The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

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Improved understanding of unsaturated fl ow and transport processes is limited by the lack of appropriate in situ measurement techniques. Th is study was conducted to determine whether two noninvasive cross-borehole geophysical methods combined could be used to estimate two important unsaturated zone transport parameters, namely the pore water velocity and longitudinal dispersivity. Cross-borehole electrical resistivity tomography and ground penetrating radar were used to estimate temporal and spatial variation of electrical resistivity and water content, respectively, during a 20-d forced infi ltration experiment. Th e resulting one-dimensional profi les and two-dimensional images of moisture content and electrical resistivity were subsequently combined to estimate solute tracer concentration. Th e results were used to analyze the downward migration and vertical spreading of water and tracer mass. Th e two geophysical methods provided independent estimates of soil moisture content and electrical resistivity that were spatially and temporally consistent. Th e observed changes in moisture content and electrical resistivity were, as a fi rst approximation, used in a one-dimensional moment analysis. Th e transport behavior was found to be very susceptible to layering of the subsurface. Even slight reductions in grain size apparently lead to fl ow barriers and associated lateral fl ow, resulting in tracer mass loss, reduced vertical pore water velocity, and increased longitudinal dispersivity. Synthetic data showed that the estimated unsaturated transport parameters (i.e., pore water velocity and longitudinal dispersivity) and the mass estimate were infl uenced by the selected electrical resistivity tomography inversion routine. In eff ect, an overprediction of all three parameters was observed.

The cosmic ray neutron method was developed for intermediate-scale soil moisture detection, but may potentially be used for other hydrological applications. The neutron signal of different hydrogen pools is poorly understood and separating them is difficult based on neutron measurements alone. Including neutron transport modeling may accommodate this shortcoming. However, measured and modeled neutrons are not directly comparable. Neither the scale nor energy ranges are equivalent, and the exact neutron energy sensitivity of the detectors is unknown. Here a methodology to enable comparability of the measured and modeled neutrons is presented. The usual cosmic ray soil moisture detector measures moderated neutrons by means of a proportional counter surrounded by plastic, making it sensitive to epithermal neutrons. However, that configuration allows for some thermal neutrons to be measured. The thermal contribution can be removed by surrounding the plastic with a layer of cadmium, which absorbs neutrons with energies below 0.5 eV. Likewise, cadmium shielding of a bare detector allows for estimating the epithermal contribution. First, the cadmium difference method is used to determine the fraction of thermal and epithermal neutrons measured by the bare and plastic-shielded detectors, respectively. The cadmium difference method results in linear correction models for measurements by the two detectors, and has the greatest impact on the neutron intensity measured by the moderated detector at the ground surface. Next, conversion factors are obtained relating measured and modeled neutron intensities. Finally, the methodology is tested by modeling the neutron profiles at an agricultural field site and satisfactory agreement to measurements is found.

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[1] This paper presents a quantitative comparison of plausible climate and land use change impacts on the hydrology of a large-scale agricultural catchment. An integrated, distributed hydrological model was used to simulate changes in the groundwater system and its discharge to rivers and drains for two climate scenarios (2071-2100). Annual groundwater recharge increased significantly (especially the B2 scenario), giving higher groundwater heads and stream discharges and amplifying the seasonal dynamics significantly. Owing to drier summers, irrigation volumes increased by up to 90% compared to current values. Changing the land use from grass to forest had a minor effect on groundwater recharge, whereas CO 2 effects on transpiration resulted in a relatively large increase in recharge. This study has shown that climate change has the most substantial effect on the hydrology in this catchment, whereas other factors such as irrigation, CO 2 effects on transpiration, and land use changes affect the water balance to a lesser extent.

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