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Time series remote sensing data provide data support for agricultural monitoring, atmospheric and hydrological research, etc. As an effective method to reconstruct time series remote sensing images, remote sensing spatiotemporal information fusion technology can supply the shortage of high spatial resolution data. Aiming at the accuracy of time series remote sensing data generated by spatiotemporal fusion model, this paper proposes a time series assessment method based on Dynamic time warping (DTW), which uses GF-2 and Landsat-8 data to product 2015 year time series data by selected four typical spatiotemporal fusion models (STARFM, FSDAF, ESPFM, Single-pair SPSTFM), and compare synthesized data with reference data. The results show that the time series assessment method has a high consistency with the existing methods which based on spatial quality evaluation. And this method can be used as an effective method to evaluate the accuracy of synthesized time series data.

Hydrological parameters have great significance for water resource management, especially for Jiuyuangou Basin in China.By using the hydrological model of ArcGIS system, we proposed an optimal method using D8 and Dinf algorithm to automatically extract and analysis the river network. In this method, we used the hydrological parameter indexes, combined with the river network density and the river length to indicate , and adopted the mean change point method to determine the optimal catchment threshold. The experimental result shows that the optimal catchment threshold is 150, and the extracted river network is supposed to be consistent with the actual river channel. This method could provide data support for hydrological research on water and soil conservation and flood prevention, and these data are helpful to comprehensive management and evaluation of the ecological environment and water resources.

The distributed watershed hydrological model can effectively simulate lake basin hydrological processes, obtain water balance data such as runoff and evapotranspiration, and provide data support for hydrological research and management of lake basin environment. However, most of distributed hydrological models lack the breadth, timeliness, and convenience in practical applications, which are affected by discrete processes, and multi-source input and output parameters. This paper establishes a virtual geographic environment platform based on WATLAC (A Water Flow Model for Lake Catchments) distributed hydrological model, introduces the principle of WATLAC modeling, and develops virtual simulation and decision support based on WATLAC hydrological model. The system was calibrated and validated against observed daily stream flows in tributaries of the Poyang Lake from 2000 to 2005. The results show that the virtual geographical environment system can be implemented to address issues in data sharing and to improve the modeling environment that traditional distributed hydrological models would be challenged and limited.

The component soil moisture within one mixed-pixel is important for predicting soil moisture contents in eco-hydrological research in arid and semi-arid regions. The multi-angular observations of passive microwave remote sensing provide the possibility to derive mixed-pixel component soil moisture. In this study, a field experiment was carried out using a truck-mounted multi-frequency microwave radiometer (TMMR). Because on the difference of radiative characteristics in parallel-to-row and perpendicular-to-row directions, an algorithm for estimating the component soil moisture within a footprint was developed. Taking the row-structure effect into consideration, the component soil moisture could be estimated based on the tau-omega model. It has shown that the RMSE decreased when comparing the component soil moisture with the corresponding soil moisture measurements in bare surface.

Comprehensive integrated research on ecological and hydrological processes and the simulation of river basin environments are critical foundations for decision making by governments and river-basin managers. The demand for a holistic understanding of environmental systems such as river basins is increasing. Eco-hydrological research needs two types of monitoring platforms to access and collect data from basins: a modeling platform to support access, select, and run models online, and build new models with the collected data, and a manipulation platform to generate forcing data, run models, and visualize the results. Consequently, we developed an e-science environment framework comprising three platforms - a monitoring platform, a model platform, and a manipulation platform. The framework allows automatic data transmission, storage, management, analysis, model management, simulation, computing, and result visualization. The e-science environment integrates land surface models such as Simplified Simple Biosphere model, the Revised Simple Biosphere model and WRF, hydrological models such as SWAT and TOPMODEL, data assimilation filters including such as Kalman filter algorithm, and several tools and methods for dealing with data, principally artificial neural networks and Markov chains. We demonstrate the application of the framework that uses an SSIB land surface model ensemble Kalman filter to improve evapotranspiration, soil moisture, and ground temperature simulation in the Heihe inland river basin. The approach proves suitable for environmental simulation for inland river research.

Eco-hydrology is a science related to the overlapping parts of ecology and hydrology, studying the relationship between vegetation and water cycle in terrestrial and aquatic ecosystems. Climate change has significant effects on carbon and water cycles in terrestrial ecosystem, which have received extensive attentions in recent years. This paper provides a detailed analysis to the major parts of carbon and water cycles, and the relationship between these processes and environmental elements under climate change circumstances. This paper also summarized the classification and characteristics of eco-hydrological simulation models. Eco-hydrological simulation is an important approach to study the interaction between ecological and hydrological processes. The interaction mechanism and coupling of vegetation and hydrology at different scales is still a challenge in eco-hydrological research. Besides, researches on the response of eco-hydrological processes to climate change and human activities, and their feedback effects on climate are the most vigorous in this field.

Accurate forecasting of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. This paper proposes a Projection Pursuit Regression and Neural Networks (PPR--NNs) model for forecasting monthly rainfall in summer. First of all, we use the PPR technology to select input feature for NNs. Secondly, the Levenberg--Marquardt algorithm algorithm is used to train the NNs. Subsequently, example of rainfall values in August of Guangxi is used to illustrate the proposed PPR--NNs model. Empirical results indicate that the proposed method is better than the conventional neural network forecasting models which PPR--NNs model provides a promising alternative for forecasting rainfall application.

A robust system for monitoring the flow of water through soil would serve as an important facilitator to hydrological research. Of particular interest is accurate monitoring of watersheds, which will aid the discovery and understanding of potential hazards such as mudslides and groundwater contamination. Our project is targeted at meeting this need with a robust autonomous sensor network. Through existing wireless communication means, this sensor network can easily be deployed by unskilled labor to monitor the effects of moisture in a wide range of soil environments.

A major objective of soil moisture-related hydrological research during NASA's SIR-C/X-SAR mission was to determine and compare soil moisture patterns within humid watersheds using SAR data, ground-based measurements, and hydrologic modeling. Currently available soil moisture inversion methods using active microwave data are only accurate when applied to bare and slightly vegetated surfaces. Moreover, as the surface dries down, the number of pixels that can provide estimated soil moisture by these radar inversion methods decreases, leading to less accuracy and confidence in the retrieved soil moisture fields at the watershed scale. The impact of these errors in microwave-derived soil moisture on hydrological modeling of vegetated watersheds has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework is used to predict surface soil moisture for both bare and vegetated areas. In the first model run, the hydrological model is initialized using a standard baseflow approach, while in the second model run, soil moisture values derived from SIR-C radar data are used for initialization. The results, which compare favorably with ground measurements, demonstrate the utility of combining radar-derived surface soil moisture information with basin-scale hydrological modeling.