bluuebunny/crossref_metadata_2025_split huggingface dataset:
Twentieth-Century Drought in the Conterminous United States
Authors: Andreadis Konstantinos M. , Clark Elizabeth A. , Wood Andrew W. , Hamlet Alan F. , Lettenmaier Dennis P. 
Published: January 2006

Abstract
Droughts can be characterized by their severity, frequency and duration, and areal extent. Depth–area–duration analysis, widely used to characterize precipitation extremes, provides a basis for the evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. Gridded precipitation and temperature data were used to force a physically based macroscale hydrologic model at 1/2° spatial resolution over the continental United States, and construct a drought history from 1920 to 2003 based on the model-simulated soil moisture and runoff. A clustering algorithm was used to identify individual drought events and their spatial extent from monthly summaries of the simulated data. A series of severity–area–duration (SAD) curves were constructed to relate the area of each drought to its severity. An envelope of the most severe drought events in terms of their SAD characteristics was then constructed. The results show that (a) the droughts of the 1930s and 1950s were the most severe of the twentieth century for large areas; (b) the early 2000s drought in the western United States is among the most severe in the period of record, especially for small areas and short durations; (c) the most severe agricultural droughts were also among the most severe hydrologic droughts, however, the early 2000s western U.S. drought occupies a larger portion of the hydrologic drought envelope curve than does its agricultural companion; and (d) runoff tends to recover in response to precipitation more quickly than soil moisture, so the severity of hydrologic drought during the 1930s and 1950s was dampened by short wet spells, while the severity of the early 2000s drought remained high because of the relative absence of these short-term phenomena.
A Comparison of the Triangle Retrieval and Variational Data Assimilation Methods for Surface Turbulent Flux Estimation
Authors: Margulis Steven A. , Kim Jongyoun , Hogue Terri 
Published: January 2006

Abstract
Future operational frameworks for estimating surface turbulent fluxes over the necessary spatial and temporal scales will undoubtedly require the use of remote sensing products. Techniques used to estimate surface fluxes from radiometric surface temperature generally fall into two categories: retrieval-based and data assimilation approaches. Up to this point, there has been little comparison between retrieval- and assimilation-based techniques. In this note, the triangle retrieval method is compared to a variational data assimilation approach for estimating surface turbulent fluxes from radiometric surface temperature observations. Results from a set of synthetic experiments and an application using real data from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site indicate that the assimilation approach performs slightly better than the triangle method because of the robustness of the estimation to measurement errors and parsimony of the system model, which leads to fewer sources of structural model errors. Future comparison work using retrieval and data assimilation algorithms will provide more insight into the optimal approach for diagnosis of land surface fluxes using remote sensing observations.
Probabilistic and Ensemble Representations of the Uncertainty in an IR/Microwave Satellite Precipitation Product
Authors: Bellerby Timothy J. , Sun Jizhong 
Published: January 2006

AbstractWhile current satellite techniques are theoretically capable of producing precipitation estimates to image pixel resolutions, significant uncertainty is present in such high-resolution products. This uncertainty is frequently difficult to characterize using scalar measures of additive error. This paper describes the development of a methodology to more fully represent the uncertainty in satellite precipitation retrievals. The methodology derives conditional probability distribution functions of rainfall on a pixel-by-pixel basis. This array of distribution functions is then combined with a simple model of the spatiotemporal covariance structure of the uncertainty in the precipitation field to stochastically generate an ensemble precipitation product. Each element of the ensemble represents an equiprobable realization of the precipitation field that is consistent with the original satellite data while containing a random element commensurate with the uncertainty in that field. The technique has been tested using data from the Tropical Rainfall Measuring Mission (TRMM) Texas and Florida Underflight Experiment (TEFLUN-B) field campaign.
Determining Meaningful Differences for SMACEX Eddy Covariance Measurements
Authors: Meek D. W. , Prueger J. H. , Kustas W. P. , Hatfield J. L. 
Published: January 2006

AbstractTwo eddy covariance instrument comparison studies were conducted before and after the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) field campaign to 1) determine if observations from multiple sensors were equivalent for the measured variables over a uniform surface and to 2) determine a least significant difference (LSD) value for each variable to discriminate between daily and hourly differences in latent and sensible heat and carbon dioxide fluxes, friction velocity, and standard deviation of the vertical wind velocity from eddy covariance instruments placed in different locations within the study area. The studies were conducted in early June over an alfalfa field and in mid-September over a short grass field. Several statistical exploratory, graphical, and multiple-comparison procedures were used to evaluate each daily variable. Daily total or average data were used to estimate a pooled standard error and corresponding LSD values at the P = 0.05 and P = 0.01 levels using univariate procedures. There were no significant sensor differences in any of the daily measurements for either intercomparison period. Hourly averaged data were used to estimate a pooled standard error and corresponding LSD values at the P = 0.05 and P = 0.01 levels using mixed model procedures. Sensor differences for pre- and post-intercomparisons were minimal for hourly and daily values of CO2, water vapor, sensible heat, friction velocity, and standard deviation for vertical wind velocity. Computed LSD values were used to determine significant daily differences and threshold values for the variables monitored during the SMACEX campaign.
Relationship of Soil Respiration to Crop and Landscape in the Walnut Creek Watershed
Authors: Parkin T. B. , Kaspar T. C. , Senwo Z. , Prueger J. H. , Hatfield J. L. 
Published: January 2006

Abstract
Soil respiration is an important component of the carbon dynamics of terrestrial ecosystems. Many factors exert controls on soil respiration, including temperature, soil water content, organic matter, soil texture, and plant root activity. This study was conducted to quantify soil respiration in the Walnut Creek watershed in central Iowa, and to investigate the factors controlling this process. Six agricultural fields were identified for this investigation: three of the fields were cropped with soybean [Glycine max (L.) Merr.] and three were cropped with corn (Zea mays L.). Within each field, soil respiration was measured at nine locations, with each location corresponding to one of three general landscape positions (summit, side slope, and depression). Soil respiration was measured using a portable vented chamber connected to an infrared gas analyzer. Soil samples were collected at each location for the measurement of soil water content, pH, texture, microbial biomass, and respiration potential. Field respiration rates did not show a significant landscape effect. However, there was a significant crop effect, with respiration from cornfields averaging 37.5 g CO2 m−2 day−1 versus an average respiration of 13.1 g CO2 m−2 day−1 in soybean fields. In contrast, laboratory measurements of soil respiration potential, which did not include plant roots, showed a significant landscape effect and an insignificant cropping system effect. Similar relationships were observed for soil organic C and microbial biomass. Additional analyses indicate that corn roots may be more important than soybean roots in their contribution to surface CO2 flux, and that root respiration masked landscape effects on total soil respiration. Also, the failure to account for soil respiration may lead to biased estimates of net primary production measured by eddy covariance.
Effects of Land Use and Meteorological Conditions on Local and Regional Momentum Transport and Roughness for Midwestern Cropping Systems
Authors: Kustas William P. , Prueger John H. , MacPherson J. Ian , Wolde Mengistu , Li Fuqin 
Published: January 2006

Abstract
Eddy covariance measurements of wind speed u and shear velocity u* from tower- and aircraft-based systems collected over rapidly developing corn- (Zea mays L.) and soybean [Glycine max (L.) Merr.] fields were used in determining the local and regional (effective) surface roughness length zo and 〈zo〉, respectively. For corn, canopy height increased from ∼1 to 2 m and the leaf area index changed from ∼1 to 4 during the study period, while for soybean, canopy height increased from ∼0.1 to 0.5 m and the leaf area index increased from ∼0.5 to 2. A procedure for the aggregation of local roughness values from the different land cover types based on blending-height concepts yielded effective surface roughness values that were from ∼1/2 to 1/4 of the magnitude estimated with the aircraft data. This indicated additional kinematic stress caused by form drag from isolated obstacles (i.e., trees, houses, and farm buildings), and the interaction of adjacent corn- and soybean fields were probably important factors influencing the effective surface roughness length for this landscape. The comparison of u* measurements from the towers versus the aircraft indicated that u* from aircraft was 20%–30% higher, on average, and that u* over corn was 10%–30% higher than over soybean, depending on stability. These results provide further evidence for the likely sources of additional kinematic stress. Although there was an increase in zo and 〈zo〉 over time as the crops rapidly developed, particularly for corn, there was a more significant trend of increasing roughness length with decreasing wind speed at wind speed thresholds of around 5 m s−1 for the aircraft and 3 m s−1 for the tower measurements. Other studies have recently reported such a trend. The impact on computed sensible heat flux H using 〈zo〉 derived from the aggregation of zo from the different land cover types, using the blending-height scheme, and that estimated from the aircraft observations, was evaluated using a calibrated single-source/bulk resistance approach with surface–air temperature differences from the aircraft observations. An underestimate of 〈zo〉 by 50% and 75% resulted in a bias in the H estimates of approximately 10% and 15%, respectively. This is a relatively minor error when considering that the root-mean-square error (rmse) value between single-source estimates and the aircraft observations of H was 15 W m−2 using the aircraft-derived 〈zo〉, and only increased to approximately 20 and 25 W m−2 using the 1/2 and 1/4 〈zo〉 values, as estimated from the blending-height scheme. The magnitude of the excess resistance relative to the aerodynamic resistance to heat transfer was a major contributing factor in minimizing the error in heat flux calculations resulting from these underestimations of 〈zo〉.
Retrieval of Soil Moisture and Vegetation Water Content Using SSM/I Data over a Corn and Soybean Region
Authors: Wen Jun , Jackson Thomas J. , Bindlish Rajat , Hsu Ann Y. , Su Z. Bob 
Published: January 2006

AbstractThe potential for soil moisture and vegetation water content retrieval using Special Sensor Microwave Imager (SSM/I) brightness temperature over a corn and soybean field region was analyzed and assessed using datasets from the Soil Moisture Experiment 2002 (SMEX02). Soil moisture retrieval was performed using a dual-polarization 19.4-GHz data algorithm that requires the specification of two vegetation parameters—single scattering albedo and vegetation water content. Single scattering albedo was estimated using published values. A method for estimating the vegetation water content from the microwave polarization index using SSM/I 37.0-GHz data was developed for the region using extensive datasets developed as part of SMEX02. Analyses indicated that the sensitivity of the brightness temperature to soil moisture decreased as vegetation water content increased. However, there was evidence that SSM/I brightness temperatures changed in response to soil moisture increases resulting from rainfall during the later stages of crop growth. This was partly attributed to the lower soil and vegetation thermal temperatures that typically followed a rainfall. Comparisons between experimentally measured volumetric soil moisture and SSM/I-retrieved soil moisture indicated that soil moisture retrieval was feasible using SSM/I data, but the accuracy highly depended upon the levels of vegetation and atmospheric precipitable water; the standard error of estimate over the 3-week study period was 5.49%. The potential for using this approach on a larger scale was demonstrated by mapping the state of Iowa. Results of this investigation provide new insights on how one might operationally correct for vegetation effects using high-frequency microwave observations.
Evaluation of AMSR-E-Derived Soil Moisture Retrievals Using Ground-Based and PSR Airborne Data during SMEX02
Authors: McCabe M. F. , Gao H. , Wood E. F. 
Published: January 2006

Abstract
A Land Surface Microwave Emission Model (LSMEM) is used to derive soil moisture estimates over Iowa during the Soil Moisture Experiment 2002 (SMEX02) field campaign, using brightness temperature data from the Advanced Microwave Sounding Radiometer (AMSR)-E satellite. Spatial distributions of the near-surface soil moisture are produced using the LSMEM, with data from the North American Land Data Assimilation System (NLDAS), vegetation and land surface parameters estimated through recent Moderate Imaging Spectroradiometer (MODIS) land surface products, and standard soil datasets. To assess the value of soil moisture estimates from the 10.7-GHz X-band sensor on the AMSR-E instrument, retrievals are evaluated against ground-based sampling and soil moisture estimates from the airborne Polarimetric Scanning Radiometer (PSR) operating at C band. The PSR offers high-resolution detail of the soil moisture distribution, which can be used to analyze heterogeneity within the scale of the AMSR-E pixel. Preliminary analysis indicates that retrievals from the AMSR-E instrument at 10.7 GHz using the LSMEM are surprisingly robust, with accuracies within 3% vol/vol compared with in situ samples. Results from these AMSR-E comparisons also indicate potential in determining soil moisture patterns over regional scales, even in the presence of vegetation. Assessment of soil moisture determined through local-scale sampling within the larger-scale AMSR-E footprint reveals a consistent level of agreement over a range of meteorological and surface conditions, offering promise for improved land surface hydrometeorological characterization.
Utility of Remote Sensing–Based Two-Source Energy Balance Model under Low- and High-Vegetation Cover Conditions
Authors: Li Fuqin , Kustas William P. , Prueger John H. , Neale Christopher M. U. , Jackson Thomas J. 
Published: January 2006

Abstract
Two resistance network formulations that are used in a two-source model for parameterizing soil and canopy energy exchanges are evaluated for a wide range of soybean and corn crop cover and soil moisture conditions during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The parallel resistance formulation does not consider interaction between the soil and canopy fluxes, whereas the series resistance algorithms provide interaction via the computation of a within-air canopy temperature. Land surface temperatures were derived from high-resolution Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM) scenes and aircraft imagery. These data, along with tower-based meteorological data, provided inputs for the two-source energy balance model. Comparison of the local model output with tower-based flux observations indicated that both the parallel and series resistance formulations produced basically similar estimates with root-mean-square difference (RMSD) values ranging from approximately 20 to 50 W m−2 for net radiation and latent heat fluxes, respectively. The largest relative difference in percentage [mean absolute percent difference (MAPD)] was for sensible heat flux, which was ≈35%, followed by a MAPD ≈ 25% for soil heat flux, ≈10% for latent heat flux, and a MAPD &lt; 5% for net radiation. Although both series and parallel versions gave similar results, the parallel resistance formulation was found to be more sensitive to model parameter specification, particularly in accounting for the effects of vegetation clumping resulting from row crop planting on flux partitioning. A sensitivity and model stability analysis for a key model input variable, that is, fractional vegetation cover, also show that the parallel resistance network is more sensitive to the errors vegetation cover estimates. Furthermore, it is shown that for a much narrower range in vegetation cover fraction, compared to the series resistance network, the parallel resistance scheme is able to achieve a balance in both the radiative temperature and convective heat fluxes between the soil and canopy components. This result appears to be related to the moderating effects of the air temperature in the canopy air space computed in the series resistance scheme, which represents the effective source height for turbulent energy exchange across the soil–canopy–atmosphere system.
Effects of Vegetation Clumping on Two–Source Model Estimates of Surface Energy Fluxes from an Agricultural Landscape during SMACEX
Authors: Anderson Martha C. , Norman J. M. , Kustas William P. , Li Fuqin , Prueger John H. , Mecikalski John R. 
Published: January 2006

Abstract
The effects of nonrandom leaf area distributions on surface flux predictions from a two-source thermal remote sensing model are investigated. The modeling framework is applied at local and regional scales over the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) study area in central Iowa, an agricultural landscape that exhibits foliage organization at a variety of levels. Row-scale clumping in area corn- and soybean fields is quantified as a function of view zenith and azimuth angles using ground-based measurements of canopy architecture. The derived clumping indices are used to represent subpixel clumping in Landsat cover estimates at 30-m resolution, which are then aggregated to the 5-km scale of the regional model, reflecting field-to-field variations in vegetation amount. Consideration of vegetation clumping within the thermal model, which affects the relationship between surface temperature and leaf area inputs, significantly improves model estimates of sensible heating at both local and watershed scales in comparison with eddy covariance data collected by aircraft and with a ground-based tower network. These results suggest that this economical approach to representing subpixel leaf area hetereogeneity at multiple scales within the two-source modeling framework works well over the agricultural landscape studied here.
Intercomparison of Spatially Distributed Models for Predicting Surface Energy Flux Patterns during SMACEX
Authors: Crow Wade T. , Li Fuqin , Kustas William P. 
Published: January 2006

AbstractThe treatment of aerodynamic surface temperature in soil–vegetation–atmosphere transfer (SVAT) models can be used to classify approaches into two broad categories. The first category contains models utilizing remote sensing (RS) observations of surface radiometric temperature to estimate aerodynamic surface temperature and solve the terrestrial energy balance. The second category contains combined water and energy balance (WEB) approaches that simultaneously solve for surface temperature and energy fluxes based on observations of incoming radiation, precipitation, and micrometeorological variables. To date, few studies have focused on cross comparing model predictions from each category. Land surface and remote sensing datasets collected during the 2002 Soil Moisture–Atmosphere Coupling Experiment (SMACEX) provide an opportunity to evaluate and intercompare spatially distributed surface energy balance models. Intercomparison results presented here focus on the ability of a WEB-SVAT approach [the TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS)] and an RS-SVAT approach [the Two-Source Energy Balance (TSEB) model] to accurately predict patterns of turbulent energy fluxes observed during SMACEX. During the experiment, TOPLATS and TSEB latent heat flux predictions match flux tower observations with root-mean-square (rms) accuracies of 67 and 63 W m−2, respectively. TSEB predictions of sensible heat flux are significantly more accurate with an rms accuracy of 22 versus 46 W m−2 for TOPLATS. The intercomparison of flux predictions from each model suggests that modeling errors for each approach are sufficiently independent and that opportunities exist for improving the performance of both models via data assimilation and model calibration techniques that integrate RS- and WEB-SVAT energy flux predictions.
A Lagrangian Analysis of the Atmospheric Branch of the Global Water Cycle. Part II: Moisture Transports between Earth’s Ocean Basins and River Catchments
Authors: Stohl Andreas , James Paul 
Published: January 2006

Abstract
A diagnostic Lagrangian method to trace the budgets of freshwater fluxes, first described in Part I of this article, is used here to establish source–sink relationships of moisture between earth’s ocean basins and river catchments. Using the Lagrangian particle dispersion model FLEXPART, driven with meteorological analyses, 1.1 million particles, representing the mass of the atmosphere, were tracked over a period of 4 yr. Via diagnosis of the changes of specific humidity along the trajectories, budgets of evaporation minus precipitation (E − P) were determined. For validation purposes, E − P budgets were calculated for 39 river catchments and compared with climatological streamflow data for these rivers. Good agreement (explained variance 87%) was found between the two quantities. The E − P budgets were then tracked forward from all of earth’s ocean basins and backward from the 39 major river catchments for a period of 10 days. As much previous work was done for the Mississippi basin, this basin was chosen for a detailed analysis. Moisture recycling over the continent and moisture transport from the Gulf of Mexico were identified as the major sources for precipitation over the Mississippi basin, in quantitative agreement with previous studies. In the remainder of the paper, global statistics for source–sink relationships of moisture between the ocean basins and river catchments are presented. They show, for instance, the evaporative capacity of monsoonal flows for precipitation over the Ganges and Niger catchments, and the transport of moisture from both hemispheres to supply the Amazon’s precipitation. In contrast, precipitation in northern Eurasia draws its moisture mainly via recycling over the continent. The atmospheric transport of moisture between different ocean basins was also investigated. It was found that transport of air from the North Pacific produces net evaporation over the North Atlantic, but not vice versa. This helps to explain why the sea surface salinity is higher in the North Atlantic than in the North Pacific, a difference thought to be an important driver of the oceans’ thermohaline circulation. Finally, limitations of the method are discussed and possible future developments are outlined.