Comparison of soil wetness from multiple models over Australia with observations. Issue 1 (22nd January 2017)

• Record Type: Journal Article
• Authors: Vinodkumar, ; Dharssi, I.; Bally, J.; Steinle, P.; McJannet, D.; Walker, J.
• Is Part Of: Water resources research. Volume 53:Issue 1(2017)
• Page Start: 633
• Page End: 646
• Publication Date: 2017-01-22
• Subjects: soil moisture -- verification -- remote sensing -- wildfire -- ASCAT -- drought index; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2015WR017738
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: The McArthur Forest Fire Danger Index used in Australia for operational fire warnings has a component representing fuel availability called the Drought Factor (DF). The DF is partly based on soil moisture deficit, calculated as either the Keetch‐Byram Drought Index (KBDI) or Mount's Soil Dryness Index (MSDI). The KBDI and MSDI are simplified water balance models driven by observation based daily rainfall and temperature. In this work, gridded KBDI and MSDI analyses are computed at a horizontal resolution of 5 km and are verified against in‐situ soil moisture observations. Also verified is another simple model called the Antecedent Precipitation Index (API). Soil moisture analyses from the Australian Community Climate and Earth System Simulator (ACCESS) global Numerical Weather Prediction (NWP) system as well as remotely sensed soil wetness retrievals from the Advanced Scatterometer (ASCAT) are also verified. The verification shows that the NWP soil wetness analyses have greater skill and smaller biases than the KBDI, MSDI and API analyses. This is despite the NWP system having a coarse horizontal resolution and not using observed precipitation. The average temporal correlations (root mean square difference) between cosmic ray soil moisture monitoring facility observations and modeled or remotely sensed soil wetness are 0.82 (0.15 ±0.02), 0.66 (0.33 ±0.07), 0.77 (0.20 ±0.03), 0.74 (0.22 ±0.03) and 0.83 (0.18 ±0.04) for NWP, KBDI, MSDI, API and ASCAT. The results from this study suggests that analyses of soil moisture can be greatly improved by using physically based land surface models, remote sensing measurements and data assimilation. Key Points: Simple water balance models have less skill than weather prediction system soil moisture analyses Weather prediction system soil moisture analyses are unbiased and capture the seasonal variations The remotely sensed ASCAT soil wetness product is of good quality