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Natural forests in southern China have been severely logged due to high human demand for timber, food, and fuels during the past century, but are recovering in the past decade. The objective of this study was to investigate how vegetation cover changes in composition and structure affected the water budgets of a 9.6-km 2 Dakeng watershed located in a humid subtropical mountainous region in southern China. We analyzed 27 years (i.e., 1967-1993) of streamflow and climate data and associated vegetation cover change in the watershed. Land use ⁄ land cover census and Normalized Difference of Vegetation Index (NDVI) data derived from remote sensing were used to construct historic land cover change patterns. We found that over the period of record, annual streamflow (Q) and runoff ⁄ precipitation ratio did not change significantly, nor did the climatic variables, including air temperature, Hamon's potential evapotranspiration (ET), pan evaporation, sunshine hours, and radiation. However, annual ET estimated as the differences between P and Q showed a statistically significant increasing trend. Overall, the NDVI of the watershed had a significant increasing trend in the peak spring growing season. This study concluded that watershed ecosystem ET increased as the vegetation cover shifted from low stock forests to shrub and grasslands that had higher ET rates. A conceptual model was developed for the study watershed to describe the vegetation cover-streamflow relationships during a 50-year time frame. This paper highlighted the importance of eco-physiologically based studies in understanding transitory, nonstationary effects of deforestation or forestation on watershed water balances.

Subglacial topography is an important feature in numerous ice-sheet analyses and can drive the routing of water at the bed. Bed topography is primarily measured with ice-penetrating radar. Significant gaps, however, remain in data coverage that require interpolation. Topographic interpolations are typically made with kriging, as well as with mass conservation, where ice flow dynamics are used to constrain bed geometry. However, these techniques generate bed topography that is unrealistically smooth at small scales, which biases subglacial water flowpath models and makes it difficult to rigorously quantify uncertainty in subglacial drainage patterns. To address this challenge, we adapt a geostatistical simulation method with probabilistic modeling to stochastically simulate bed topography such that the interpolated topography retains the spatial statistics of the ice-penetrating radar data. We use this method to simulate subglacial topography using mass conservation topography as a secondary constraint. We apply a water routing model to each of these realizations. Our results show that many of the flowpaths significantly change with each topographic realization, demonstrating that geostatistical simulation can be useful for assessing confidence in subglacial flowpaths.

A random three-dimensional (3D) porous medium can be reconstructed from a two-dimensional (2D) image by reconstructing an image from the original 2D image, and then repeatedly using the result to reconstruct the next 2D image. The reconstructed images are then stacked together to generate the entire reconstructed 3D porous medium. To perform this successfully, a very important issue must be addressed, i.e., controlling the continuity and variability among adjacent layers. Continuity and variability, which are consistent with the statistics characteristic of the training image (TI), ensure that the reconstructed result matches the TI. By selecting the number and location of the sampling points in the sampling process, the continuity and variability can be controlled directly, and thus the characteristics of the reconstructed image can be controlled indirectly. In this paper, we propose and develop an original sampling method called three-step sampling. In our sampling method, sampling points are extracted successively from the center of 5×5 and 3×3 sampling templates and the edge area based on a two-point correlation function. The continuity and variability of adjacent layers were considered during the three steps of the sampling process. Our method was tested on a Berea sandstone sample, and the reconstructed result was compared with the original sample, using tests involving porosity distribution, the lineal path function, the autocorrelation function, the pore and throat size distributions, and two-phase flow relative permeabilities. The comparison indicates that many statistical characteristics of the reconstructed result match with the TI and the reference 3D medium perfectly.

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Direct sampling (DS) is a versatile multiple‐point statistics method for generating spatial‐temporal geostatistical models. DS is known for being able to address a variety of training images and hence spatiotemporal stochastic modeling problems. One limitation of DS is the central processing unit (CPU) time, mostly attributed to the use of a random search for patterns in the training image. To improve CPU performance, we propose a tree‐based direct sampling (TDS) method. In our method, training patterns are grouped according to their similarities combined with a clustering tree for fast lookup. Rather than patterns, we store locations in our database. During the simulation, TDS applies a tree‐driven search approach. Two objectives, similarity and diversity, are used to rapidly retrieve patterns and prevent trapping into local optima. We also introduce a way to speed up simulation by means of pasting patterns with adaptive size. The performance of our TDS is investigated using a 2‐D benchmark training image. Moreover, we apply the proposed method to two real cases including gap filling the bedrock topography in Antarctica from radar to better understand subglacial hydrology and creating 3‐D groundwater models in the Danish aquifer system. Based on several quantitative evaluations, we find the proposed TDS is comparable to DS in terms of simulation quality, while significantly saves CPU time.

Determination of rates of mineralization of organic nitrogen (N) into ammonium-N (NH4+-N) and nitrification of NH4+-N into nitrate-N (NO3−-N) could be used to evaluate inorganic N supply capacity, which, in turn, could guide N fertilizer application practices in crop cultivation systems. However, little information is available on the change of mineralization and nitrification in soils under fruit cultivation systems converted from forestlands in karst regions. In a 15N-tracing study, inorganic N supply capacity in forest soils and three typical fruit crop soils under long-term cultivation was investigated, in addition to factors influencing the supply, in calcareous soils in the karst regions in southwestern China. Long-term fruit crop cultivation decreased soil organic carbon (SOC), total N, and calcium concentrations, cation exchange capacity (CEC), water holding capacity (WHC), pH, and sand content, significantly, but increased clay content. Compared to that of forests, long-term fruit crop cultivation significantly decreased mineralization and nitrification rates to 0.61–1.34 mg N kg−1 d−1 and 1.95–5.07 mg N kg−1 d−1, respectively, from 2.85–6.49 mg N kg−1 d−1 and 8.17–15.5 mg N kg−1 d−1, respectively, but greatly increased the mean residence times of NH4+-N and NO3−-N. The results indicate that long-term fruit crop cultivation could decrease soil inorganic N supply capacity and turnover in karst regions. Both mineralization and nitrification rates were significantly and positively correlated with SOC and total N concentrations, CEC, and WHC, but negatively correlated with clay content, suggesting that decreased soil organic matter and increased clay content were responsible for the decline in mineralization and nitrification rates in soils under long-term cultivation of fruit crops. The results of the present study highlight the importance of rational organic fertilizer application in accelerating soil inorganic N supply and turnover under long-term cultivation of fruit crops in karst regions.