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.

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.

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.

Noah (version 2.7.1), the community landsurface model (LSM) of National Centers for Environmental Predictions-National Center for Atmospheric Research (NCEP-NCAR), which is widely used to describe the landsurface processes either in stand-alone or in coupled landatmospheric model systems, is recognized to underestimate snow-water equivalent (SWE). Noah's SWE bias can be attributed to its simple snow sub-model, which does not effectively describe the physical processes during snow accumulation and melt period. To improve SWE simulation in the Noah LSM, the Utah Energy Balance (UEB) snow model is implemented in Noah to test alternate snow surface temperature and snowmelt outflow schemes. Snow surface temperature was estimated using the force-restore method and snowmelt event is regulated by accounting for the internal energy of the snowpack. The modified Noah's SWE simulations are compared with the SWE observed at California's NRCS SNOTEL stations for 7 water years: 2002-2008, while the model's snow surface temperature is verified with observed surface-temperature data at an observation site in Utah. The experiments show that modification in Noah's snow process substantially reduced SWE estimation bias while keeping the simplicity of the Noah LSM. The results suggest that the model did not benefit from the alternate temperature representation but primary improvement can be attributed to the substituted snowmelt process.

In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measure are used to quantify the quality of ensemble prediction. Copyright 2006 by the American Geophysical Union.

Rainfall frequency analysis, which is an important tool in hydrologic engineering, has been traditionally performed using information from gauge observations. This approach has proven to be a useful tool in planning and design for the regions where sufficient observational data are available. However, in many parts of the world where ground-based observations are sparse and limited in length, the effectiveness of statistical methods for such applications is highly limited. The sparse gauge networks over those regions, especially over remote areas and high-elevation regions, cannot represent the spatiotemporal variability of extreme rainfall events and hence preclude developing depth-duration-frequency curves (DDF) for rainfall frequency analysis. In this study, the PERSIANN-CDR dataset is used to propose a mechanism, by which satellite precipitation information could be used for rainfall frequency analysis and development of DDF curves. In the proposed framework, we first adjust the extreme precipitation time series estimated by PERSIANN-CDR using an elevation-based correction function, then use the adjusted dataset to develop DDF curves. As a proof of concept, we have implemented our proposed approach in 20 river basins in the United States with different climatic conditions and elevations. Bias adjustment results indicate that the correction model can significantly reduce the biases in PERSIANN-CDR estimates of annual maximum series, especially for high elevation regions. Comparison of the extracted DDF curves from both the original and adjusted PERSIANN-CDR data with the reported DDF curves from NOAA Atlas 14 shows that the extreme percentiles from the corrected PERSIANN-CDR are consistently closer to the gauge-based estimates at the tested basins. The median relative errors of the frequency estimates at the studied basins were less than 20% in most cases. Our proposed framework has the potential for constructing DDF curves for regions with limited or sparse gauge-based observations using remotely sensed precipitation information, and the spatiotemporal resolution of the adjusted PERSIANN-CDR data provides valuable information for various applications in remote and high elevation areas.

Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of this model to microphysics and cumulus schemes. This study investigated the performance of the WRF in forecasting precipitation, hurricane track, and landfall time using various microphysics and cumulus schemes. A total of 20 combinations of microphysics and cumulus schemes were used, and the model outputs were validated against ground-based observations. While the choice of microphysics and cumulus schemes can significantly impact model output, it is not the case that any single combination can be considered "ideal"for modeling all characteristics of a hurricane, including precipitation amount, areal extent, hurricane track, and the time of landfall. For example, the model’s ability to simulate precipitation (with the least total bias) is best achieved using Betts-Miller-Janjić (BMJ) cumulus parameterization in combination with the WRF single-moment five-class microphysics scheme (WSM5). It was determined that the WSM5-BMJ, WSM3 (the three-class version of theWSMscheme)-BMJ, and Ferrier microphysics in combination with the Grell-Devenyi cumulus scheme were the best combinations for simulation of the landfall time. However, the hurricane track was best estimated using the Lin et al. and Kessler microphysics options withBMJ cumulus parameterization. Contrary to previous studies, these results indicated that the use of cumulus schemes improves model outputs when the grid size is smaller than 10 km. However, it was found that many of the differences between parameterization schemes may be well within the uncertainty of the measurements. © 2012 American Meteorological Society.

In the framework of Soil Moisture and Ocean Salinity (SMOS) Calibration/Validation (Cal/Val) activities, this study addresses the use of the PERSIANN-CCS(1)database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50x50 km(2)). The study focuses on the Valencia Anchor Station (VAS) experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe. A faithful representation of the soil moisture distribution at SMOS pixel scale (50x50 km(2)) requires an accurate estimation of the amount and temporal/spatial distribution of precipitation. To quantify the gain of using the comprehensive PERSIANN database instead of sparsely distributed rain gauge measurements, comparisons between in situ observations and satellite rainfall data are done both at point and areal scale. An overestimation of the satellite rainfall amounts is observed in most of the cases (about 66%) but the precipitation occurrences are in general retrieved (about 67%). To simulate the high variability in space and time of surface soil moisture, a Soil Vegetation Atmosphere Transfer (SVAT) model - ISBA (Interactions between Soil Biosphere Atmosphere) is used. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil is depicted by a comparison between different soil moisture data. Point-like and spatialized simulated data using rain gauge observations or PERSIANN - CCS database as well as ground measurements are used. It is shown that a good adequacy is reached in most part of the year, the precipitation differences having less impact upon the simulated soil moisture. The behaviour of simulated surface soil moisture at SMOS scale is verified by the use of remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E). We show that the PERSIANN database provides useful information at temporal and spatial scales in the context of soil moisture retrieval.