Airborne lidar (light detecting and ranging) is a useful tool for probing the structure of forest canopies. Such information is not readily available from other remote sensing methods and is essential for modern forest inventories. In this study, small-footprint lidar data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the San Pedro River basin near Benson, Arizona. The lidar data were acquired in June 2004, using Optech's 1233 ALTM during flyovers conducted at an altitude of 600 m. Canopy height, crown diameter, stem dbh, canopy cover, and mean intensity of return laser pulses from the canopy surface were estimated for the cottonwood trees from the data. Linear regression models were used to develop equations relating lidar-derived tree characteristics with corresponding field acquired data for each age class of cottonwoods. The lidar estimates show a good degree of correlation with ground-based measurements. This study also shows that other parameters of young, mature, and old cottonwood trees such as height and canopy cover, when derived from lidar, are significantly different (P < 0.05). Additionally, mean crown diameters of mature and young trees are not statistically different at the study site (P = 0.31). The results illustrate the potential of airborne lidar data to differentiate different age classes of cottonwood trees for riparian areas quickly and quantitatively. Copyright © 2006 by the Society of American Foresters.

This study examines the capabilities and limitations of the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) in predicting the precipitation and circulation features that accompanied the 2004 North American monsoon (NAM). When the model is reinitialized every 5 days to restrain the growth of modeling errors, its results for precipitation checked at subseasonal time scales (not for individual rainfall events) become comparable with ground- and satellite-based observations as well as with the NAM's diagnostic characteristics. The modeled monthly precipitation illustrates the evolution patterns of monsoon rainfall, although it underestimates the rainfall amount and coverage area in comparison with observations. The modeled daily precipitation shows the transition from dry to wet episodes on the monsoon onset day over the Arizona-New Mexico region, and the multiday heavy rainfall (>1 mm day-1) and dry periods after the onset. All these modeling predictions agree with observed variations. The model also accurately simulated the onset and ending dates of four major moisture surges over the Gulf of California during the 2004 monsoon season. The model reproduced the strong diurnal variability of the NAM precipitation, but did not predict the observed diurnal feature of the precipitation peak's shift from the mountains to the coast during local afternoon to late night. In general, the model is able to reproduce the major, critical patterns and dynamic variations of the NAM rainfall at intraseasonal time scales, but still includes errors in precipitation quantity, pattern, and timing. The numerical study suggests that these errors are due largely to deficiencies in the model's cumulus convective parameterization scheme, which is responsible for the model's precipitation generation. © 2007 American Meteorological Society.

The role that soil moisture plays in terms of modulating hydrologic processes including infiltration and runoff generation makes it an essential component to capture for hydrologic modeling. This work aims to leverage information gained from SMOS to improve surface soil moisture simulations in the Russian River Basin (California, U.S.A). The basin's complex terrain offers a rigorous testing ground for SMOS soil moisture products. Data from seven in situ observation sites are used to assess model performance after assimilating SMOS-based soil saturation ratios. For a comparison of "best case" scenarios, the in situ observations themselves are assimilated. Results show that SMOS assimilated simulations shows modest improvement at most in situ locations. Despite the observed decrease in model performance at some locations, overall performance of simulations assimilated with SMOS-based saturation ratios remains high. Findings suggest that even in a complex environment, useful information may be extracted from SMOS estimates for hydrologic modeling.

In the PILPS 2(e) experiment, the Snow Atmosphere Soil Transfer (SAST) land-surface scheme developed from the Biosphere-Atmosphere Transfer Scheme (BATS) showed difficulty in accurately simulating the patterns and quantities of runoff resulting from heavy snowmelt in the high-latitude Torne-Kalix River basin (shared by Sweden and Finland). This difficulty exposes the model deficiency in runoff formations. After representing subsurface runoff and calibrating the parameters, the accuracy of hydrograph prediction improved substantially. However, even with the accurate precipitation and runoff, the predicted soil moisture and its variation were highly "model-dependent". Knowledge obtained from the experiment is discussed. © 2003 Elsevier Science B.V. All rights reserved.

As part of a regional integrated assessment of climate vulnerability, a survey was conducted from June 1998 to May 2000 of weather, climate, and hydrologic forecasts with coverage of the US Southwest and an emphasis on the Colorado River Basin. The survey addresses the types of forecasts that were issued, the organizations that provided them, and techniques used in their generation. It reflects discussions with key personnel from organizations involved in producing or issuing forecasts, providing data for making forecasts, or serving as a link for communicating forecasts. During the survey period, users faced a complex and constantly changing mix of forecast products available from a variety of sources. The abundance of forecasts was not matched in the provision of corresponding interpretive materials, documentation about how the forecasts were generated, or reviews of past performance. Potential existed for confusing experimental and research products with others that had undergone a thorough review process, including official products issued by the National Weather Service. Contrasts between the state of meteorologic and hydrologic forecasting were notable, especially in the former's greater operational flexibility and more rapid incorporation of new observations and research products. Greater attention should be given to forecast content and communication, including visualization, expression of probabilistic forecasts and presentation of ancillary information. Regional climate models and use of climate forecasts in water supply forecasting offer rapid improvements in predictive capabilities for the Southwest. Forecasts and production details should be archived, and publicly available forecasts should be accompanied by performance evaluations that are relevant to users.

Verification has become an integral component in the development of precipitation algorithms used in satellite-based precipitation products and evaluation of numerical weather prediction models. A number of object-based verification methods have been developed to quantify the errors related to spatial patterns and placement of precipitation. In this study, an image processing technique known as watershed transformation, capable of detecting closely spaced, but separable precipitation areas, is adopted in the object-based approach. Several key attributes of the segmented precipitation objects are selected and interest values of those attributes are estimated based on the distance measurement of the estimated and reference images. An overall interest score is estimated from all the selected attributes and their interest values. The proposed object-based approach is implemented to validate satellite-based precipitation estimation against ground radar observations. The results indicate that the watershed segmentation technique is capable of separating the closely spaced local-scale precipitation areas. In addition, three verification metrics, including the object-based false alarm ratio, object-based missing ratio, and overall interest score, reveal the skill of precipitation estimates in depicting the spatial and geometric characteristics of the precipitation structure against observations.

Estimating the water budgets in a small-scale basin is a challenge, especially in the mountainous western United States, where the terrain is complex and observational data in the mountain areas are sparse. This manuscript reports on research that downscaled 5-yr (1999-2004) hydrometeorological fields over the upper Rio Grande basin from a 2.5° NCEP-NCAR reanalysis to a 4-km local scale using a regional climate model [fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5), version 3]. The model can reproduce the terrain-related precipitation distribution - the trend of diurnal, seasonal, and interannual precipitation variability - although poor snow simulation caused it to overestimate precipitation and evapotranspiration in the cold season. The outcomes from the coupled model are also comparable to offline Variable Infiltration Capacity (VIC) and Land Data Assimilation System (LDAS)/Mosaic land surface simulations that are driven by observed and/or analyzed surface meteorological data. © 2007 American Meteorological Society.

The contemporary usage of hydrologic models has been to rely on a single model to perform the simulation and predictions. Despite the tremendous progress, efforts and investment put into developing more hydrologic models, there is no convincing claim that any particular model in existence is superior to other models for various applications and under all circumstances. This results to reducing the size of the plausible model space and often leads to predictions that may well-represent some phenomena or events at the expenses of others. Assessment of predictive uncertainty based on a single model is subject to statistical bias and most likely underestimation of uncertainty. This endorses the implementation of multi-model methods for more accurate estimation of uncertainty in hydrologic prediction. In this study, we present two methods for the combination of multiple model predictors using Bayesian Model Averaging (BMA) and Sequential Bayesian Model Combination (SBMC). Both methods are statistical schemes to infer a combined probabilistic prediction that possess more reliability and skill than the original model members produced by several competing models. This paper discusses the features of both methods and explains how the limitation of BMA can be overcome by SBMC. Three hydrologic models are considered and it is shown that multi-model combination can result in higher prediction accuracy than individual models. © 2008 ASCE.

Anew satellite-based rainfall monitoring algorithm that integrates the strengths of both low Earth-orbiting (LEO) and geostationary Earth-orbiting (GEO) satellite information has been developed. The Lagrangian Model (LMODEL) algorithm combines a 2D cloud-advection tracking system and a GEO data-driven cloud development and rainfall generation model with procedures to update model parameters and state variables in near-real time. The details of the LMODEL algorithm were presented in Part I. This paper describes a comparative validation against ground radar rainfall measurements of 1- and 3-h LMODEL accumulated rainfall outputs. LMODEL rainfall estimates consistently outperform accumulated 3-h microwave (MW)-only rainfall estimates, even before the more restricted spatial coverage provided by the latter is taken into account. In addition, the performance of LMODEL products remains effective and consistent between MW overpasses. Case studies demonstrate that the LMODEL provides the potential to synergize available satellite data to generate useful precipitation measurements at an hourly scale. © 2009 American Meteorological Society.

The 1997-98 El Niño was exceptional, not only because of its magnitude, but also because of the visibility and use of its forecasts. The 3 to 9 mo advance warning of a wet winter with potential flooding in the US Southwest, easily accessible by water management agencies, was unprecedented. Insights about use of this information in operational water management decision processes were developed through a series of semi-structured in-depth interviews with key personnel from a broad array of agencies responsible for emergency management and water supply, with jurisdictions ranging from urban to rural and local to regional. Interviews investigated where information was acquired, how it was interpreted and how it was incorporated into specific decisions and actions. In addition, technical and institutional barriers to forecast use are explored. Study findings emphasize (1) the need for special handling of tailored forecast products on a regional scale, (2) the need for systematic regional forecast evaluation and (3) the potential for climate information to directly affect water management decisions through integrating climate forecasts into water supply outlooks where appropriate.