大别山板栗片热泵干燥特性及动力学模型分析

为了研究大别山板栗片热泵干燥过程和特性,建立其干燥动力学模型,并优化干燥工艺以提高干燥后的品质,以大别山板栗为研究对象进行热泵干燥动力学研究,通过对7 种常用干燥模型线性化处理,筛选适合用来描述板栗片热泵干燥特性的数学模型,并利用试验数据与模型进行回归拟合,确定并建立最适合的数学模型。结果表明：Page 模型的预测值曲线和实测值曲线高度吻合,Page模型可用来描述板栗片热泵干燥过程中水分的变化规律。研究结果可为板栗片实际生产中的热泵干燥加工提供一定参考。     

基于广义混合效应模型的云冷杉林天然更新计数方法研究

天然更新是森林恢复较好的办法,对未来林分结构和生物多样性具有深远的影响。更新模型能够模拟天然更新的现实和未来状况,为森林经营者提供准确的森林计划。以吉林省汪清林业局2013年设置的12块云冷杉针阔混交林为例,选择泊松和负二项分布形式,考虑样地间的随机效应,构建基于林分因子的云冷杉针阔混交林天然更新模型。结果表明：各个树种对林分因子的反应不一,白桦更新株数与林分每公顷株数和平均直径均呈负相关;红松和水曲柳更新株数不受各个林分因子的影响,更新株数是随机的;冷杉更新株数与林分每公顷断面积呈负相关;色木槭和云杉更新株数均与林分平均直径呈负相关。在考虑样地的随机截距效应后,模型的模拟效果显著提高。在构建天然更新模型时,林分密度是十分重要的因子,如果要人工促进天然更新,就要科学合理的采取经营措施,以确保合理的林分密度。另外样地间的差异是不容忽略的因素,需要利用混合效应模型方法来降低预测误差。     

Analysis of regional variation of height growth and slenderness in populations of six urban tree species using a quantile regression approach

Inventory data of six urban tree species from seven cities across Germany were used to model regional variation of height growth, allometric scaling, and slenderness over a wide range of size and age using a quantile regression approach. Variation within and between species and cities was large. Height did not reach an asymptote but declined at higher ages, presumably because of reduction cuts. Allometric scaling and slenderness varied with wind climate, modulus of elasticity, and coefficient of drag. Our data do not support the use of a threshold value of slenderness in tree risk assessment.     

Accounting for uncertainty in pedotransfer functions in vulnerability assessments of pesticide leaching to groundwater

A simulation tool for site-specific vulnerability assessments of pesticide leaching to groundwater was developed, based on the pesticide fate and transport model MACRO, parameterized using pedotransfer functions and reasonable worst-case parameter values. The effects of uncertainty in the pedotransfer functions on simulation results were examined for 48 combinations of soils, pesticides and application timings, by sampling pedotransfer function regression errors and propagating them through the simulation model in a Monte Carlo analysis. An uncertainty factor, f(u), was derived, defined as the ratio between the concentration simulated with no errors, c(sim), and the 80th percentile concentration for the scenario. The pedotransfer function errors caused a large variation in simulation results, with f(u) ranging from 1.14 to 1440, with a median of 2.8. A non-linear relationship was found between f(u) and c(sim), which can be used to account for parameter uncertainty by correcting the simulated concentration, c(sim), to an estimated 80th percentile value. For fine-textured soils, the predictions were most sensitive to errors in the pedotransfer functions for two parameters regulating macropore flow (the saturated matrix hydraulic conductivity, K(b), and the effective diffusion pathlength, d) and two water retention function parameters (van Genuchten's N and alpha parameters). For coarse-textured soils, the model was also sensitive to errors in the exponent in the degradation water response function and the dispersivity, in addition to K(b), but showed little sensitivity to d. To reduce uncertainty in model predictions, improved pedotransfer functions for K(b), d, N and alpha would therefore be most useful.     

中国六省土地沙漠化敏感性时空格局与趋势分析

以我国新疆维吾尔自治区、青海省、内蒙古自治区、甘肃省、宁夏回族自治区和陕西省六省区为研究区,分析了1990年、2000年、2005年、2010年4个时期的土地沙漠化敏感性空间格局分布和时间动态变化特征,探讨变化成因并采用CA-Markov耦合模型对土地沙漠化敏感性格局变化趋势进行预测。结果表明:中国六省四期土地沙漠化敏感性格局相似,各级敏感区按分布面积大小排序为:轻度敏感区域 > 中度敏感区域 > 高度敏感区域 > 不敏感区域 > 极敏感区域。极敏感区域主要分布在土壤质地为流动沙地的沙漠区域,不敏感区域主要分布在高山、湖泊附近;人口增多、人为活动强度增加的区域敏感性程度增高;建立治沙工程的区域敏感性降低。对2020年土地沙漠化敏感性格局预测结果显示,相比2010年,极敏感区域在原有的基础上向外围扩张了7 120.04 km²,增幅为4.63%。本文通过分析中国六省土地沙漠化敏感性时空格局与趋势,为中国土地沙漠化防治分区策略制定和划分沙漠化扩展屏障区提供科学依据。     

樟子松人工林生长与收获预估模型的研究

以可变密度全林分预估模型理论为指导,探讨建立了樟子松人工林地位级指数和林分密度指数经验方程,结合林分密度动态预估模型,建立了樟子松人工林断面积预估模型和蓄积量预估模型。经检验模型预测值与实测值误差为3.64%,模型精度96.37%。     

关中灌区粮食生产“两高一优”机械化模式

根据关中灌区自然经济条件，提出了一种新的机械化模式。经过９年实践表明，采用该模式，无论丰年、平年、灾年均可大幅度增产，能提高抵抗自然灾害的能力，使丰、灾年间产量波幅减小，更趋稳定。     

Statistical Model on the Time and Time Difference Error of Arrival in TDOA Location Estimation

Additional time delay of arrival resulted from non-line of sight(NLOS)propagation and muti-path effect in(cellular) environment is the dominant factor resulting in TDOA location error,study on the statistical property of TDOA error is useful for improving the accuracy of TDOA location.Based on the exponential distribution property of NLOS propagation delay,and considering the system error resulted from the signal detection,the authors establish a statistical model of arrival time and a model of TDOA error distribution by statistics when non-light of sight and multi-path propagation exist.The models reflect the statistical property of arrival time and TDOA error in cell network,the simulation shows that the proposed models have a good accuracy and a satisfactory behavior.     

基于深度学习算法的京津冀地区综合干旱评估模型构建

及时准确的干旱评估对社会经济发展和农业生产具有重要的指导意义,当前的干旱评估指标通常仅考虑植被或降水等单方面影响因素,在实际干旱评估中存在一定的局限性。本研究综合考虑降水、温度、地形等多个干旱致灾因子,以主要产粮基地京津冀地区为例,基于2007-2017年地表温度（Land Surface Temperature,LST）、归一化植被指数（Normalized Difference Vegetation Index,NDVI）以及降水等多源数据,利用深度学习框架Tensorflow构建以标准化降水蒸散发指数（Standardized Precipitation Evapotranspiration Index,SPEI）为目标值的综合干旱评估模型。利用决定系数（R2）和均方根误差（RMSE）对模型进行测试;利用站点标准化降水指数（Standardized Precipitation Index,SPI）、土壤相对湿度数据以及2016年京津冀地区的气象灾害数据,从时间和空间上对模型的可靠性进行验证。结果表明：模型的训练集和测试集在不同月份上均表现较好（R2均大于0.5而RMSE均小于0.55）。模型输出的综合干旱指数（Comprehensive Drought Index,CDI）在密云站上与SPI和SPEI接近,变化趋势基本一致,并且与站点SPI和土壤相对湿度的相关系数分别大于0.7和0.4,均通过了0.01水平的显著性检验。空间上,相较于SPEI,CDI计算的2016年3-7月京津冀地区干旱事件结果与实际情况符合度更高,表明该模型适用于京津冀地区干旱评估。     

Prediction of irrigation return flows through a hierarchical modeling approach

The quantity of water available for irrigation is getting scarce in many countries and it assumes great importance for assured crop production, especially in view of the erratic behavior of the monsoon. Thus, there is a pressing need to improve the water efficiency of irrigation systems. One-way of improving the efficiency of the irrigation system is reusing the return flow from the irrigation system. This task requires quantification of return flow, which still remains as a grey area in irrigation water management. The estimation of return flow from the irrigation system is usually obtained using thumb rules depending upon the site-specific conditions like command area conditions and soil properties. In this paper, a hierarchical modeling technique, namely, regression tree is developed for return flow estimation. Regression tree is built through binary recursive partitioning. The effective rainfall, inflow, consumptive water demand, and percolation loss are taken as predictor variables and return flow is treated as the target variable. The applicability of the hierarchical model is demonstrated through a case study of Periyar-Vaigai Irrigation System in Tamil Nadu, India. The model performance shows a good match between the simulated and the field measured return flow values. Results of statistical analysis indicated that the correlation coefficients are high for both single as well as double crop seasons.