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Haolu Shang, Li Jia, and Massimo Menenti

Abstract

The soil wetness condition is a useful indicator of inundation hazard in floodplains, such as the Poyang Lake floodplain. Special Sensor Microwave Imager (SSM/I) passive microwave data were used to monitor water-saturated soil and open water areas of the Poyang Lake floodplain from 2001 to 2008, capturing the inundation patterns of this area in space and time. The polarization difference brightness temperature (PDBT) at 37 GHz is sensitive to the water extension even under dense vegetation. The zero-order radiative transfer model was simplified to retrieve the vertical–horizontal (V–H)-polarized effective emissivity difference from the PDBT at 37 GHz. Vegetation fractional area and vegetation transmission function were derived from NDVI to represent the vegetation attenuation. This effective emissivity difference has a quasi-linear relationship with the fractional area of water-saturated soil and standing water, no matter the frequency. Using the multifrequency-polarization surface emission (Q p) model and the Dobson model of the soil–water mixture, the two segments of this relationship were combined into a quasi-linear model. Comparing the retrieved water-saturated soil and standing water area of Poyang Lake with the lake area obtained from the MODIS and synthetic aperture radar (SAR) image at higher spatial resolution, the calculations show a good fit with the MODIS and SAR data, with R 2 = 0.7664 and relative RMSE = 17.74%. The cross-correlation analysis shows that the Poyang Lake extension fluctuates with a 5-day time lag with the upstream land area of water-saturated soil and standing water. Since the closure of the Three Gorges Dam, this relationship is more evident.

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Rafael Pimentel, Javier Herrero, Yijian Zeng, Zhongbo Su, and María J. Polo

Abstract

Snow cover simulation is a complex task in mountain regions because of its highly irregular distribution. GIS-based calculations of snowmelt–accumulation models must deal with nonnegligible scale effects below cell size, which may result in unsatisfactory predictions depending on the study scale. Terrestrial photography, whose scales can be adapted to the study problem, is a cost-effective technique, capable of reproducing snow dynamics at subgrid scale. A series of high-frequency images were combined with a mass and energy model to reproduce snow evolution at cell scale (30 m × 30 m) by means of the assimilation of the snow cover fraction observation dataset obtained from terrestrial photography in the Sierra Nevada, southern Spain. The ensemble transform Kalman filter technique is employed. The results show the convenience of adopting a selective depletion curve parameterization depending on the succession of accumulation–melting cycles in the snow season in these highly variable environments. A reduction in the error for snow depth to 50% (from 463.87 to 261.21 mm and from 238.22 to 128.50 mm) is achieved if the appropriate curve is selected.

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Donghai Zheng, Rogier van der Velde, Zhongbo Su, Martin J. Booij, Arjen Y. Hoekstra, and Jun Wen
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Donghai Zheng, Rogier van der Velde, Zhongbo Su, Martijn J. Booij, Arjen Y. Hoekstra, and Jun Wen

ABSTRACT

Current land surface models still have difficulties with producing reliable surface heat fluxes and skin temperature (T sfc) estimates for high-altitude regions, which may be addressed via adequate parameterization of the roughness lengths for momentum (z 0m) and heat (z 0h) transfer. In this study, the performance of various z 0h and z 0m schemes developed for the Noah land surface model is assessed for a high-altitude site (3430 m) on the northeastern part of the Tibetan Plateau. Based on the in situ surface heat fluxes and profile measurements of wind and temperature, monthly variations of z 0m and diurnal variations of z 0h are derived through application of the Monin–Obukhov similarity theory. These derived values together with the measured heat fluxes are utilized to assess the performance of those z 0m and z 0h schemes for different seasons. The analyses show that the z 0m dynamics are related to vegetation dynamics and soil water freeze–thaw state, which are reproduced satisfactorily with current z 0m schemes. Further, it is demonstrated that the heat flux simulations are very sensitive to the diurnal variations of z 0h. The newly developed z 0h schemes all capture, at least over the sparse vegetated surfaces during the winter season, the observed diurnal variability much better than the original one. It should, however, be noted that for the dense vegetated surfaces during the spring and monsoon seasons, not all newly developed schemes perform consistently better than the original one. With the most promising schemes, the Noah simulated sensible heat flux, latent heat flux, T sfc, and soil temperature improved for the monsoon season by about 29%, 79%, 75%, and 81%, respectively. In addition, the impact of T sfc calculation and energy balance closure associated with measurement uncertainties on the above findings are discussed, and the selection of the appropriate z 0h scheme for applications is addressed.

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Chiara Corbari and Marco Mancini

Abstract

Distributed hydrological models of energy and mass balance need as inputs many soil and vegetation parameters, which are usually difficult to define. This paper will try to approach this problem by performing a pixel to pixel calibration procedure of soil hydraulic and vegetation parameters based on satellite land surface temperature data as a complementary method to the traditional calibration with ground discharge measurements at river control cross sections. These analyses are performed for the upper Po River basin (Italy) closed at the river cross section of Ponte della Becca with a total catchment area of about 38 000 km2, for a calibration period from 2000 to 2003, and a validation period from 2004 to 2010. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data and a distributed hydrological model, Flash-Flood Event-Based Spatially Distributed Rainfall-Runoff Transformation Energy Water Balance model (FEST-EWB), that solves the system of energy and mass balance equations as a function of the representative equilibrium temperature will be used. This equilibrium surface temperature is comparable to the land surface temperature as retrieved from operational remote sensing data. Results suggest that a combined calibration based on satellite land surface temperature and ground discharge is needed to correctly reproduce volume discharge and also spatially distributed maps of representative equilibrium temperature and evapotranspiration. Improvements of about 10 mm/8 days are obtained on evapotranspiration from the model calibrated with Q and land surface temperature (LST) respect to the calibration based only on discharge.

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Gift Dumedah and Jeffrey P. Walker

Abstract

Data assimilation (DA) methods are commonly used for finding a compromise between imperfect observations and uncertain model predictions. The estimation of model states and parameters has been widely recognized, but the convergence of estimated parameters has not been thoroughly investigated. The distribution of model state and parameter values is closely linked to convergence, which in turn impacts the ultimate estimation accuracy of DA methods. This demonstration study examines the robustness and convergence of model parameters for the ensemble Kalman filter (EnKF) and the evolutionary data assimilation (EDA) in the context of the Soil Moisture and Ocean Salinity (SMOS) soil moisture assimilation into the Joint UK Land Environment Simulator in the Yanco area in southeast Australia. The results show high soil moisture estimation accuracy for the EnKF and EDA methods when compared with the open loop estimates during evaluation and validation stages. The level of convergence was quantified for each model parameter in the EDA approach to illustrate its potential in the retrieval of variables that were not directly observed. The EDA was found to have a higher estimation accuracy than the EnKF when its updated members were evaluated against the SMOS level 2 soil moisture. However, the EnKF and EDA estimations are comparable when their forward soil moisture estimates were validated against SMOS soil moisture outside the assimilation time period. This suggests that parameter convergence does not significantly influence soil moisture estimation accuracy for the EnKF. However, the EDA has the advantage of simultaneously determining the convergence of model parameters while providing comparably higher accuracy for soil moisture estimates.

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