Songhao Shang

Tidal Effects on Groundwater Dynamics in Coastal Aquifer Under Different Beach Slopes

The tide induced groundwater fluctuation and the seawater intrusion have important effects on hydrogeology and ecology of coastal aquifers. Among previous studies, there were few quantitative evaluations of the joint effects of the beach slope and the tide fluctuation on the groundwater dynamics. In this article, a numerical model is built by using the software FEFLOW with consideration of seawater intrusion, tide effects, density dependent flow and beach sloping effects. The simulation results are validated by laboratory experimental data in literature. More numerical scenarios are designed in a practical scale with different beach slopes. Results show that the groundwater fluctuation decays exponentially with the distance to the beach, i.e., A = β1e−γ x, and our simulation further shows that the beach slope influence β1 can be expressed in the form of a logarithm function. While for the same location, the amplitude increases logarithmically with the beach angle in the form A = βln(α) + γ′, where β2 and γ′ are related with the horizontal distance (x) in the form of a logarithm function. The beach slope has no influence on the phase lag, although the latter increases regularly with the distance from the sea. The beach slope effect on the seawater intrusion is investigated through the quantitative relationship among the relative intrusion length (λ), the relative enhancement of the tide induced seawater intrusion (κ) and the beach angle (α). It is shown that the tide effects on a milder beach is much greater than on a vertical one, and both λ and κ can be expressed in logarithm functions of α. The tidal effect on the flow field in the transition zone for a particular mild beach is also studied, with results showing that the tide induced fluctuation of Vx is similar to the groundwater table fluctuation while Vz shows a distinct variation along both directions.

Toward the Use of the MODIS ET Product to Estimate Terrestrial GPP for Nonforest Ecosystems

Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) data (MOD17), based on the light-use-efficiency algorithm, have been widely used to assess large-scale carbon budgets. However, systemic errors of this product have been reported, particularly for nonforest ecosystems. Here, we test a simple and operational way to estimate GPP in nonforest ecosystems by inverting the MODIS evapotranspiration (ET) product (MOD16) using ecosystem water use efficiency (WUE = GPP/ET) . Field measurements from 17 nonforest AmeriFlux sites of GPP were used for validation. Results show that the inverted GPP from MOD16 (MOD16 GPP) agrees better with the observed GPP than MOD17 does. The overall root-mean-square error (RMSE) and mean bias of MOD16 GPP are 19.63 g C/m2/8-day and -4.06 g C/m2 /8-day, respectively, which are lower than the corresponding values of MOD17 GPP ( RMSE = 23.82 g C/ m2/8-day and mean bias = -9.07 g C/m2/8-day). This finding suggests the potential to achieve a better assessment of GPP for nonforest ecosystems with a fine resolution.

Lake surface area method to define minimum ecological lake level from level-area-storage curves

Lake level assessment is essential for the protection of ecosystem in shrunk or shrinking lakes. Minimum ecological lake level is the critical lake level below which there should be no human activities to further decrease the lake level, and this level can provide a certain protection for the lake ecosystem. Lake surface area method was proposed to define the minimum ecological lake storage as the breakpoint of the lake surface area-storage curve, where the curve slope equals to the ratio of maximum lake surface area to maximum lake storage. If the curve can be expressed as a simple analytical function, the minimum ecological lake storage can be calculated analytically. Otherwise, it can be calculated numerically using the ideal point method for an equivalent multi-objective optimization model that balances ecosystem protection and water use. Then the minimum ecological lake level can be estimated from the lake level-storage curve. Compared with available lake morphology analysis methods, the lake surface area method is superior in its definition of minimum ecological lake level, applicable range of lake morphology, and calculation complexity. The proposed method was applied to two representative lakes in China, including one freshwater lake (the Dongting Lake in Hunan province in Central China) and one saltwater lake (the Ebinur Lake in Xinjiang Uygur autonomous region in Northwest China). The estimated minimum ecological lake level for the Dongting Lake is 26.7 m, at which 31% of the maximum lake storage provides 87% of the maximum lake surface area. The result for the Ebinur Lake is 191.2 m, at which 24% of the maximum lake storage provides 54% of the maximum lake surface area. The estimated minimum ecological lake level balances the conflict between economical and ecological water uses, and can provide a relatively larger habitat for the lake ecosystem with relatively smaller lake storage. These results are rational compared with the results of other methods. The calculated minimum ecological lake level can be used in the protection of lake ecosystems and the planning and rational use of water resources in lake basins.

Mapping interannual variability of maize cover in a large irrigation district using a vegetation index - phenological index classifier

Vegetation index and phenological index derived from MODIS NDVI series are used to establish the maize classifier. This classifier performs reasonably well in mapping interannual variability of maize cover in a large irrigation district with heterogeneous crop cover.Display Omitted A vegetation index - phenological index classifier is developed for maize based on MODIS data.Maize cover is mapped for a large irrigation district from 2003 to 2012.The classifier accuracy is acceptable in an area with complex planting structure.The classifier can be applied in multiple years without further field investigation. Accurate mapping interannual variability of crop cover is a pre-request for modern agricultural management, while most published algorithms require re-calibration when crop cover is mapped over multiple years, and hence greatly hinder their applicability. In addition, these algorithms are often not applicable for areas with complex planting patterns. Here we propose a vegetation index - phenological index (VI-PI) classifier to map interannual variability of crop cover (using maize, which is one of the major crops in the study area as a demonstration case) in the Hetao Irrigation District of North China from 2003 to 2012 using the MODIS data at 250m spatial resolution. Representative MODIS Normalized Difference Vegetation Index (NDVI) time series of maize is obtained during a field survey in late August, 2012, which is fitted with an asymmetric logistic curve to obtain the phenological indices. The maize classifier (an ellipse on the VI-PI space) is shaped based on the in situ data and adjusted by the official statistics in 2010-2012. The performance of the developed classifier is then tested with the official data from 2003 to 2009. Results show that the asymmetric logistic curve performs excellent in describing the NDVI time series of maize, and the estimated distribution of maize agrees reasonably well with the independent official data. The relative errors are lower than 7% in the training years, and lower than 30% during the testing years which is considered acceptable for crop mapping in an area with complex planting patterns. And the kappa coefficient was as high as 0.86. These results indicate that the proposed VI-PI classifier can be used effectively for crop mapping over multiple planting years and in areas with a complex planting structure.

Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation District of China with High Spatial and Temporal Resolution Vegetation Index Series

Crop identification in large irrigation districts is important for crop yield estimation, hydrological simulation, and agricultural water management. Remote sensing provides an opportunity to visualize crops in the regional scale. However, the use of coarse resolution remote sensing images for crop identification usually causes great errors due to the presence of mixed pixels in regions with complex planting structure of crops. Therefore, it is preferable to use remote sensing data with high spatial and temporal resolutions in crop identification. This study aimed to map multi-year distributions of major crops (maize and sunflower) in Hetao Irrigation District, the third largest irrigation district in China, using HJ-1A/1B CCD images with high spatial and temporal resolutions. The Normalized Difference Vegetation Index (NDVI) series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to find the NDVI characteristics and phenological metrics for both maize and sunflower. Nine combinations of NDVI characteristics and phenological metrics were compared to obtain the optimal classifier to map maize and sunflower from 2009 to 2015. Results showed that the classification ellipse with the NDVI characteristic of the left inflection point in the NDVI curve and the phenological metric from the left inflection point to the peak point normalized, with mean values of corresponding grassland indexes achieving the minimum mean relative error of 10.82% for maize and 4.38% for sunflower. The corresponding Kappa coefficient was 0.62. These results indicated that the vegetation and phenology-based classifier using HJ-1A/1B data could effectively identify multi-year distribution of maize and sunflower in the study region. It was found that maize was mainly distributed in the middle part of the irrigation district (Hangjinhouqi and Linhe), while sunflower mainly in the east part (Wuyuan). The planting sites of sunflower had been gradually expanded from Wuyuan to the north part of Hangjinhouqi and Linhe. These results were in agreement with the local economic policy. Results also revealed the increasing trends of both maize and sunflower planting areas during the study period.

Downscaling Crop Water Sensitivity Index Using Monotone Piecewise Cubic Interpolation

Abstract Crop-water production functions quantitatively describe the relationship between crop yield and field evapotranspiration. The crop water sensitivity indexes of crop-water production functions, a key factor for optimizing irrigation scheduling in case of water scarcity, are usually obtained from field experiments or other sources for crop growth stages, while their values in shorter intervals are preferred for practical irrigation scheduling. We proposed a method to downscale the sensitivity index from growth stages to shorter intervals by monotone piecewise cubic interpolation of the cumulative sensitivity index curve. This method was used to estimate sensitivity indexes in irrigation intervals of about 10 d for corn and wheat in central Shanxi Province of China. Results showed that the downscaled sensitivity index could reflect the impact of water stress on crop growth both at different growth stages and within each stage. Scenario analysis of water stress at a single growth stage of wheat showed the rationality of downscaling water sensitivity index from growth stages to shorter intervals through interpolation of cumulative sensitivity index, and this proposed downscaling method was superior to the traditional linear downscaling method.

A Physicoempirical Model for Soil Water Simulation in Crop Root Zone

To predict soil water variation in the crop root zone, a general exponential recession (GER) model was developed to depict the recession process of soil water storage. Incorporating the GER model into the mass balance model for soil water, a GER-based physicoempirical (PE-GER) model was proposed for simulating soil water variation in the crop root zone. The PE-GER model was calibrated and validated with experimental data of winter wheat in North China. Simulation results agreed well with the field experiment results, as well as were consistent with the simulation results from a more thoroughly developed soil water balance model which required more detailed parameters and inputs. Compared with a previously developed simple exponential recession (SER) based physicoempirical (PE-SER) model, PE-GER was more suitable for application in a broad range of soil texture, from light soil to heavy soil. Practical application of PE-GER showed that PE-GER could provide a convenient way to simulate and predict the variation of soil water storage in the crop root zone, especially in case of insufficient data for conceptual or hydrodynamic models.

A general multi-objective programming model for minimum ecological flow or water level of inland water bodies

Assessment of ecological flow or water level for water bodies is important for the protection of degraded or degrading ecosystems caused by water shortage in arid regions, and it has become a key issue in water resources planning. In the past several decades, many methods have been proposed to assess ecological flow for rivers and ecological water level for lakes or wetlands. To balance water uses by human and ecosystems, we proposed a general multi-objective programming model to determine minimum ecological flow or water level for inland water bodies, where two objectives are water index for human and habitat index for ecosystems, respectively. Using the weighted sum method for multi-objective optimization, minimum ecological flow or water level can be determined from the breakpoint in the water index-habitat index curve, which is similar to the slope method to determine minimum ecological flow from wetted perimeter-discharge curve. However, the general multi-objective programming model is superior to the slope method in its physical meaning and calculation method. This model provides a general analysis method for ecological water uses of different inland water bodies, and can be used to define minimum ecological flow or water level by choosing appropriate water and habitat indices. Several commonly used flow or water level assessment methods were found to be special cases of the general model, including the wetted perimeter method and the multi-objective physical habitat simulation method for ecological river flow, the inundated forest width method for regeneration flow of floodplain forest and the lake surface area method for ecological lake level. These methods were applied to determine minimum ecological flow or water level for two representative rivers and a lake in northern Xinjiang of China, including minimum ecological flow for the Ertix River, minimum regeneration flow for floodplain forest along the midstream of Kaxgar River, and minimum ecological lake level for the Ebinur Lake. The results illustrated the versatility of the general model, and can provide references for water resources planning and ecosystem protection for these rivers and lake.

Modeling winter wheat response to water in North China with feed-forward neural networks

The model of crop response to water describes the quantitative relationship between crop yield and water input, and is essential for the rational regulation of field water regime and the improvement of water use efficiency. A multi-layer feed-forward neural network (MFNN) was used to simulate the crop response to water for winter wheat in Xiaohe irrigation district in North China. The MFNN was trained with field experiment results using the Hybrid Algorithm of genetic algorithms and back-propagation algorithm. It was found that the MFNN is capable to describe wheat yield response to water well when using suitable parameters and training algorithms, while the over-fitting of the MFNN can be improved by decreasing the number of hidden nodes and introducing calibration samples. The simulation results indicate that the yield of winter wheat is sensitive to water stress during three mid-growing stages. Moderate water stress in these three stages has little influence on the yield, and thresholds of moderate water stress for these three stages can be used in irrigation scheduling.

Simulation-based optimization of irrigation scheduling for maize in arid region to increase field application efficiency

Abstract. . In arid regions, optimization of irrigation scheduling for crops is crucial to solve the problem of irrigation water shortage. In this study, a simulation-based optimization model was developed by coupling a soil water balance simulation model in farmland and an optimization model for irrigation scheduling. The Yingke Irrigation District in the middle reaches of Heihe River basin in northwest China was chosen as the study area. The water balance simulation model was first calibrated and validated based on field experiment results of maize in 2012 and 2013, respectively. Then, considering distribution of soil types in the study area, the model was used to solve the optimal irrigation schedules for the scenarios of status quo and typical climate years. Results showed that the current irrigation schedules in the study area was inefficient to increase field application efficiency because there were much drainage water transferred from irrigation. Analysis of the scenario simulations indicated that the irrigated water could be best utilized and the shortage of irrigated water resources should be relieved after irrigation schedules optimization by the simulation-optimization model in the farmland of this region.