太子河流域参考作物腾发量演变特征及气候影响因素分析

采用太子河流域内8个气象站1960～2005年间气象资料，应用Penman-Montieth公式计算了46年间逐月参考作物腾发量(ET₀)，对参考作物腾发量及气象要素的年际变化特征、月际变化特征及趋势进行了分析，应用统计检验方法分析了影响流域参考作物腾发量变化的主要气象因素。结果表明：近46年间太子河流域ET₀值呈现缓慢下降趋势，年内ET₀值分布以5、6月份最高，1月份最低。影响ET₀的主要气候要素按影响程度强弱依次为日照、风速、温度、相对湿度。     

基于极限学习机的参考作物蒸散量预测模型

为实现气象资料缺乏情况下参考作物蒸散量（reference crop evapotranspiration, ET0）高精度预测,以气象因子的不同组合为输入参数,利用FAO-56 Penman-Monteith公式计算的ET0作为预测标准值建立基于极限学习机（extreme learning machine, ELM）的ET0预测模型。选取川中丘陵区7个气象站点1963－2012年逐日气象资料进行模型训练与测试,并将模拟结果同Hargreaves、Priestley-Taylor、Makkink及Irmark-Allen等4种常用模型进行对比。结果表明：ELM模型能很好地反映气象因子同ET0间复杂的非线性关系,且模拟精度较高;基于最高和最低温度的ELM模型模拟精度（均方根误差和模型效率系数分别为0.504 mm/d和0.827）高于Hargreaves模型（均方根误差和模型有效系数分别为0.692 mm/d和0.741）;基于最高、最低温度和辐射的ELM模型模拟精度（均方根误差和模型有效系数分别为0.291 mm/d和0.938）明显高于Priestley-Taylor（均方根误差和模型有效系数分别为0.467 mm/d和0.823）、Makkink（均方根误差和模型有效系数分别为0.540 mm/d和0.800）和Irmark-Allen模型（均方根误差和模型有效系数分别为0.880 mm/d和0.623）。因此基于最高、最低温度和辐射的ELM模型可以作为气象资料缺乏情况下川中丘陵区ET0计算的推荐模型。该研究可为川中丘陵区气象资料缺乏情境下ET0精确计算提供科学依据。     

基于MODIS遥感数据计算无定河流域日蒸散

为研究无定河流域日蒸散分布规律，应用遥感数据、农业气象站测量数据及Nishida模型等对该流域日蒸散进行了模拟。首先用2001～2002年晴天中国科学院禹城生态试验站Lysimeter测量日蒸散验证模型，模拟与测量的日蒸散相关系数达到0.61。随后，用该模型计算了无定河流域日蒸散，发现无定河流域日蒸散存在较为明显的空间分布规律：2001～2003年连续3年的8月份日蒸散都表现为东北部蒸散明显小于西南部，这是因为东北部基本是荒漠而东南部多是农田，且8月份日蒸散基本在2～5 mm之间变化；从2001年8月份第222 d日蒸散空间分布看，无定河主干道两边蒸散显著高于其他位置，这是由于8月份无定河流域为多雨季节，河谷土壤水分较高的缘故；从2002年内变化来看，不同的土地利用/覆被类型日平均蒸散差别不显著。     

青海大通退耕还林工程区的林木耗水特性

 为了给退耕还林工程建设的树种选择提供依据,2002—2003年,通过实地观测并应用PenmanMonteith方程和定位通量法,对青海大通黄土高寒区自20世纪80年代陆续退耕还林后营造的青海云杉、紫果云杉、华北落叶松、白桦、青杨和沙棘进行了林木耗水特性研究。结果表明:2种方法测算林地蒸散的月平均相对误差是57%。中龄林的青杨沙棘混交林、白桦纯林、青杨灌木混交林和白桦青海云杉沙棘混交林的生长季蒸散总量为488～538mm,其中蒸腾量占蒸散总量的74%～79%,林地土面蒸发总量占蒸散总量的10%～12%,林地无地表径流产生;处于幼龄生长阶段的紫果云杉、青杨、华北落叶松、青海云杉混交林生长季蒸散总量为450～510mm,其中蒸腾总量占蒸散总量的45%～69%,林地土面蒸发总量占蒸散总量的16%～44%,地表径流量占林地降水量的1%～6%。大部分林分6—8月的蒸腾耗水量占全年的80%。     

华北平原冬小麦相对蒸散与叶面积指数及表层土壤含水量的关系

冬小麦相对蒸散(农田蒸散量ET与自由水面蒸发量ET_0之比)表征冬小麦受土壤水分和作物生长状况制约下的耗水规律。冬小麦生长季利用大型蒸渗仪测定农田蒸散,用E601型水面蒸发器测定水面蒸发,并用平行观测方法测定叶面积指数,分析冬小麦相对蒸散与叶面积指数和表层土壤含水量的关系,并建立了冬小麦返青～收获期相对蒸散与叶面积指数和0～60cm表层土壤含水量的经验公式为。在田间条件下由RE和ET_0推算出小麦耗水量ET,并可用于冬小麦适时、适量灌溉管理。     

基于偏最小二乘回归的水稻腾发量建模

利用气象因子计算水稻腾发量过程中,各自变量之间经常存在多重相关性,从而导致传统的多元回归模型(基于最小二乘法)的失真,丧失稳健性,预测精度降低。该文采用偏最小二乘回归建模,借助主成分分析与典型相关分析的思路,采用成分提取的方法,有效地解决了各气象因子之间的多重相关性,建立了水稻腾发量模型,并对模型进行了辅助分析,得到满意效果     

北京地区常用草坪草的耗水规律及适宜灌溉量研究

研究了在北京地区不同供水条件下，草地早熟禾、高羊茅、多年生黑麦草、野牛草、结缕草和狗牙根整个生长季的蒸散量差异和耗水规律。结果表明，蒸散量主要与草坪草的生物学特性有关，受水分条件影响；冷季型草坪草中多年生黑麦草耗水最少，暖季型草坪草中结缕草耗水最少；通过比较坪草和北方主要农作物的K_C值，得出草坪与大田作物的需水量相当；北京地区常用草坪草整个生长季的适宜灌溉量为：高羊茅(391.8 mm)>草地早熟禾(368.3 mm)>多年生黑麦草(315.1 mm)>狗牙根(300.7 mm)>野牛草(184.0 mm)>结缕草(110.5 mm)。     

温室封闭式栽培自适应灌溉方法

为了改进和提高温室封闭式栽培精细灌溉控制方法,针对利用Penman-Monteith(P-M)公式和传感器数据信息相结合进行灌溉控制中因为涉及参数较多而使用不便、需要近似计算导致建立的作物蒸腾模型精度不够等问题,该文根据封闭式栽培可以回收并循环利用多余灌溉水的特点,利用灌溉量与排出量的差值和温室小气候环境数据建立相对精确的作物蒸腾量计算模型,并在此基础上利用人工神经网络算法实现了温室封闭式栽培自适应灌溉控制,结果表明,在10 d内灌溉用水量为实际蒸腾量的97.8%,基本实现了按照作物需水量进行灌溉。研究对于实现按照作物蒸腾量进行准确的水分供给、节约灌溉用水量、提高水分利用效率具有一定的现实意义。     

Corn crop response under managing different irrigation and salinity levels

Non-uniformity of water distribution under irrigation system creates both deficit and surplus irrigation areas. Water salinity can be hazard on crop production; however, there is little information on the interaction of irrigation and salinity conditions on corn (Zea Mays) growth and production. This study evaluated the effect of salinity and irrigation levels on growth and yield of corn grown in the arid area of Egypt. A field experiment was conducted using corn grown in northern Egypt at Quesina, Menofia in 2009 summer season to evaluate amount of water applied, salinity hazard and their interactions. Three salinity levels and five irrigation treatments were arranged in a randomized split-plot design with salinity treatments as main plots and irrigation rates within salinity treatments. Salinity treatments were to apply fresh water (0.89 dS m⁻¹), saline water (4.73 dS m⁻¹), or mixing fresh plus saline water (2.81 dS m⁻¹). Irrigation treatments were a ratio of crop evapotranspiration (ET) as: 0.6ET, 0.8ET, 1.0ET, 1.2ET, and 1.4ET. In well-watered conditions (1.0ET), seasonal water usable by corn was 453, 423, and 380 mm for 0.89EC, 2.81EC and 4.73EC over the 122-day growing season, respectively. Soil salt accumulation was significantly increased by either irrigation salinity increase or amount decrease. But, soil infiltration was significantly decreased by either salinity level or its interaction with irrigation amount. Leaf temperature, transpiration rate, and stomata resistance were significantly affected by both irrigation and salinity levels with interaction. Leaf area index, harvest index, and yield were the greatest when fresh and adequate irrigation was applied. Grain yield was significantly affected in a linear relationship (r² ≥ 0.95) by either irrigation or salinity conditions with no interaction. An optimal irrigation scheduling was statistically developed based on crop response for a given salinity level to extrapolate data from the small experiment (uniform condition) to big field (non-uniformity condition) under the experiment constraints.     

Evapotranspiration and crop coefficient of Populus euphratica Oliv forest during the growing season in the extreme arid region northwest China

Based on successive observation, fifteen-day evapotranspiration (ET_c) of Populus euphratica Oliv forest, in the extreme arid region northwest China, was estimated by application of Bowen ratio-energy balance method (BREB) during the growing season in 2005. During the growing season in 2005, total ET_c was 446.96 mm. From the beginning of growing season, the ET_c increased gradually, and reached its maximum value of 6.724 mm d⁻¹ in the last fifteen days of June. Hereafter the ET_c dropped rapidly, and reached its minimum value of 1.215 mm d⁻¹ at the end of growing season. The variation pattern of crop coefficient (K_c) was similar to that of ET_c. From the beginning of growing season, the K_c value increased rapidly, and reached its maximum value of 0.623 in the last fifteen days of June. Afterward, with slowing growth of P. euphratica, the value dropped rapidly to the end of growing season. According to this study, the ET_c of P. euphratica forest is affected not only by meteorological factors, but by water content in soil.