Drought is a recurring and extreme hydroclimatic hazard with serious impacts on agriculture and overall society. Delineation and forecasting of agricultural and meteorological drought are essential for water resource management and sustainable crop production. Agricultural drought assessment is defined as the deficit of root-zone soil moisture (RZSM) during active crop growing season, whereas meteorological drought is defined as subnormal precipitation over months to years. Several indices have been used to characterize droughts, however, there is a lack of study focusing on comprehensive comparison among different agricultural and meteorological drought indices for their ability to delineate and forecast drought across major climate regimes and land cover types. This study evaluates the role of RZSM from Soil Moisture Active Passive (SMAP) mission along with two other soil moisture (SM) based indices (e.g., Palmer Z and SWDI) for agricultural and meteorological drought monitoring in comparison with two popular meteorological drought indices (e.g., SPEI and SPI) and a hybrid (Comprehensive Drought Index, CDI) drought index. Results demonstrate that SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought events better than meteorological (e.g., SPI, SPEI) and hybrid (CDI) drought indices, whereas the latter three performed better in delineating meteorological drought across the contiguous USA during 2015–2019. SM-based indices showed skills for forecasting agricultural drought (represented by end-of-growing season gross primary productivity) in the early growing seasons. The results further confirm the key role of SM on ecosystem dryness and corroborate the SM-memory in land-atmosphere coupling.

Rice (Oryza sativa) is a major staple food crop in India occupying about 44 million ha (Mha) of cropped land in meeting food requirements for about 65% of the population. As water scarcity has become a major concern in changing climatic scenarios precise measurements of actual evapotranspiration (ETa) and crop coefficients (Kc) are needed to better manage the limited water resources and improve irrigation scheduling. The eddy covariance (EC) method was used to determine ETa and Kc of tropical lowland rice in eastern India over two years. Reference evapotranspiration (ETa) was estimated by four different approaches– the Food and Agriculture Organization-Penman-Monteith (FAO-PM) method, the Hargreaves, and Samani (HS) method, the Mahringer (MG) method, and pan evaporation (Epan) measurements. Measurements of turbulent and available energy fluxes were taken using EC during two rice growing seasons: dry season (January-May) and wet season (July-November) and also in the fallow period where no crop was grown. Results demonstrated that the magnitude of average ETa during dry seasons (2.86 and 3.32 mm d-1 in 2015 and 2016, respectively) was higher than the wet seasons (2.3 and 2.2 mm d-1) in both the years of the experiment. The FAO-PM method best-represented ETa in this lowland rice region of India as compared to the other three methods. The energy balance was found to be more closed in the dry seasons (75–84%) and dry fallow periods (73–81%) as compared to the wet seasons (42–48%) and wet fallows (33-69%) period of both the years of study, suggesting that lateral heat transport was an important term in the energy balance calculation. The estimated Kc values for lowland rice in dry seasons by the FAO-PM method at the four crop growth stages; namely, initial, crop development, reproductive, and late-season were 0.23, 0.42, 0.64, and 0.90, respectively, in 2015 and 0.32, 0.52, 0.76 and 0.88, respectively, in 2016. The FAO-PM, HS, and MG methods produced reliable estimates of Kc values in dry seasons, whereas Epan; performed better in wet seasons. The results further demonstrated that the Kc values derived for tropical lowland rice in eastern India are different from those suggested by the FAO implying revision of Kc values for regional-scale irrigation planning.