Abstract
Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044∘ N/S) using an L-band (1.4 GHz) microwave radiometer in 2–3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May–October) and cold (November–April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov–Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters – which also gives the best coverage of the grids throughout CONUS – was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0–D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3-month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.
Highlights
Drought is an extreme condition when water in one or a combination of water stores or water fluxes drops below a defined condition for a prolonged period of time (Wilhite and Glantz, 1985; Wilhite, 2000; AMS, 2012)
The advantages of the Standard Precipitation Index (SPI) include the following: it only relies on precipitation, it can characterize both drought and pluvial conditions, its computation over different timescales can be related to various water resource stores, and it is more comparable across regions with different climates than the Palmer Severity Drought Index (PDSI)
Contrary to the warm season, southern California shows a high a correlation with variable infiltration capacity (VIC) during the cold season, at around 0.9. We attribute this change from cold season to warm season in southern and southern-central California to the irrigation that Soil Moisture Active Passive (SMAP) picks up (Lawston et al, 2017) but VIC does not, since the version used here does not have water management effects
Summary
Drought is an extreme condition when water in one or a combination of water stores (e.g., river, lake, reservoir, snowpack, soil water or groundwater) or water fluxes (precipitation, evapotranspiration or runoff) drops below a defined condition for a prolonged period of time (Wilhite and Glantz, 1985; Wilhite, 2000; AMS, 2012). The advantages of the SPI include the following: it only relies on precipitation, it can characterize both drought and pluvial conditions, its computation over different timescales can be related to various water resource stores (such as soil moisture and groundwater), and it is more comparable across regions with different climates than the Palmer Severity Drought Index (PDSI). Sheffield et al (2004) used simulations from the NLDAS VIC model forced with observed precipitation and near-surface meteorology to develop a drought index based on soil moisture. Especially for applications in parts of the globe with sparse in situ data, is to have an SMAP-based monitoring product that expresses soil moisture in terms of probability percentiles for dry (drought) or wet (pluvial) conditions (Entekhabi et al, 2010).
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