Northwest Watershed Research Center

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide wellcalibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> .

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation.

Snowmelt is the principal source for soil moisture, ground-water re-charge, and stream-flow in mountainous regions of the western US, Canada, and other similar regions of the world. Information on the timing, magnitude, and contributing area of melt under variable or changing climate conditions is required for successful water and resource management. A coupled energy and mass-balance model ISNOBAL is used to simulate the development and melting of the seasonal snowcover in several mountain basins in California, Idaho, and Utah. Simulations are done over basins varying from 1 to 2500 km2, with simulation periods varying from a few days for the smallest basin, Emerald Lake watershed in California, to multiple snow seasons for the Park City area in Utah. The model is driven by topographically corrected estimates of radiation, temperature, humidity, wind, and precipitation. Simulation results in all basins closely match independently measured snow water equivalent, snow depth, or runoff during both the development and depletion of the snowcover. Spatially distributed estimates of snow deposition and melt allow us to better understand the interaction between topographic structure, climate, and moisture availability in mountain basins of the western US. Application of topographically distributed models such as this will lead to improved water resource and watershed management. Copyright © 1999 John Wiley & Sons, Ltd.

A warm, very wet Pacific storm caused significant flooding in the Pacific Northwest during February 1996. Rapid melting of the mountain snow cover contributed to this flooding. An energy balance snowmelt model is used to simulate snowmelt processes during this event in the Central Cascade Mountains of Oregon. Data from paired open and forested experimental sites at locations at and just below the Pacific Crest were used to drive the model. The event was preceded by cold, stormy conditions that developed a significant snow cover down to elevations as low as 500 m in the Oregon Cascades. At the start of the storm, the depth of the snow cover at the high site (1142 m) was 1·97 m with a snow water equivalent (SWE) of 425 mm, while at the mid-site (968 m) the snow cover was 1·14 m with a SWE of 264 mm. During the 5–6 day period of the storm the open high site received 349 mm of rain, lost 291 mm of SWE and generated 640 mm of runoff, leaving only 0·22 m of snow on the ground. The mid-site received 410 mm of rain, lost 264 mm of SWE to melt and generated 674 mm of runoff, completely depleting the snow cover. Simulations at adjacent forested sites showed significantly less snowmelt during the event. The snow cover under the mature forest at the high site lost only 44 mm of SWE during the event, generating 396 mm of runoff and leaving 0·69 m of snow. The model accurately simulated both snow cover depth and SWE during the development of the snow cover prior to the storm, and the depletion of the snow cover during the event. This analysis shows that because of the high temperature, humidity and relatively high winds in the open sites during the storm, 60–90% of the energy for snowmelt came from sensible and latent heat exchanges. Because the antecedent conditions extended the snow cover to very low elevations in the basin, snowmelt generated by condensation during the event made a significant contribution to the flood. Lower wind speeds beneath the forest canopy during the storm reduced the magnitude of the turbulent exchanges at the snow surface, so the contribution of snowmelt to the runoff from forested areas was significantly less. This experiment shows the sensitivity of snowmelt processes to both climate and land cover, and illustrates how the forest canopy is coupled to the hydrological cycle in mountainous areas. © 1998 John Wiley & Sons, Ltd.

Wind is widely recognized as one of the dominant controls of snow accumulation and distribution in exposed alpine regions. Complex and highly variable wind fields in rugged terrain lead to similarly complex snow distribution fields with areas of no snow adjacent to areas of deep accumulation. Unfortunately, these complexities have limited inclusion of wind redistribution effects in spatial snow distribution models. In this study the difficulties associated with physically exhaustive wind field modeling are avoided and terrain-based parameters are developed to characterize wind effects. One parameter, , was based on maximum upwind slopes relative to seasonally averaged winds to characterize the wind scalar at each pixel location in an alpine basin. A second parameter, , measured upwind breaks in slope from a given location and was combined with an upwind application of to create a drift delineator parameter, D0, which was used to delineate sites of intense redeposition on lee slopes. Based on 504 snow depth samples from a May 1999 survey of the upper Green Lakes Valley, Colorado, the correlation of the developed parameters to the observed snow distribution and the effect of their inclusion in a spatial snow distribution model were quantified. The parameter was found to be a significant predictor, accounting for more of the variance in the observed snow depth than could be explained by elevation, solar radiation, or slope. Samples located in D0-delineated drift zones were shown to have significantly greater depths than samples located in nondrift zones. A regression tree model of snow distribution based on a predictor variable set of , D0, elevation, solar radiation, and slope explained 8%–23% more variance in the observed snow distribution, and performed noticeably better in unsampled areas of the basin, compared to a regression tree model based on only the latter three predictors.

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

Correlations between suspended sediment load rating parameters, river basin morphology, and climate provide information about the physical controls on the sediment load in rivers and are used to create predictive equations for the sediment rating parameters. Long‐term time‐averaged values of discharge, suspended load, flow duration, flow peakedness, and temporally averaged values of precipitation, temperature, and range in temperature were coupled with the drainage area and basin relief to establish statistical relationships with the sediment rating parameters for 59 gauging stations. Rating parameters ( a and b ) are defined by a power law relating daily discharge values of a river ( Q ) and its sediment concentration C s , where C s = aQ b . The rating coefficient a (the mathematical concentration at Q = 1 m 3 /s) is inversely proportional to the long‐term mean discharge and is secondarily related to the average air temperature and the basin's topographic relief. The rating exponent b (the log‐log slope of the power law) correlates most strongly with the average air temperature and basin relief and has lesser correlations with the long‐term load of the river (which is related to basin relief and drainage area). The rating equation describes the long‐term character of the suspended sediment load in a river. Each river undergoes higher‐frequency variability (decadal, interannual, and storm event) around this characteristic response, controlled by weather patterns and channel recovery from extreme precipitation events.

Abstract Hydraulic connectivity on hillslopes and the existence of preferred soil moisture states in a catchment have important controls on runoff generation. In this study we investigate the relationships between soil moisture patterns, lateral hillslope flow, and streamflow generation in a semi‐arid, snowmelt‐driven catchment. We identify five soil moisture conditions that occur during a year and present a conceptual model based on field studies and computer simulations of how streamflow is generated with respect to the soil moisture conditions. The five soil moisture conditions are (1) a summer dry period, (2) a transitional fall wetting period, (3) a winter wet, low‐flux period, (4) a spring wet, high‐flux period, and (5) a transitional late‐spring drying period. Transitions between the periods are driven by changes in the water balance between rain, snow, snowmelt and evapotranspiration. Low rates of water input to the soil during the winter allow dry soil regions to persist at the soil–bedrock interface, which act as barriers to lateral flow. Once the dry‐soil flow barriers are wetted, whole‐slope hydraulic connectivity is established, lateral flow can occur, and upland soils are in direct connection with the near‐stream soil moisture. This whole‐slope connectivity can alter near‐stream hydraulics and modify the delivery of water, pressure, and solutes to the stream. Copyright © 2005 John Wiley &amp; Sons, Ltd.

Abstract 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.

Abstract This work advances a unified approach to process‐based hydrologic modeling, which we term the “Structure for Unifying Multiple Modeling Alternatives (SUMMA).” The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within‐canopy and below‐canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty.

The standard 8" nonrecording precipitation gauge has been used historically by the National Weather Service (NWS) as the official precipitation measurement instrument of the U.S. climate station network. From 1986 to 1992, the accuracy and performance of this gauge (unshielded or with an Alter shield) were evaluated during the WMO Solid Precipitation Measurement Intercomparison at three stations in the United States and Russia, representing a variety of climate, terrain, and exposure. The double-fence intercomparison reference (DFIR) was the reference standard used at all the intercomparison stations in the Intercomparison project. The Intercomparison data collected at different sites are compatible with respect to the catch ratio (gauge measured/DFIR) for the same gauges, when compared using wind speed at the height of gauge orifice during the observation period. The effects of environmental factors, such as wind speed and temperature, on the gauge catch were investigated. Wind speed was found to be the most important factor determining gauge catch when precipitation was classified into snow, mixed, and rain. The regression functions of the catch ratio versus wind speed at the gauge height on a daily time step were derived for various types of precipitation. Independent checks of the equations have been conducted at these intercomparison stations and good agreement was obtained. Application of the correction procedures for wind, wetting loss, and trace amounts was made on a daily basis at Barrow, Alaska, for 1982 and 1983, and, on average, the gauge-measured precipitation was increased by 20% for rain and 90% for snow.

We model the spatial distribution of snow depth across a wind‐dominated alpine basin using a geostatistical approach with a complex variable mean. Snow depth surveys were conducted at maximum accumulation from 1997 through 2003 in the 2.3 km 2 Green Lakes Valley watershed in Colorado. We model snow depth as a random function that can be decomposed into a deterministic trend and a stochastic residual. Three snow depth trends were considered, differing in how they model the effect of terrain parameters on snow depth. The terrain parameters considered were elevation, slope, potential radiation, an index of wind sheltering, and an index of wind drifting. When nonlinear interactions between the terrain parameters were included and a multiyear data set was analyzed, all five terrain parameters were found to be statistically significant in predicting snow depth, yet only potential radiation and the index of wind sheltering were found to be statistically significant for all individual years. Of the five terrain parameters considered, the index of wind sheltering was found to have the greatest effect on predicted snow depth. The methodology presented in this paper allows for the characterization of the spatial correlation of model residuals for a variable mean model, incorporates the spatial correlation into the optimization of the deterministic trend, and produces smooth estimate maps that may extrapolate above and below measured values.

Abstract In mountainous regions, wind plays a prominent role in determining snow accumulation patterns and turbulent heat exchanges, strongly affecting the timing and magnitude of snowmelt runoff. In this study, digital terrain analysis was employed to quantify aspects of the upwind topography related to wind shelter and exposure, to efficiently develop a distributed time‐series of snow accumulation rates and wind speeds to force a distributed snow model. Parameters are presented that determined each grid cell's topographic exposure and potential for drift development relative to observed winds. Using meteorological data taken from both an exposed and a sheltered site in the Reynolds Mountain East watershed (0·38 km 2 ) in southwestern Idaho, the terrain parameters were used to distribute rates of snow accumulation and wind speeds at an hourly time step for input to ISNOBAL, an energy and mass balance snow model. Model runs were initiated prior to the development of the seasonal snow cover and continued through complete meltout for the 1986 (precipitation 128% of average), 1987 (66%), and 1989 (108%) water years. A comprehensive dataset consisting of a time series of aerial photographs taken during meltout, measured runoff, and snow data from the sheltered meteorological site were used to validate the simulations. ISNOBAL forced with accumulation rates and wind fields generated from the applied terrain parameterizations accurately modelled the observed snow distribution (including the formation of drifts and scoured wind‐exposed ridges) and snowmelt runoff for all three years of study. By contrast, ISNOBAL forced with spatially constant accumulation rates and wind speeds taken from the sheltered meteorological site, a typical snow‐monitoring site, overestimated peak snowmelt inputs and tended to underestimate snowmelt inputs prior to the runoff peak. Published in 2002 by John Wiley &amp; Sons, Ltd.

Abstract. The controls on the spatial distribution of soil moisture include static and dynamic variables. The superposition of static and dynamic controls can lead to different soil moisture patterns for a given catchment during wetting, draining, and drying periods. These relationships can be further complicated in snow-dominated mountain regions where soil water input by precipitation is largely dictated by the spatial variability of snow accumulation and melt. In this study, we assess controls on spatial and temporal soil moisture variability in a small (0.02 km2), snow-dominated, semi-arid catchment by evaluating spatial correlations between soil moisture and site characteristics through different hydrologic seasons. We assess the relative importance of snow with respect to other catchment properties on the spatial variability of soil moisture and track the temporal persistence of those controls. Spatial distribution of snow, distance from divide, soil texture, and soil depth exerted significant control on the spatial variability of moisture content throughout most of the hydrologic year. These relationships were strongest during the wettest period and degraded during the dry period. As the catchment cycled through wet and dry periods, the relative spatial variability of soil moisture tended to remain unchanged. We suggest that the static properties in complex terrain (slope, aspect, soils) impose first order controls on the spatial variability of snow and resulting soil moisture patterns, and that the interaction of dynamic (timing of water input) and static influences propagate that relative constant spatial variability through most of the hydrologic year. The results demonstrate that snow exerts significant influence on how water is retained within mid-elevation semi-arid catchments and suggest that reductions in annual snowpacks associated with changing climate regimes may strongly influence spatial and temporal soil moisture patterns and catchment physical and biological processes.

Widespread interest in soil water content (θ, m 3 m −3 ) information for both management and research has led to the development of a variety of soil water content sensors. In most cases, critical issues related to sensor calibration and accuracy have received little independent study. We investigated the performance of the Hydra Probe soil water sensor with the following objectives: (i) quantify the inter‐sensor variability, (ii) evaluate the applicability of data from two commonly used calibration methods, and (iii) develop and test two multi‐soil calibration equations, one general, “default” calibration equation and a second calibration that incorporates the effects of soil properties. The largest deviation in the real component of the relative dielectric permittivity ε r ′ determined with the Hydra Probe using 30 sensors in ethanol corresponded to a water content deviation of about 0.012 m 3 m −3 , indicating that a single calibration could be generally applied. In layered (wet and dry) media, ε r ′ determined with the Hydra Probe was different from that in uniform media with the same water content. In uniform media, θ was a linear function of √ε r ′ We used this functional relationship to describe individual soil calibrations and the multi‐soil calibrations. Individual soil calibrations varied independently of clay content but were correlated with dielectric loss. When applied to the 19‐soil test data set, the general calibration outperformed manufacturer‐supplied calibrations. The average θ difference, evaluated between and , was 0.019 m 3 m −3 for the general equation and 0.013 m 3 m −3 for the loss‐corrected equation.

Spatial variability in snow accumulation and melt owing to topographic effects on solar radiation, snow drifting, air temperature and precipitation is important in determining the timing of snowmelt releases. Precipitation and temperature effects related to topography affect snowpack variability at large scales and are generally included in models of hydrology in mountainous terrain. The effects of spatial variability in drifting and solar input are generally included only in distributed models at small scales. Previous research has demonstrated that snowpack patterns are not well reproduced when topography and drifting are ignored, implying that larger scale representations that ignore drifting could be in error. Detailed measurements of the spatial distribution of snow water equivalence within a small, intensively studied, 26-ha watershed were used to validate a spatially distributed snowmelt model. These observations and model output were then compared to basin-averaged snowmelt rates from a single-point representation of the basin, a two-region representation that captures some of the variability in drifting and aspect and a model with distributed terrain but uniform drift. The model comparisons demonstrate that the lumped, single-point representation and distributed terrain with uniform drift both yielded poor simulations of the basin-averaged surface water input rate. The two-point representation was a slight improvement, but the late season melt required for the observed stream-flow was not simulated because the deepest drifts were not represented. These results imply that representing the effects of subgrid variability of snow drifting is equally or more important than representing subgrid variability in solar radiation. © 1998 John Wiley & Sons, Ltd.

Soil water retention characteristics are needed to describe the movement and storage of water in soils. Since conventional laboratory methods of obtaining these are expensive and time consuming, a method of estimating soil water retention at various matric potentials from readily available soil properties was investigated. Data from 2543 soil horizons representing many agricultural areas of the country were utilized. The soil properties investigated were the percent sand, silt and clay, the percent organic matter, the bulk density, median particle size, particle size index, and the -0.33 and -15 bar soil water contents. A series of regression equations for predicting soil water retention at 12 matric potentials ranging from -0.04 to -15 bar were developed utilizing different combinations of soils data.

Abstract Measurements were conducted in coniferous forests of differing density, insolation and latitude to test whether air temperatures are suitable surrogates for canopy temperature in estimating sub‐canopy longwave irradiance to snow. Air temperature generally was a good representation of canopy radiative temperature under conditions of low insolation. However during high insolation, needle and branch temperatures were well estimated by air temperature only in relatively dense canopies and exceeded air temperatures elsewhere. Tree trunks exceeded air temperatures in all canopies during high insolation, with the relatively hottest trunks associated with direct interception of sunlight, sparse canopy cover and dead trees. The exitance of longwave radiation from these relatively warm canopies exceeded that calculated assuming canopy temperature was equal to air temperature. This enhancement was strongly related to the extinction of shortwave radiation by the canopy. Estimates of sub‐canopy longwave irradiance using either two‐energy source or two thermal regime approaches to evaluate the contribution of canopy longwave exitance performed better than did estimates that used only air temperature and sky view. However, there was little evidence that such corrections are necessary under cloudy or low solar insolation conditions. The longwave enhancement effect due to shortwave extinction was important to sub‐canopy longwave irradiance to snow during clear, sunlit conditions. Longwave enhancement increased with increasing solar elevation angle and decreasing air temperature. Its relative importance to longwave irradiance to snow was insensitive to canopy density. As errors from ignoring enhanced longwave contributions from the canopy accumulate over the winter season, it is important for snow energy balance computations to include the enhancement in order to better calculate snow internal energy and therefore the timing and magnitude of snowmelt and sublimation. Copyright © 2009 John Wiley &amp; Sons, Ltd.

The amount and spatial distribution of deep drainage (downward movement of water across the bottom of the root zone) and groundwater recharge affect the quantity and quality of increasingly limited groundwater in arid and semiarid regions. We synthesize research from the fields of ecology and hydrology to address the issue of deep drainage in arid and semiarid regions. We start with a recently developed hydrological model that accurately simulates soil water potential and geochemical profiles measured in thick (>50 m), unconsolidated vadose zones. Model results indicate that, since the climate change that marked the onset of the Holocene period 10 000–15 000 years ago, there has been no deep drainage in vegetated interdrainage areas and that continuous, relatively low (<−1 MPa) soil water potentials have been maintained at depths of 2–3 m. A conceptual model consistent with these results proposes that the native, xeric-shrub-dominated, plant communities that gained dominance during the Holocene generated and maintained these conditions. We present three lines of ecological evidence that support the conceptual model. First, xeric shrubs have sufficiently deep rooting systems with low extraction limits to generate the modeled conditions. Second, the characteristic deep-rooted soil–plant systems store sufficient water to effectively buffer deep soil from climatic fluctuations in these dry environments, allowing stable conditions to persist for long periods of time. And third, adaptations resulting in deep, low-extraction-limit rooting systems confer significant advantages to xeric shrubs in arid and semiarid environments. We then consider conditions in arid and semiarid regions in which the conceptual model may not apply, leading to the expectation that portions of many arid and semiarid watersheds supply some deep drainage. Further ecohydrologic research is required to elucidate critical climatic and edaphic thresholds, evaluate the role of important physiological processes (such as hydraulic redistribution), and evaluate the role of deep roots in terms of carbon costs, nutrient uptake, and whole-plant development.