Each soil moisture data set is characterized by its specific mean value, variability, and dynamical range. For the assimilation of soil moisture observations into numerical models observation operators have to be developed, which reduce systematic differences. In this study, cumulative distribution function (CDF) matching is used to derive observation operators for TRMM Microwave Imager (TMI) derived soil moisture for the southern US, modeled soil moisture fields from the European Centre for Medium‐Range Weather Forecasts (ECMWF), and model output from the Variable Infiltration Capacity model (VIC). It is found that the transferability of these observation operators in space and time strongly depends on the geographical region. In the Central US, where the assimilation of soil moisture is most promising, the observation operator exhibits little variability in time. The temporal variability in the observation operator can result in substantial differences between the modeled field and the observation. In Numerical Weather Prediction (NWP) applications, where the model tends to be updated on a regular basis, dynamic observation operators will be necessary to assimilate soil moisture. For climate studies or re‐analysis projects long time series are required to define an observation operator, which correctly reproduces interannual variability.
Statistical downscaling and dynamical downscaling are two approaches to generate high‐resolution regional climate models based on the large‐scale information from either reanalysis data or global climate models. In this study, these two downscaling methods are used to simulate the surface climate of China and compared. The Statistical Downscaling Model (SDSM) is cross validated and used to downscale the regional climate of China. Then, the downscaled historical climate of 1981–2000 and future climate of 2041–2060 are compared with that from the Weather Research and Forecasting (WRF) model driven by the European Center‐Hamburg atmosphere model and the Max Planck Institute Ocean Model (ECHAM5/MPI‐OM) and the L'Institut Pierre‐Simon Laplace Coupled Model, version 5, coupled with the Nucleus for European Modelling of the ocean, low resolution (IPSL‐CM5A‐LR). The SDSM can reproduce the surface temperature characteristics of the present climate in China, whereas the WRF tends to underestimate the surface temperature over most of China. Both the SDSM and WRF require further work to improve their ability to downscale precipitation. Both statistical and dynamical downscaling methods produce future surface temperatures for 2041–2060 that are markedly different from the historical climatology. However, the changes in projected precipitation differ between the two downscaling methods. Indeed, large uncertainties remain in terms of the direction and magnitude of future precipitation changes over China.
Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.
It is of great significance to establish a scientific and reasonable water resources carrying capacity evaluation system and evaluation method on the basis of studying the interdependence and mutual relations of water resources, society, economy and the ecological environment. This can guide water resources utilization and economic and social development planning, and promote the sustainable development of water resources and the socio-economic system. Projection pursuit technology can achieve automatic index selection and index weight confirmation. When used to assess water resources carrying capacity, the subjectivity and uncertainty of index weights can be avoided. Meanwhile, it can also be used to optimize the index system, and can improve the accuracy of evaluation results and discrimination. In this paper, the projection pursuit grade model of water resources carrying capacity is established. The evaluation criteria are determined by combining the theory with practice. Grades I to IV indicate that the water resources capacity declines gradually. This is the first study of water resources carrying capacity in four municipalities in China. The results show that the water resources carrying capacity of the four municipalities in 2012 belong to the third level, Chongqing is close to the second level and Tianjin is close to the fourth level.
Abstract. A bell-shape vertical profile of chlorophyll a (Chl a) concentration, conventionally referred as Subsurface Chlorophyll Maximum (SCM) phenomenon, has frequently been observed in stratified oceans and lakes. This profile is assumed to be a general Gaussian distribution in this study. By substituting the general Gaussian function into ecosystem dynamical equations, the steady-state solutions for SCM characteristics (i.e. SCM layer depth, thickness, and intensity) in various scenarios are derived. These solutions indicate that: (1) The maximum in Chl a concentrations occurs at or below the depth with the maximum in growth rates of phytoplankton locating at the transition from nutrient limitation to light limitation, and the depth of SCM layer deepens logarithmically with an increase in surface light intensity; (2) The shape of SCM layer (thickness and intensity) is mainly influenced by nutrient supply, but independence of surface light intensity; (3) The intensity of SCM layer is proportional to the diffusive flux of nutrient from below, getting stronger as a result of this layer being shrank by a higher light attenuation coefficient or a larger sinking velocity of phytoplankton. The analytical solutions can be useful to estimate environmental parameters difficultly obtained from on-site observations.
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