The water quality in Poyang Lake, the largest freshwater lake in China, has deteriorated steadily in recent years and local governments have made efforts to manage the potential eutrophication. In order to investigate the transport and retention processes of dissolved substances, the hydrodynamic model, Environmental Fluid Dynamics Code (EFDC) was applied by using the concept of water age. The simulated results showed agreement with the measured water level, discharge, and inundation area. The water age in Poyang Lake was significantly influenced by the variations of hydrological conditions. The annual analysis revealed that the largest averaged water age was observed during the wet year (2010) with 28.4 days at Hukou, the junction of the Yangtze River and Poyang Lake. In the normal season (April), the youngest age with 9.1 days was found. The spatial distribution of water quality derived from the remote sensing images suggested that a higher chlorophyll-a concentration, lower turbidity, and smaller water age in the eastern area of Poyang Lake might threaten the regional aquatic health. The particle tracking simulation reproduced the trajectories of the dissolved substances, indicating that the water mass with greater nutrient loading would further lead to potential environmental problems in the east lake. Moreover, the water transfer ability would be weakened due to dam (Poyang Project) construction resulting in the rising water levels in periods of regulation. Generally, this study quantified an indicative transport timescale, which could help to better understand the complex hydrodynamic processes and manage wetland ecosystems similar to Poyang Lake.

China's largest freshwater lake, Poyang Lake, is characterized by rapid changes in its inundation area and hydrodynamics, so in this study, a hydrodynamic model of Poyang Lake was established to simulate these long-term changes. Inundation information was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and used to calibrate the wetting and drying parameter by assessing the accuracy of the simulated inundation area and its boundary. The bottom friction parameter was calibrated using current velocity measurements from Acoustic Doppler Current Profilers (ADCP). The results show the model is capable of predicting the inundation area dynamic through cross-validation with remotely sensed inundation data, and can reproduce the seasonal dynamics of the water level, and water discharge through a comparison with hydrological data. Based on the model results, the characteristics of the current velocities of the lake in the wet season and the dry season of the lake were explored, and the potential effect of the current dynamic on water quality patterns was discussed. The model is a OPEN ACCESSRemote Sens. 2015, 7 4859 promising basic tool for prediction and management of the water resource and water quality of Poyang Lake.

Abstract. To solve the problem of estimating and verifying stream flow without direct observation data, we estimated stream flow in ungauged zones by coupling a hydrological model with a hydrodynamic model, using the Poyang Lake basin as a test case. To simulate the stream flow of the ungauged zone, we built a soil and water assessment tool (SWAT) model for the entire catchment area covering the upstream gauged area and ungauged zone, and then calibrated the SWAT model using the data in the gauged area. To verify the results, we built two hydrodynamic scenarios (the original and adjusted scenarios) for Poyang Lake using the Delft3D model. In the original scenario, the upstream boundary condition is the observed stream flow from the upstream gauged area, while, in the adjusted scenario, it is the sum of the observed stream flow from the gauged area and the simulated stream flow from the ungauged zone. The experimental results showed that there is a stronger correlation and lower bias (R 2 = 0.81, PBIAS = 10.00 %) between the observed and simulated stream flow in the adjusted scenario compared to that (R 2 = 0.77, PBIAS = 20.10 %) in the original scenario, suggesting the simulated stream flow of the ungauged zone is reasonable. Using this method, we estimated the stream flow of the Poyang Lake ungauged zone as 16.4 ± 6.2 billion m 3 a −1 , representing ∼ 11.24 % of the annual total water yield of the entire watershed. Of the annual water yield, 70 % (11.48 billion m 3 a −1 ) is concentrated in the wet season, while 30 % (4.92 billion m 3 a −1 ) comes from the dry season. The ungauged stream flow significantly improves the water balance with the closing error decreased by 13.48 billion m 3 a −1 (10.10 % of the total annual water resource) from 30.20 ± 9.1 billion m 3 a −1 (20.10 % of the total annual water resource) to 16.72 ± 8.53 billion m 3 a −1 (10.00 % of the total annual water resource). The method can be extended to other lake, river, or ocean basins where observation data is unavailable.

Research Impact Statement: Both CFSR and CMADS data were proved to provide alternatives to quickly implement a hydrologic model. However, CMADS data perform better than CFSR in capturing extreme streamflow events. ABSTRACT: Evaluating the accuracy of gridded weather data is important because these data significantly affect the results of hydrologic simulations. In this study, we evaluated the applicability of Climate Forecast System Reanalysis (CFSR) and China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) datasets for capturing the hydrologic extreme events in the Fuhe River Basin (FRB) of the Poyang Lake, a typical humid area in eastern China. First, both the CFSR and CMADS temperature and precipitation data obtained from 2008 to 2013 were validated using ground-based meteorological station data. Then, the SWAT model was driven by the CFSR and CMADS datasets for hydrologic predictions. The results show that both CFSR and CMADS temperature data are of high quality. The CMADS data underestimate precipitation, whereas CFSR data overestimate precipitation. Both datasets have their own advantage and disadvantage to detect extreme rainfall events. For the FRB case study, SWAT models driven by two datasets yield good streamflow simulation results. The CMADS-driven model performs slightly better than CFSRdriven model in predicting extreme streamflow events in the simulation time-period (2009-2013). In general, both the CFSR and CMADS data can be used to obtain satisfactory simulation results for hydrologic predictions. They provide alternatives that enable quickly implementing a hydrologic model in data-scarce areas.

An increase in vegetation greenness can improve ecosystem productivity, but also reduce the water supply, creating the potential for conflicting water demands between ecosystems and humans. This problem has been well-assessed and is most evident in dry environments. However, in humid regions, the potential effects of vegetation greenness on water yields under drought conditions are not well understood. To address this gap, we focused on the Poyang Lake watershed in the humid region of southern China. Based on the Standardized Precipitation Evapotranspiration Index and a satellite-derived leaf area index dataset during the growing seasons of 1984 to 2013, several typical dry growing seasons were selected as the study conditions. An existing Water Supply Stress Index model was modified to investigate how the changes in vegetation greenness affected water yield and to explore potentially conflicting water demands between ecosystems and humans under drought conditions. Our results showed that an increase of 20-80% in vegetation greenness generally resulted in a reduction of 3-27% in water yield under drought conditions. Large reductions in water yield mainly were observed in forested areas due to large increases in forest greenness. Moreover, increased vegetation greenness caused a 2 to 3 times greater reduction in water yield during continuing and intensifying droughts than during a short moderate drought period. Thus, in this study, during continuing and intensifying droughts, increased vegetation greenness can cause or aggravate water conflicts in sub-watersheds with high forest cover and high human water demands. Therefore, given the increasing frequency of extreme climatic events, afforestation with a targeted approach should be implemented as it would provide the most benefits. In addition, selective harvesting in forested areas with high density could be an effective strategy to maintain water supply in humid regions.

The Poyang Lake ungauged area (PLUA) is an essential hydrology buffer surrounding Poyang Lake. For such a data-scarce area, a novel spatially distributed runoff coefficient model (SDRCM) was developed based on the underlying surface properties using remotely sensed precipitation and reanalysis data after their validation. The runoff simulated by the SDRCM based on both sets of gridded precipitation data were validated in a subbasin where R2 and ENS are larger than 0.87. In addition, a hydrodynamic model was applied to validate the proposed model further by considering the estimated water yield for PLUA that involves boundary inputs, in which the result more closely aligns to the monthly observed discharge. On an annual basis, the PLUA water flow accounted for 12%–19% of the total annual water flow within the watershed, which was approximately equal to the proportion of the area of PLUA in relation to the entire watershed. Finally, the water balance between inflow and outflow of Poyang Lake was investigated, with relative errors observed at the Hukou gauging station all being less than 10% from 1998 to 2009. The proposed model will be helpful in understanding the significance of water yields of such ungauged plain area when evaluating the water balance.

No abstract

In flood-prone areas, the delineation of the spatial pattern of historical flood extents, damage assessment, and flood durations allow planners to anticipate potential threats from floods and to formulate strategies to mitigate or abate these events. The Chenab plain in the Punjab region of Pakistan is particularly prone to flooding but is understudied. It experienced its worst riverine flood in recorded history in September 2014. The present study applies Remote Sensing (RS) and Geographical Information System (GIS) techniques to estimate the riverine flood extent and duration and assess the resulting damage using Landsat-8 data. The Landsat-8 images were acquired for the pre-flooding, co-flooding, and post-flooding periods for the comprehensive analysis and delineation of flood extent, damage assessment, and duration. We used supervised classification to determine land use/cover changes, and the satellite-derived modified normalized difference water index (MNDWI) to detect flooded areas and duration. The analysis permitted us to calculate flood inundation, damages to built-up areas, and agriculture, as well as the flood duration and recession. The results also reveal that the floodwaters remained in the study area for almost two months, which further affected cultivation and increased the financial cost. Our study provides an empirical basis for flood response assessment and rehabilitation efforts in future events. Thus, the integrated RS and GIS techniques with supporting datasets make substantial contributions to flood monitoring and damage assessment in Pakistan.