilation, to have the same statistics as the model, using quantile mapping approaches so that the observational climatology matches that of the land model. The article focuses on the impact of various a priori bias correction approaches in data assimilation when the distributions of the model and the observations are significantly different due to unmodeled irrigation processes
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