Xinyi Shen

Refining a Distributed Linear Reservoir Routing Method to Improve Performance of the CREST Model

AbstractAs one of the most important components of hydrologic models, routing module determines model performance to a large degree. In this study, the authors proposed a fully distributed linear r...

Bare Surface Soil Moisture Estimation Using Double-Angle and Dual-Polarization L-Band Radar Data

Based on todays most widely used surface scattering model, the advanced integral equation model (AIEM), this study proposes a novel soil moisture inversion model that estimates bare surface soil moisture using double-incidence angle and dual-polarized L-band radar data. Compared with previous studies at L-/C-band, the proposed method provides the estimation of soil moisture without referring to the measured soil roughness and eliminates the requirement of an initial dry season condition. The root-mean-square error (rmse) of volumetric soil moisture varies from 0.8% to 3.2% at different incidence-angle combinations validated by simulated solving and from 4.0% to 7.9% by field measurements when the paired incidence angles are not both large. In case the paired angels are both large, not particularly suitable for soil moisture estimation, the rmse increases to 10.3%. Therefore, this method is applicable to bare surface soil moisture retrieval when at least one of the incidence angles is not large.

A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval

This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on todays most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., khRMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP.

A global distributed basin morphometric dataset

Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in addition we present formulas for generating twenty-one additional morphometric variables based on combination of the prime variables. The dataset can aid different applications including studies of land-atmosphere interaction, and modelling of floods and droughts for sustainable water management. The validity of the dataset has been consolidated by successfully repeating the Hack’s law.

Decomposition methods for the estimation of bare soil surface parameters using fully polarimetric SAR data 1

This study wants to demonstrate that two different polarimetric target decomposition methods can improve SAR data accuracy for estimating the parameters of bare soil surface. To achieve this goal, two experiments are conducted: (1) both Freeman and Cloude decomposition methods are performed on JPL/AIRSAR L-band fully polarimetric data; and (2) Advanced Integral Equation Model (AIEM) is used to simulate backscatting coefficients. The root mean square errors (RMSEs) of σ0hh, σ0vv between original data and AIEM simulated data are 1.96 and 1.25 dB. However, if Cloude method is used to decompose original data, the RMSEs will be reduced to 1.45 and 1.14dB, respectively; for Freeman method, the RMSEs are 1.64 and 1.35 dB. Therefore, polarimetric target decomposition compensation, especially Cloude method, can help to improve the accuracy of SAR data for estimating the parameters of bare soil surface.

A Comprehensive Database of Flood Events in the Contiguous United States from 2002 to 2013

AbstractNotwithstanding the rich record of hydrometric observations compiled by the U.S. Geological Survey (USGS) across the contiguous United States (CONUS), flood event catalogs are sparse and incomplete. Available databases or inventories are mostly survey- or report-based, impact oriented, or limited to flash floods. These data do not represent the full range of flood events occurring in CONUS in terms of geographical locations, severity, triggering weather, or basin morphometry. This study describes a comprehensive dataset consisting of more than half a million flood events extracted from 6,301 USGS flow records and radar-rainfall fields from 2002 to 2013, using the characteristic point method. The database features event duration; first- (mass center) and second- (spreading) order moments of both precipitation and flow, flow peak and percentile, event runoff coefficient, base flow, and information on the basin geomorphology. It can support flood modeling, geomorphological and geophysical impact stud...