No abstract

Evapotranspiration (ET) comprises a major portion of the water budget in forests, yet few studies have measured or estimated ET in semi‐arid, high‐elevation ponderosa pine forests of the south‐western USA or have investigated the capacity of models to predict ET in disturbed forests. We measured actual ET with the eddy covariance (eddy) method over 4 years in three ponderosa pine forests near Flagstaff, Arizona, that differ in disturbance history (undisturbed control, wildfire burned, and restoration thinning) and compared these measurements (415–510 mm year−1 on average) with actual ET estimated from five meteorological models [Penman–Monteith (P‐M), P‐M with dynamic control of stomatal resistance (P‐M‐d), Priestley–Taylor (P‐T), McNaughton–Black (M‐B), and Shuttleworth–Wallace (S‐W)] and from the Moderate Resolution Imaging Spectroradiometer (MODIS) ET product. The meteorological models with constant stomatal resistance (P‐M, M‐B, and S‐W) provided the most accurate estimates of annual eddy ET (average percent differences ranged between 11 and −14%), but their accuracy varied across sites. The P‐M‐d consistently underpredicted ET at all sites. The more simplistic P‐T model performed well at the control site (18% overprediction) but strongly overpredicted annual eddy ET at the restoration sites (92%) and underpredicted at the fire site (−26%). The MODIS ET underpredicted annual eddy ET at all sites by at least 51% primarily because of underestimation of leaf area index. Overall, we conclude that with accurate parameterization, micrometeorological models can predict ET within 30% in forests of the south‐western USA and that remote sensing‐based ET estimates need to be improved through use of higher resolution products. Copyright © 2014 John Wiley & Sons, Ltd.

No abstract

Several studies have reported on the detection of perchlorate (ClO(4)(-)) in edible leafy vegetables irrigated with Colorado River water. However, there is no information on spinach as related to ClO(4)(-) in irrigation water nor on the effect of other anions on ClO(4)(-) uptake. A greenhouse ClO(4)(-) uptake experiment using spinach was conducted to investigate the impact of presence of chloride (Cl(-)) and nitrate (NO(3)(-)) on ClO(4)(-) uptake under controlled conditions. We examined three concentrations of ClO(4)(-), 40, 220, and 400 nmol(c)/L (nanomoles of charge per liter of solution), three concentrations of Cl(-), 2.5, 13.75, and 25 mmol(c)/L, and NO(3)(-) at 2, 11, and 20 mmol(c)/L. The results revealed that ClO(4)(-) was taken up the most when NO(3)(-) and Cl(-) were lowest in concentration in irrigation water. More ClO(4)(-) was detected in spinach leaves than that in the root tissue. Relative to lettuces, spinach accumulated more ClO(4)(-) in the plant tissue. Perchlorate was accumulated in spinach leaves more than reported for outer leaves of lettuce at 40 nmol(c)/L of ClO(4)(-) in irrigation water. The results also provided evidence that spinach selectively took up ClO(4)(-) relative to Cl(-). We developed a predictive model to describe the ClO(4)(-) concentration in spinach as related to the Cl(-), NO(3)(-), and ClO(4)(-) concentration in irrigation water.

High-resolution evapotranspiration (ET) maps can assist demand-based irrigation management. Development of high-resolution daily ET maps requires high-resolution land surface temperature (LST) images. Earth-observing satellite sensors such as the Landsat 5 Thematic Mapper (TM) and MODerate resolution Imaging Spectroradiometer (MODIS) provide thermal images that are coarser than simultaneously acquired visible and near-infrared images. In this study, we evaluated the TsHARP downscaling technique for its capability to downscale coarser LST images using finer resolution normalized difference vegetation index (NDVI) data. The TsHARP technique was implemented to downscale seven coarser scale (240, 360, 480, 600, 720, 840, and 960 m) synthetic images to a 120 m LST image. The TsHARP was also evaluated for downscaling a coarser 960 m LST image to 240 m to mimic MODIS datasets. Comparison between observed 120 m LST images and 120 m LST images downscaled from coarser 240, 360, 480, 600, 720, 840, and 960 m images yielded root mean square errors of 1.0, 1.3, 1.5, 1.6, 1.7, 1.8, and 1.9°C, respectively. This indicates that the TsHARP method can be used for downscaling coarser (960 m) MODIS-based LST images using finer Landsat (120 m) or MODIS (240 m)-derived NDVI images. However, the TsSHARP method should be evaluated further with real datasets before using it for an operational ET remote sensing program for irrigation scheduling purposes.