Real Time Observing and Forecasting System for Soil Moisture in Anhui Province

Chen Jinhua; Yang Zaiqiang; Yang Taiming; Li Yongxiu

Vol.22, NO.2, 2011

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2011 > 22(2): 249-256

Chen Jinhua, Yang Zaiqiang, Yang Taiming, et al. Real time observing and forecasting system for soil moisture in Anhui Province. J Appl Meteor Sci, 2011, 22(2): 249-256.

Citation:

Chen Jinhua, Yang Zaiqiang, Yang Taiming, et al. Real time observing and forecasting system for soil moisture in Anhui Province. J Appl Meteor Sci, 2011, 22(2): 249-256.

Chen Jinhua, Yang Zaiqiang, Yang Taiming, et al. Real time observing and forecasting system for soil moisture in Anhui Province. J Appl Meteor Sci, 2011, 22(2): 249-256.

Citation:

Chen Jinhua, Yang Zaiqiang, Yang Taiming, et al. Real time observing and forecasting system for soil moisture in Anhui Province. J Appl Meteor Sci, 2011, 22(2): 249-256.

PDF( 2042 KB)

Real Time Observing and Forecasting System for Soil Moisture in Anhui Province

1.
College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044
2.
Anhui Institute of Meteorological Science, Hefei 230031

Received Date: 2010-08-03
Rev Recd Date: 2010-12-02
Publish Date: 2011-04-30

Abstract

Abstract

In order to meet the needs of flood control and drought relief, the operation of soil moisture observation is launched routinely in meteorological department, by artificial boring stick all the time or by automatic measurer in recent years. However, the use of soil moisture data is always lagging with poor matching service and continuity. Based on the soil water observation network (including the manual and automatic network) and many kinds of approaches for data transmission, Real Time Observing and Forecasting System for Soil Moisture in Anhui Province (SMRTOFS) is developed. SMRTOFS is composed of data observation and transmission subsystem, forecast subsystem, and display subsystem. In data observation and transmission subsystem, the data from manual observers and automatic observation stations is collected in real time and stored in standard soil moisture database, and the data from unexpected transmission approach is also automatically gathered and conserved by defining an intermediate file. In forecast subsystem, predicting models of soil water content for each season are established, and soil moisture forecast is achieved using the latest soil water observation data and the coming 10-day weather information. In the display subsystem, based on the secondary development of Golden Software Surfer 8.0 and line bar chart control, the results of soil water observation and prediction in different seasons and different depths are exported and displayed dynamically, with the patterns of data table, the filled contour in spatial scale, bar chart, and so on. In the system, four-level files from observation to application are constructed including observation raw data, standard database, primary products and user products. The operation flow of soil moisture observation and forecast is reduced to transforming the four-level files. With higher applicability and compatibility, the system is applied triumphantly to the service of agricultural drought and waterlogging operation in Anhui Province. The information could be used to avoid the loss of flood and drought disaster. However, the soil moisture forecasting is based on statistical method, so the model parameters need modification for other regions. Implementing better Soil-Plant-Atmosphere Continuum model can also improve the performance of this system.
- soil water;
- real time observing;
- forecasting system

FullText(HTML)

References(31)

Figures and Tables

Fig. 1 The structure and data flow of SMRTOFS

DownLoad: Full-Size Img PowerPoint

Fig. 2 The process of interpreting and forecasting of soil water data

DownLoad: Full-Size Img PowerPoint

Fig. 3 The principle of data interpolation and contour map filling

DownLoad: Full-Size Img PowerPoint

图 4 土壤水分监测实例 (a) 线状图显示，(b) 动态播放最近N个时次土壤水分空间变化

Fig. 4 The example of soil water observing

(a) linear graph display, (b) dynamically showing of the latest soil water data on spatial scale

DownLoad: Full-Size Img PowerPoint

图 5 土壤水分预测实例

(a) 0~20 cm预测，(b)20~50 cm预测，(c)0~20 cm预测值与实测值比较，(d)20~50 cm预测值与实测值比较

Fig. 5 The example of soil water forecasting (a) forecasting in 0—20 cm depth, (b) forecasting in 20—50 cm depth, (c) comparison between predicting value and observing value in 0—20 cm depth, (d) comparison between predicting value and observing value in 20—50 cm depth

DownLoad: Full-Size Img PowerPoint

Table 1 Drought index of soil relative humidity

等级	类型	土壤相对湿度/%
1	过湿	θ′＞90
2	正常	60＜θ′≤90
3	轻旱	50＜θ′≤60
4	中旱	40＜θ′≤50
5	重旱	θ′≤40

DownLoad: Download CSV

Table 2 The parameters of soil water forecasting model

	季节	a₀	a₁	a₂	a₃	a₄
P≤P₀	春季	10.70	0.768	-0.034	0.538	-0.024
	夏季	32.80	0.657	-0.067	0.540	-0.070
	秋季	10.82	0.833	-0.035	0.386	-0.027
	冬季	0.72	0.930	-0.016	0.295	-0.015
P＞P₀	春季	33.28	0.420	-0.020	7.320	-0.137
	夏季	44.13	0.280	-0.049	7.580	-0.030
	秋季	39.31	0.405	-0.051	7.750	-0.112
	冬季	35.08	0.441	-0.021	5.870	-0.010

DownLoad: Download CSV

Table 3 Table structure of soil water

列名	中文名称	类型	单位
Rq	日期	character
Zhh	站号	character
Zhm	站名	character
Sjlx^*	数据类别	integer
Tzlx	台站类型	integer
Sgg	灌溉标示	integer
S10	0~10 cm	single	%
S20	10~20 cm	single	%
S30	20~30 cm	single	%
S40	30~40 cm	single	%
S50	40~50 cm	single	%
S60	50~60 cm	single	%
S70	60~70 cm	single	%
S80	70~80 cm	single	%
S90	80~90 cm	single	%
S100	90~100 cm	single	%
Sgtc	干土层厚度	integer	cm
注：*数据类别有3种：0表示相对湿度，1表示体积含水率，2表示重量含水率。

DownLoad: Download CSV

Table 4 Property of the class of SurferIni

属性	数据类型	备注
Back_layer	string	背景图层路径
Grid_txt	string	图形数据文件路径
LongLat (1 to 4)	single	图像经纬度区间
Level_file	string	自定义的色标文件路径
Bln_file	string	白化时省边界文件路径
Bmp_width	integer	输出图像宽
Bmp_Height	integer	输出图像高
Bmp_output	string	图像输出路径
BmpWHbili	boolean	是否按高/宽比输出
Srf_output	string	*.srf输出路径

DownLoad: Download CSV

References

[1]	王善型, 宣春生, 王效瑞, 等.安徽省志·气象志.合肥:安徽人民出版社, 1990.
[2]	陈家宙, 陈明亮, 何圆球.各具特色的当代土壤水分测量技术.湖北农业科学, 2001(3):25-28. http://www.cnki.com.cn/Article/CJFDTOTAL-HBNY200103012.htm
[3]	冶林茂, 吴志刚, 牛素军, 等. GStar-Ⅰ型电容式土壤水分监测仪设计与应用.气象与环境科学, 2008, 31(3):82-85. http://www.cnki.com.cn/Article/CJFDTOTAL-HNQX200803018.htm
[4]	Menziani M, Pugnaghi S, Pilan L, et al. TDR soil moisture measurements at the Lago Maggiore MAP target area: Preliminary results.Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 2001, 26: 431-436. doi: 10.1016/S1464-1909(01)00031-4
[5]	范佳林, 梁秀清.土壤墒情自动化监测及应用.现代农业科技, 2010(7):323; 327. http://www.cnki.com.cn/Article/CJFDTOTAL-ANHE201007217.htm
[6]	国家防汛抗旱总指挥部办公室. 全国旱情监测规划. 2007: 32-35. http://www.docin.com/p-8690746.html.
[7]	邵晓梅, 严昌荣, 徐振剑.土壤水分监测与模拟研究进展.地理科学进展, 2004, 23(3): 59-66. http://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ200403007.htm
[8]	黄妙芬, 康玲玲, 王云璋.气象、水文干旱指数计算访求研究概述.水资源与水工程学报, 2004, 15 (3):15-18. http://www.cnki.com.cn/Article/CJFDTOTAL-XBSZ200403004.htm
[9]	陈金华, 杨太明, 马晓群, 等.安徽省长江以北地区土壤水分动态模拟初探.中国农业气象, 2007, 28(3): 289-291. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200703012.htm
[10]	元来福, 王继琴.从农业需水量评价我国的干旱状况.应用气象学报, 1995, 6(3):86-92. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19950355&flag=1
[11]	王晓云, 郭文利, 奚文, 等.利用"3S"技术进行北京地区土壤水分监测应用技术研究.应用气象学报, 2002, 13(4):422-431. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20020457&flag=1
[12]	李玉中, 程延年, 安顺清.北方地区干旱规律及抗旱综合技术.北京:中国农业科学技术出版社, 2002.
[13]	董振国.作物层温度与土壤水分关系.科学通报, 1986, 31(8):186-190. http://www.cnki.com.cn/Article/CJFDTOTAL-KXTB198608012.htm
[14]	Caporali E, Entekhabi D, Castelli F. Rainstorm statistics conditional on soil moisture index: Temporal and spatial characteristics. Meccanica, 1996, 31:103-116. doi: 10.1007/BF00444158
[15]	Wang J R. An overview of the measurements of soil moisture and modeling of moisture flux in FIFE. J Geophys Res, 1992, 97(D17): 955-959. https://www.researchgate.net/publication/4708806_An_overview_of_the_measurements_of_soil_moisture_and_modeling_of_moisture_flux_in_FIFE
[16]	郭以明, 郭相平, 樊峻江, 等.蓄水控灌模式对水稻产量和水分生产效率的影响.灌溉排水学报, 2010, 29(3): 61-63;73. http://www.cnki.com.cn/Article/CJFDTOTAL-GGPS201003015.htm
[17]	王传河.小麦不同指标对旱涝反应敏感性差异的比较.中国农业通报, 2003, 19(6):33-40. http://www.cnki.com.cn/Article/CJFDTOTAL-ZNTB200306012.htm
[18]	张爱民, 马晓群.安徽省旱涝灾害及其对农作物产量影响.应用气象学报, 2007, 18(5):619-625. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20070595&flag=1
[19]	邵光成, 张展羽, 蔡焕杰, 等.膜下滴灌棉花缺水诊断指标的试验研究.河海大学学报 (自然科学版), 2004, 32(5):546-553. http://www.cnki.com.cn/Article/CJFDTOTAL-HHDX200405017.htm
[20]	谭宗锟.广西农业气象灾害风险评价及灾害风险区划.广西气象, 1997, 18(1):44-50. http://www.cnki.com.cn/Article/CJFDTOTAL-GXQX701.012.htm
[21]	李世奎, 霍治国, 王素艳, 等.农业气象灾害风险评估体系及模型研究.自然灾害学报, 2004, 13(1): 77-86. http://www.cnki.com.cn/Article/CJFDTOTAL-ZRZH200401013.htm
[22]	宋丽莉, 王春霖, 董永春.水稻干旱动态模拟及干旱损失评估.应用气象学报, 2001, 12(2):23-26. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20010230&flag=1
[23]	Naor A. Relationship between leaf and stem water potential and stomatal conductance in three field-grown woody species. J Hort Sci & Biotechnology, 1998, 73:431-436. https://www.researchgate.net/publication/290014488_Relations_between_leaf_and_stem_water_potentials_and_stomatal_conductance_in_three_field-grown_woody_species
[24]	Nicola M, John D A. Multi-scale assimilation of surface soil moisture data for robust root zone moisture predictions. Advances in Water Resources, 2003, 26(1): 33-44. doi: 10.1016/S0309-1708(02)00103-3
[25]	Nicola B, Maelle A. Operational mapping of soil moisture using synthetic aperture radar data: Application to the Touch Basin (France). Sensors, 2007, 7: 2458-2483. doi: 10.3390/s7102458
[26]	王越, 江志红, 张强, 等.用Palmer湿润指数作西北地区东部冬小麦旱涝评估.应用气象学报, 2008, 19(3):342-349. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20080356&flag=1
[27]	刘云辉, 朱渐臣.丹东地区中部春播期土壤水分特征及早涝评价.中国农业气象, 2008, 29(2):174-176. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200802016.htm
[28]	鞠笑生, 杨贤为, 陈丽娟, 等.我国单站早涝指标确定和区域早涝级别划分的研究.应用气象学报, 1997, 8(1):26-33. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=yyqx701.003&dbname=CJFD&dbcode=CJFQ
[29]	侯琼, 郝文俊.内蒙古地区玉米农田土壤墒情动态预测模式.干旱地区农业研究, 2000, 12(4): 49-56. http://www.cnki.com.cn/Article/CJFDTOTAL-GHDQ200004010.htm
[30]	申惠娟, 严昌荣, 戴亚平.等.农田土壤水分预测模型的研究进展及应用.生态科学, 2003, 22(4):366-370. http://www.cnki.com.cn/Article/CJFDTOTAL-STKX200304019.htm
[31]	盛绍学, 胡雯, 马晓群, 等.安徽省农业干旱遥感监测指标的确定及应用.安徽气象, 2000(3):16-18. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200104008.htm