The effect of expansion ratio on the critical Reynolds number in single fracture flow with sudden expansion

Flow in a single fracture (SF) is an important research subject in groundwater hydrology, hydraulic engineering, radioactive nuclear waste repository and geotechnical engineering. An abruptly changing aperture is a unique type of SF. This study discusses the relation between the values of the critical Reynolds number (Re_c) for the onset of symmetry breaking of flow and the expansion ratio (E) of SF, which is defined as the ratio between the outlet (D) and inlet (d) apertures. This study also investigates the effect of inlet aperture d on Re_c for flow in an SF with abruptly changing apertures (SF‐ACA) using the finite volume method. Earlier numerical and experimental results showed that flow is symmetric in respect to the central plane of the SF‐ACA at small Reynolds number (Re) but becomes asymmetric when Re is sufficiently large. Our simulations show that the value of Re_c decreases with the increasing E, and the relationship between the logarithm of Re_c and E can be described accurately using either a quadratic polynomial function or a logarithmic function. However, the relationship of Re_c and d for a given E value is vague, and Re_c becomes even less sensitive to d when E increases. This study also reveals that the hydraulic gradient (J) and flow velocity (v) follow a super‐linear relationship that can be fitted almost perfectly by the Forchheimer equation. The inertial component (J_i) of J increases monotonically with Re, whereas the viscous component (J_v) of J decreases monotonically with Re. The Re value corresponding to equal inertial and viscous components of J (named as the transitional point Re) decreases when E increases, and such a transitional point Re should be closely related to the critical Reynolds number Re_c, although a rigorous theoretical proof is not yet available. Copyright © 2015 John Wiley & Sons, Ltd.     

Semantic segmentation of high spatial resolution images with deep neural networks

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.     

Hydrological cycle and water balance estimates for the megadune–lake region of the Badain Jaran Desert,China

The hydrology and water balance of megadunes and lakes have been investigated in the Badain Jaran Desert of China. Field observations and analyses of sand layer water content, field capacity, secondary salt content, and grain size reveal 3 types of important natural phenomenon: (a) vegetation bands on the leeward slope of the megadunes reflect the hydrological regime within the sandy vadose zone; (b) seepage, wet sand deposits, and secondary salt deposits indicate the pattern of water movement within the sandy vadose zone; (c) zones of groundwater seeps and descending springs around the lakes reflect the influence of the local topography on the hydrological regime of the megadunes. The seepage exposed on the sloping surface of the megadunes and gravity water contained within the sand layer confirm the occurrence of preferential flow within the vadose zone of the megadunes. Alternating layers of coarse and fine sand create the conditions for the formation of preferential flows. The preferential flows promote movement of water within the sand layer water that leads to deep penetration of water within the megadunes and ultimately to the recharging of groundwater and lake water. Our results indicate that a positive water balance promotes recharge of the megadunes, which depends on the high permeability of the megadune material, the shallow depth of the surface sand layer affected by evaporation, the occurrence of rainfall events exceeding 15 mm, and the sparse vegetation cover. Water balance estimates indicate that the annual water storage of the megadunes is about 7.5 mm, accounting for only 8% of annual precipitation; however, the shallow groundwater per unit area under the megadunes receives only 3.6% of annual precipitation, but it is still able to maintain a dynamic balance of the lake water. From a water budget perspective, the annual water storage in the megadunes is sufficient to serve as a recharge source for lake water, thereby enabling the long‐term persistence of the lakes. Overall, our findings demonstrate that precipitation is a significant component of the hydrological cycle in arid deserts.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

Changes in potential evapotranspiration and surface runoff in 1981–2010 and the driving factors in Upper Heihe River Basin in Northwest China

Changes in potential evapotranspiration and surface runoff can have profound implications for hydrological processes in arid and semiarid regions. In this study, we investigated the response of hydrological processes to climate change in Upper Heihe River Basin in Northwest China for the period from 1981 to 2010. We used agronomic, climatic and hydrological data to drive the Soil and Water Assessment Tool model for changes in potential evapotranspiration (ET₀) and surface runoff and the driving factors in the study area. The results showed that increasing autumn temperature increased snow melt, resulting in increased surface runoff, especially in September and October. The spatial distribution of annual runoff was different from that of seasonal runoff, with the highest runoff in Yeniugou River, followed by Babaohe River and then the tributaries in the northern of the basin. There was no evaporation paradox at annual and seasonal time scales, and annual ET₀ was driven mainly by wind speed. ET₀ was driven by relative humidity in spring, sunshine hour duration in autumn and both sunshine hour duration and relative humility in summer. Surface runoff was controlled by temperature in spring and winter and by precipitation in summer (flood season). Although surface runoff increased in autumn with increasing temperature, it depended on rainfall in September and on temperature in October and November. Copyright © 2016 John Wiley & Sons, Ltd.     

Precipitation variability assessment of northeast China: Songhua River basin

Variability in precipitation is critical for the management of water resources. In this study, the research entropy base concept was applied to investigate spatial and temporal variability of the precipitation during 1964–2013 in the Songhua River basin of Heilongjiang Province in China. Sample entropy was applied on precipitation data on a monthly, seasonally, annually, decade scale and the number of rainy days for each selected station. Intensity entropy and apportionment entropy were used to calculate the variability over individual year and decade, respectively. Subsequently, Spearman’s Rho and Mann–Kendall tests were applied to observe for trends in the precipitation time series. The statistics of sample disorder index showed that the precipitation during February (mean 1.09, max. 1.26 and min. 0.80), April (mean 1.12, max. 1.29 and min. 0.99) and July (mean 1.10, max. 1.20 and min. 0.98) contributed significantly higher than those of other months. Overall, the contribution of the winter season was considerably high with a standard deviation of 0.10. The precipitation variability on decade basis was observed to increase from decade 1964–1973 and 1994–2003 with a mean value of decadal apportionment disorder index 0.023 and 0.053, respectively. In addition, the Mann–Kendall test value (1.90) showed a significant positive trend only at the Shangzhi station.