Jinkang Du

Hydrological Simulation by SWAT Model with Fixed and Varied Parameterization Approaches Under Land Use Change

Land use and land cover (LULC) change within a watershed is recognized as an important factor affecting hydrological processes and water resources. Modeling the hydrological effects of land-use change is important not only for after-the-fact analyses, but also for understanding and predicting the potential hydrological consequences of existing land-use practices. The main aim of the study is to understand and quantify the hydrological processes in a rapid urbanization region. The SWAT model and the Qinhuai River basin, one of the most rapidly urbanizing regions in China were selected to perform the study. In the study, a varied parameterization strategy was developed by establishing regression equations with selected SWAT parameters as dependent variables and catchment impermeable area as independent variable. The performance of the newly developed varied parameterization approach was compared with the conventional fixed parameterization approach in simulating the hydrological processes under LULC changes. The results showed that the model simulation with varied parameterization approach has a large improvement over the conventional fixed parameterization approach in terms of both long-term water balance and flood events simulations. The proposed modeling approach could provide an essential reference for the study of assessing the impact of LULC changes on hydrology in other regions.

Extraction of impervious surface based on multi-source satellite data of Qinhuai River basin from 1979–2009

Impervious surface (here after IMP) is a typical characteristic of urban area and is one of the most important environmental indicators. A 30 year time series (1979–2009) of Landsat imagery and CBERS imagery for Qinhuai River basin was analyzed to estimate the IMP. A new approach was proposed to quantify impervious surface as a continuous variable by using multi-temporal and multi-source datasets. The principal component analysis (PCA) approach was performed in CBERS imagery to increase information of image. Linear Spectral Mixture Analysis (LSMA) was used to determine the fractional composition of vegetation, high- and low-albedo and soil for each pixel of the normalized data. Supervised classification technique and MNDWI (Modified Normalized Difference Water Index) method were used to extract water. IMP was then estimated by adding all of high-albedo and part of low-albedo fraction images. Temporal rule, that minimized classification error, was developed based on each pixels classified trajectory over the time series of imagery. Overall cross-date classification accuracies for impervious vs. non-impervious surface were greater than 85%. The results indicated that the area of impervious surface in the Qinhuai River basin increased by 963% over 30 years, and impervious surface rate was from 1.70% in 1979 to 18.02% in 2009. The increase rate of IMP was 7.1% before 2003 and 12.9% after 2003. This approach demonstrated that impervious surface distribution could be derived from multi-temporal and multi-source satellite datasets with promising accuracy

Vertical distribution of snow cover and its relation to temperature over the Manasi River Basin of Tianshan Mountains, Northwest China

How snow cover changes in response to climate change at different elevations within a mountainous basin is a less investigated question. In this study we focused on the vertical distribution of snow cover and its relation to elevation and temperature within different elevation zones of distinct climatology, taking the mountainous Manasi River Basin of Xinjiang, Northwest China as a case study. Data sources include MODIS 8-day snow product, MODIS land surface temperature (LST) data from 2001 to 2014, and in situ temperature data observed at three hydrological stations from 2001 to 2012. The results show that: (1) the vertical distribution of snow areal extent (SAE) is sensitive to elevation in low (<2100 m) and high altitude (>3200 m) regions and shows four different seasonal patterns, each pattern is well correspondent to the variation of temperature. (2) The correlation between vertical changes of the SAE and temperature is significant in all seasons except for winter. (3) The correlation between annual changes of the SAE and temperature decreases with increasing elevation, the negative correlation is significant in area below 4000 m. (4) The snow cover days (SCDs) and its long-term change show visible differences in different altitude range. (5) The long-term increasing trend of SCDs and decreasing trend of winter temperature have a strong vertical relation with elevation below 3600 m. The decreasing trend of SCDs is attributed to the increasing trend of summer temperature in the area above 3600 m.

Flood Simulation with Distributed Hydrological Approach Using DEMs and Remotely Sensed Data

The spatial variability of both meteorological and geographic features has a great influence on rainfall runoff process. When making flood forecasting, the influence of this variability on runoff should be considered. In this paper, the distributed hydrological modeling technique was adopted to simulate flood discharge with consideration of spatial variability using DEMs and remotely sensed data. In our approach, the grid based digital elevation data was used for the presentation of watershed discretization and deriving flow path system and parameters. The infiltration and rainfall excess on each hillslope grid cell were calculated using the Green-Ampt infiltration equation. Soil lateral flow was estimated based on Darcys law and continuity equation, overland and channel flows were calculated by using one-dimensional kinematic wave equations on hillslope and channel grid cells respectively. The land use and land cover data derived from remotely sensed images was used for the determination of Mannings roughness coefficients. The soil type data was used for deriving soil hydraulic parameters needed for calculation. The approach was applied in Jiaokou Watershed with area of 259 km2, a sub-basin of Yongjiang River in Zhejiang Province, China. The data collected for this study included land cover processed from Landsat TM images, soil type distribution from the soil maps, and 100-m spatial resolution DEMs produced from digital topographic maps. Spatial distribution of rainfall for each flood event was calculated by using distance inverse interpolation methods. Nine flood events were simulated, two of which were used for calibration, and the others for validation. The simulated hydrographs at basin outlet showed good agreement with observed ones for both calibrated and validated flood events, and average coefficients of efficiency were 94% for calibrated flood events and 92% for validated flood events.

Effect of DEM Uncertainty on the Distributed Hydrological Model TOPMODEL

TOPMODEL is a semi-distributed hydrological model in which the distributed predictions of catchments response to rainfall are made. In this process of simulation, digital elevation models (DEM) is required to provide the values of parameters, such as topographic index, cumulative area of catchment and distance from catchment outlet; thus DEM play a dominant role in TOPMODEL implementation. Generally, DEM has inherent errors, referred to as DEM uncertainty. Lacking of knowledge about DEM errors, DEM data is often used in hydrological applications by using TOPMODEL without quantifying the effects of DEM uncertainty. In fact, the uncertainty of DEM may strongly influence the simulation results produced by TOPMODEL. Unfortunately, this effect is largely ignored in many empirical researches. This study aimed to examine the impacts of DEM uncertainty on the simulation results of TOPMODEL from the study area - Jiaokou watershed, a sub- basin with an area of 259 km2 of the Yongjiang River in southeast China. This paper mainly discussed the effects in both quantitative and qualitative aspects. First, DEM uncertainty was simulated using the Monte Carlo method, and for every realization of the DEM, the topographic index, cumulative area of catchment and distance from catchment outlet were calculated. Second, TOPMODEL was tested and the results saved as the four statistical indices of the simulation: EFF (the Nash and Sutcliffe efficiency criterion), SSE (Sum of squared residuals over all time steps), SLE (Sum of squared log residuals over all time steps) and SAE (Sum of absolute errors over all time steps) under the condition of seven storm events. Third, the statistic results - min, max, range, standard deviation, and Mean Value, of the four indices from the simulated DEM were compared in each case of flood and Mean value of the four indices were picked up to evaluate the effect of uncertainty of DEM on TOPMODEL. Finally, the simulation hydrographs were compared with the hydrographs using the original DEM under each flood event. The biggest errors for the indices of EFF, SSE, SLE, and SAE were 0.0169, 2.3x10-5, 1.1375, and 0.0033 respectively, which showed that the effect of DEM uncertainty on TOPMODEL was inconsiderable and could be ignored in the models application.

Evaluating functions of reservoirs′ storage capacities and locations on daily peak attenuation for Ganjiang River Basin using Xinanjiang model

Flooding is the most prevalent and costly natural disaster in the world and building reservoirs is one of the major structural measures for flood control and management. In this paper, a framework was proposed to evaluate functions of reservoirs′ locations and magnitudes on daily peak flow attenuation for a large basin of China, namely Ganjiang River Basin. In this study, the Xinanjiang model was adopted to simulate inflows of the reservoirs and flood hydrographs of all sub-catchments of the basin, and simple reservoir operation rules were established for calculating outflows of the reservoirs. Four reservoirs scenarios were established to analyze reservoirs′ locations on daily peak flow attenuation. The results showed that: 1) reservoirs attenuated the peak discharges for all simulated floods, when the flood storage capacities increase as new reservoirs were built, the peak discharge attenuation by reservoirs showed an increasing tendency both in absolute and relative measures; 2) reservoirs attenuated more peak discharge relatively for small floods than for large ones; 3) reservoirs reduced the peak discharge more efficiently for the floods with single peak or multi peaks with main peak occurred first; and 4) effect of upstream reservoirs on peak attenuation decreased from upper reaches to lower reaches; upstream and midstream reservoirs played important roles in decreasing peak discharge both at middle and lower reaches, and downstream reservoirs had less effect on large peak discharge attenuation at outlet of the basin. The proposed framework of evaluating functions of multiple reservoirs′ storage capacities and locations on peak attenuation is valuable for flood control planning and management at basin scale.