Wanchang Zhang

A simple empirical topographic correction method for ETM+ imagery

A simple topographic correction approach, the Variable Empirical Coefficient Algorithm (VECA), was developed using theoretical and statistic analyses of the radiance values of remotely sensed data acquired for rugged terrain and the cosine of the solar illumination angle (cos i). Visual comparison and statistical analysis were used for evaluation of the proposed algorithm and the performance of the VECA approach was compared with 10 commonly used methods. The test site selected for this study is located on the south hill of the Qinling Mountain in Shanxi province, China, and the remotely sensed data used were from Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) images. The results indicate that the Cosine‐T, Cosine‐C, sun–canopy–senor (SCS) and Cosine‐b correction have the problem of overcorrection, and the other corrections can be classed into three ranks: the VECA, b correction and C models performed the best, followed by the Teillet‐regression correction model, and the SCS+C, Minnaert and Minnaert‐SCS corrections performed the worst. The proposed VECA correction and the b correction are the most capable of removing the topographic effects of the ETM+ image. The VECA is not only simple in theory but also easy to operate, indicating that the VECA is an effective topographic correction tool in remote sensing techniques.

Assessment of Water Quality and Identification of Polluted Risky Regions Based on Field Observations & GIS in the Honghe River Watershed, China

Water quality assessment at the watershed scale requires not only an investigation of water pollution and the recognition of main pollution factors, but also the identification of polluted risky regions resulted in polluted surrounding river sections. To realize this objective, we collected water samplings from 67 sampling sites in the Honghe River watershed of China with Grid GIS method to analyze six parameters including dissolved oxygen (DO), ammonia nitrogen (NH3-N), nitrate nitrogen (NO3-N), nitrite nitrogen (NO2-N), total nitrogen (TN) and total phosphorus (TP). Single factor pollution index and comprehensive pollution index were adopted to explore main water pollutants and evaluate water quality pollution level. Based on two evaluate methods, Geo-statistical analysis and Geographical Information System (GIS) were used to visualize the spatial pollution characteristics and identifying potential polluted risky regions. The results indicated that the general water quality in the watershed has been exposed to various pollutants, in which TP, NO2-N and TN were the main pollutants and seriously exceeded the standard of Category III. The zones of TP, TN, DO, NO2-N and NH3-N pollution covered 99.07%, 62.22%, 59.72%, 37.34% and 13.82% of the watershed respectively, and they were from medium to serious polluted. 83.27% of the watershed in total was polluted by comprehensive pollutants. These conclusions may provide useful and effective information for watershed water pollution control and management.

Topographic correction algorithm for remotely sensed data accounting for indirect irradiance

The solar irradiance incidents upon terrain surface are composed of three parts, i.e. direct solar irradiance, diffuse sky irradiance and reflected irradiance from the adjacent surface, respectively. Most of the topographic correction models only account for the topographic effect induced from direct solar irradiance, and few models take the topographic effects from the last two components of solar irradiance into account. A physically based topographic correction algorithm aiming to overcome this shortcoming, called a three-factor correction model, was developed based on theoretical analysis of radiation transferring processes along an undulating surface, atmosphere and satellite sensor geometry under the assumption of Lambertian surface. On the basis of this three-factor correction model, an advanced algorithm accounting for the bidirectional reflectance distribution function (BRDF) nature of non-Lambertian surface, called the three-factor+C topographic correction model, was developed by introducing an empirical parameter C to approximate the indirect irradiance contribution of non-Lambertian surface. Performances of these two newly developed algorithms were tested and compared with those of Cosine and C correction algorithms for a selected rugged terrain on the south flank of the Qinling Mountain, China. Visual comparison and statistical analysis were adopted for quantitative evaluation on topographic corrections of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image in the study. The results suggested that the general performance of the algorithms for topographic correction ranks the three-factor+C correction, C correction, three-factor correction and Cosine correction from excellent to poor in order, which implies the promising potential of the proposed algorithms in effective topographic correction applications in remote sensing techniques.

Spatial statistical properties and scale transform analyses on the topographic index derived from DEMs in China

The topographic index (TI), frequently used in approximately characterizing the spatial distribution of variable source areas within a watershed, has been widely applied in topography-based land-surface process schemes coupled in regional or global climatic models. The TI concept, however, was originally developed for studying hill-slope scale hydrological processes and was most commonly used in simulations from small- to medium-sized watersheds. It is still questionable whether the TI computed from coarse-resolution digital elevation models (DEMs) for large-scale hydrology and climate studies can effectively reflect the spatial distribution of soil moisture, surface saturation, and runoff generation processes in most areas. In this study, we first proposed an improved multiple flow direction algorithm (IMFD) for accurately computing the TI distribution. We then evaluated the IMFD algorithm quantitatively by using four types of artificial mathematical surfaces. Subsequently, we conducted statistical analyses on the TI distributions computed with IMFD from 90x90m^2 and 1000x1000m^2 resolution DEM blocks sampled from across the whole of China. We found there are linear relationships between the mean TI values computed from the two different resolution DEMs in three sampled blocks of different sizes, i.e., 0.1^ox0.1^o, 0.5^ox0.5^o and 1^ox1^o. Systematic analyses further suggested that the forms of these linear relationships are evidently affected by the algorithm used for the TI computation, while the size, location, and number of the selected TI samples have minor effects on them. Finally, we investigated the influence of DEM resolution on the spatial statistical properties of TI. From the viewpoint of terrain discretization and smoothing effects, we also discussed the mechanism and the reasons causing the similarity on TI at different spatial resolutions.

Distributed hydrological modeling study with the dynamic water yielding mechanism and RS/GIS techniques

Water yielding in the hydrologic cycle is a temporally and spatially varied process. However, water yielding mechanics expressed in hydrological simulations seldom accurately characterize such dynamic processes thus weakens the simulation capabilities of present hydrological modeling systems. In this study a conceptual distributed hydrological model entitled ESSI (infiltration Excess and Saturation excess Soil-water Integration model for hydrology) was developed for flooding simulation and long term water resource management studies by means of RS, GIS and data mining techniques. This distributed hydrological modeling system has three significant characteristics: 1) capable of determining temporally and spatially varied water yielding mechanics over the most basic simulated grid by comparing with real-time computed rainfall and soil water variables; 2) excellent weather adoptability to ensure the model perform excellently for either wet and dry watershed conditions; 3) fully distributed simulating capabilities enable the model output about 20 distributed hydrological process components in different time scales, i.e. evapotranspiration (potential and actual), canopy storage, and soil moisture contents in different soil depth etc. Calibration and validation of the modeling system was conducted on two carefully selected climatologically typical watersheds in China, one located in the typical humid climate condition of upper stream of the Hanjiang river Basin, gauged by the Jiangkou hydrometric station (drainage area: 2413 km2), and another the Yingluoxia watershed (drainage area: 10029 km2), situated in typical cold and arid Heihe Mountainous region. With the calibrated model parameters and the appropriate combination of hydrological simulating module, ESSI successfully reproduced the flooding events and long term hydrological processes for the both experiment watershed, which implies the model an excellent hydrological simulation tool under various weather conditions.

Risk mapping of integrated natural disasters in China

We explored the regional shifts in hazards at the county level in China for three different time periods (1956–1965, 1976–1985, and 1996–2005). The frequency of natural disasters was used as the assessment index. Based on the probability of natural disasters in each county, the spatial change in risk with increasing intensity of natural disasters was studied. The results show that the frequency of natural disasters is increasing and the area affected by natural disasters extends across the whole country. The patterns of risk distribution in the periods 1956–1965, 1976–1985, and 1996–2005 are defined as Eastern-intensive, National-dispersed, and National-intensive, respectively. The high-risk areas are located in the north of the Tianshan Mountains in Xinjiang Province, the Silim Gol Plateau and the Hulun Buir Plateau in Inner Mongolia Autonomous Region, the eastern coastal provinces (Jiangsu, Zhejiang, Fujian, and Guangdong Provinces), and most parts of the Yunnan–Guizhou Plateau. This research offers a scientific basis for the regionalization of disaster risk and a disaster reduction policy for highly vulnerable regions.

Establishment of a hybrid rainfall-runoff model for use in the Noah LSM

There is an increasing trend to incorporate the basin hydrological model into the traditional land surface model (LSM) to improve the description of hydrological processes in them. For incorporating with the Noah LSM, a new rainfall-runoff model named XXT (the first X stands for Xinanjiang, the second X stands for hybrid, and T stands for TOPMODEL) was developed and presented in this study, based on the soil moisture storage capacity distribution curve (SMSCC), some essential modules of the Xinanjiang model, together with the simple model framework of the TOPMODEL (a topography based hydrological model). The innovation of XXT is that the water table is incorporated into SMSCC and it connects the surface runoff production with base flow production. This improves the description of the dynamically varying saturated areas that produce runoff and also captures the physical underground water level. XXT was tested in a small-scale watershed Youshuijie (946 km2) and a large-scale watershed Yinglouxia (10009 km2) in China. The results show that XXT has better performance against the TOPMODEL and the Xinanjiang model for the two watersheds in both the calibration period and the validation period in terms of the Nash-Sutcliffe efficiency. Moreover, XXT captures the largest peak flow well for both the small- and large-scale watersheds during the validation period, while the TOPMODEL produces significant overestimates or underestimates, so does the Xinanjiang model.

Mid-short-term daily runoff forecasting by ANNs and multiple process-based hydrological models

In recent decades, the daily runoff forecasting based on artificial Neural Network (ANN) models has become quite important to deliver sustainable use and effective planning and management of water resources. The performance of the existent ANN models for 1 day in advance forecasting are frequently reported. However, the mid-term forecasting by ANN is scarce in the literature. In this study, a feed forward network trained with a back-propagation learning algorithm (BP-ANN) is used to construct a mid-short-term daily runoff forecasting system. ANN models having various input variables were constructed and the best structure was investigated. Moreover, the performance of ANN models and multiple process-based rainfall-runoff models, including Xinanjiang, ESSI, SWAT and XXT, is compared. Baohe River basin, located in central China, is chosen as a case study area. The results show that in general the performance of ANN models decrease as the lead time increase when the lead time is less than 11 days; while it varies slightly with lead time when the lead time is larger than 11 days. The ANN model with an appropriate combination of stream flow, precipitation as input variables performs much better than all the process-based rainfall-runoff models in terms of Nash-Sutcliffe efficiency for mid-short-term daily runoff forecasting.

Climate Change and Anthropogenic Impacts on Wetland and Agriculture in the Songnen and Sanjiang Plain, Northeast China

Influences of the increasing pressure of climate change and anthropogenic activities on wetlands ecosystems and agriculture are significant around the world. This paper assessed the spatiotemporal land use and land cover changes (LULCC), especially for conversion from marshland to other LULC types (e.g., croplands) over the Songnen and Sanjiang Plain (SNP and SJP), northeast China, during the past 35 years (1980–2015). The relative role of human activities and climatic changes in terms of their impacts on wetlands and agriculture dynamics were quantitatively distinguished and evaluated in different periods based on a seven-stage LULC dataset. Our results indicated that human activities, such as population expansion and socioeconomic development, and institutional policies related to wetlands and agriculture were the main driving forces for LULCC of the SJP and SNP during the past decades, while increasing contributions of climatic changes were also found. Furthermore, as few studies have identified which geographic regions are most at risk, how the future climate changes will spatially and temporally impact wetlands and agriculture, i.e., the suitability of wetlands and agriculture distributions under different future climate change scenarios, were predicted and analyzed using a habitat distribution model (Maxent) at the pixel-scale. The present findings can provide valuable references for policy makers on regional sustainability for food security, water resource rational management, agricultural planning and wetland protection as well as restoration of the region.

Using a BP Neural Network for Rapid Assessment of Populations with Difficulties Accessing Drinking Water Because of Drought

ABSTRACT Accurately predicting the populations with difficulties accessing drinking water because of drought and taking appropriate mitigation measures can minimize economic loss and personal injury. Taking the 2013 Guizhou extreme summer drought as an example, on the basis of collecting meteorological, basic geographic information, socioeconomic data, and disaster effect data of the study area, a rapid assessment model based on a backpropagation (BP) neural network was constructed. Six factors were chosen for the input of the network: the average monthly precipitation, Digital Elevation Model (DEM), river density, population density, road density, and gross domestic product (GDP). The population affected by drought was the models output. Using samples from 50 drought-affected counties in Guizhou Province for network training, the models parameters were optimized. Using the trained model, the populations in need were predicted using the other 74 drought-affected counties. The accuracy of the prediction model, represented by the coefficient of determination (R2) and the normalized root mean square error (N-RMSE), yielded 0.7736 for R2 and 0.0070 for N-RMSE. The method may provide an effective reference for rapid assessment of the population in need and disaster effect verification.