In this study we estimate a time series of regional groundwater anomalies by combining terrestrial water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) satellite mission with in situ soil moisture observations from the Oklahoma Mesonet. Using supplementary data from the Department of Energy's Atmospheric Radiation Measurement (DOE ARM) network, we develop an empirical scaling factor with which to relate the soil moisture variability in the top 75 cm sampled by the Mesonet sites to the total variability in the upper 4 m of the unsaturated zone. By subtracting this estimate of the full unsaturated zone soil moisture anomalies, we arrive at a time series of groundwater anomalies, spatially averaged over a region approximately 280,000 km² in area. Results are compared to observed well level data from a larger surrounding region, and show consistent phase and relative inter-annual variability.

Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r ² = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D ₂ class, moderate drought) to 77 % (0 and D ₀ class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.