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地球信息科学学报 >

2016 , Vol. 18 >Issue 7: 977 - 986

DOI: https://doi.org/10.3724/SP.J.1047.2016.00977

地理空间分析综合应用

黑河中游地区作物用水效率比较及种植结构调整方向研究

  • 郑璐倩 , 1, 2, 3 ,
  • 谈明洪 , 1, 2, *
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  • 1. 中国科学院地理科学与资源研究所,北京 100101
  • 2. 中国科学院陆地表层格局与模拟重点实验室,北京 100101
  • 3. 中国科学院大学,北京 100049
*通讯作者：谈明洪(1970-),男,博士,副研究员,主要从事土地利用/覆被变化与城市用地增长及其效应研究。E-mail:

作者简介：郑璐倩(1991-),女,硕士生,主要从事土地利用/覆被变化及其环境效应研究。E-mail:

收稿日期: 2015-07-09

  要求修回日期: 2015-10-01

  网络出版日期: 2016-07-15

基金资助

国家自然科学基金重大研究计划项目（91325302）

国家自然科学基金重大国际合作项目(41161140352)

国家自然科学基金面上项目（41271119）

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A Comparative Study of Water Use Efficiency of Different Crops in the Middle Reaches of Heihe River and Its Implications for Planting Structure Adjustment

  • ZHENG Luqian , 1, 2, 3 ,
  • TAN Minghong , 1, 2, *
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  • 1. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. Key Laboratory of Land Surface Pattern and Simulation, Beijing 100101, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China
*Corresponding author: TAN Minghong, E-mail:

Received date: 2015-07-09

  Request revised date: 2015-10-01

  Online published: 2016-07-15

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《地球信息科学学报》编辑部 所有
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摘要

黑河流域地处西北干旱区,水资源短缺是限制其中游绿洲农业发展、下游生态环境保护的首要原因。该流域的中游绿洲农业用水约占总用水量的80%,因此农业节水对流域发展至关重要。在干旱区绿洲农业节水探索中,众多学者主张通过节水技术来提高用水效率,而关于农业种植结构调整对农业节水影响的定量研究较少。本文采用2012年黑河流域蒸散发数据、土地利用数据、降水数据和农业经济统计数据,定量分析黑河中游主要作物需水特征和用水效率差异,尝试从调整作物种植结构角度为其绿洲农业节水提供依据。结果表明：(1)研究区4种主要作物中,玉米生长期需水量最大,其次为小麦、油菜和大麦;(2)考虑降水补给,发现大麦和油菜生长需水可很大程度上依赖降水,而小麦和玉米则需要灌溉,且玉米灌溉需水量远超小麦;(3)作物用水效率由高到低依次为大麦、油菜、小麦和玉米。从用水效率角度而言,考虑种植区位,在黑河中游适当扩大小麦种植规模更有利于提高中游农业用水效率。

关键词： 黑河流域; 农业结构; 蒸散发; 用水效率

本文引用格式

郑璐倩 , 谈明洪 . 黑河中游地区作物用水效率比较及种植结构调整方向研究[J]. 地球信息科学学报, 2016 , 18(7) : 977 -986 . DOI: 10.3724/SP.J.1047.2016.00977

Abstract

The Heihe River Basin is located in the northwest arid region of China. Water shortage is the primary restrictive factor for oasis agriculture development and ecological environment protection in this region. Since the water consumed by the oasis agriculture in the middle reaches occupies about 80% of the total water resources in this basin, how to efficiently use the agricultural water resources is the key factor affecting the regional development. In the exploration of agricultural water saving measures in the arid region, most scholars advocate to improve the water use efficiency (WUE) through water-saving techniques. However, there are few quantitative studies focusing on the water saving effect of planting structure adjustment. This study takes the adjustment of agricultural planting structure as a breakthrough point to achieve the goal of agricultural water saving, considering that different crops have different water demands. We aim to explore the characteristics of the water demands of the main crops in the middle reaches of Heihe River and examine the different levels of WUE for those crops. In this paper, with the help of ArcGIS technique and based on the evapotranspiration (ET) data of Heihe River Basin and crop spatial distribution data, we extracted the ET information of the main crops during their growing seasons in 2012, which was represented as the water requirement of crop. Then, the crop ET data and crop yield data were used to calculate the WUE of four main crops. The results show that: (1) maize has the largest ET during its growing seasons, followed by wheat, rapeseed and barley; (2) considering the case of precipitation recharge, the water demands of barley and rapeseed may largely depend on rainfall, while maize and wheat require irrigation instead, and maize needs more water than wheat; (3) the WUE of crops ranking from high to low is barley, rapeseed, wheat and maize. This study argues that the appropriate increase in the size of wheat planting is helpful to improve the efficiency of agricultural water in the middle reaches of Heihe River.

Key words： Heihe River Basin; agricultural structure; evapotranspiration; water use efficiency

1 引言

黑河流域是中国第二大内陆河流域,流域上、中、下游年均降水量分别为350、140和47 mm。降水量是黑河流域生态环境演变的关键控制因子[1],对流域生态安全与经济发展影响巨大。近年来,随着全球变暖,黑河出山口天然来水保持相对稳定并略有增加的趋势[2],但是黑河下游依然出现河流断流、湖泊干涸和水质恶化等现象[3],其主要原因是中游流域大量抽调水资源。研究显示,中游用水占全流域用水的83%[3],其中,中游张掖市农业用水占中游总用水的88%~95%[4-5]。因此,绿洲农业节水是黑河流域水资源合理利用研究的重要切入点。
当前,干旱区农业节水研究主要集中在2方面：（1）利用节水技术提高农业用水效率。目前已有众多学者对干旱区农业关键节水技术（包括覆膜、沟灌、喷灌、滴灌、保护性耕作技术等[6-8]）的节水效率进行了研究。吴丽丽等指出节水技术在黑河流域前期农业节水中取得较大成效,但2007年后节水技术水平停滞不前[9],实地考察发现黑河中游农业中铺设地膜、修建水泥渠等节水措施得到较好实行,但滴灌等先进节水技术则有待推广。（2）种植结构调整对农业用水效率的影响。这方面的研究多以定性研究为主[10-12],定量化研究相对缺乏。根据统计数据,黑河绿洲农业结构中高耗水作物（如玉米、小麦等）种植比例较大,鉴于不同作物生长需水不同,种植结构调整可有利于农业节水,提高农业用水效率[13-15]。
作物用水效率计算关键在于确定作物的需水量,国内外研究中通用作物生长期蒸散量表征作物需水量,表示作物生长实际利用的水量[16]。目前,针对作物蒸散量的估算方法有经验公式实测法、土壤水量平衡法、参考作物蒸散量-作物系数法等[17-18],现阶段对作物蒸散量的研究广泛采用参考作物蒸散量-作物系数法。一般计算方法是利用气象数据代入彭曼(P-M)公式计算参考作物蒸散量,再乘以作物系数得到实际作物蒸散量,此方法得到的作物蒸散量往往是站点尺度上的,很难推广到区域尺度,空间局限性大。本文充分利用遥感反演的蒸散发数据,结合作物分类数据,通过实地调查选取面积较大的均一作物地块作为样本,以获得不同种植区域的作物蒸散量,从而相对准确地表征区域范围内该作物实际蒸散量的平均水平。通过对黑河中游主要作物生长期需水特征及用水效率差异的比较分析,从作物种植结构调整角度为黑河绿洲农业节水提供依据和相关建议,为实现黑河流域水资源均衡利用、改善整体生态环境提供参考。

2 研究区概况

黑河发源于祁连山中段,流域范围介于96°42′~102°00′ E、37°50′~42°40′ N之间,总面积为14.29 万km2,东邻石羊河流域,西接疏勒河流域,北至内蒙古自治区额济纳旗境内的居延海。流域以莺落峡、正义峡为界分为上、中、下游：上游属祁连山区,为产流区;中游地势平坦,人工绿洲面积较大,是径流利用区;下游大部分为沙漠戈壁,为径流消失区。
黑河绿洲农业集中在中游流域,包括甘肃省张掖市的甘州、山丹、民乐、临泽、高台、肃南等区县和甘肃省酒泉市的肃州区。鉴于中游绿洲农业面积有82.6%分布在张掖市,而张掖市肃南县为典型牧业县,2012年作物播种面积不到全市作物总播种面积的3%[19];同时,为保证数据、资料的可获取性和标准化,采用研究区范围尽量与行政边界相一致的原则,将研究区域确定为张掖市下辖农业区县,即甘州区、山丹县、民乐县、临泽县和高台县（图1）。
Fig. 1 The location of the study area

图1 研究区示意图

研究区地处祁连山冲积平原,属大陆性气候,其近10年的年平均气温为7.57 ℃,年日照时数为2969.1 h,年降水量为195.43 mm,年蒸发量为1710.23 mm（数据来源于2005-2013年《张掖市统计年鉴》。）。干旱区水热条件特性决定了研究区农业多为单季种植,以夏收和秋收2种类型作物为主,夏收作物主要为小麦和大麦,秋收作物主要有玉米、油菜等,作物生长期在4-9月间。2012年研究区内大麦、小麦、玉米和油菜播种面积占农作物总播种面积的63%,年产量达99万t[19],为黑河中游主要作物。由于研究区降水少、蒸发量大,故该区域内农业多为灌溉农业,且灌溉可分为渠灌和井灌2种。2012年研究区渠道总长度达1.9万km,拥有机井6530眼,有效灌溉面积达172千hm2,其中,节水灌溉面积所占比例不到20%[20],大水漫灌现象普遍存在。
Fig. 2 Evapotranspiration data of 2012

图2 2012年蒸散量图

3 数据与方法

3.1 数据

3.1.1 蒸散发数据
蒸散发数据来源于黑河流域2000-2012年 1 km分辨率月尺度地表蒸散发数据集,该数据集由吴炳方课题组基于ETWatch系统生产而成。ETWatch运用P-M公式和余项单层蒸散模型,采用多尺度、多源遥感数据,结合黑河典型区野外观测数据,对该区域的蒸散发(ET)进行估算[21-23],所得黑河全流域ET估算值与实测ET间的平均相对误差为-11.8%( Wu B F, Zhu W W, Yan N N, et al. Integration of parameterization based on multi-source remote sensing data for estimation evapotranspiration: A case study on Heihe River Basin[J]. Manuscript under preparation.)。该数据以像元为基础,可结合作物空间分布数据提供不同作物的ET信息。
受作物精分类数据缺乏的限制,本文选择2012年作物生长期间的月蒸散发数据进行研究。图2为黑河流域2012年蒸散发示意图。利用ArcGIS进行区域统计可知：研究区2012年农田平均蒸散量约为467 mm,这与周剑基于遥感和SEBS模型估算的黑河中游绿洲蒸散量449.9 mm[24]、杨永民基于遥感测算的黑河中游农田绿洲区蒸散量500 mm[25]等研究结果相近;农田区年降水量为220 mm左右,约为农田蒸散量的1/2;根据张掖市2012年水利管理年报,研究区单位面积灌溉水量约为583 mm[20],表明作物生长很大程度上依赖于灌溉。
3.1.2 作物数据
作物空间分布数据主要来源于2012年黑河流域土地利用数据集,该数据集由仲波课题组利用HJ/CCD数据解译完成[26-27]。该数据中除了传统土地利用类别信息,还结合实地调查数据对耕地范围增加了作物精细分类,数据分类总体精度达到92.2%,作物分类精度为84.1%[28]。该土地利用数据中包括大麦、小麦、玉米、油菜、苜蓿和棉花等作物信息,本文选取大面积种植的大麦、小麦、玉米和油菜4种作物做进一步分析。
结合2012年黑河流域作物空间分布数据,本文于2014年8月对研究区进行实地考察采点工作,根据农户调查,确定面积较大且近年来作物种植类型不变的地块,为更好地选取单一作物样本提供参考。采点数据见图3,样地实况见图4。
Fig. 3 The distribution of crop samples

图3 作物采点及样本分布图

Fig. 4 Some field samples of crops

图4 实地样本

3.1.3 降水数据
降水数据来源于中国气象科学数据共享服务网的中国地面降水月值0.5°×0.5°格点数据集。为保证研究单元一致性,利用专业气象数据空间插值软件ANUSPLIN对原始降水数据进行插值处理,生成1 km分辨率的2012年逐月降水数据集。
ANUSPLIN软件由澳大利亚科学家Hutchinson基于薄盘光滑样条函数原理编写完成[29],目前已得到广泛应用。该软件允许引入多个影响因子作为协变量,与IDW法、Kriging法相比具有更高的插值精度[30],且能同时进行多个表面的插值。软件提供18个薄盘光滑样条函数模型,研究表明以高程作为协变量,样条次数为3的TVPTPS3模型能使降水要素插值精度最高[31]。本文采用该模型对2012年全国降水量进行插值,并运用交叉验证法[32-33]分析插值结果精度,结果显示：模型模拟降水量与站点实际降水量的平均绝对误差（MAE）为6.45 mm,平均相对误差（MRE)为12%,均方根误差（RMSE）为13.32 mm,其中MAE、RMSE均小于刘正佳全国降水插值的验证结果（MAE=48.19 mm,MRE=6%,RMSE=76.38 mm）[34]。
3.1.4 作物经济数据
各作物产量数据来源于2012年《张掖统计年鉴》[19];作物价格数据来源于联合国粮农组织统计数据库,为2012年中国市场统一价格(表1)。
Tab. 1 Yields and prices of different crops

表1 不同作物产量及价格

指标 作物
大麦 小麦 玉米 油菜
产量/(kg/hm2) 6349.50 6189.75 7656.75 3051.60
价格/(元/kg) 2.30 2.04 2.42 5.00

3.2 研究方法

本文首先选取大麦、小麦、玉米和油菜4种主要作物,基于2012年黑河流域作物空间分布数据,结合实地调查,确定面积较大、种植结构单一的作物样本区域;然后,利用ET时空分布数据,分别提取4种作物各月蒸散量,计算出各作物生长期需水量;最后,结合作物产量和价格数据,进行不同作物用水效率的计算和比较。
3.2.1 用水效率的定义及计算
国内外对农业用水效率、效益的研究较多,命名不一,定义也因不同观点、不同学科而存在差异。20世纪70年代以后,从作物生理学角度,学术界对农业用水效率的定义多采用水分利用效率（Water Use Efficiency）这一概念[16],也称用水效率,即单位水量产生的作物产量。考虑农业生产实际应用,作物产量应指经济产量。作物用水量则主要分为作物需水量和灌溉用水量[16,35]。本研究着眼于作物自身耗水特性差异,故基于作物需水量定义作物用水效率,这也是目前国际上普遍所指的水分利用效率。国内外研究中普遍用作物产量和作物生长期蒸散量的关系描述作物用水效率[36],通用计算方法[37-40]如式（1）所示。
WUE = Y ET (1)
式中：WUE为用水效率;Y为作物产量;ET为作物生长期蒸散量。
本文旨在比较不同种类作物的用水效率差异,而式(1)中基于作物产量的用水效率并不能表征作物之间的绝对差异。广义上,农业用水效率可指农业水资源利用的效率和效益[41-42],因此有研究用作物经济收益代替作物产量来评价作物用水效率[43]。本文借鉴该方法,将作物经济产量统一按其市场价值进行换算,从而统一作物产量的衡量标准,以单位用水量的经济收益来表征作物用水效率的高低[35],计算方法如式（2）所示。
WU E e = ( Y × P ) ET (2)
式中：P为作物的市场价格。
3.2.2 作物蒸散量提取
由于蒸散发数据分辨率为1 km,而土地利用数据分辨率为30 m,故提取作物蒸散量关键在于选取适宜的作物样本。图5给出了作物蒸散量提取技术流程：以小麦为例,为获得单一小麦样本,将小麦分布数据按蒸散发数据分辨率重新划分成1 km×1 km的像元,选择像元内小麦面积占比超过75%的像元作为小麦样本;同时,结合实地采点数据,尽量选取大面积且种植结构不变的地块,生成最终小麦样本。将小麦样本数据与黑河流域蒸散发数据进行空间关联,得到各小麦样本的蒸散量值,进一步平均,得到小麦蒸散量值。经处理得到各作物样本数为：大麦36个、小麦7个、玉米301个、油菜19个（图3）。
Fig. 5 The technical flowchart of crop evapotranspiration extraction

图5 作物蒸散量提取技术流程（以小麦为例）

4 结果与分析

4.1 作物分布情况

利用2012年黑河流域土地利用数据提取作物信息,对中游绿洲农业中主要作物(大麦、小麦、玉米和油菜)的分布进行分析（图6）。由图6可见,大麦主要分布于民乐县、山丹县及甘州区的南部;小麦主要分布在民乐县的北部、山丹县城周边这些海拔相对低的地区;玉米是黑河中游主要作物,在民乐县北部、甘州区、临泽县、高台县均有成片种植,尤以甘州区最为密集;油菜则主要分布在民乐县东南部和山丹县西南部,这些地区海拔高、气温低,相对干旱,作物产量不高,但大面积种植油菜可以发展特色旅游业。
Fig. 6 The distribution of main crops in the study area

图6 研究区主要作物分布图

4.2 作物生长期需水量特征

由于地区不同作物生长期会略有差异,根据实地调研来确定各作物生长期：大麦和小麦生长期相近,均为4-7月;玉米生长期为4-9月;油菜生长期为5-9月。由各作物生长期蒸散量过程曲线（图7(a)）可知,玉米生长期需水峰值出现在6月,而小麦、大麦和油菜生长期需水峰值为7月。
Fig. 7 Evapotranspiration, precipitation and their relationship of crops during the growing seasons

图7 作物生长期蒸散量、降水量及其关系

统计各作物生长期蒸散量发现,玉米是需水量最高的作物,生长期蒸散量达514 mm;其次,依次是小麦和油菜,分别为314 mm和260 mm;大麦需水量最少,生长期蒸散量为193 mm。
已有大量研究对小麦和玉米生长需水量进行估算,连彩云等采用田间试验法,利用水量平衡方程计算非充分灌溉条件下张掖小麦和玉米的生长期实际蒸散量,分别为507.5 mm和570.3 mm[44];王瑶等基于参考作物蒸散量-作物系数法,利用14个气象站点数据计算黑河中游30年小麦年均生长期蒸散量为573~781 mm,其中民乐小麦生长期蒸散量为598.2 mm[45];尹海霞等用该方法,选取黑河中游7个气象站点数据计算了该区域43年来小麦和玉米生长期蒸散量均值,分别为453 mm和602 mm,其中民乐小麦生长期蒸散量为390 mm[46]。本文所得小麦生长需水量与连彩云、王瑶和尹海霞的研究结果相比均偏小;玉米生长需水量与连彩云研究结果相近,与尹海霞估算结果则存在差异。这些差异可能和研究区域的选择不同有关。本研究中小麦和玉米样本分别集中在民乐县和甘州区,而连彩云测算蒸散量的试验田位于甘州区,民乐县相比甘州区较为干旱且作物灌溉用水少,故小麦蒸散量可能低于甘州区。另外,王瑶、尹海霞利用参考作物蒸散量-作物系数法估算作物蒸散量,实际得到的是气象站点附近区域的作物生长需水量;而本文采用遥感反演的蒸散发数据,结合作物空间样本,提取作物蒸散量,一定程度上能代表区域范围内作物生长需水量的平均水平。王海波等利用遥感和P-M公式法测算的黑河中游玉米生长期平均蒸散量为552.2 mm[47],与本文所得玉米生长期蒸散量结果基本接近。针对大麦和油菜的作物生长需水量测算研究目前则较为匮乏。

4.3 降水补给下作物生长期水分需求分析

由于研究区地处干旱区且地势平坦,故不考虑降水形成径流和深层渗漏的损失。研究区降水补给的作物用水需求可用作物生长期蒸散量与降水量之差来衡量。各作物生长期月降水量峰值都出现在7月（图7(b)）,与大麦、小麦、油菜的生长期需水量趋势相契合;研究区不同作物生长期蒸散量和降水量差异如图7(c)所示;图7(d)则进一步表征不同作物各生长阶段蒸散量与降水量之差。
由图7(c)可见,4种作物中,大麦和油菜生长期的降水量总和大于蒸散量,分别超出22 mm和 62 mm,说明降水补给可基本满足大麦和油菜生长期总需水要求;而小麦和玉米生长期内降水量均低于蒸散量,说明降水不能满足这2种作物的生长需水,仍需要灌溉。其中,玉米由于生长期需水量大且生长期长,除降水补给外,其灌溉用水需求仍远大于小麦,达322 mm。
由图7(d)进一步分析不同作物生长期内各月蒸散量与降水量差异,大麦生长初期（4月）蒸散量高于同期降水量12 mm,需要其他途径的水分补给;生长期内其余各月降水量均大于同期蒸散量。油菜生长期（6月）的蒸散量略高于降水量,其余生长阶段降水量均高于同期蒸散量,说明油菜生长可基本依赖于降水补给,这也与油菜旱地种植的实际情况相符。小麦和玉米生长期各月降水量均小于其蒸散量,二者降水补给下的用水需求呈现“倒U型”规律,在6月达到最高。6月为作物灌溉需水高峰期,一定程度上可为作物灌溉水配置提供参考。

4.4 作物用水效率分析

根据各作物单位面积产量、作物需水量及作物价格数据,由式(2)计算得到各作物用水效率(表2)。由表2可见,大麦用水效率最高,达7.57元/m3;油菜次之,单位水产生经济收益为5.86元;小麦次之,为4.02元/m3;玉米用水效率最低,为3.60元/m3。
Tab. 2 Water use efficiencies of different crops

表2 不同作物用水效率

作物 指标
产量（Y）/（kg/hm2） 生长期需水（ET）/mm 价格（P）/（元/kg） 用水效率（WUEe）/（元/m3）
大麦 6349.50 192.92 2.30 7.57
油菜 3051.60 260.34 5.00 5.86
小麦 6189.75 314.29 2.04 4.02
玉米 7656.75 514.03 2.42 3.60
目前针对作物用水效率的研究多集中在小麦和玉米。盖力强等研究华北平原小麦、玉米生产用水情况[48],结果表明生产相同产量的作物,小麦需水量明显高于玉米。而本研究区玉米需水量远超小麦,造成该研究结果差异的原因可能是华北平原种植的冬小麦与黑河中游种植的春小麦在生长需水量上存在较大差异。黑河中游研究结果说明,在产生相同收益时,在该区域种植小麦比种植玉米所需水量少。
综合4种作物用水效率特征和种植区位差异,考虑在水资源紧缺、灌溉用水得不到保障时,应多种植用水效率高的作物,如大麦和油菜;而玉米用水效率在4种作物中虽为最低,但由于其产量高,在灌溉用水充足的地区可适当种植。目前,研究区作物种植结构也表现出这样的规律,灌溉水资源充分的地区,作物种植种类选择以考虑经济收益为主,如甘州区、临泽县、高台县广泛种植玉米,但大面积玉米种植是以过度抽取水资源为代价,这直接导致地下水位下降和下游可用水资源减少。由于中游地区小麦种植区位与玉米相似,而小麦用水效率高于玉米,从该角度来说,在黑河中游适当扩大小麦种植能有效减少水资源的消耗,提高本区域农业用水效率。

5 结论

本文基于作物生长期蒸散量,结合同期降水量,分析黑河中游绿洲农业主要作物的生长期需水特征;结合作物产量和价格数据,分析不同作物用水效率差异,得出如下结论：
（1）由作物生长期蒸散量看,作物需水量特征为：黑河中游绿洲农业4种主要作物生长需水存在较大差异,玉米生长需水量为514 mm,显著高于其他作物,与大麦相差达321 mm;小麦、油菜生长需水量处二者之间,分别为314 mm、260 mm。
（2）大麦和油菜生长期降水量高于其蒸散量,进一步分析其生长期各月蒸散量与降水量关系发现,大麦仅在4月需要灌溉,油菜生长需水则基本可依赖于降水补给;小麦和玉米生长期蒸散量均高于同期降水量,对灌溉用水需求量较大,分别为 322 mm和137 mm。
（3）以货币价值表征各作物产值,作物用水效率从高到低依次为：大麦、油菜、小麦和玉米。考虑作物种植区位,在广泛种植玉米的黑河中游地区适当扩大小麦种植,将有利于提高农业用水效率。
本研究中基于ET数据所得的作物蒸散量相比实测蒸散量可能偏小,但鉴于作物蒸散量是基于同一数据和方法提取,得到各作物生长期蒸散量的偏差一致,故ETWatch估算误差对不同作物间需水特征和用水效率的比较研究影响不大。另外,本研究只分析了大麦、小麦、玉米和油菜4种主要作物,而实际黑河中游绿洲农业种植结构更为复杂,且近年蔬菜、马铃薯等作物种植面积均呈增加趋势。因此,后续研究中将进一步细化作物分类,同时结合农户问卷调查,考虑作物投入成本和实际灌溉水量,完善作物用水效率计算,为干旱区绿洲农业种植结构调整提供更全面的依据与建议。

The authors have declared that no competing interests exist.

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徐凯,陆垂裕,季海萍.作物蒸发腾发量计算研究综述[J].人民黄河,2013,35(4):61-65.作物蒸发腾发量是农业用水最主 要的部分。准确计算作物蒸发腾发量是制定农作物灌溉制度的基础,也是促进农业节水的关键。介绍了作物蒸发腾发量计算的基本理论及发展情况,按照理论基础, 将目前作物蒸发腾发量计算的常用方法分类,并对经验公式法、参照作物蒸发腾发量法等的适用条件进行了分析。对常用作物蒸发腾发量计算方法在大时空尺度和非 充分灌溉模式下的计算改进方式及应用进行了说明。基于作物蒸发腾发量计算方法问题、改进及实际应用情况,认为作物蒸发腾发量计算方法及实用技术研究未来的 发展方向为基于多学科、多专业联合攻关,研究既有理论基础又便于实际应用的多尺度作物蒸发滕发量估算模式。

DOI

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Wu B ., Yan N ., Xiong ., et al.Validation of ETWatch using field measurements at diverse landscapes: a case study in Hai Basin of China[J]. Journal of Hydrology, 2012,436:67-80.The latent heat of evapotranspiration (ET) plays an important role for water resource management in water scarcity areas. Compared to the water balance method or to in situ measurements, an operational integrated monitoring method of regional surface ET from remote sensing data is a potentially useful approach to achieve water saving. This study presents new algorithms for the aerodynamic roughness length for complex landscape, for gap filling for cloud days, and for data fusion at different resolutions, based on the Penman鈥揗onteith equation. It also presents an improved algorithm for ET calculation with remotely sensed data for clear days. Algorithms were integrated into the ETWatch. The research objective was to present the enhanced features of the ETWatch algorithm and its validation in the 320,000km 2 Hai Basin in Northern China. This area faces serious over-exploitation of groundwater. ET was modeled and extensive field campaigns were done to collect data on soil moisture depletion, lysimeter measurements, eddy covariance measurements, and water balance calculations at diverse landscapes. The overall deviation for individual fields on a seasonal basis was 12% and decreased to 6% for an annual cycle. For larger areas, the deviation was 3% for an annual cycle. These levels of deviation are within the error bands for in situ measurements. The study concludes that data sets from ETWatch are able to aid consumptive water use reduction management in the study area.

DOI

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Wu B ., Xiong ., Yan N N.ETWatch: Models and methods[J]. Journal of Remote Sensing, 2011,15(2):224-239.Evapotranspiration (ET) is not only an important part of the coupled Eco-Hydrological processes, but also primary wayof eco-agricultural consumption. A better description of the temporal-spatial pattern of a watershed greatly will enhance people’s understandingof hydrological processes and the water management approach. As quantitative measurement of surface heterogeneity,remote sensing and surface observations are combined to develop operational methods and determine eco-hydrological variables.ETWatch is such an operational platform which is designed for practical needs of watershed planning and agricultural water managementusing remote sensing techniques that can describe the spatial distribution and time process of surface net radiation, sensibleheat, and latent heat (ET). The reviewing of algorithms and approaches show that the parametric approach is the core componentto improve the accuracy of ET estimation at regional scale and apply remote sensed ET for practical goals. The other bottlenecksinclude scaling, multi-source data integration and validation of modeling. Potential approaches used in ETWatch to the above issuesare summarized and commented.

DOI

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DOI

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DOI

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DOI

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钱永兰,吕厚荃,张艳红.基于ANUSPLIN软件的逐日气象要素插值方法应用与评估[J].气象与环境学报,2010,26(2):7-15.气象要素是资源、环境、灾害以及全球变化等领域研究的数据基础，格点化数据在未来研究应用中显得日益重要。本文基于中国境内667个基本和基准地面气象观测站点的基本气象资料，使用ANUSPLIN专用气候插值软件对1961-2006年逐日气温、降水进行插值，并利用未参与插值的全国1667个加密站点对插值结果的准确性进行检验，同时与反向距离权重法和普通克吕格法等插值方法的结果进行对比。结果表明，利用667个站点使用ANUSPLIN软件进行逐日平均气温插值有92.0%的误差在2.0℃以内，75.0%的误差在1.0℃以内，0.9%的误差在5.0℃以上，平均绝对误差为0.8℃；对逐日降水进行插值，75.0%的误差小于5.0mm，85%的误差小于10.0mm，平均绝对误差为6.4mm，误差大小与降水量呈现出正相关性，对局地强降水的插值效果不好,这可能与参与局部拟合插值的样本数太少有关；同时，夏季的温度插值误差小于冬季，而冬季的降水误差小于夏季。将ANUSPLIN的局部薄盘样条插值结果分别与反向距离权重法和普通克吕格法的插值结果进行对比，显示ANUSPLIN软件的插值误差最小。结果同样表明，适当增加站点数量和提高DEM精度可进一步提高ANUSPLIN软件的插值精度。

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潘耀忠,龚道溢,邓磊,等.基于DEM的中国陆地多年平均温度插值方法[J].地理学报,2004,59(3):366-374.<p>以1961~2000年全国726个气象站点旬平均温度为基础数据,在分析了多年月平均温度和年平均温度的空间分布与经度、纬度、高度的内在关系后,提出了一种基于DEM和智能搜索距离的温度空间插值方法 (SSI),并与反距离平方 (IDS) 等传统方法进行了对比。交叉验证结果表明：1) 传统的IDS方法最优结果的MAE范围是1.44 <sup>o</sup>C~1.63 <sup>o</sup>C,平均1.51<sup>o</sup>C；而SSI温度插值方法的平均绝对误差为0.53 <sup>o</sup>C~0.92 <sup>o</sup>C,平均值0.69 <sup>o</sup>C,精度超过IDS等方法一倍以上。2) 随着距离的增大,站点间温度的相关性逐渐降低,会降低估算精度；小于一定的搜索半径,被估算点周围的相邻站点的数目逐渐减少,同样会降低插值的精度,因而对中国陆地部分温度插值而言,最优的空间插值搜索半径介于150~250 km之间。最后,结合DEM数据,生成了0.1<sup>o</sup> &times; 0.1<sup>o</sup>中国陆地区域多年月平均和年平均温度栅格图像数据集,该结果表明：利用SSI方法不仅可以生成高精度、高空间分辨率的网格温度结果,而且其插值结果能客观细致的反映温度随经度、纬度和高度梯度变化的地带性特征。</p>

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刘正佳,于兴修,王丝丝,等.薄盘光滑样条插值中三种协变量方法的降水量插值精度比较[J].地理科学进展,2012,31(1):56-62.在薄盘光滑样条插值中,高相关协变量的选取决定了插值结果的精确性。以2001-2009年全国728个气象站点日降水为数据源,提取年降水量数据,在分析多年平均降水量与两协变量高程(DEM)和距海岸线距离(DCL)的空间相关性基础上,利用ANUSPLIN软件,比较不同协变量下降水量插值结果精度在全国尺度以及区域尺度上的差异。以DEM、DCL及DEM-DCL分别为协变量对降水量数据进行空间插值发现:①在全国尺度上,DEM法的平均绝对误差(MAE)为47.79,略低于DEM-DCL法(48.90),但显著低于DCL法(55.54);且DEM法的平均相对误差和均方根误差也明显低于其它两种方法。②在区域尺度上,除西藏地区外的其他7个区域,3种方法的插值误差与全国尺度上相一致。西藏地区降水插值结果以DCL法的精度最高,而DEM法则较差。研究建议除在西藏地区的降水量插值研究中采用DCL法,在全国其他大部分区域采用DEM法。

DOI

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段爱旺. 水分利用效率的内涵及使用中需要注意的问题[J].灌溉排水学报,2005,24(1):8-11.对水分利用效率的基本内涵进行了分析,根据耗水总量的取值方法,总结了水分利用效率的4种基本形式,并建议将田间水分利用效率作为水分利用效率的标准值使用.文章还对不同层次的水分利用效率的确定方法进行了简要的介绍,对节水实践中使用水分利用效率指标评价节水农业发展状况与发展水平时需要特别注意的一些问题进行了较为详细的探讨,对于水分利用效率的正确取值与科学使用具有一定的指导意义.

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Nair ., Johnson ., Wang C.Efficiency of irrigation water use: A review from the perspectives of multiple disciplines[J]. Agronomy Journal, 2013,105(2):351-363.ABSTRACT The increasing global population and dwindling water supplies make efficient use of irrigation water the prime priority in agricultural production. Th e importance of irrigation water efficiency is more intensified now owing to concerns about climate change, which may lead to a drier climate in the future. Even though irrigation efficiency is a term widely used by plant physiologists, agronomists, irrigation engineers, and economists, both the definition and the perspectives of the term vary with the different disciplines. This review presents and compares irrigation water use efficiency from the perspective of different disciplines. This can help researchers in different fields understand each other&rsquo;s perspective and stimulate multidisciplinary thinking and research.

DOI

[40]
Miriti J ., Kironchi ., Esilaba A ., et al.Yield and water use efficiencies of maize and cowpea as affected by tillage and cropping systems in semi-arid Eastern Kenya[J]. Agricultural Water Management, 2012,115(115):148-155.Soil water conservation through tillage is widely accepted as one of the ways of improving crop yields in rainfed agriculture. Field experiments were conducted between 2007 and 2009 to evaluate the effects of conservation tillage on the yields and crop water use efficiency of maize ( Zea mays L.) and cowpea ( Vigna unguiculata L.) in eastern Kenya. Experimental treatments were a combination of three tillage practices and four cropping systems. Tillage practices were tied-ridges, subsoiling-ripping and ox-ploughing. The cropping systems were single crop maize, single crop cowpea, intercropped maize鈥揷owpea and single crop maize with manure. The treatments were arranged in split plots with tillage practices as the main plots and cropping systems as the sub-plots in a Randomized Complete Block Design (RCBD). The results showed that tied-ridge tillage had the greatest plant available water content while subsoiling-ripping tillage had the least in all seasons. Averaged across seasons and cropping season, tillage did not have a significant effects on maize grain yield but it did have a significant effect on crop grain and dry matter water use efficiency (WUE). Nevertheless, maize grain yields and WUE values were generally greater under tied-ridge tillage than under subsoiling-ripping and ox-plough tillages. The yields and WUE of cowpea under subsoiling-ripping tillage were less than those of ox-plough tillage. When averaged across the seasons and tillage systems, the cropping system with the manure treatment increased ( P 聽鈮ぢ0.05) maize grain yield, grain WUE and dry matter WUE by 36%, 30%, 26% respectively, compared to treatments without manure. Maize and cowpea when intercropped under ox-plough and ripping tillage systems did not have any yield advantage over the single crop.

DOI

[41]
凡炳文,陈文.甘肃省农业用水效率控制红线研究[J].干旱地区农业研究,2012,30(3):101-106,113.

[ Fan B ., Chen W.Research on red line of control over agricultural water use efficiency in Gansu Province[J]. Agricultural Research in the Arid Areas, 2012,30(3):101-106,113. ]

[42]
刘军,朱美玲.农业用水效率评价指标体系研究[J].节水灌溉,2013(5):61-63.

[ Liu ., Zhu M L.Research on evaluation index system of agricultural water use efficiency[J]. Water Saving Irrigation, 2013,5:61-63. ]

[43]
彭世琪. 农田水资源利用效益观测与评价方法探讨[J].节水灌溉,2013(4):38-41.

[ Peng S Q.Observation and evaluation method for water use efficiency on farm level[J]. Water Saving Irrigation, 2013,4:38-41. ]

[44]
连彩云,马忠明,吕晓东,等.河西绿洲灌区主要作物需水量及作物系数试验研究[J].灌溉排水学报,2012,31(5):136-139.利用Penman-Monteith公式计算了甘肃张掖绿洲主要 作物各生育期参考作物蒸散量,利用农田水量平衡方程及土壤水分胁迫系数计算了作物实际蒸发蒸腾量,并计算比较了充分灌溉和非充分灌溉条件下不同生育期作物 需水特征,确定了非充分灌溉条件下主要作物的作物系数。结果表明,非充分灌溉条件下,主要作物各生育期需水规律和充分灌溉具有一致变化趋势。非充分灌溉条 件下,小麦、玉米、马铃薯全生育期作物系数平均值分别为0.81、0.7和0.73。在全生育期当中,随生育期的延续,主要作物叶面蒸腾比例逐渐增大,棵 间蒸发逐渐减少。

[ Lian C ., Ma Z ., Lv X ., et al.Effect of land use types on infiltration characteristics in the Ziquejie Terraces Region[J]. Journal of Irrigation ang Drainage, 2012,31(5):136-139. ]

[45]
王瑶,赵传燕,田风霞,等.黑河中游春小麦需水量空间分布[J].生态学报,2011,31(9):2374-2382.合理估计春小麦的需水量(<em>ET</em><sub>c</sub>)是进行干旱区水资源配置的有效方法,利用黑河中游14个气象站1970-2009年的逐日气象资料,应用Penman-Monteith公式估算各站点的参考作物蒸散量,并根据春小麦生长期的作物系数,在地理信息系统(GIS)技术支持下得出黑河中游春小麦需水量的空间分布及变化趋势。结果表明:1970-2009年黑河中游春小麦作物需水量整体分布具有从南向北递增的趋势,全生长期需水量在573-781 mm之间;高台、张掖、临泽、民乐、山丹、酒泉的春小麦需水量分别为731.26、686.88、598.24、728.89、719.77、713.59 mm,其中生长中期需水量最大,分别占全生长期的51.67%、51.11%、50.96%、51.24%、50.83%和50.77%;日平均气温、日照时数、风速、降水量、最小相对湿度和各因子的影响力由大到小分别占总影响力的57.29%、26.92%、15.15%、1.41%和0.78%。

[ Wang ., Zhao C ., Tian F ., et al.Spatial variation of water requirement for spring wheat in the middle reaches of Heihe River Basin[J]. Acta Ecologica Sinica, 2011,31(9):2374-2382. ]

[46]
尹海霞,张勃,王亚敏,等.黑河流域中游地区近43年来农作物需水量的变化趋势分析[J].资源科学,2012,34(3):409-417.根据黑河流域中游地区7个气象站点1967年-2009年的逐日气象资料,利用FAO Penman-Monteith模型和所提供的作物系数计算出主要作物不同生育阶段的需水量,采用Mann-Kendall 趋势分析法对主要作物需水量的变化特征进行了研究,并对引起作物需水量的驱动因子进行了相关性分析,在此基础上,应用基于分型理论的R/S方法对作物需水量的变化趋势进行了预测。结果表明：近43年来,中游地区春小麦和玉米<i>ET<sub>c</sub></i>均呈波动下降趋势,春小麦下降斜率为6mm/10a,玉米下降斜率为8mm/10a;其中,民乐和甘州站春小麦和玉米的<i>ET<sub>c</sub></i>呈缓慢上升趋势,其他站点春小麦和玉米的<i>ET<sub>c</sub></i>均呈下降趋势,且部分站点下降趋势显著。春小麦和玉米<i>ET<sub>c</sub></i>的主要影响因子均为相对湿度、日照时数和平均风速。中游地区各站点春小麦和玉米需水量Hurst指数H均大于0.5,同时分维数D均小于1.5,因此未来一段时间<i>ET<sub>c</sub></i>仍然保持与过去相一致的变化趋势。

[ Yin H ., Zhang ., Wang Y ., et al.Research on the change trend of crop water requirement in the middle reaches of Heihe River Basin in the recent 43 years[J]. Resources Science, 2012,34(3):409-417. ]

[47]
王海波,马明国.基于遥感和Penman-Monteith模型的内陆河流域不同生态系统蒸散发估算[J].生态学报,2014,34(19):5617-5626.遥感数据具有很好的时空连续性，它是区域蒸散发通量估算的有效方法。引入了一个简单的具有生物物理基础的Penman-Monteith （P-M）模型，分别利用黑河流域高寒草地阿柔站和干旱区农田盈科站2008-2009年的气象数据和MODIS （Moderate Resolution Imaging Spectroradiometer）叶面积指数（LAI），实现了2008-2009年日蒸散发的估算，并同时实现了对植被蒸腾和土壤蒸发的分别估算。结果表明，利用P-M公式模拟的蒸散发与实测的蒸散发具有较好的一致性，日蒸散发模拟的决定系数（<em>R</em><sup>2</sup>）超过0.8。估算的高寒草甸和干旱区农田玉米全年平均的蒸腾分别为0.78 mm/d和1.20 mm/d，分别占总蒸散发的60%和61%，土壤蒸发分别为0.53和0.77 mm/d，占总蒸发的40%和39%。可见两种生态系统的作物蒸腾均强于土壤蒸发，同时农田玉米蒸腾强于高寒草甸蒸腾。研究结果证明了基于遥感的P-M公式可以很好地实现对高寒草地和干旱区农田生态系统蒸散发的估算。通过考虑土壤水分变化对气孔导度的影响，可以提高模型对农田蒸散发的模拟精度。

DOI

[ Wang H ., Ma M G.Estimation of transpiration and evaporation of different ecosystems in an inland river basin using remote sensing data and the Penman-Monteith equation[J]. Acta Ecologica Sinica, 2014,34(19):5617-5626. ]

[48]
盖力强,谢高地,李士美,等.华北平原小麦、玉米作物生产水足迹的研究[J].资源科学,2010,32(11):2066-2071.本文借助水足迹的概念，计算了华北平原地区（河北、北京、天津）小麦、玉米的虚拟水含量及其生长生产水足迹，并就绿水的重要性和灰水对环境的不利影响进行了相关分析。结果显示：2007年华北平原主要地区小麦虚拟水含量为1.054 m3/㎏，生长用水以蓝水为主；玉米虚拟水含量为0.808 m3/㎏，生长用水以绿水为主；小麦生产水足迹为172×10<sup>8</sup> m<sup>3</sup>，其中绿水足迹30.85×10<sup>8</sup> m<sup>3</sup>，蓝水足迹102.5×10<sup>8</sup> m<sup>3</sup>，灰水足迹38.65×10<sup>8</sup> m<sup>3</sup>；玉米生产水足迹为173.07×10<sup>8</sup> m<sup>3</sup>，其中绿水足迹101.06×10<sup>8</sup> m<sup>3</sup>，蓝水足迹26.92×10<sup>8</sup> m<sup>3</sup>，灰水足迹45.09×10<sup>8</sup> m<sup>3</sup>。通过分析表明：绿水在当地农作物生产中占有重要的地位，绿水的使用与作物的生长特点及作物生长周期有关；小麦、玉米总水足迹约为当地水资源总量的2.2倍，减少小麦、玉米作物生产水足迹对华北平原具有重要的意义。

[ Gai L ., Xie G ., Li S ., et al.A study on production water footprint of winter-wheatand maize in the North China Plain[J]. Resources Science, 2010,32(11):2066-2071. ]

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