Water table over an arid region can be elevated to a critical level to sustain terrestrial ecosystem along the natural channel by the stream water conveyance. Estimation of water table depth and soil moisture on river channel profile may be reduced to a two-dimensional moving boundary problem with soil water-groundwater interaction. The two-dimensional soil water flow with stream water transferred is divided into an unsaturated vertical soil water flow and a horizontal groundwater flow. Therefore, a prediction model scheme for water table depths under the interaction between soil water and groundwater with stream water transferred is presented, which includes a vertical soil water movement model, a horizontal groundwater movement model, and an interface model. The synthetic experiments are conducted to test the sensitivities of the river elevation, horizontal conductivity, and surface flux, and the results from the experiments show the robustness of the proposed scheme under different conditions. The groundwater horizontal conductivity of the proposed scheme is also calibrated by SCE-UA method and validated by data collected at the Yingsu section in the lower reaches of the Tarim River, which shows that the model can reasonably simulate the water table depths.
DI ZhenHuaXIE ZhengHuiYUAN XingTIAN XiangJunLUO ZhenDongCHEN YaNing
Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite(GOSAT) and ground-based the Total Carbon Column Observing Network(TCCON) data. It was found that CO2 concentrations based on GOSAT satellite retrievals were generally higher than those simulated by GEOS-Chem. The differences over the land area in January and April ranged from 1 to 2 ppm, and there were major differences in June and August. At high latitudes in the Northern Hemisphere in June, as well as south of the Sahara, the difference was greater than 5 ppm. In the high latitudes of the Northern Hemisphere the model results were higher than the GOSAT retrievals, while in South America the satellite data were higher. The trend of the difference in the high latitudes of the Northern Hemisphere and the Saharan region in August was opposite to June. Maximum correlation coefficients were found in April, reaching 0.72, but were smaller in June and August. In January, the correlation coefficient was only 0.36. The comparisons between GEOS-Chem data and TCCON observations showed better results than the comparison between GEOS and GOSAT. The correlation coefficients ranged between 0.42(Darwin) and 0.92(Izana). Analysis of the results indicated that the inconsistency between satellite observations and model simulations depended on inversion errors caused by data inaccuracies of the model simulation's inputs, as well as the mismatch of satellite retrieval model input parameters.
Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
The regional climate model RegCM3 incorporating the crop model CERES,called the RegCM3CERES model,was used to study the efects of crop growth and development on regional climate and hydrological processes over seven river basins in China.A 20-year numerical simulation showed that incorporating the crop growth and development processes improved the simulation of precipitation over the Haihe River Basin,Songhuajiang River Basin and Pearl River Basin.When compared with the RegCM3 control run,RegCM3CERES reduced the negative biases of monthly mean temperature over most of the seven basins in summer,especially the Haihe River Basin and Huaihe River Basin.The simulated maximum monthly evapotranspiration for summer(JJA)was around 100 mm in the basins of the Yangtze,Haihe,Huaihe and Pearl Rivers.The seasonal and annual variations of water balance components(runof,evapotranspiration and total precipitation)over all seven basins indicate that changes of evapotranspiration agree well with total precipitation.Compared to the RegCM3,RegCM3CERES simulations indicate reduced local water recycling rate over most of the seven basins due to lower evapotranspiration and greater water flux into these basins and an increased precipitation in the Heihe River Basin and Yellow River Basin,but reduced precipitation in the other five basins.Furthermore,a lower summer leaf area index(1.20 m2m 2),greater root soil moisture(0.01 m3m 3),lower latent heat flux(1.34 W m 2),and greater sensible heat flux(2.04 W m 2)are simulated for the Yangtze River Basin.
Simulations were conducted with the regional climate model RegCM incorporating water table dynamics from 1 September 1982 to 28 August 2002 to detect precipitation and temperature extremes. Compared with observed r10(number of days with precipitation ≥ 10 mm d–1), RegCM3_Hydro(the regional climate model with water table dynamics considered) simulated rain belts, including those in southern China and the middle and lower reaches of the Yangtze River, and provided data for arid to semi-arid areas such as the Heihe River Basin in northwestern China. RegCM3_Hydro indicated a significant increasing trend of r95p(days with daily precipitation greater than the 95th percentile of daily amounts) for the Yangtze, Yellow, and Pearl River basins, consistent with r95p observations. The Haihe River Basin was also chosen as a specific case to detect the effect of groundwater on extreme precipitation using peaks over threshold(POT)-based generalized Pareto distribution(GPD) with parameters estimated by the L-moment method. Quantile plots showed that all but a few of the plotted points were distributed near diagonal lines and the modeled data fitted well with the samples. Finally, the effects of water table dynamics on temperature extremes were also evaluated. In the Yellow River Basin and Songhuajiang River Basin, the trends of the number of warm days(TX95n) from RegCM3_Hydro matched observed values more closely when water table dynamics were considered, and clearly increasing numbers of warm days from 1983 to 2001 were detected.
The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar) through Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The feasibility and effectiveness of the PODEn4DVar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations(OSSEs) in comparison with the original version of the PODEn4DVar with B-localization and that without localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.
In this study, a historic simulation covering the period from 1951 to 2000 and three projected scenario simulations covering 2001-2050 were conducted em- ploying the regional climate model RegCM4 to detect the changes of terrestrial water storage （TWS） in major river basins of China, using the Intergovernmental Panel on Climate Change （IPCC） Special Report on Emissions Scenarios （SRES）： A1B, A2, and B1. The historic simula- tion revealed that the variations of TWS, which are dominated by precipitation in the basins, rely highly on their climatic features. Compared with the historic simu- lation, the changes of TWS in the scenario simulations showed strong regional differences. However, for all sce- narios, TWS was found to increase most in Northeast China and surrounding mountains around the Tibetan Plateau, and decrease most in eastern regions of China. Unlike the low seasonal variations of TWS in arid areas, the TWS showed strong seasonal variations in eastern monsoon areas, with the maximum changes usually oc- curring in summer, when TWS increases most in a year. Among the three scenario simulations, TWS increased most in Songhua River Basin of B1 scenario, and de- creased most in Pearl River Basin of A2 scenario and Hal River Basin of A1B scenario, accompanied by different annual trends and seasonal variations.
ZOU JingXIE Zheng-HuixXIE Zheng-HuiQIN Pei-HuaMA QianSUN Qin
