SATELLITE WATER MONITORING AND FLOW FORECASTING SYSTEM FOR THE YELLOW RIVER BASIN
Sino-Dutch Cooperation Project ORET 02/09-CN00069 Scientific Final Report December 2008
Title page
SATELLITE WATER MONITORING AND FLOW FORECASTING SYSTEM FOR THE YELLOW RIVER BASIN
Sino-Dutch Cooperation Project ORET 02/09-CN00069 Scientific Final Report December 2008
Authors
Andries Rosema, Marjolein de Weirdt and Yuanze Gu, Weimin Zhao, Chunqing Steven Foppes Wang, Xiaowei Liu, Suqiu Rao, Dong EARS, Kanaalweg 1, 2628 EB Delft, Dai, Yong Zhang, Liye Wen, Dongling Netherlands. Email: [email protected] Chen, Yanyan Di, Shuhui Qiu, Qingzhai Wang, Liuzhu Zhang, Jifeng Liu, Raymond Venneker and Shreedar Maskey Longqing Liu, Li Xie, Ronggang Zhang, UNESCO-IHE Institute for Water Education, Jian Yang, Yawei Zhang, Meng Luo, Bo Westvest 7, 2611 AX, Delft, Netherlands. Hou, Lai Zhao, Lihua Zhu, Xiaodong Chen and Tequn Yang. Hydrology Bureau, Yellow River Hongqi Shang, Songchang Ren, Feng Sun, Conservancy Commission, Ministry of Yangbo Sun, Falu Zheng, Yunpeng Xue, Water Resources, P.R. China, No.12 Zhongqun Yuan and Hui Pang. Chengbe East Road, Zhengzhou 450004, Bureau of Science, Technology and Foreign China. Affairs, Yellow River Conservancy Commission, Ministry of Water Resources, Chengyang Lu, Gensheng Liu, Xijun Guo PR. China, No.11, Jinshui Road, Zhengzhou and Deyan Du. 450003, China Upper Hydrology Bureau of YRCC, No. 157 Wudu Road, Lanzhou 730030, China.
Bastiaan Bink and Xiaobo Wu Xiaoying He, Xinwu Tu and Wenjuan Sun. Hofung Limited, The Hague, Netherlands Sanmenxia Hydrology Bureau of YRCC, and Beijing, China. Email: No.7 Hepingxiduan, Sanmenxia 472000, [email protected] China
Scientific final report of the project Establishment of a Satellite Based Water Monitoring and Flow Forecasting System in the Yellow River Basin, commissioned by the Yellow River Conservancy Commission to a consortium consisting of EARS Earth Environment Monitoring BV, UNESCO-IHE Institute for Water Education and Hofung Ltd. The project was co-funded by the Yellow River Conservancy Commission and the Government of the Kingdom of the Netherlands through Grant Agreement CN200400105 related to ORET project 02/09 – CN00069.
This report may be referred to as: Rosema, A; De Weirdt, M; Foppes, S; Venneker, R; Maskey, S; Gu, Y; Zhao, W; Wang, C; Liu, X; Rao, S; Dai, D; Zhang, Y; Wen, L; Chen, D; Di, Y; Qiu, S; Wang, Q; Zhang, L; Liu, J; Liu, L; Xie, L; Zhang, R; Yang, J; Zhang, Y; Luo, M; Hou, B; Zhao, L; Zhu, L; Chen, X; Yang, T; Shang, H; Ren, S; Sun, F; Sun, Y; Zheng, F; Xue, Y; Yuan, Z; Pang, H; Lu, C; Liu, G; Guo,X; Du, D; He, X; Tu , X; Sun, W; Bink, B; Wu, X. (2008) “Satellite Monitoring and Flow Forecasting System for the Yellow River Basin”, Scientific final report of ORET project 02/09-CN00069, EARS, Delft, the Netherlands, 144 pg, December 2008.
Cover: Yellow River at Tangke
3 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Acknowledgement
This project has been approved and supported by Chinese Ministry of Water Resources, Chinese Ministry of Finance and the Yellow River Conservancy Commission. We especially thank Yongfu Zheng of the Ministry of Finance, Jianming Liu, Zhiguang Liu, Xingjun Yu, Ge Li, Hai Jin, Qingping Zhu, Mengzhuo Guo and Yubo Shi of the Ministry of Water Resources, Zijiang Huang, Xiaoyan Liu, Yuguo Niu, Hanxia Yang, Hongyue Zhang, Shuili Tian, Shiqing Huo and Long Wang of the Yellow River Organisation. We are grateful for their interest and support during project initiation, development and implementation.
The authors also wish to thank the many people from the hydrology stations at Jingchuan, Tangneihai, Jungong Jun and Tangke for their work on the establishment and maintenance of the Large Aperture Scintillometer Systems, as well as for reading and forwarding the measuring data. We thank Wouter Meijninger of Kipp & Zonen in Delft for his support in questions related to the LAS data processing.
We are also grateful to Rongzhang Wu of the China National Satellite Meteorological Centre, Yun Bai of the Shinetek Company, Guang Zhu and Qian Qian of CITC CMC International Tendering Corporation for their support in different phases of the project.
We thank Dave van den Nieuwenhof, Huub Lavooij, Albert de Haas and Liqun Li of Royal Dutch Embassy in Beijing for their interest and support. The project would not have been possible without the grant received from the Government of the Netherlands, through the ORET organization. We are grateful for the fair and proper settlement of all administrative, financial and contractual matters related to this project.
4 Contents
CONTENTS
Section Title Page
1 INTRODUCTION 7 1.1 China’s water resources problems 7 1.2 Flooding 8 1.3 Water shortages 8 1.4 Need for basin wide water resources monitoring and management 9 1.5 Sino-Dutch water monitoring and flow forecasting project 10 1.6 Project objectives 10 1.7 Project deliverables 11 1.8 Project approach 11 1.8.1 Development phase 12 1.8.2 Implementation and testing phase 13 1.8.3 Demonstration phase 14 1.9 Project impact 15 1.10 References 16 2 THE YELLOW RIIVER TARGET AREAS 17 2.1 The source area of the Yellow River 17 2.1.1 Hydrological observations in the source area 18 2.1.2 Information acquisition and transmission 22 2.1.3 Hydrological forecasting 22 2.2 The lower Weihe River 23 2.2.1 Hydrological observations 24 2.2.2 Information acquisition and transmission 25 2.2.3 Flood forecasting 25 3 ENERGY AND WATER BALANCE MONITORING SYSTEM 29 3.1 System components 31 3.1.1 Pre-processing 31 3.1.2 Precipitation mapping 32 3.1.3 Energy balance monitoring 33 3.1.4 Snow and snowmelt 42 3.1.5 Drought monitoring 43 3.2 LAS measurements 45 3.2.1 LAS theory 45 3.2.2 LAS equipment and installation 47 3.2.3 LAS measuring sites 46 3.2.4. Data collection 46 3.2.5 Data processing 47 3.2.6 LAS results 48 3.3 EWBMS software system 52 3.3.1 Satellite data reception and pre-processing 53 3.3.2 Rain gauge data reception and pre-processing 54 3.3.3 Precipitation module 55 3.3.4 Energy balance module 55 3.3.5 Freeze/Thaw module 58 3.3.6 Drought monitoring system 58 3.3.7 Processing information data base 62 3.3.8 Imageshow-2 analysis tool 62 3.4 Catchment drought monitoring system 64 3.4.1 Climatic drought 64 3.4.2 Hydrological drought 65 3.4.3 Agricultural drought 67 3.5 Validation of EWBMS products 69
5 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
3.5.1 Validation of precipitation 69 3.5.2 Validation of air temperature 74 3.5.3 Validation of net radiation 77 3.5.4 Validation of sensible heat flux 79 3.5.5. Validation of catchment water budget 81 3.6 References 85 4 LARGE SCALE HYDROLOGICAL MODEL 87 4.1 Technical reference 87 4.1.1 Land component transport 88 4.1.2 River routing 90 4.1.3 Land-river coupling 91 4.1.4 Forecasting of river flows 92 4.2 System implementation 93 4.2.1 Software components 93 4.2.2 User interface 94 4.3 Upper Yellow River Water Resources Forecasting System 96 4.3.1 Description of the data requirements 96 4.3.2 Description of the terrain data 96 4.4 Weihe basin High Water Forecasting System 98 4.4.1 Description of the data requirements 98 4.4.2 Description of the terrain data 98 4.5 Evaluation of the simulation results 99 4.5.1 Validation data 99 4.5.2 WFRS validation results 101 4.5.3 HWFS validation results 103 4.5.4 Discussion 105 4.6 References 108 5 SYSTEM IMPLEMENTATION AT YRCC 111 5.1 System set-up 111 5.1.1 Satellite receiving and processing system 111 5.1.2 Computer network 113 5.1.3 Data base 115 5.1.4 Organization and operation 117 5.1.5 LAS station, data collection and processing 118 5.2 Catchment monitoring bulletin 118 5.2.1 Reporting flood and drought information 118 5.2.2 Bulletin contents 119 5.3 Catchment monitoring website 119 5.3.1 Target users 119 5.3.2 Website design and structure 120 6 CONCLUSIONS, OUTLOOK AND RECOMMENDATIONS 123
ANNEX 1: LAS STATIONS INFORMATION 127 ANNEX 2: CATCHMENT MONITORING BULLETIN 131
6 Chapter 1 - Introduction
1 INTRODUCTION
This document is the final report of the project “Satellite Based Water Monitoring and Flow Forecasting System in the Yellow River Basin”. This Sino-Dutch project was funded by the Chinese and Dutch Government. The Dutch funding contribution was provided through the ORET program, a program that supports export transactions that are relevant for social economic development and for the environment, but are not feasible in a commercial sense. After signature of the contract in November 2003 and the Grant agreement in May 2004, the project started in June 2004. The project was completed in the last month of 2008.
1.1 China’s water resources problems.
Water is one of the most important issues in relation to China's development. With a growing population and a booming economy the water demand is increasing steadily. At the same time water availability – especially in the north of the country - is limited and characterized by a highly uneven distribution geographically and seasonally.
The Yellow river (Huanghe) is, after the Yangtze, the second largest river in China. The river basin is situated in the arid, semi-arid and sub-humid zones, which zones are characterized by relatively low but highly variable rainfall. The average annual run-of is about 58 billion m 3. The lower reach of the Yellow river runs through a relatively narrow corridor towards the sea. By consequence the Yellow river basin has only a short coast line.
While water availability in the Yellow river catchment is highly irregular, water demand is steadily increasing. China’s impressive economic growth and improving living standards - alongside a still increasing population - put pressure on water resources. Urban areas, industry, agriculture and nature are all competing for a share of the precious natural resource. Also some large cities, which are situated outside of the catchment (e.g. Tianjin), depend on water from the river. Due to water intake from the Yellow river for industry, agriculture and residential use, the flow tends to dry up in the lower reach during the summer period. However, in case of high precipitation the risk of flooding looms in the lower reach, where the riverbed runs elevated high above the land. Growth and development are held back or become ‘non sustainable’ in water shortage areas, resulting in damage to local economy and nature. This puts more and more pressure on decision making concerning the allocation and development of water resources.
A detailed assessment of water resources in the Yellow river is currently carried out every 10 years. But in view of the climatic variability, more frequent assessments are highly desirable. Monitoring (measuring time series) is currently restricted to precipitation and river flow at a limited number of locations. These measurements suffer from a lack of overview. A new satellite based water resources monitoring and flow forecasting technology has been implemented to help addressing this problem.
There are two neighbouring basins, that of the Hai and Huai river respectively. The Yellow (Huanghe), the Hai and Huai river basins are together referred to as the 3-H basins. 40% of the Chinese population lives here. The 3-H basins are the breadbaskets of China and produce 67% of its wheat, 44% of its corn and 72% of its millet. In addition they produce 65% of its peanuts, 64% of its sunflower and 42% of its cotton. At the same time these basins have only 10% of China's water resources.
7 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Water resources issues in the 3-H basins are related. Excess water, if available, may be transported from the Yellow river to the densely populated areas along the coast in particularly the Hai River basin (Beijing, Tianjin). The whole region is regularly hit by disasters of both water scarcity and excess.
1.2 Flooding
The Yellow river carries a huge amount of sediments, originating from the Loess Plateau in the upper and middle reaches. 75 % of the sediments is deposited in the lower reaches and the river estuary. As a result the river floor is rising 5-10 centimetre per year, and the levees in the floodplain had to be rebuilt 4 times during the second half of the 20th century: in 1950, 1955, 1964 and 1977. Costs are approximately 2 Billion US$ each time. The river water level is now up to 10 meter above the surrounding land. The combination of this phenomenon, with the sometimes-high precipitation in the middle reach, creates a high risk of flooding.
Since 600 BC, dike bursts occurred 1590 times and the river changed its course 26 times. Very serious floods occurred in the 1930's. A major flood in 1933 caused more than 50 dike bursts. 18000 people were killed and 3.6 million ha of farmland was damaged. In 1938 another flood inundated 27 counties and caused 3.4 million victims.
According to the report Agenda for Water Sector Strategy for North China (Worldbank 2001) flood losses in the Yellow river basin increased from 1500 RMB/ha in the 1950's to 9000 RMB/ha in the 1990's. In 1998 losses were in the order of 1% of the GDP. In the Yellow river basin the provinces most affected were Henan and Shandong, followed by Shanxi and Shaanxi.
The total flood prone area of the Yellow river is 118000 km 2. 71 million people are living in this area. An estimation of losses in case of complete flooding of this area, based on the fore mentioned figure of 9000 RMB/ha, would then be 100 billion RMB or 13 billion US$.
According to the document "An overview of Chinese water issues" (Unknown 1997) the losses due to flooding in China were 20 billion US$ in 1990 and 10 billion $ in 1996. Very severe flooding occurred in 1998. These floods killed 3500 people, damaged 7 million houses and submerged 250.000 km 2 of farmland. Total damage amounted to 30 billion US$ nation wide. In these events 250,000 km 2 of land was inundated.
1.3 Water shortages
Since the 1980's water shortages in the 3-H basins have been growing in magnitude and frequency of occurrence. This has created severe economic losses. Water demand in 2000 was 169 billion m 3 and exceeds the total supply, which is 132 billion m 3 per year (table 1.1). Shortages are expected to grow from 37 billion m 3 to 56 billion m 3 in 2050 if no measures are taken (Worldbank 2001). Current demands and shortages for these basins are presented in following table.
8 Chapter 1 - Introduction
Table 1.1: Water demand, supply and shortages in the 3-H basins (billion m 3/yr) Huanghe Huai Hai Total Demand 47 72 50 169 Supply total 37 55 32 124 Shortage 10 17 18 37 • domestic + industry 2 4 4 5 • agriculture 8 13 14 32
Roughly 80% of all water is used in agriculture, and within this sector the use for irrigation is far dominant. The water demand structure in the Yellow river basin is as follows.
Table 1.2: Water demand in the Yellow river basin Category Share (%) Urban life 3 Urban industry 12 Rural life 2 Rural industry 2 Irrigation 76 Livestock 1 Fisheries / Pasture 3
As a result of surface water shortages, there is an increasing reliance on groundwater. Groundwater extraction in the 3H basins is about 50 billion m 3 per year and about 13 billion m 3 in the Yellow river basin alone.
Table 1.3: Utilization of ground water in the 3H basins (billion m 3/yr) Huanghe Huai Hai Total Groundwater extraction 13 16 22 51
In many areas ground water resources are over exploited and ground water tables are falling as much as several meter per year. As a result the cost of ground water extraction is growing. Other effects are: saline water intrusion in the coastal areas (now covering an area of 142 km 2) and ground subsidence. The urban areas of Tianjin and Beijing suffer from subsidence, causing settlement of structures, bridge collapse, storm water drainage problems and reduction in flood protection. Ground subsidence has caused 1.4 billion RMB damages to structures during the 1990's
The earlier mentioned Worldbank study also estimates the economic value of water for different sectors. In agriculture, predominantly irrigation, the value is 0.8-1.6 RMB/m 2, or 1.2 RMB/m 3 on average. For domestic and industrial use the value varies between 3 and 6 RMB/m 3.
1.4 Need for basin wide water resources monitoring and management
Provinces in the upper reach of the Yellow river have been overusing the available water, which has lead to shortages and even drying up of the river in downstream areas. In 1997 the irrigation rate in the upper reach was 12000 m3/ha on average, while 3700 m 3 was used in the middle reach and 4500 m 3 in the lower reach of the river. According to Changming Liu (2000) average gross irrigation water use in the north-east of China was 8400 m3/ha. This is about 4 times the water required for a single crop. The problem of water shortage versus overuse has caused intense
9 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
conflicts between political entities. Therefore a basin wide control of the limited water resources has become essential.
China's Agenda for Water Sector Strategy for North China recommends that: t he River Basin Councils are to be charged with, and given the necessary legislative support for: (a) Determining water resources allocations (surface and groundwater) for the provinces, (b) Developing policies and programs to promote sustainable water resources management, particularly with respect to flood control and drought relief, ground water management, water resources protection and pollution control, promotion of increased water use (especially irrigation) efficiency, and comprehensive basin development planning.
The Yellow River Conservancy Commission is the first river commission that has been charged with such far reaching tasks. It is clear however, that proper decision making in relation to these tasks, requires a lot of information on river flow and water resources on the one side, and water needs on the other side. On the demand side water needs in agriculture is by far the largest and most important part. It is also the most difficult demand to assess, because of the variability of “natural” water supply by precipitation. In dry years, water needs for irrigation will be much larger than in more wet years.
1.5 Sino-Dutch water monitoring and flow forecasting project
The Sino-Dutch project “Satellite Based Water Monitoring and Flow Forecasting System in the Yellow River Basin” has developed and implemented an operational water balance monitoring and flow forecasting system for the Yellow river basin. The main components of this system are the “Energy and Water Balance Monitoring System” (EWBMS) developed by the Dutch remote sensing company EARS and the Large Scale Hydrological Model (LSHM) developed by UNESCO-IHE, both in Delft, Netherlands. Based on these technological components the following dedicated subsystems have been developed and implemented for the Yellow River basin: • Flow Forecasting system in the upper reach of the Yellow river • Flow and high water forecasting system for the Weihe tributary. • Drought monitoring system for the entire Yellow river basin.
The system is to become a major tool in the hands of the Yellow River Conservancy Commission. It will help to carry out tasks with respect to (1) the management of water resources in the Yellow river basin, (2) flood forecasting and early warning, and (3) the monitoring and early warning of drought.
1.6 Project objectives
The satellite based water monitoring and flow forecasting project has been carried out with the following objectives: • To develop a system for energy and water balance monitoring. • To develop a system for drought monitoring and early warning. • To develop a prototype system for flow forecasting in the Upper Reach. • To develop a prototype system flow and flood forecasting in the Weihe. • To calibrate, test and improve these systems. • To implement these systems at the YRCC premises. • To train the partners in understanding and effectively using these systems. • To assist the YRCC in monitoring of water resources and drought.
10 Chapter 1 - Introduction
1.7 Project deliverables
In course of project the partners have provided the following deliverables to YRCC:
• Two FY2c geostationary satellite receiving systems. • EARS Energy and Water Balance Monitoring System (EWBMS), providing daily data fields of: surface temperature, 1.5 m air temperature, global and net radiation, actual and potential evapotranspiration, rainfall, snow height and effective precipitation. • 4 Surface flux measuring systems consisting of a Large Aperture Scintillometer (LAS), a CNR1 net radiometer and a data logger. • Drought Monitoring and early warning System (DMS) for the entire basin. • Water Resources Forecasting System for the Upper Reach (WRFS). • Flood Forecasting System for the Weihe (HWFS). • EWBMS, DMS, WRFS and HWFS methodology description reports. • EWBMS, DMS, WRFS and HWFS user manuals. • A project final report (this document). • Half yearly project progress reports. • 17 man-years of technical assistance. • 30 man-months of training in understanding, use and application of the monitoring system and its technological components. • 36 man-months of research fellowships, to carry out joint research and development.
1.8 Project approach
The project has been carried out in 3 phases. The 1st phase, the system development phase has been used to design the various systems, to resolve a number of technical and methodological questions, to develop the proto-types and to train the Chinese partners in understanding the backgrounds and potential of the technology. The first phase took about 2 years.
The 2nd phase is for implementation and testing . The prototype monitoring systems were installed at the YRCC premises. Sites for the surface flux measuring systems have been selected and the LAS, radiation, temperature and wind sensors were installed; three on the Qinghai plateau in the upper reach, and one in the Loess plateau area. The flux data measured with these systems have been used to validate the satellite based systems and to optimize their performance. For validation also data from regular weather and flow measuring stations have been used. This phase has also taken at least two years with considerable time overlap with the 1 st and 3 rd phase.
The 3rd phase is the demonstration phase . With a total duration of also 2 years, the system has been run by YRCC in a semi-operational way. Water resources, water level and drought information have been generated operationally. Monthly river flow bulletins have been developed and published. A website has been developed too inform a larger public. The activities carried out in the different phases are briefly described hereafter.
11 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
1.8.1 Development phase
Satellite data receiving system
At the beginning of the project two PC based receiving systems for the geostationary satellite data have been selected and were purchased from the Beijing based company Shinetek. One system has been implemented in Zhengzhou and the second one in Lanzhou. They are both receiving the Chinese geostationary meteorological satellite FengYun-2c, and serve as mutual back-up. FY2 is one of the most operational satellite systems. Its follow-up has already been launched and in case of unexpected failure can fast replace the current satellite. With the two receiving systems visual and thermal infrared images are received every hour. The data are stored on hard disk until being further processing once every day.
EWBMS adaptation
The EWBMS has been adapted to the needs of YRCC. User requirements have been discussed early in the project phase on the basis of a report describing the current methodology. On the basis of this report discussions were held and modifications were agreed on in the interest of the present application of the EWBMS system. The most important user requirements that were agreed for implementation are the following two: 1) Generation of all basic data fields on a daily basis in stead of 10 daily. 10 daily data products are also generated, but they are shifting averages, i.e. each day a 10 daily average is produced on the basis of the last 10 days. 2) Extension of the EWBMS software so as to take care of precipitation at below zero temperatures, the storage of snow during winter, and melting of snow in spring. This in view of the climatic conditions in the upper reaches of the river. The second modification was a considerable effort and required the development of the theoretical framework and a special algorithm.
Drought monitoring system
For the drought monitoring system (DMS) it is proposed to use the Climatic Moisture Index as proposed in the framework of the UN Convention to Combat Desertification
CMI = P / E P (1.1)
Where P is the precipitation, EP the potential evapotranspiration and CMI the Climatic Moisture Index, a parameter which indicates climatic drought. More directly related to the drought conditions at the ground surface is the "Soil Moisture Index" (SMI), which may be defined as
SMI = E / E P (1.2)
Where E denotes the actual evapotranspiration. In both indices the potential evapotranspiration is not one of the basic products. The potential evapotranspiration can be derived in several ways. The "EARS method" estimates the potential evapotranspiration as 0.8 times the net radiation. The factor 0.8 is derived by approximation from the Penman-Monteith equation. A second approach is to estimate the potential evapotranspiration from the satellite observed air temperatures using the Thornthwaite formula. Other drought products may be developed according to the YRCC needs, for example Number of Dry Days, etc.
12 Chapter 1 - Introduction
Water Resources Forecasting System (WRFS) for the upper reach
The WRFS has been developed as a grid-based modelling system, which can assimilate the precipitation, snow melt and evaporation data from the EWBMS at the time-scale of 1 day, without the need to aggregate on the spatial scale. In this phase, the distributed water balance model has been developed and the components have been fit into the structure arising from the geometrical terrain arrangement. The initial model parameterisation of other hydrological characteristics was carried out. The data transfer from the EWBMS to the water resources forecasting model component was developed as separate module, which enhances the capability of independent incremental upgrading during later stages of the project. During definition of the required key model parameters to be incorporated, and during construction of the model, extensive use of information and experience from YRCC has been incorporated through intense collaboration. Similarly, data requirements and procedures for calibration and validation of the model components were jointly defined. The possibilities and limitations for calibration and validation, however, depend to a large degree on existing historical data records. Part of the procedure consisted of a sensitivity assessment for the individual parameters.
Flood Forecasting System (HWFS) for the lower Weihe River
A flood forecasting systems has been developed, initially by extending the presently available flow routing tools used by the YRCC. This requires establishing a coupling with a grid-based model structure, to be built as a separate module, which is capable to the EWBMS generated data in a similar fashion as for the WRFS component. Starting from pilot sub-catchment, progressive improvements have been introduced and tested, finally covering the entire Weihe catchment. Similarly to the development of the WRFS, procedures, data requirements and selection of data records for the calibration and validation phase have been established and carried out. YRCC participated actively in the development of the flood forecasting system and contributed essential information and knowledge of the area and its hydrological behaviour.
Training
A detailed training program including satellite reception, energy and water balance processing, drought monitoring, water resources forecasting and high water level forecasting has been prepared during the development phase of the project.
1.8.2 Implementation and testing phase
EWBMS and drought monitoring system
At the beginning of the second year the Energy and Water Balance Monitoring system and the Drought Monitoring System were implemented at the YRCC premises in Zhengzhou, in addition to the satellite data receiving system. Also rain gauge data reception ran smoothly by that time. The EWBMS system was made operational in such a way that it could produce the basic products, such as precipitation, melt water, evapotranspiration, radiation and air temperature on a daily basis. Dekadal or monthly drought monitoring products (CMS, DMS) could be generated as well. YRCC operators were trained in using the system and in generating the products.
13 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The first part of this phase was used to test and validate the quality of the distributed data products. For testing of the data products the following methods were used.
1) Precipitation data will were tested by means of the Jack-knifing method.
2) Radiation data were tested by comparison with radiometer data; mainly for the net radiometers installed together with the LAS systems (see point 3)
3) Sensible heat flux data were tested by means of Large Aperture Scintillometer (LAS) systems. 4 systems have been installed, 3 in the Upper Reach and 1 in the Loess plateau area, two different climatic regions of the Yellow river basin.
4) Actual evapotranspiration can be considered validated if the radiation and sensible heat flux are validated and calibrated (see 2 and 3).
5) 1.5-meter air temperatures derived from satellite will be tested with measured air temperatures available from existing weather stations.
6) In addition the water balance (rainfall minus actual evapotranspiration) has been tested by comparing this quantity for a period of one or more years with the discharge measured at the basin outlet.
By comparison of the data derived from the satellite and those measured on the ground the EWBMS have been tested. Deviations were studied and the system was improved wherever deficiencies in the models could be identified.
Having calibrated the basic satellite data products, also the drought monitoring indices (CMI and SMI), which are ratio's of the previous fluxes, can be considered reliable. At the end of this phase a Validation and Calibration Report has been generated.
Water Resources and High Water Level Forecasting Systems (WRFS and HWFS)
During this phase, the WRFS and HWFS have been implemented, tested and assessed at YRCC. A first appraisal of their functioning was done on the basis of comparing test results against hydrological response observations and measurements from field studies and measuring sites, i.e. validation. Where necessary, improvements were made to the individual modules and alternative solutions to certain were carried through. The HWFS component was extended to comprise the full area of the sub- basin. Training was conducted in order to familiarise YRCC staff with the systems operation.
1.8.3 Demonstration phase
During the demonstration phase the EWBMS, the drought monitoring system (DMS), the Water Resources Forecasting System (WRFS) for the upper river, and the Flood Forecasting System (HWFS), for the Weihe River, have been run in an operational way. Products were generated and provided to end-users. A satellite monitoring bulletin and a website were developed for this purpose. At the end of the demonstration phase the project has been assessed by means of a validation workshop.
14 Chapter 1 - Introduction
EWBMS and Drought Monitoring System (DMS)
The EWBMS and drought monitoring system were demonstrated operationally during the last two years of the project. A drought monitoring and early warning bulletin for the Yellow River basin was developed and will be published regularly. Drought related products will be generated and published in this bulletin, for example: rainfall and the soil moisture index (= relative evapotranspiration), sub-catchment water balances, etc. Other drought products, such as the cumulative number of dry days, may be added.
Water Resources and High Water Level Forecasting Systems (WRFS and HWFS)
Based on monitoring of the results and evaluation of the performance of both systems since the implementation and testing phase, the systems implementation was finalized and the documentation completed. Particular attention will be paid to ensure that the improvements arising from the systems performance assessment, in combination with those from initial calibration and validation are implemented. Training of system operators at the YRCC Hydrological Bureau continued, so as to assure that they master the technology well and the products become established tools in support of operations duly carried out by the YRCC.
1.9 Project impact
Water budget monitoring in the upper reach and forecasting of the rivers base flow leads to earlier knowledge of the amount of water that is available for use in the middle reach of the river and will enable a more rational and sustainable water allocation to users. Lack of information has so far prohibited this. The main water intake along the river is for irrigation and it has been noted that water quota for irrigation have been far too high. This has adversely affected users in the middle and lower reaches of the river. For an equitable water distribution the system provides early information on river run-off and drought, i.e. actual water needs. This will allow water managers sufficient time to prepare rational water distribution plans based on actual water supply and needs.
Water resources forecasting
The system is expected to bring an overall increase of water use efficiency, which is conservatively estimated at 1%. Given a total water use for irrigation in the Yellow river basin of 27 billion m 3 per year, and a related average economic value of water of 1.2 RMB/m 3, the estimated yearly social economic value that can be attributed to this part of the system may then be estimated at 0.01*1.2*27.10 9 RMB/year or 324 million RMB/year.
Flood forecasting
The flood forecasting system implemented for the Weihe tributary provides run-off and related river level forecasts on a daily basis. These forecasts are based on satellite based daily distributed data fields of precipitation and evapotranspiration which cover the whole sub-catchment. The system will help to provide improved predictions of high water levels and to timely alert authorities and the population to take flood protection measures.
15 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
According to the Worldbank (2001), flood losses in the Yellow River basin increased from 1500 RMB/ha in the 1950’s to 9000 RMB in the 1990’s. The total flood prone area is about 118000 km 2. Based on these figures the potential costs of a complete flooding could nowadays be estimated at 120 billion RMB. If such a flood occurs once every century and by improved high water forecasting 10% of the damage could be prevented, than the social economic value of the system would be 120 million RMB per year.
Drought monitoring
Another important functionality of the EWBMS is drought and desertification monitoring. Such information has significant meaning in relation to food security and food trade, ass well as in relation to land degradation. The drought monitoring system may be used to monitor and forecast the production of crop and grass lands. Crop yield forecasts help to optimize food market performance and reduce price fluctuations. According to Hayami and Peterson (1972) improved market monitoring and reduced price fluctuations increase population welfare. They also allow realizing better prices in food trade. Finally the system can help to reduce desertification damage. The total benefits of drought monitoring and early warning for China would be about 3 billion RMB per year, or about 300 million RMD in the Yellow river area.
Summarizing it may be concluded that the impact and social economic returns of the project can be very high, in the order of 700 million RMB per year.
1.10 References
Hayami, Y and Peterson, W. (1972) “Social Returns to Public Information Services: Statistical Reporting of US Farm Commodities”, The American Economic Review, Vol. 62, 1972, pp 119-130.
Worldbank (2001) “Agenda for the Water Sector Strategy for North China”, Worldbank report 22040-CHA, April 2, 2001.
Unknown (1997) “An Overview of Chinese Water Issues”, China Environment Series, 10 September 1997, pp 46-48.
Changming Liu (2000) “Water Resources Development in the First Half of the 21 th Century in China”, 2 nd World Water Forum, China Water Session, pp 1-16), March 2000.
16 Chapter 2 – The Yellow River Target Areas
2 THE YELLOW RIVER TARGET AREAS
2.1 The source area of the Yellow River
The upper reach of the Yellow River covers the area above Tangnaihai hydrological station on the main stream. The drainage area of the upper reach covers 121972 km 2, located between 95°00 ′ and 103°30 ′ E and from 32°19 to 36°08 ′ N. The river length to the upstream source is 1552.4 km. With an altitude over 3000 m, this area has a low air density, with oxygen content between 0.166 and 0.186 g/m 3. There are many mountains in the source area, such as Bayankala, Animaqin and Min Mountain, and there are scattered plains, rivers, basins and hills. The mountain summits over 4000 m are bare, while the lower slopes are covered with grassland. The glacier of the Animaqin covers an area of 191.95 km 2. Its melting water makes up 1.0% of the runoff at Tangnaihai.
Temperature and Ice
The annual temperature is below zero and only July and August are frost-free. The difference between yearly minimum and maximum temperature amounts to 75 oC. The recorded lowest temperature is –53 oC. The warmest period generally falls in August, the coldest in January. The following table provide an overview of the ice situation in the upper reach
Table 2.1: Characteristics of river ice in the YR source area Reach Ice flooded from Fully frozen Melting from Above Jimai October D3 January March D3 Mengtang-Maqu November D3 December March D3 Jungong-Tangnaihai November D1-2 March D1-2 D=dekad, period of 10 days
Figure 2.1: The project focal areas: the Yellow River Upper Reach and the lower reach of the Weihe River
17 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Rainfall and Evaporation
The average annual rainfall in the river source area is 474.6 mm. The rainfall in the area of Hongyuan, Ruoergai ，Maqu and Jiuzhi is in the range of 500-800 mm, while rainfall, while rainfall in the reaches from Maqu to Jungong is much lower: 250-400 mm. 91% of the rain falls from May to October. Incidental rain falls between October and May. Above Maduo rain is scattered throughout the year. Pan evaporation is between 1200 and 2300 mm/yr and declines from north to south.
River runoff
The average annual rainfall on the area amounts to 69.9 billion m 3. The average runoff is 20.5 billion m 3, corresponding to 168 mm water depth, which on average is 35% of the rainfall. There are usually two peaks in the run-off: in July and September. Most runoff occurs between May and October (78.5%).
2.1.1 Hydrological observations in the source area
Figure 2.2 and table 2.2 and 2.3 provide an overview of the hydrological observations network in the source area of the Yellow River.
The Development of Automatic Observation and Reporting System
The project Automatic Observation and Reporting System for the River Source Areas of the Yellow River (here in after referred to as the River Source Project) is composed of four sub-systems: data collection system, data transmission system, monitoring and information centre, and water resources monitoring system. In consideration of the hydrological situation in the upper reaches of the Yellow River, the development of the system is mainly based on the construction of the data
Figure 2.2: Hydrological station network in the source area
18 Chapter 2 – The Yellow River Target Areas collection platform. The system will function only with people in charge, but without requiring their permanent presence. The data will be collected and processed in an automatic, digital, distance-observed and remote-controlled way, supported by regular inspection visits. The River Source Project has completed the rainfall collection sub-project and the construction of the sub-centre. The other projects are scheduled for construction. Considerable progress is expected to be made in the observation and reporting techniques and in modernization of the data collection.
Distribution of the stations
There are 10 hydrological stations or water level stations in the river source areas, of which 14 are all administered by the Xining Hydrological Reconnaissance Bureau of the Yellow River Conservancy Commission (YRCC). The stations are located at: Huangheyan, Jimai, Mengtang, Maqu, Jungong, and Tangnaihai station, on the mainstream, while the following stations are situated on the branches: Hanghe on the Requ, Jiuzhi on the Shakequ, Tangke on the Baihe river and Dashui on Heihe river. In addition, there are 5 entrusted rainfall stations in: Awancang, Longriba, Waqie, Maiwa and Dongqinggou. See also table 2.2.
Items of Hydrological Observation
The stations are responsible for the observation and reporting of the following items:
- Rainfall and Evaporation : The rainfall observation instruments include the manual rainfall device and the siphon rainfall device; the evaporation observation instruments include E601 Evaporator and 20 cm Diameter Evaporator. Recording and processing of the data is manual.
- Water level : Three instruments are used: vertical rule, floater stage, and ultrasonic no-touching stage. Vertical rules are used in all stations. Some stations are equipped with HW-1000 ultrasonic devices, and only a few use the floater device. Water level recording is automatic in 4 stations: Tangnaihai, Xunhua, Guide and Xiangtang. In the other 10 stations data are recorded manually. Computation and processing is also manually.
- Discharge : The major way of discharge observation is by continuous measurement. Only a few stations are measured discontinuously or at regular intervals. The tools used include observation boat, running speed cableway, hanging box cableway and buoy projector. Boat observation is used at Huangheyan, Jimai, Mengtang, Maqu, Jungong, and Tangnaihai. In addition at Tangnaihai, the half-automatic running speed cableway is used to measure low and high flows. Bridge observation is adopted in Huanghe station. Rubber boats are used in Jiuzhi,Tangke and Dashui. The stations at Maqu and Tangnaihai are equipped with a buoy projector to measure flood. However, most of them were built already in the 1960s and 1970s, and the trestles and cables have expired. The same data can be collected by buoy projectors. The collection, analysis, computation, and processing of the data involved is mainly done manually.
- Sediment. Sampling of silt is done manually with the help of a horizontal sampling device. The processing and analysis of the sample sediment, and the related data processing is done by hand.
19 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Table 2.2 Information of the hydrological station network in the source area No. River Station Station type Esta- Coordinates Drainage Alti- Observation blished Long. Lat. area tude Item (yr.m) （km 2） (m) 1 Zhaling Lake Zhaling Lake water level planned 97°30 ′ 34°51 ′ 17728 water level 2 Eling Lake Eling Lake water level planned 97°45 ′ 35°05 ′ 18428 Water level 3 Yellow River Huang- hydrology 1955.6 98°10 ′ 34°53 ′ 20930 4215 Water level, heyan volume of flow, silt, evaporation 4 Yellow River Jimai hydrology 1958.6 99°39 ′ 33°46 ′ 45019 3948 Water level, volume of flow, silt, evaporation 5 Yellow River Mengtang hydrology 1987.8 101°03 ′ 33°46 ′ 59655 3636 Water level, volume of flow, rainfall 6 Yellow River Maqu hydrology 1959.1 102°05 ′ 33°58 ′ 86048 3400 Water level, volume of flow, silt, rainfall, evaporation 7 Yellow River Jungong hydrology 1979.8 100°39 ′ 34°42 ′ 98414 3079 Water level, volume of flow, silt, rainfall, evaporation 8 Yellow River Tang-naihai hydrology 1955.8 100°39 ′ 35°30 ′ 121972 2665 Water level, volume of flow, silt, rainfall 9 Yellow River Duide hydrology 1954.1 101°24 ′ 36°02 ′ 133650 2201 Water level, volume of flow, silt, rainfall, evaporation 10 Yellow River Xunhua hydrology 1945.10 102°30 ′ 35°50 ′ 145459 1850 Water level, volume of flow, silt, rainfall 11 Requ Huanghe hydrology 1978.8 98°16 ′ 34°36 ′ 6446 4200 Water level, volume of flow, rainfall 12 Shakequ Jiuzhi hydrology 1978.9 101°30 ′ 33°26 ′ 1248 3560 Water level, volume of flow, rainfall 13 Baihe River Tangke hydrology 1978.9 102°28 ′ 33°25 ′ 5374 3410 Water level, volume of flow, silt, rainfall, evaporation 14 Heihe River Dashui hydrology 1984.6 102°16 ′ 33°59 ′ 7421 3400 Water level, volume of flow, rainfall 15 Datong River Xiangtang hydrology 1940.1 102°50 ′ 36°21 ′ 15126 1776 Water level, volume of flow, silt 16 Huang-shui Minhe hydrology 1940.1 102°48 ′ 36°20 ′ 15432 1752 Water level, River volume of flow, silt, rainfall, evaporation 17 Saierqu Awangcang rainfall 1977 101°42 ′ 33°47 ′ Rainfall 18 Baihe River Longriba rainfall 1976 102°22 ′ 32°27 ′ Rainfall 19 Baihe River Waqie rainfall 1976 102°37 33°08 ′ Rainfall 20 Black River Maiwa rainfall 1977 102°54 ′ 32°03 ′ Rainfall 21 Qiemuqu Dongqinggo rainfall 1977 99°58 ′ 34°32 ′ Rainfall u 22 Gequ Maqin rainfall planned 100°15 ′ 34°29 ′ Rainfall 23 Shakequ Jiuzhi rainfall planned 101°29 ′ 33°25 ′ Rainfall 24 Baihe River Hongyuan rainfall planned 102°34 ′ 32°48 ′ Rainfall 25 Heihe River Ruoergai rainfall planned 102°58 ′ 33°35 ′ Rainfall 26 Zequ Zeku rainfall planned 101°28 ′ 35°02 ′ Rainfall 27 Gande Xikequ rainfall planned 99°54 ′ 33°58 ′ Rainfall 28 Henan Zequ rainfall planned 101°35 ′ 34°45 ′ Rainfall Rem 3 additional tour surveying sections are established in Ruoergai on the mainstream, Jimai and Jiaqu on the branches.
20 Chapter 2 – The Yellow River Target Areas
Table 2.3: Data collection in the river source area No River Station Instruments and manner of observation Water level Discharge Sediment Sediment load Rainfall Evaporation concentration 1 Yellow Huang- Manual vertical Conttin. in flood , Manual , horiz. 1year every 4 Purchase from local Purchase from local River heyan rule discont. in non-flood sampling device years met.eo station meteo station season , hanging boat 2 Yellow Jimai Manual vertical Conttin. in flood , Manual , horiz. 1year every 4 Manual., horiz. Manual River rule discont. in non-flood sampling device years sampling device 20cm evaporator season , hanging boat 3 Yellow Mengtang Manual vertical Conttin. in flood , N/A N/A Manual , rainfall N/A River rule discont. in non-flood device season , hanging boat 4 Yellow Maqu Manual vertical Continuous., hanging Manual , horiz. 1 year every 4 Man. & recording Manual River rule boat, buoy projector sampling device years siphon rainfall and 20cm evaporator rainfall device 5 Yellow Jungong Manual vertical 1 year discont. every 4 Manual , horiz. 1 year every 4 Man. & recording Manual River rule years, hanging boat sampling device years siphon rainfall and 20cm evaporator rainfall device 6 Yellow Tangnaihai Record. water Contin. , cableway Manual , horiz. Boat., horizontal Man. & recording N/A River level, floater with streaml. weight, sampling device sampling device siphon rainfall and fluviograph, hanging boat, buoy rainfall device vertical rule projector 7 Yellow Guide Record. water Contin. , cableway Manual , horiz. Boat., horizontal Man. & recording Manual, River level, floater with streaml. weight, sampling device sampling device siphon rainfall and E601 evaporator, fluviograph, hanging boat, buoy rainfall device 20CM evaporator vertical rule projector 8 Yellow Xunhua Record. water Contin. , cableway Manual , horiz. Boat., horizontal Man. & recording N/A River level, floater with streaml. weight, sampling device sampling device siphon rainfall and fluviograph, hanging boat, buoy rainfall device vertical rule projector 9 Requ Yellow Manual, vertical Disc. bridge observ. N/A N/A Manual, N/A River rule rainfall device 10 Shakequ Jiuzhi Manual, vertical 1 year discont.. every 4 N/A N/A N/A N/A rule years, rubber boat 11 Baihe Tangke Manual, vertical Regular in flood, Manual ， N/A Manual, Manual, River rule irregular in non-flood horizontal rainfall device 20CM evaporator season, rubber boat sampling device 12 Heihe Dashui Manual, vertical 1year observ, every N/A N/A Manual, N/A River rule 4years, rubber boat rainfall device 13 Datong Xiangtang Record. water Regular, cableway Manual, horiz. Boat , horiz. N/A N/A River level, floater with streamlined sampling device sampling device fluviograph, weight, hanging boat, vertical rule buoy projector 15 Saierqu Awancang Manual observ. rainfall device 16 Baihe Longriba Manual observ. River rainfall device 17 Baihe Waqie Manual observ. River rainfall device 18 Heihe Maiwa Manual observ. River rainfall device 19 Qiermuqu Dongqing- Manual observ. gou rainfall device
21 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
2.1.2 Information acquisition and transmission
At present, there are 3 levels of real time water regime data collection: the hydrological stations, the data collection sub-centres and the data collection centre. The sub-centre of the upper Yellow River is located in the Upper Yellow River Hydrology and Water Resources Bureau of YRCC (Lanzhou, Gansu province). The data collection centre is in Hydrology Bureau of YRCC (Zhengzhou, Henan province).
All reporting stations are communicating by PSTN (Public Switched Telephone Network), GSM (Global System for Mobile Communications) or satellite. Most stations use PSTN and GSM, and part of them use GSM and satellite.
Rain gauges have been realized that automatically collect and transmit the rainfall information, while hydrological stations automatically transmit the discharge information after putting them manually into the computer. More than 90% of data can be transmitted to Zhengzhou within 20 minutes, and more than 95% of the data within 30 minutes. The sub-centre of the Upper Yellow River is in charge of the real time water information transmission. The sub-centre communicates with the centre in Zhengzhou through SDH (Synchronous Digital Hierarchy) with 2Mbaud rate.
The centre in Zhengzhou is in charge of the real time water information reception, transmission and decoding, and the storage of these data into the real time water information database.
2.1.3 Hydrological forecasting
So far, the hydrological forecasting tasks in the source area of the Yellow River mainly concern the middle and long-term runoff forecasting and flood forecasting, and which consists particularly of the following: - Before the end of April, the long term monthly runoff forecast at Tangnaihai for the period May-October should be made. - During the flood season (May-October), every first dekad the updated forecast of runoff for the next month should be made. - During the flood season, short-term flood forecasts at Tangnaihai should be made when heavy and large-scale rainfall may cause flood in the source area. - At the end of the flood season, a long-term runoff forecast for the non-flood season (November-June) should be made.
22 Chapter 2 – The Yellow River Target Areas
2.2 The lower Weihe River
The Weihe is the largest tributary of the Yellow River. It originates from the Niaoshu Mountain in Weiyuan county in Gansu province, flows through Gansu, Ningxia and Shannxi provinces and flows into the Yellow River in Tongguan county in Shanxi Province. The total length of the main flow is 818 km and the basin area is 134800 km2. The reach from Xianyang to the outlet is the lower Weihe River. The length of this part is 211 km. The river bed slope is around 0.68-0.15‰. Down of Lintong the river is most winding. The Weihe River water system is dissymmetrically developed. On its left bank, the tributaries are long, with larger catchments, and carry more sediment. But on the right bank, the tributaries are short and steep, and carry more water and less sediment.
According to physical and geographic conditions, the lower Weihe basin can be divided in four types: soil-tor, loess hill, loess terrace and plain region. The soil-tor region is found in the upper and middle reaches of the south tributary. It has steep slopes, abundant precipitation, dense vegetation, little water and soil loss, high runoff coefficient and easily generates runoff. The loess hill region is mostly situated on the north side of the upper and middle Shichuan River. There is little vegetation, serious water and soil loss and not easily generates runoff .The loess terrace is mainly found on the south side of the lower Shichuan river and middle reaches. Finally the plain region is situated in the vicinity of Lower Weihe River. The area is flat with fertile soil and has a lower runoff coefficient.
There are many tributaries in lower Weihe. The tributaries on the north side mostly originate from the loess hill and plateau, such as the Shichuan, Jinghe and Beiluohe rivers. They have a large catchment, slow fall, high sediment load and are main sources of sediment. The Jinghe is the largest tributary. With a length of 455.1 km and a basin area of 45400 km 2, it makes up 33.7% of the Weihe river basin. The Beiluohe is the second largest tributary. Its length is 680 km 2 and its basin area 26900 km 2, thus covering 20% of the Weihe basin. There are a number of smaller rivers on the south side: Fenghe, Zaohe, Chanbahe, Dayuhe, Xihe, Linghe, Youhe, Chishuihe, Yuxianhe, Shidihe, which originate from the Qinling Mountain. They are short, have rapid flow, great runoff and low sediment concentration. They represent major storm flood sources in lower Weihe River.
The lower Weihe River belongs to the warm temperate zone and semi-arid and humid climate. The annual mean temperature is 6-13 oC, the annual mean precipitation 500- 800 mm and the annual mean pan evaporation is 700-1000 mm. The rainfall in southern mountainous region is more than that in the valley and northern part of the basin. Rain storms occur from July to October and bring 50-60% of the yearly precipitation. Maximum precipitation mostly occurs in July and August, as a result of strong and short-duration storms. Autumn rainfall occurs from September to October, and may last 5-10 days or longer.
Hydrological Characteristics
Runoff comes mostly from the main stream and south tributaries. The mean yearly runoff is 7.96 Bm3, 7.26 Bm3 at Huaxian, 1.37 Bm3 at Zhangjiashan of the Jinghe River and 0.696 Bm3 at Zhuangtou on the Beiluohe River, respectively. The inter- annual variation amounts to a factor 10. The maximum runoff of 20.6 Bm3 occurred in 1964, while the minimum runoff, only 2.1 Bm3, took place in 1995. The yearly runoff is also not equally distributed: 60% occurs during the flood season.
23 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The multi-annual average sediment runoff is 444 Mton in the lower Weihe River, 359Mton at Huaxian, 246 Mton Zhangjiashan and 85 Mton in Zhuangtou. The sediment is mainly from north tributaries, especially the Jinghe and Beiluohe rivers. They contribute 55.4% and 19.1%, respectively, to the total of the Weihe River. Most sediment transport takes place during the flood season.
The flood in the lower Weihe basin comes mainly from upper Xianyang, Jinghe River and the south tributaries. Discharge and sediment concentration are high, and rise and drop steeply. The flood is of the flat type, with high runoff volume in autumn.
`2.2.1 Hydrological observations
The observation stations belong to Hydrology bureau of YRCC, Shannxi Hydrology Bureau (SHB) and Shannxi Sanmenxia Bureau (SSB). There are 12 reporting hydrological stations, 3 water level stations, 6 reporting rain gauge stations. Except the reporting rain gauging stations, there are 55 basic rain gauge stations, of which 33 in south of the Weihe River, and 1 basic hydrological station at Liulin in the Shishanchuan River. They are not reporting. They belong to the Shannxi Hydrology Bureau. See figure 2.3 and table 2.4 and 2.5.
A tele-metering system of water level and rainfall is built since 1998 by the Shannxi Sanmenxia Bureau. The system includes 3 water level stations (Gengzhen, Jiaokou, Weinan), and 4 rain gauge stations (Quancao, Dianzi, Wulongshan, Shanghuichi). Furthermore 44 silting cross-sections from Xianyang to the outlet of the Weihe and 22 from Zhuangtou to the outlet of the Beiluohe, have been set up by the Shannxi Sanmenxia Reservoir Administrative Bureau for measuring the channel sedimentation changes caused by the Sanmenxia Reservoir.
Figure 2.3: Reporting station network in the lower Weihe river.
24 Chapter 2 – The Yellow River Target Areas
The hydrological observations in the lower Weihe include 9 items including: precipitation, evaporation, water level, discharge, sediment concentration, sediment delivery rate, particles size analysis, water temperature and water quality. The same items except water temperature but including ice regime are observed at Xianyang and Huaxian.
Precipitation is observed by using solid-storage and tipping-bucket rainfall recording every 2 hours, both at Xianyang and Huaxian station. At Xianyang evaporation data are obtained manually from the local meteorological station. The station at Huaxian has a vertical gauge, an HW-1000 ultrasonic recorder to measure the water level. Huayin station has the vertical gauge. Xianyang station has not only the vertical gauge and HW-1000 ultrasonic recorder, but also the wire weight gauge to observe water level. The measurement facilities at Xianyang station consist of a double permanent cable with ferroconcrete brackets, which serve as electrical current measuring cables. The measuring facilities at Huaxian station consist of a single cable with free-standing steel bracket lifting ship, one suspending ship, one hydrological capstan, two electric boats. Xianyang and Huaxian stations measure the sediment concentration using a horizontal-bottle sediment sampler. The sediment particle size is analysed at both stations.
2.2.2 Information acquisition and transmission
The information sub-centre for the Weihe River is located at the Sanmenxia Reservoir Hydrology and Water Resources Bureau of YRCC (Sanmenxia City, Heman Provence). The centre is again the Hydrology Bureau of YRCC in Zhengzhou. All reporting stations are communicating by PSTN (Public Switched Telephone Network), GSM (Global System for Mobile Communications) or satellite. Most of them use PSTN and GSM, and part of them use GSM and satellite.
Rain gauges have been realized that automatically collect and transmit rainfall information, while hydrological stations automatically transmit discharge after manually putting the information into a computer. More than 90% of data can be transmitted to Zhengzhon within 20 minutes, and more than 95% within half an hour. The sub-centre of Sanmenxia is in charge of real time informatiom transmission. The sub-centre is communicating with the centre in Zhengzhou by SDH (Synchronous Digital Hierarchy) at 2Mbaud rate. The centre in Zhengzhou is in charge of real time water informatiom reception, transmission and decoding, and storage of the information into the real time water information database.
2.2.3 Flood forecasting
Huaxian is the forecasting station in the lower Weihe. The forecasting items include the discharge hydrograph, in particular the flood peak and its time of occurrence. The peak is usually over 2000 m3/s. The forecast is based on the correlation between the peaks at Lintong and Huaxian station. The parameters in the scheme are the momentaneous discharge at Huaxian and the coefficient of excess in Lintong. In addition the correlations of the flood peak at Xianyang and Zhangjiashan with that at Huaxian are developed. The routing times of each are based on the discharges at Xianyang and Zhangjiashan separately.
25 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Table 2.4: Reporting stations in the lower Weihe River River name Station name Station type East Long. North Lat. Owner Rem. Weihe Xianyang Hydrology 108 42 34 19 YRCC Weihe Lintong Hydrology 109 12 34 26 SSB Weihe Huaxian Hydrology 109 46 34 35 YRCC Weihe Huayin Water level YRCC Jinghe Zhangjiashan Hydrology 108 08 34 38 SHB Beiluohe Zhuangtou Hydrology 109 50 35 03 SHB Beiluohe Nanronghua Hydrology 109 53 34 46 SSB Beiluohe Chaoyi Water level 109 52 34 46 SSB Chanhe Qinduzhen Hydrology 108 46 34 06 SHB South Juhe Gaoqiao Hydrology 108 49 34 06 SHB South Dayuhe Dayu Hydrology 109 07 34 00 SHB South Bahe Luolicun Hydrology 109 22 34 09 SHB South Bahe Maduwang Hydrology 109 09 34 14 SHB South Qishuihe Yaoxian Hydrology 108 59 34 55 SHB North Yeyuhe Chunhua Hydrology 108 35 34 47 SHB North Wangchuanhe Gepaizhen Rain gauging 109 30 33 55 SHB South Linghe Tielu Rain gauging 109 27 34 24 SHB South Shichuanhe Fuping Rain gauging 109 10 34 45 SHB North Shichuanhe Meiyuan Rain gauging 109 21 34 54 SHB North Shichuanhe Yaoqu Rain gauging 108 53 35 12 SHB North Weihe Weinan Meteorology 109 30 34 30 SHB
The flood peak correlation forecasting is based on the antecedent rainfall, local rainfall and the coefficient of excess. The hydraulic characteristics can be included in the scheme, which is easy in use. But the channel siltation and different hydraulic characteristics of the main flow and overflow floods are not taken into account.
The river channel is wide and shallow in the lower Weihe. The cross section is compound. Normal flow is through the main channel, while the high flood is overflowing. Considering the different flood characteristics and the routing rules through the main channel and overbank, a Muskingum layered routing scheme has been developed. The layered outputs are calculated with the different parameters of the main channel and the overbank, and subsequently added. The scheme parameters can be optimized when overflow occurs in order to forecast the flood peak, the time it occurs and its progression. Local inflow, however, is not considered. In fact, the flood peak correlation and the Muskingum layered routing scheme are combined. The forecast is also optimized on the basis of real-time rain and flow information so as to improve the forecasting accuracy.
Currently also the national flood forecasting system (NFFS) is being studied and tested for flood forecasting in the lower Weihe River, and will become operational soon.
26 Chapter 2 – The Yellow River Target Areas
Table 2.5: Basic stations in the lower Weihe River No River name Station name Station type East long. North lat. Remark 1 Fenghe Jiwozi Rain gauging 108 50 33 52 south 2 Fenghe Qinggangshu Rain gauging 108 51 33 55 south 3 Jianhe Bianzigou Rain gauging 108 54 34 04 south 4 Taipinghe Meichang Rain gauging 108 39 33 56 south 5 Taipinghe Taipingyu Rain gauging 108 43 34 00 south 6 Gaoguanyu Xingjialing Rain gauging 108 44 33 53 south 7 Shibianyu Xianrencha Rain gauging 108 56 33 56 south 8 Shibianyu Shibianyu Rain gauging 108 57 33 59 south 9 Xiangzihe Wangqu Rain gauging 108 58 34 05 south 10 Dayuhe Banmiaozi Rain gauging 109 08 33 57 south 11 Dayuhe Xinguansi Rain gauging 109 07 33 59 south 12 Fenghe Doumen Rain gauging 108 45 34 14 south 13 Weihe Mazhuang Rain gauging 108 39 34 26 south 14 Weihe Yaodian Rain gauging 108 51 34 24 south 15 Weihe Bayuan Rain gauging 109 41 34 09 south 16 Weihe Mujiayan Rain gauging 109 32 34 11 south 17 Wukonghe Muhuguan Rain gauging 109 30 34 03 south 18 Qinghe Lanqiao Rain gauging 109 27 34 06 south 19 Wangchuanhe Yuchuan Rain gauging 109 23 33 58 south 20 Wangchuanhe Longwangmiao Rain gauging 109 20 33 54 south 21 Wangchuanhe Wangchuan Rain gauging 109 22 34 05 south 22 Wangchuanhe Gepaizhen Rain gauging 109 30 33 55 south 23 Bahe Pantaowan Rain gauging 109 14 34 13 south 24 Bahe Xiqu Rain gauging 109 05 34 18 south 25 Tangyuhe Gaobaozhen Rain gauging 109 12 34 02 south 26 Chanhe Mingdu Rain gauging 109 06 34 08 south 27 Weihe Tongyuanfang Rain gauging 109 03 34 33 south 28 Yuchuanhe Yuchuan Rain gauging 109 19 34 21 south 29 Donggou Qingcaoping Rain gauging 108 48 35 17 north 30 Juhe Miaowan Rain gauging 108 46 35 10 north 31 Qishuihe Jinsuoguan Rain gauging 109 03 35 13 north 32 Wujiahe Yunmeng Rain gauging 109 11 35 13 north 33 Qishuihe Chenlu Rain gauging 109 09 35 02 north 34 Qishuihe Huangbao Rain gauging 109 02 35 01 north 35 Qishuihe Shizhu Rain gauging 108 58 35 04 north 36 Zhaoshihe Potou Rain gauging 108 53 34 52 north 37 Shichuanhe Caocunzhen Rain gauging 109 12 34 54 north 38 Shichuanhe Guanshan Rain gauging 109 23 34 42 north 39 Yeyuhe Anziwa Rain gauging 108 35 35 01 north 40 Yeyuhe Qinhe Rain gauging 108 36 34 55 north 41 Dongyuhe Nancun Rain gauging 108 38 34 53 north 42 Yeyuhe Bujiacun Rain gauging 108 31 34 56 north 43 Yeyuhe Kouzhen Rain gauging 108 42 34 42 north 44 Yeyuhe yunyang Rain gauging 108 48 34 38 north 45 Qingyuhe Xiaoqiu Rain gauging 108 47 34 55 north 46 Qingyuhe Fangli Rain gauging 108 44 34 50 north 47 Qingyuhe Fanjiahe Rain gauging 108 54 34 44 north 48 Qingyuhe Sanyuan Rain gauging 108 56 34 37 north 49 Qingyuhe Duli Rain gauging 109 04 34 38 north 50 Linghe Jinshanzhen Rain gauging 109 23 34 17 south 51 Qiuhe Houzizhen Rain gauging 109 31 34 16 south 52 Qiuhe Chongning Rain gauging 109 35 34 23 south 53 Chishuihe Longjiawan Rain gauging 109 41 34 25 south 54 Weihe Gushi Rain gauging 109 35 34 38 south 55 Shidihe Huichi Rain gauging 109 48 34 25 south 56 Juhe Liulin hydrological 108 49 35 03 north
27 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
28 Chapter 3 – Energy and Water Balance Monitoring System
3 ENERGY AND WATER BALANCE MONITORING SYSTEM
Meteorological satellites have been used mainly for weather analysis and forecasting. Since the 1980’s new applications, related to the energy and water balance of the earth surface, have emerged. Surface reflectance, measured in the visible wavelength band (VIS) enables the estimation of the amount of solar energy that is absorbed by the ground. Surface temperatures, measured in the thermal infrared band (TIR) enable the assessment of the partitioning of this absorbed energy between sensible and latent heat, the latter representing the evapotranspiration of water. Geostationary meteorological satellites provide thermal infrared and visible data at 3 or 5 km resolution. Polar orbiting meteorological satellites may also be used to measure planetary temperatures. But, the lower repeat coverage makes them less suitable for cloud and rainfall monitoring. The time of data capture, the large scan angles and the variable imaging geometry makes them also less valuable for energy balance monitoring
In figure 3.1 the overview of the Energy and Water Balance System (EWBMS), as used in this project, is shown. Images from the geostationary meteorological FY2c and GMS satellites are received hourly. Cloud top level frequencies or "cloud durations" are determined. From the hourly full image data, composites are prepared which represent local noon and local midnight VIS and TIR values. The extracted data are then processed to quantitative, spatially continuous image maps of rainfall, radiation, sensible heat flux, temperatures and evapotranspiration. Besides the satellite images, hardly any additional input is needed. Only ground point precipitation data, used for generating the rainfall maps, are required. The actual evapotranspiration, rainfall and temperature are the inputs for the drought monitoring model, the freeze/thaw model and the Large Scale Hydrological Model (LSHM). The latter is discussed in detail in chapter 4.
Theoretical backgrounds of the EWBMS and the generation of products is discussed in section 3.1: System Components . To collect validation data, use is made of four Large Aperture Scintillometers (LAS) and some additional instrumentation, which has been established for this purpose at 4 sites in the Yellow River basin. The theory and set-up of these measurements are presented in section 3.2: LAS measurements . In section 3.3 the EWBMS software system is described. All modules: pre-processing, basic product modules, application modules, analysis tools and the EWBMS processing information metadata base are discussed briefly.
FY2c GMS Precipitation ground point data Hydrological Flow & Flood Rainfall model forecasts Cloud Rainfall durations mapping Drought Drought/ Evaporation monitoring Desert. Hourly VIS, TIR Local noon Energy model indices and midnight balance composites processing Freeze/Thaw Snow and Temperature model Snowmelt
Pre-processing Basic products Applications
Figure 3.1: Energy and Water Balance System (EWBMS) overview.
29 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Section 3.4 discusses some results of the Catchment drought monitoring system . Climatologic, hydrologic and agricultural drought indices are calculated and presented in spatially continuous maps, and an analysis of the drought situation in the Yellow River basin is made.
Section 3.5, Evaluation of EWBMS results is dedicated to the validation of the EWBMS basic products. The satellite derived data, precipitation, air temperature, sensible heat flux, and global and net radiation, are compared with data from the LAS systems and other sources. Evapotranspiration is evaluated indirectly, since no such data are measured regularly on the ground. It is assumed that if the validation results of two components of the energy balance: sensible heat flux and net radiation, are satisfactory, also the remaining component, obtained by subtracting the previous two, can be trusted. A final approach to validation is by comparing the net precipitation, i.e. rainfall minus evapotranspiration, with the river discharge at the outlet of the catchment.
30 Chapter 3 – Energy and Water Balance Monitoring System
3.1 System Components
3.1.1 Pre-processing
The pre-processing calculates cloud durations and composes local noon and local midnight images from the hourly VIS and TIR images, obtained with the satellite receiving system (section 3.3.1). The cloud duration mapping uses only the TIR images. The radiance of an observed object in the infrared spectrum, measured in counts, is directly related to the temperature of that object. The object observed from the satellite is the earth’s surface or the top of the highest clouds present. The cloud temperature is proportional to the height above the ground: a typical lapse rate is –6.5 °C per 1000 m. Based on analysis of image histograms, four cloud level classes are discriminated. The thresholds in TIR counts are converted to planetary temperatures. The corresponding temperatures and heights are shown in table 3.1.
Table 3.1: Definition of cloud levels and corresponding temperatures and heights. CLOUD LEVEL TEMPERATURE RANGE HEIGHT RANGE Cold < 226 K > 10.8 km High 226 – 240 K 8.5 – 10.8 km Medium high 240 – 260 K 5.2 – 8.5 km Medium low 260 – 279 K 2.2 – 5.2 km
For every hour a new TIR image is received, for each pixel is determined if there is a cloud present and to which cloud class the cloud belongs. The results are stored in 4 files (one for every cloud class). These files are updated every day, so daily multilevel cloud class frequencies are produced.
The main input data for the energy balance are the local noon and local midnight composites, representing the situation at local noon or midnight in one image. In the pre-processing these images are composed from the hourly VIS and TIR images. For each pixel in the image, the time of local noon or midnight is calculated based on its longitude position, and expressed in GMT (Greenwich Mean Time). Then, the two hourly images closest in GMT-time to the local noon or midnight are selected. Pixel values for the composite images are calculated by interpolating the pixel values of the two selected hourly images. Both VIS and TIR local noon composites are produced, but local midnight composites are only calculated with TIR images. When one of the
Figure 3.2: Example of a thermal infrared (left) and a visual (right) satellite image with sea mask overlay.
31 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
two hourly images, closest in time to the local noon or midnight of a pixel, is not present, no interpolation is carried out. Therefore, if many hourly images are missing, the composite images will show distinct lines. When 3 or more subsequent hourly images, necessary for the creation of the composite images, are missing, no composite image for that noon or midnight day is created.
3.1.2 Precipitation mapping
The estimation of the spatially distributed precipitation is based on two sources of information: (1) point precipitation data from meteorological stations and (2) cloud frequency data from derived from the FY2c meteorological geostationary satellite. Point data are obtained from the WMO Global Telecommunication System (GTS) and additional meteorological station data from the China Meteorological Administration (CMA). The GTS data consist of meteorological measurements from approximately eleven thousand meteorological stations spread over the globe. 95% of these measurements are available within six hours through the GTS. In China, there are about 400 meteorological stations reporting precipitation through the WMO-GTS network. CMA provides data from another 800 national stations.
In the past several methods have been developed to create rainfall fields from meteorological satellite data. Well known is the so-called Cold Cloud Duration (CCD) technique, which relates the presence of very high and “cold” clouds to rain gauge measurements. Calibration is done on historical data sets. The CCD technique is only suitable to estimate convective rainfall. The method of EARS differs in two ways. First it uses four cloud levels and the so-called “temperature threshold excess” in contrast to only one cloud level. So also lower cloud levels, associated with frontal precipitation, are accounted for. Secondly, the method uses no calibration on the basis of historic data, but combines rain gauge data and cloud durations in near real time.
EWBMS rainfall processing starts with the derivation of a multiple ‘local’ regression between the satellite derived cloud data and the precipitation data for each pixel that contains a rain gauge. This ‘local’ regression is based on the station under consideration and its 12 nearest neighbours. The resulting equation for station j is:
Pj,est = Σ(a j,n · CD j,n ) + b j (3.1)
where CD n is the cloud duration (frequency) at cloud level n. The regression equation, however, is an imperfect estimator of precipitation P. Therefore at each station the residual Dj between the estimated and the observed precipitation is determined:
Dj = P j,obs – P j,est (3.2)
Subsequently, the regression coefficients aj,n , bj from (3.1) and the residual Dj from (3.2) are interpolated between 6 precipitation stations, using a weighed inverse distance method, so as to obtain the corresponding values for pixel i. The spatially distributed precipitation is finally calculated pixel by pixel with:
Pi,est = Σ(a i,n · CD i,n ) + b i + D i (3.3)
Note that the estimated precipitation at the location of a station is always equal to the reported precipitation. In the current project, which includes considerable parts of the Tibetan plateau area, the previous technique has been extended so as to include effects of altitude. Such effects are insufficiently present in the point rainfall data,
32 Chapter 3 – Energy and Water Balance Monitoring System
because the measuring stations are usually at relatively low altitude. It is known that precipitation depends on the amount of precipitable water between the surface and the tropopause at ∼11km. Therefore precipitation can be expected to be proportional with the height or mass of the atmosphere column between the surface and the tropopause. We have investigated these options. A correction based on height gave the best result in the overall water balance of the upper Yellow river:
Pi,cor = P i,est · (H trp – H pix ) / (H trp – H stat,avg ) (3.4)
Where P i,cor is the corrected rainfall, H trp the height of the tropopauze, H pix the altitude of the pixel and H stat,avg the average altitude of the 6 rain gauge stations involved. In this way the precipitation at high altitudes, where no meteorological stations are located, will be lower than at lower altitudes.
Figure 3.3: The energy balance of the earth surface
3.1.3 Energy balance monitoring
The purpose of the energy balance monitoring component of the EWBMS is to determine the components of the surface energy balance, which reads:
LE = I n – H – E – G (3.5)
where: LE = latent heat flux (W/m 2) In = net radiation (W/m 2) H = sensible heat flux (W/m 2) E = photosynthetic electron transport (W/m 2) G = soil heat flux (W/m 2)
Surface albedo and surface temperature are the main input data. The energy used for the evaporation of surface water (LE) equals the net radiation energy provided to the ground surface (I n) minus the energy used for heating the air (H), the energy used by vegetation for photosynthetic electron transport and the energy for heating the soil (G). On a daily basis the soil heat flux can be considered zero (G ≈0). Consequently, the surface energy balance can be rewritten as:
33 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
LE ≈ I n – H – E (3.6)
Only noon and midnight satellite images are used for the processing of energy fluxes. Fourier analysis of the daily solar cycle, a chopped cosine function, is used to relate the noon value of radiation and sensible heat flux to daily averages. As additional information the geographic coordinates and day number is required. This approach assumes that atmospheric transmission remains unchanged during the day. A correction to the daily radiation is applied based on cloud presence in the hours around noon.
Atmospheric correction
By calibration of the VIS and TIR infrared digital values the planetary albedo (reflectivity) and temperature are obtained, i.e. as observed through the atmosphere. However, to calculate the different components of the surface energy balance, the surface albedo and temperature are needed. To make this conversion, atmospheric corrections procedures are carried out. Absorption and scattering of solar radiation in the atmosphere cause the planetary albedo to differ from the surface albedo. Absorption in the atmosphere is mainly due to water vapour. Scattering occurs as a result of the presence of air molecules (e.g. N 2, O 2) and aerosols. Planetary temperatures are lower than actual surface temperatures because of the absorption and re-emission of infrared radiation by particularly water vapour. Scattering plays only a minor role. The atmospheric correction procedures have been designed such that they do not require information on atmospheric composition and stratification, but make use of reference information in the image, for example visual contrast. Image contrast will decrease with increasing atmospheric turbidity.
For the visible band we use the global radiation transmission model of Kondratyev (1969). The model is applied to direct and diffuse solar radiation. Simultaneous differential equations are formulated for downward and upward global radiation fluxes separately. The down welling flux is direct; the up welling flux is diffuse (figure 3.4). The radiation transmission model of Kondratyev only accounts for backscatter and ignores absorption of radiation in the atmosphere. Atmospheric absorption, however, is in the order of 10%. An absorption factor (k) was introduced in the model. In the slab δτ in figure 3.4 the global flux is modified due to backscatter and absorption. The approximate differential equations are:
Figure 3.4: Visualisation of downward and upward radiation fluxes.
34 Chapter 3 – Energy and Water Balance Monitoring System
δI/ δτ = + a.I - b.J (3.7) δJ/ δτ = - c.J + d.I (3.8) where:
a = ( α+k) / cos(i s) b = 2 α c = 2( α+k)
d = α/cos(i s) α = Backscatter coefficient of light ( ≈ 0.1) k = absorption coefficient of light ( ≈ 0.03) is = solar zenith angle
The differential equations are solved analytically. As a result two functions may be derived. One relates the surface albedo to the planetary albedo and the optical depth (τ). The other relates the solar radiation transmission through a cloud free atmosphere (t) to the surface albedo and the optical depth. The optical depth is an indication of the amount of optical active matter in the atmosphere.
A = f(A’ , τ) (3.9) t = f(A , τ) (3.10) with: A’ = observed planetary albedo (-) A = surface albedo (-) τ = optical depth of the atmosphere (-)
Figure 3.5 shows the surface albedo and absorbed solar radiation as a function of planetary albedo and optical depth. Solar radiation absorbed by the earth’s surface is defined as 1 minus the surface albedo (1-A) times the transmission through the atmosphere (t). Once the optical depth is known, equation 3.9 converts the planetary albedo to a surface albedo for each pixel. The influence of the optical depth is highest at minimum surface albedo, which is found in densely vegetated areas. To determine the daily optical depth, the first step is to determine for every pixel in the image the minimum 10-daily planetary albedo. Subsequently the “darkest” pixels with the lowest planetary albedo are obtained. These are related to a minimum surface albedo. When sufficient dense forest is present in the image, this value is typically 7%. From the minimum planetary albedo and the minimum surface albedo, the optical depth can be calculated. Having determined the optical depth, which is applied for the whole image, the planetary albedo of each pixel can be converted to a surface albedo.
0.50 1.00
0.40 0.80 tau=0
0.30 0.60
tau=2.5 0.20 0.40 tau=0
Surface albedo Surface (A) 0.10 0.20 tau=2.5 Absorbedradiation solar (1-A)*t
0.00 0.00 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 Planetary albedo (A') Planetary albedo (A')
Figure 3.5: Surface albedo and absorbed solar radiation as a function of planetary
albedo and optical depth ( τ). α=0.12, i s=0, k=0.03
35 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The transmission model has been compared with global radiation measurements. Some tuning of the EBMS global radiation output is required to achieve a better match. For this purpose, two calibration coefficients have been introduced in (3.10):
t = C 1 * f(A, C 2 * τa) (3.11)
There are two reasons to do so. First, the transmission through the atmosphere is determined at noon. On a daily basis the effective transmission may be somewhat lower because of lower solar inclinations during most of the day. Second, the transmission of solar radiation through the atmosphere is on average less than in the visible window. A best match with observed global radiation values has been obtained with the following values C 1 = 0.77 and C 2 = 0.65.
For the thermal infrared band a different method of atmospheric correction is used. The relation between the planetary temperature (T 0') and the surface temperature (T 0) is described as:
()− = k ( ' − ) T0 Ta T0 Ta (3.12) cos( im) wth: k = atmospheric correction coefficient im = satellite zenith angle Ta = air temperature at the top of the atmospheric boundary layer (K)
The air temperature at the top of the boundary layer (T a), is obtained on the basis of a linear regression between the noon and midnight pixel temperatures, as illustrated in figure 3.6. An estimate of the air temperature is found for the case of perfect heat transfer so that T 0,noon = T 0,midnight = T a. The top of the atmospheric boundary layer varies at daytime usually between one and two kilometres. A map of the air temperature at the top of the boundary layer covering the whole region is obtained by applying this method to a shifting window of 200*200 km. In order to calculate the correction coefficient, the driest pixels in the image are selected and are assumed to correspond with the condition of no evapotranspiration. For each pixel a dryness index (DI) is calculated, which is defined as follows:
360 350 To max 340 To' max 330 LE = 0 320 310 300 290 Ta 280 270 noon planetary temperature (Kelvin) (Kelvin) temperature planetary noon 260 260 270 280 290 300 310 320 midnight planetary temperature (Kelvin)
Figure 3.6: Derivation of reference temperatures from the scatter gram of planetary noon and midnight temperatures
36 Chapter 3 – Energy and Water Balance Monitoring System
T0'−Ta DI = (3.13) In
Where I n is the net radiation. For the driest pixels in the image it is assumed that the latent heat flux (LE) is zero and therefore the net radiation equals the sensible heat flux (H). Once the sensible heat flux for the driest pixels is known, the corresponding surface temperature can be calculated from the net radiation and air temperature with
T0 = T a + I n/α. Because in equation 3.8 the correction coefficient (k) is then the only unknown variable, its value can be determined. The correction coefficient is applied to the whole image. After the correction coefficient and the air temperature are known, it is possible to calculate the surface temperature for each pixel. These surface temperatures may then be used for calculating the sensible heat flux.
Cloud detection
The EBMS system calculates evapotranspiration for both cloudy and cloud free conditions. A cloud detection algorithm is used which separates cloudy pixels from cloud free pixels. When cloudy or partly cloudy pixels are erroneously classified as cloud free, evapotranspiration will be overestimated. This has to be prevented as much as possible.
For the purpose of cloud detection, threshold tests on visible and infrared images are used. Four cloud detection criteria have been defined. If one of these four criteria returns true, a pixel is flagged cloudy. The four tests are:
1) A’ ≥ A’ min + threshold 1 2) T0’noon ≤ T 0’noon, max – threshold 2 3) T0’noon ≤ T 0’midnight – threshold 3 4) T0’noon ≤ T a where: A’ min = minimum planetary albedo as observed in a dekad (-) T0’noon, max = cloud free planetary temperature at noon (K)
The first and second cloud detection tests are based on a comparison between observed pixel values and pixel values associated with a cloud free situation. To obtain a cloud free minimum planetary albedo value, used in test 1, a minimum albedo map is composed from a sequence of 10 daily noon VIS channel composite images. It is assumed that during a 10-day period (a dekad) each pixel was at least once cloud free. Test 1 can only be applied during daylight hours.
Test 2 compares planetary pixel temperatures with cloud free planetary temperatures. A special methodology is applied to estimate the cloud free planetary temperature. This method uses TIR channel data from a square window of 1000*1000 km. Within this window the pixels with the highest planetary temperature are localized. It is assumed that these pixels represent cloud free spots. The temperature for the centre pixels is then calculated with a weighting distance averaging method. The application of this method was demonstrated for Europe by De Valk et al (1998). Test 2 is applied to detect cloudy pixels in noon and midnight TIR images. The first and second test, separately detect both about 70% of cloudy pixels. In combination the detection result increases to 80%. Test 3 and 4 add only few extra cloudy pixels to the ones already detected by test 1 and 2. The threshold values used in test 1 and 2 have to be determined empirically. De Valk et al (1998) determined threshold values for test 1 and test 2. They found that a fixed threshold could be used both during winter and summer.
37 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
After applying these tests relative evapotranspiration values at the edge of cloud systems often appeared to be very high compared to the relative evapotranspiration values a bit further from the cloud’s edge. This high relative evapotranspiration value results from a low planetary temperature, suggesting that clouds still contaminate the pixels. Therefore after processing of the energy balance products, an additional procedure is used to detect such cloud contaminated pixels which were not flagged cloudy by the four cloud tests. This procedure consists in finding pixels, which border cloudy pixels and which have a significant higher relative evapotranspiration value then the non-cloudy pixels in their surrounding. These pixels are flagged cloudy and their energy balance is recalculated.
Global radiation
Global radiation transmission through the atmosphere (t) may be calculated with the noon Kondratyev model. The global radiation at the Earth surface at noon (I g ) is then obtained with:
noon = × ( )× Ig t cos i S (3.14) where: i = solar zenith angle at noon S = intensity of solar radiation at the edge of the atmosphere (1355 W.m -2)
noon The next step is to convert the global radiation at noon (I g ) to the daily average noon value of the global radiation (I g). The conversion factor ν=I g/ I g is determined by integration of the daily solar cycle and is a function of latitude and day number. When a pixel is flagged cloudy, the solar radiation transmission through clouds (t c) is calculated using a relation derived from Kubelka-Munk theory.
= ( )2 × + 2 × A' t-1 c A Cb t c A (3.15)
Where A Cb is the albedo of cumulus nimbus clouds (= 0.92). For the determination of the surface albedo (A) on a pixel-by-pixel basis, a minimum dekadal ground albedo map is composed from daily VIS images. It is assumed that during 10 days each pixel was cloud free at least once. Figure 3.7 shows the relation between the planetary albedo and t c for three different values of the surface albedo. Global solar radiation on noon cloudy days at noon at the Earth surface (I g ) is then estimated by:
cloud transmission 1
0.9
0.8
0.7
0.6 surface albedo
0.08 0.5 0.2 0.4 0.32 0.3
0.2
0.1
0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 cloud transmission (ratio) Figure 3.7: Transmission of solar radiation through clouds
38 Chapter 3 – Energy and Water Balance Monitoring System
noon = × ( )× Ig t c cos i S (3.16)
The conversion of global solar radiation at noon to the daily average value is the same as in cloud free conditions.
Cloud duration
If the EBMS system only uses noon composite images, the radiation will be overestimated if noon conditions are clear, while the morning and afternoon the conditions are cloudy and reverse. In order to generate more accurate daily radiation values, also hourly TIR images, depicting the amount of cloud cover between 9AM and 3PM local time, are taken into account. Daily cloud cover information files are generated, giving information on the degree of cloudiness between 9h and 15h. These data fields are used as additional input and enable the calculated daily radiation values to be corrected and improved significantly.
Detection of clouds in the TIR channel is done using threshold values, which are a function of local time, day of the year and latitude. This, because in the morning temperatures are lower and a pixels could be classified as cloudy if the threshold value for noon was not adapted to the colder conditions in de morning. The same applies for day of the year and latitude. Also taken into account is difference in incoming solar radiation between morning/afternoon and noon. For adaptation the radiation values that are solely based on the noon composites, the cloud cover conditions at 11 hour and 13 hour local time are for instance more important than the cloud cover conditions at 9 hour and 15 hour local time.
Net radiation
Net radiation (I n) at the earth’s surface is calculated as the net result of the short wave (solar) and long wave (terrestrial, thermal, infrared) radiative fluxes. Expressed in terms of daily averages:
In = (1 – A)*I g + L n (3.17) 4 4 Ln = ε0La – L 0 = ε0 εa σ T a – ε0 σ T o (3.18)
2 where: Ig = daily average global solar radiation at the earths surface (W.m ) 2 ε0La = absorbed downward long wave (thermal) radiation (W.m ) 2 L0 = long wave radiation emitted by the surface (W.m ) ε0 = surface emissivity (-) σ = Stefan-Boltzmann constant (W.m -2.K -4)
εa = atmosphere emissivity (-)
The net long wave radiation (L n) is calculated from the surface and atmospheric temperatures and emissivities. Land surface emissivities ( ε0) generally vary between 0.85 (desert) and 0.95 (vegetation). We assume an average value of 0.9. The atmospheric emissivity ( εa) is derived with the empiric Brunt equation, using the specific air humidity (S a ) as input parameter:
0.5 εa = 0.58 + 2.73 * S a (3.19)
Equation 3.18 may be transformed into:
39 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
= ε σ 4 − ε σ 4 + ε σ 4 − ε ε σ 4 Ln 0 T0 0 Ta 0 Ta 0 a Ta ≈ ε σ 3 ()− + ε − ε σ 4 4 0 T T0 Ta 0 1( a ) Ta (3.20) = Hr + Lnc
Where T = (T o + T a )/2 is the mean temperature. On average the climatic net long -2 wave radiation (L nc ) is in the order of 80 W.m . The first term on the right hand of (3.20) may be called the radiative heat flux (H r). It depends on the temperature difference between the surface and the top of the boundary layer . Hr may for practical reasons be combined with the sensible heat flux (H) in the energy balance equation.
For the calculation of net radiation when a pixel is cloud covered, it is assumed that the long wave radiation fluxes below clouds cancel (Ln = 0). Net radiation is then estimated by:
In = (1 – A)*I g (3.21)
Sensible heat flux
At noon and at clear conditions, the heat exchange (H) with the atmosphere may be calculated with:
H = H c + H r H= αc (T o - T a ) + αr (T o - T a ) (3.22) H = α (T o - T a ) where:
αc = C * v a (3.23) α ε σ 3 r = 4 0 T (3.24) and C = drag coefficient (W.m -3.s.K -1) -1 va = average wind speed (m.s ) ε0 = earth surface emissivity (-) σ = Stefan-Bolzman constant (W.m -2.K -4)
T = Mean temperature (K) = (T o + T a ) / 2
The atmospheric sensible heat transfer coefficient ( α) is the sum of the convective sensible heat transfer coefficient ( αc) and the radiative sensible heat transfer coefficient ( αr). Fixed values of the average wind speed and the earth surface emissivity are used . Therefore, the difference between the surface temperature and the air temperature determines the magnitude of the sensible heat flux (H).
Rewriting (3.22) results in:
3 H = ( αc + αr ) (T 0 – T a )=Cv a(T 0 – T a ) + 4 ε0σT (T 0 – T a ) (3.25)
To relate the noon sensible heat flux value to the average daily sensible heat flux, it is assumed that the Bowen ratio, i.e. the energy distribution, remains constant during the day. Consequently the daily average sensible heat flux (H) is calculated from:
noon noon H = H * (I n / I n ) (3.26)
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The drag coefficient C, formerly taken constant, has been reformulated so as to take account for the specific conditions in the Yellow river source area on the Tibetan plateau:
C = (0.37 10 -3 h+0.92) exp(-h/H) (3.27) where h the elevation of the surface and H the scaling height. The first term on the right in (3.27) quantifies the effect of elevation on aerodynamic roughness of the area. The second term represents the influence of decreasing air density with elevation. Roughness is assumed to increase slightly with altitude when the terrain becomes more irregular and the surface more complex at higher elevations. The effect of air density is quantified by the scaling height H which was determined from the relationship between air pressure and elevation:
H = - h /( log P – log P o) (3.28) where P the atmospheric pressure and Po the sea level atmospheric pressure. With Eq. 3.28 and using pressure measurements at four locations in the Yellow River basin, the scaling height H was determined to be 8439 m.
Photosynthetic energy consumption
When vegetation is present, a part of the solar radiation is used for photosynthetic electron transport (E) and the fixation of CO 2. The amount of energy used may be estimated with:
E = ε*(1-A) * I g * C v (3.29) with: ε = photosynthetic light use efficiency on a daily basis (-) Cv = fraction of the surface covered by vegetation (-)
The photosynthetic light use efficiency is estimated on the basis of the Photosystem Deactivation Model (Rosema et al. 1998). The vegetation cover C v is not known independently. It is clear however that presence of vegetation is usually characterised by high evapotranspiration values. We therefore use the relative evapotranspiration (LE/LE p) as a proxy of crop coverage.
Cv = LE/LE p ≈ LE / (0.8*I n) (3.30)
Actual evapotranspiration
Having determined the net radiation (I n), the sensible heat flux (H) and the photosynthetic electron transport (E), the latent heat flux (i.e. the actual evapotranspiration in energy units) is obtained as:
LE = I n – H – E (3.31)
The soil heat flux, on a daily time scale, may be neglected. If a pixel is flagged cloudy, the surface temperature is not known. As a consequence the sensible heat flux (H) and the latent heat flux (LE) cannot be calculated. In that case it is assumed that the Bowen ratio ( β = H/LE) is the same as on the last cloud free day. The Bowen ratio is determined by moisture availability, which is assumed to remain constant. The actual evapotranspiration is then estimated by:
41 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
I LE = n (3.32) ()1+ β
1.5 m Air temperature
The daily average air temperature at 1.5 m above the surface (T1.5m ) is determined with the air temperature at the top of the atmospheric boundary layer (Ta) and the surface temperature (To). The daily average surface temperature To is defined as the average of the surface temperature at noon (To,n ) and the surface temperature at midnight (To,m ). It is calculated by means of a weighing function, which has been derived by comparing the satellite derived with observed 1.5 m air temperature data from 25 WMO-GTS stations in the Yellow River basin.
T1.5m = 5.73+0.58T o+0.26T a (3.33)
T1.5m temperatures, for which the absolute difference with the decadal temperature is larger than 10°C, are replaced with the 10-daily value to correct for erroneous satellite temperature measurements.
3.1.4 Snow and snowmelt
Outputs of the precipitation mapping, discussed in section 3.1.2 and the energy balance monitoring, discussed in section 3.1.3, are used to calculate snow storage and snowmelt. In order to determine whether precipitation is rain or snow, the 1.5m air temperature is used. Precipitation falls as snow when the air temperature is at or below 0 °C. If precipitation is snow, it is added to the snow storage (SS). If the temperature is higher than zero precipitation is rain. The decrease of snow storage depends on the available latent energy LE a and the temperature.
When the temperature is at or below 0°C, the available latent energy LE a is used to sublimate snow. The snow storage changes as a result of precipitation and sublimation:
∆SS = P - LE/L s (3.34)
where the latent heat of sublimation L s = 2833 J/mm. When the snow storage is depleted the sublimated water is assumed to be withdrawn from the soil.
When the temperature is above 0°C, it is assumed that, as long as there is snow available, potential sublimation occurs. If there is, after potential sublimation, any latent energy and snow left, the remaining latent energy is used to calculate the snow melt M:
M = (LE - LE P) / L f (3.35)
where the latent heat of fusion L f = 333 J/mm. The energy used for potential sublimation in this equation has been studied and is taken equivalent to 1mm water per day. The change in snow storage is given by:
∆SS = -LE P/L s -M (3.36)
If at a certain day the snow storage is completely sublimated or melted, the remaining energy is used for evaporating soil water.
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3.1.5 Drought monitoring
The drought monitoring system uses the information from the energy balance model and the rainfall mapping module to generate three different drought indicators: the Climatic Moisture Index (CMI), the Soil Moisture Index (SMI) and the Evapotranspiration Drought Index (EDI). These indices have different definition and time scale and should be evaluated together to provide a comprehensive and complete evaluation of the drought conditions. Drought originates from a rainfall deficiency. Therefore rainfall and the CMI are important to quantify drought. Another, perhaps even more useful parameter to quantify the severity of drought is the relative evapotranspiration. Relative evapotranspiration (LE/LE P) is an output of the energy balance module and is defined as the ratio of actual to potential evapotranspiration. The SMI and EDI are based on relative evapotranspiration data. They indicate water availability at the surface, in particular water availability to plants.
Climatic Moisture Index
The Climatic Moisture Index (CMI) is a numerical indicator of the degree of dryness of a climate. The CMI is an aridity index that was proposed by UNEP in 1992, serving to classify climatic regions that suffer from water shortage and desertification. The CMI is defined as:
CMI = P / E P (3.37)
where: P: annual precipitation in mm EP: annual potential evapotranspiration in mm.
The index is multiplied by 100 and expressed in %. Five climatic zones are classified on the basis of the following thresholds:
Climatic zone CMI hyper arid area <5 arid area 5-20 semi-arid area 20-50 dry sub-humid area 50-65 humid area >65
The CMI is a numerical indicator of the degree of dryness of the climate and provides a scientific and practical indicator for desertification monitoring and combating. The United Nations Convention to Combat desertification (UNCCD) has defined desertification as “land degradation in arid, semi-arid and sub-humid” areas. Therefore countries that have subscribed this convention must prepare a CMI map so as to identify their national areas where the combat of desertification should be focussed on, and which areas by consequence would be entitled to related funds.
Soil Moisture Index
In analogy with the UNEP Climatic Moisture Index, we have introduced the Soil Moisture Index (SMI), in which the rainfall is replaced by the actual evapotranspiration.
SMI = LE / LE p (3.39)
43 Satellite Water Monitoring and Flow Forecasting System for the Yellow River where: LE : annual actual evapotranspiration; LE P: annual potential evapotranspiration.
The index is multiplied by 100 and expressed in %. While the CMI is an indicator of wetness of climate, the SMI reflects the dryness at the soil surface. The SMI is well related to soil water content when vegetation is present. While the CMI only indicates a climate type, the SMI also includes the effect of vegetation cover, soil type, terrain slope, land use and other factors that influence the drought situation.
Evapotranspiration Drought Index
The Evapotranspiration Drought Index (EDI) is an agricultural drought indicator. Agricultural drought occurs when there is not enough moisture available to meet the needs of crops. The EDI is more than an indicator of soil water content. It also integrates effects of soil properties, plant physiology and weather. In fact the relative evapotranspiration and the EDI are directly related to photosynthesis and dry matter production. It is therefore an excellent indicator of agricultural drought. The agricultural drought is evaluated for two monthly periods, a suitable time scale to represent the conditions during critical phases of the growing season.
2m 2m EDI = LE / LE P (3.40) where: LE 2m : 2 monthly actual evapotranspiration; 2m LE P : 2 monthly potential evapotranspiration.
The index is multiplied by 100 and expressed in %. Like the SMI, the agricultural drought indicator is strongly related to the soil moisture content in the root zone, but more important: it is a direct measure of crop dry matter production. Five different agricultural drought classes have been defined according to the EDI value:
Agricultural Drought EDI Extreme drought <30 Severe drought 30-50 Moderate drought 50-60 Light drought 60-80 No drought >80
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3.2 LAS measurements
Four Large Aperture Scintillometer (LAS) systems have been installed to measure the sensible heat flux and the net radiation. The scintillometer is an optical device used to monitor the fluctuations in the refractive index Cn 2 of the turbulent atmosphere over a relatively large area (Meijninger 2003). Cn 2 is derived from the relative intensity fluctuations of a received signal. With additional data from a meteorological weather station installed with each LAS system, heat fluxes from the surface layer into the atmosphere can be calculated. The advantage of the LAS is that it measures the sensible heat flux along a path up to a few kilometers length. The scale of the measurement is thus fairly similar to that of the satellite pixel. Combined with measurements of net radiation, the actual evapotranspiration can be derived as the remaining component in the energy balance (see section 3.1.3).
3.2.1 LAS theory
When an electromagnetic (EM) beam of radiation propagates through the atmosphere, it is distorted by small fluctuations in the refractive index of air (n). The refractive index fluctuations lead to intensity fluctuations of the beam, known as scintillations. The scintillations measured by the LAS instrument are expressed in terms of the 2 structure parameter of the refractive index of air C n . Temperature (T), humidity (Q) and to a lesser extend pressure (P) fluctuations in the atmosphere cause air density fluctuations and, as a result, fluctuations in the refractive index of air. The structure 2 function parameter of the refractive index C n can be decomposed into the structure 2 2 parameters of temperature C T , humidity C Q and the covariance term C TQ in the following way (Hill et al. 1980):
C2 C2 C C2 = A 2 T + A 2 Q + 2A A TQ (3.41) n T T 2 Q Q 2 T Q TQ
AT and A Q are a function of the wavelength λ and the mean values of temperature, specific humidity and atmospheric pressure. In the visible and near infrared wavelength region of the EM spectrum the coefficients A T and A Q are defined by (Andreas 1989): = − ⋅ −6 P + −6 A T 78.0 10 .0 126 10. R vQ T (3.42) A = − .0 126 ⋅10 −6 R Q Q v (3.43)
-1 -1 Rv is the specific gas constant for water vapour (461.5 J K kg ). Because under normal atmospheric conditions A T is much larger than A Q the contribution of humidity related scintillations is much smaller than temperature related scintillations. Therefore a simplified expression can be derived in which C T is expressed as (Kohsiek 1982b):
−2 T 2 03.0 C 2 = C 2 1 + (3.44) T n 2 β A T
where β is the Bowen ratio, included in a correction term for humidity related scintillations. The Bowen ratio is the ratio between sensible heat (H) and latent heat flux (LE) and is large (>3) over dry areas. This means that the correction term in Eq. 2 2 2.4 is small. When surface conditions are very dry, C T is directly proportional to C N :
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T 2 C 2 = C 2 (3.45) T n (− 78.0 10. −6 )P 2
2 Once C T is known, the sensible heat flux can be derived from similarity relationships 2 that have been derived for C T which are based on the Monin-Obukhov similarity theory
− C 2 z( − )d 3/2 z − d 3/2 T LAS = − s 2 cT1 1 cT2 (3.46) θ * L d is the zero-displacement height, z LAS is the height of the scintillometer beam above the ground, c T1 and c T2 are empirical constants, θ* is a temperature scale defined as:
− H θ = (3.47) * ρ cp u* and L is the Obukhov length:
Tu L = * (3.48) θ k vg *
ρ is the density of air, c p the specific heat of air at constant, u* the friction velocity, k v the von Karman constant (~0.40) and g the gravitational acceleration (9.81 m.s -2). The set of equations is completed by the expression for the friction velocity u*:
= k z(u u ) u* (3.49) z − d z − d z u − Ψ u + Ψ 0 ln m m z 0 L L
Where, u is the mean wind speed, z u is the height above the ground where the wind speed is measured and Ψm is the integrated stability function for momentum 2 (Panofsky and Dutton 1984). With data on C T , mean wind speed u at one height, mean absolute temperature T and an estimate of the roughness length z 0, the sensible heat flux can be determined iteratively from the combination of (3.46) to (3.49). However, under conditions of free convection u* is no longer relevant and the sensible heat flux can be derived from scintillometer data only in combination with the mean absolute temperature (T):
2/1 g 2 4/3 H = ρc z(b − )d ()C (3.50) p s T T
In this equation b is an empirical constant which equals 0.48 (Kohsiek 1982b).
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3.2.2 LAS equipment and installation
In August 2005, four LAS systems were installed in the Yellow River basin. Three locations on the Qinghai-Tibetan Plateau in the upper reaches were chosen: Maqin, Tangke and Xinghai, located at altitudes above 3000 m. A fourth LAS system was installed in the WeiHe catchment, in the Sanmenxia region of the Loess Plateau, at Jingchuan (see figure 3.8). The coordinates and altitudes of the four stations are given in Table 3.2. Each LAS-ET system consists of two parts: a scintillometer and a 2 weather station. The structure parameter C N is measured by the scintillometer and additional meteorological data from the weather stations are used to determine the sensible heat flux H. A data logger (Combilog 1020) collects the signals from the meteorological sensors and from the scintillometer.
Table 3.2: Coordinates and altitude of the four LAS stations LAS site Latitude Longitude Altitude (m) Jingchuan 35˚ 20’ N 107˚ 21’ W 1061 Maqin 34°28’ N 100°14 ′ W 3725 Xinghai 35°36’ N 99°59 ′ W 3327 Tangke 33°24’ N 102°28’ W 3445
The weather station consists of two resistance thermometers, a wind speed sensor mounted at the top of the meteorological mast, a net radiation (NR-Lite) sensor and a barotransmitter. Two air temperature sensors (shelter platinum resistance thermometers) are mounted on the meteorological mast at 1m and 3 m above the surface. The difference between upper and lower temperature indicates the stability of the atmosphere; the calculation method of the sensible heat flux is different for stable and unstable conditions. Each LAS station is also equipped with a wind speed sensor, mounted on top of the meteorological mast at about 4 m above the surface. Wind speed data are needed to determine the friction velocity u * in (3.47). The wind speed is measured electronically with a 3-D cup assembly reflecting light barrier frequency output which has an accuracy of ± 0.3 m/s.
Figure 3.8: Location of the four LAS-ET systems: Xinghai, Maqin, Tangke and Jingchuan
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The NR-Lite net radiometer has an upward and downward facing conical shaped sensor surface of black coated Teflon that measures the difference between the downward and upward short-wave and long-wave radiation fluxes. The down facing sensor reading is automatically subtracted from the up facing sensor value and converted to a single output signal. A pressure sensor is also installed in the data logger protection box. The sensor is a barotransmitter with a piezo-resistive sensing element and has an accuracy of ± 1hPa. Measurements of atmospheric pressure are 2 needed to derive the structure parameter of the temperature (C T ) from the structure 2 parameter of refractive index of air (C n ), as given by equation (3.45).
In 2006, power supply problems occurred at the LAS stations on the plateau, which worked on solar panels. This happened during the night and in the early morning on days with high cloud cover. Solar power supply from the panels appeared insufficient to keep the system operational during the entire day. To prevent fall out of the systems it was decided to reduce daily power. An overview of the daily power consumption of the LAS system is given in table 3.3. The table shows that the power consumption of the heater of the LAS window is very high (36 W) compared to the other components. A programmable timer was connected to the heater of the LAS instrument window reducing its functioning to 4 hours (12 Ah) per day. This intervention reduced the daily power consumption of the LAS-ET system from 97.2 Ah to 37.2 Ah. The heater is needed to keep the window free from condensation which is important for an optical instrument like the LAS. At the plateau, rainfall is not so high and relative humidity is quite low so the heater is switched on only in the morning from 5h00 until 9h00 local time to prevent dew and condensation on the window.
Table 3.3: Daily power consumption of LAS-ET system instrumentation Power (A) Time (h) (Ah) Combilog Data logger 0.004 24 0.096 Heater of the wind speed sensor 0.5 24 12 Heater of the air pressure sensor 0.01 24 0.24 LAS 0.5 24 12 Heater of the LAS window 18 24 72
3.2.3 LAS measuring sites
The LAS stations measure the scintillation as well as wind speed, air pressure, net radiation and temperature. 10 Minute averages are stored on a Combilog 1020 data logger. In Maqin, Xinghai and Tangke, on the Qinghai plateau, the LAS systems are measuring from April to October. The measuring equipment is brought indoor during the winter months to protect it against the extreme low temperatures (< -20°C) and high wind speeds. The system on the Loess plateau in Jingchuan reports the entire year and is measuring uninterrupted since August 2005. The Upper Reach Branch Hydrology Bureau of YRCC in Lanzhou is responsible for the three LAS stations on the Qinghai-Tibetan Plateau. The Hydrology and Water Resources Bureau of Sanmenxia is taking care of the LAS station in Jingchuan, Henan province. A detailed specification of the LAS stations is presented in Annex 1.
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3.2.4 Data collection
The head of the local hydrological bureau is in charge of maintaining the LAS-ET and keeping the system operational. Every two weeks the data are downloaded and at the same time the system is inspected. The LAS data are downloaded by replacing the PCMCIA memory card or connecting a laptop to the RS232 interface of the Combilog data logger. The physical conditions of all sensors are checked for malfunctioning. If necessary, the sensors are cleaned and any dirt is removed. The condition of all cables and wires is checked. Weather conditions, changes in configuration of the system, changes in the landscape at the site, as well as possible malfunctioning are reported in a LAS operations form. After downloading the data all information is send to the respective hydrological bureau and then to the Yellow River Hydrology Bureau in Zhengzhou and EARS, as shown in figure 3.9.
Jingchuan Xinghai Maqin Tangke
Sanmenxia Upper Reach Hydrology Bureau Hydrology Bureau
Zhengzhou Hydrology Bureau
EARS Delft
Figure 3.9: LAS data collection and data transfer
3.2.5 Data processing
With the EVATION software provided with the LAS by Kipp&Zonen in Delft, Netherlands, the collected scintillometer and meteorological data are processed to fluxes of sensible heat and actual evapotranspiration. The software interface is shown in figure 3.10. Before running the software, the user selects a working directory for each of the four LAS systems. EVATION automatically creates the subdirectories “input”, “output”, “config” and “auxiliary”. The configuration parameters of each LAS system are set in the configuration tab sheet (figure 3.10 right side). These include the surface profile, the height above the surface of the receiver and the transmitter, the wind speed and temperature sensor data. Also information on path length and terrain characteristics (roughness length, etc) is added.
The data files from the Combilog contain 10 minute averages of the scintillometer signals and the meteorological sensors. To be able to compare with the daily EWBMS products, daily averaged values are calculated with EVATION.
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Figure 3.10: Interface of the EVATION software
Some stations on the Tibetan plateau however were not logging continuously during the night due to power failures in 2005 and 2006. These input data need some additional manual pre-processing to fill up the gaps in the data sets. For each month hourly averages were calculated from all available data in 2007, i.e. a matrix of 12 by 24 average values was created. These values were then used to replace the missing data. However, for the specific purpose of validation (section 3.5.3), only days with more than 80% of the actual measurements available were used.
Additional pre-processing was necessary in relation to the temperature measurements. The EVATION algorithm uses the temperature difference to determine whether the atmosphere is stable or unstable. However in some cases the temperature difference did erroneously not change sign and could not be used as such. Therefore an alternative method was used based on the sign of the measured net radiation I n. The raw data input into the EVATION software was modified as follows:
- + If I n > 0 then T = T + 0.5 °C - + If I n < 0 then T = T - 0.5 °C
Where T - is the value of the lower temperature sensor and T + that of the upper temperature sensor. As a result the sign of temperature difference is determined by the change in sign of the net radiation and the EVATION software will apply the correct formula for the calculation of sensible heat flux H.
3.2.6 LAS results
Some examples of ten minutes average data of temperature, wind speed and pressure signals, which were collected at Jingchuan in April 2007 are shown in figures 3.11 to 3.14. Figure 3.13 shows the structure parameter of the refractive index Cn 2 as measured by the scintillometer, and the demodulated carrier signal U DEMOD . The latter is not used in the calculations, but it is an important parameter for monitoring the signal transmission along the scintillometer path and the related quality of the Cn 2 measurements.
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30 25 20 15 10
5 0 Temperature (°C) -5 T diff (Tlow - Tup) T upper T lower -10 05.04.2007 06.04.2007 07.04.2007 08.04.2007 09.04.2007 01:30:00 01:30:00 01:30:00 01:30:00 01:30:00
Figure 3.11: Temperature daily variation upper and lower sensor in Jingchuan
14 950 940 12 930 10 Wind Speed Air Pressure 920
8 910 900 6 890 880 Wind Speed [m/s] Speed Wind 4 AirPressure [hPa] 870 2 860 0 850 02.04.2007 02.04.2007 03.04.2007 04.04.2007 05.04.2007 00:00:00 00:00:00 00:00:00 00:00:00 00:00:00
Figure 3.12: Ten minute averages of wind speed and air pressure in Jingchuan
100 0
Cn2 Signal Strength -100 75
-200 50 -300
25 (mV) Strength Signal -400 Refractive(m-2/3) Cn2 Index
0 -500 02.04.2007 02.04.2007 03.04.2007 04.04.2007 05.04.2007 00:00:00 00:00:00 00:00:00 00:00:00 00:00:00
Figure 3.13: Structure parameter of the refractive index of air Cn 2 and the demodulated carrier signal in Jingchuan
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3.3 EWBMS software system
The EWBMS software needs two types of input data: specially prepared satellite images and WMO-GTS rain gauge data. To obtain effective precipitation and freeze/thaw products for the large scale hydrological model, a sequence of three processing steps has to be followed as shown in figure 3.14. The EWBMS software is supplemented with utility software tools for quick evaluation of the results, display of output products and conversion of outputs into the ASCII format of the Large Scale Hydrological Model. All software modules are called from the main EWBMS menu bar (figure 3.15). A module is started with a single click on an icon. The format of the output data fields is generic 8 or 16 bit raw binary data. This format can easily be imported in most GIS software systems like ArcMap or Idrisi. The EWBMS is accompanied with the Imageshow 2 GIS tool, specially developed to view in an easy and convenient way the generic binary output data sets and to do some basic calculations and analysis with the data.
Figure 3.14: EWBMS processing flow chart
Figure 3.15: EWBMS main menu bar
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All processing times the processing information and metadata from the various software modules are stored in the EWBMS processing data base. Changes in the user interface settings of the modules are saved on each pc in files with the extension *.ini. EWBMS core software modules like the Energy Balance and Rainfall module need a licensed hardware key to run. Application processing tools and analysis tools are not protected and will also run without the hardware key.
3.3.1 Satellite data reception and pre-processing
The Cloud Durations and Image Composition module of step 1, has been developed to create the input data that are needed to run the EWBMS precipitation and energy balance monitoring modules. It produces noon and midnight composites from visible (VIS) and thermal infrared (TIR) satellite images. These composites are used as input for the energy balance monitoring software. The module also produces daily and six hourly cloud duration maps, which are used as input for the rainfall mapping software, as well as daily cloud cover information maps, used in the energy balance monitoring software.
Cloud duration maps
The cloud duration maps are used as input for the precipitation mapping module (section 3.1.2). They contain the number of hours that clouds of a certain cloud height class have been detected on a location. Cloud duration maps are based on classification of hourly TIR images, and stored as daily and six hourly files. All available hourly TIR images are used for cloud duration mapping. The more hourly TIR images are available, the more reliable the output of the precipitation mapping module is.
Figure 3.16: Output tab sheet of the cloud durations and image composition module.
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Figure 3.17: Cloud duration image for the medium low cloud class (June 12, 2008).
Daily cloud cover information maps
The daily cloud cover information maps are used as secondary input files by the energy balance monitoring module. They represent the amount of cloud cover from 9AM to 3PM solar time. The maps are used to improve the radiation values (and thus all derived energy fluxes), that are calculated by the energy balance module.
Noon and midnight composites
The inputs for the noon and midnight composite images are the hourly VIS and TIR images received. Each day one VIS composite file (noon) and two composite TIR files (noon and midnight) are produced. In these composite images, the local noon (or midnight) values are extracted for each pixel by interpolation from the hourly images. See also section 3.1.1.
3.3.2 Rain gauge data reception and pre-processing
The rainfall mapping software uses cloud duration files and rainfall data from meteorological stations (WMO-GTS and CMA stations). The rainfall mapping software runs either with a time step of 6 hours or 24 hours. The CMA sends coded files with data covering 1 to 24 hours (so called Synops files). These files are decoded, error filtered and aggregated to the proper time scale. The GTS Synops Decode software produces a database with daily and six hourly meteorological data. Apart from the GTS and CMA stations, YRCC also has its own station network. The GTS Synops Decode module enables the user to import these data to the database used for precipitation mapping.
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Figure 3.18: The settings tab sheet of the GTS synops decode module enables erroneous data filtering.
3.3.3 Precipitation Module
The Precipitation module calculates spatially continuous precipitation maps based on rain gauge measurements and satellite derived cloud durations. The input tab sheet is shown in figure 3.19. The GTS Synops Decode module (section 3.3.2) provides a database with precipitation ground point measurements that is used as input for the Precipitation module. The cloud duration files are generated by the Cloud Duration and Image Composition module (section 3.3.1). The precipitation map contains daily or six hourly spatially continuous precipitation values in mm. Apart from the precipitation map, a comma-separated quality check file is generated. The quality check file gives information to evaluate the accuracy of the rainfall map. For instance, reported rainfall and predicted rainfall figures are given. The predicted rainfall was calculated through a jack-knifing procedure: rainfall at the location of a rainfall station is calculated without using data from the rainfall station itself. For analysis, the jack-knifing file can be loaded into MS Excel. To extract certain stations for a longer period, the jack-knifing analysis tool may be used.
3.3.4 Energy Balance Module
The Energy Balance Module (EBM) has been developed to derive energy balance products from geostationary satellite imagery, collected in the visible and thermal infrared. The module can produce daily and 10 daily-averaged maps of evapotranspiration, radiation, sensible heat flux, temperature, albedo, photosynthetic light use and cloud cover. The methodology is described in detail in section 3.1.3.
Input
The main inputs required to run the Energy Balance Mapping module are the noon and midnight visible and infrared composites. The daily cloud cover information files, generated with the Cloud Duration and Image Composition pre-processing
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Figure 3.19: Input tab sheet of the Precipitation Module.
module, are used in addition to improve the results. Next to the composite files and the cloud duration maps, additional information like sea mask map, satellite settings, relative humidity file, digital elevation model and calibration data is needed. The sea mask map shows the location of land and water (sea, rivers, lakes). Only land pixels are processed. The digital elevation map is used to correct the optical depth and the air density for altitude. The daily relative humidity values used are average climatic averages for the whole region.
Both VIS and TIR images have attached a footer containing a calibration table determined pre-launch. The VIS calibration in the footer of the FY2C images is not realistic in space and an after launch alternative vicarious calibration table for the VIS channel is used instead.
Output
The Energy Balance module can generate daily and ten-daily averaged output data fields of latent heat flux, boundary layer temperature, cloud mask, surface albedo, potential evapotranspiration, actual evapotranspiration, global radiation, net radiation, photosynthetic light use, relative evapotranspiration, noon surface temperature and 1.5 m air temperature. The required outputs are chosen on the daily and dekadely output product tab sheets shown in figure 3.21 and 3.22.
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Figure 3.20: Main window of the Energy Balance module
Figure 3.21: Daily output products tab sheet..
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Figure 3.22: Dekadely output products tab sheet.
3.3.5 Freeze/Thaw module
The Freeze/Thaw module calculates spatially continuous winter products. Based on inputs from the precipitation module (section 3.3.3) and the energy balance module (section 3.3.4) snow height, snowmelt and snow cover are calculated. Figure 3.23 shows the input tab sheet of the module. To define freezing/melting and snow/rainfall conditions, the 10 daily-averaged temperatures from the energy balance module is used. To calculate the snow height, snowmelt, sublimation/evaporation, precipitation from the precipitation module, and daily actual evapotranspiration from the energy balance is used.
Figure 3.24 shows the output tab sheet of the freeze/thaw module. The snow cover output maps show where the earth’s surface is covered by snow and for how many days already. The snow height maps show how much snow there is, and the snowmelt maps how much snow has melted on a certain day (both in tenths of mm water). Since snow is not available for run-off, the input for the hydrological model (chapter 4), also called effective precipitation, is redefined. Melt water is included, while snowfall is excluded from the ‘effective’ precipitation. Sublimation from the snow pack is excluded for the same reason. The total water balance will be slightly different from the situation without the freeze/thaw module, since sublimation uses more energy than evapotranspiration, and thus less energy is left for evaporation when the freeze/thaw module is used.
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Figure 3.23: The input tab sheet of the freeze/thaw module.
Figure 3.24: The output tab sheet of the freeze/thaw module.
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Figure 3.25: Settings tab sheet of the Drought Monitoring System
3.3.6 Drought Monitoring System
The Drought Monitoring system generates drought information on a pixel-by-pixel basis. The module can generate three different drought indices: CMI, SMI and EDI. More explanation on the drought indices is given in section 3.1.5.
Input
The Drought Monitoring module needs actual evapotranspiration and potential evapotranspiration information from the Energy Balance Module. To calculate the Climatic Moisture Index (CMI), also rainfall products are needed as an input. The user has to specify the processing period and in which directory the energy balance and the rainfall products are stored.
Output
The user can choose the desired drought indices and the desired output directory in which the drought products are stored. The processing period of the module is set to one year for generating CMI or SMI. For EDI, the processing period is two months at least. The output products are expressed in %.
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Figure 3.26: Selection of output products in Drought Monitoring tab sheet.
Figure 3.27: Structure of the Access EWBMS processing information database
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3.3.7 Processing information database
Every time the user runs a EWBMS software module on his computer, the processing information is automatically stored in a Microsoft Access metadata base called ‘EWBMS processing database’. The processing information database holds information on execution time, input data specifications and the generated output data, quality of the input and output data, values of daily changing variables in the energy balance mapping module, etc. The database does not hold information on analysis work done with ImageShow2.
Figure 3.27 gives an overview of the various tables in the EWBMS processing Access database. The main table ProcessingJobs stores the basic processing information. Here the settings and time of execution of all EWBMS processing modules are stored. Every time the user runs an EWBMS module, one line of processing information is added to this table. The main table is linked to seven other tables that contain specific processing information on each of the EWBMS processing modules. More information on this metadata base may be found in the EWBMS user manual (EARS 2008).
3.3.8 Imageshow-2 analysis tool
The processing modules are the core of the EWBMS software. Next to the processing modules an additional analysis tool, ImageShow2 is provided. ImageShow2 is a GIS tool, intended for quick evaluation, analysis and post processing of the EWBMS products. Figure 3.28 shows the interface of ImageShow2, which has the following main functions.
1. Display : quick and easy display of the generic raw binary format EWBMS products. Several additional functionalities are available: scaling, zoom, histogram, display of boundary overlays, display of legend, creating a scatter plot with a second input map, etc.
Figure 3.28: Interface of the Analysis software tool ImageShow2
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2. Calculation : calculations on a series of maps or on a single map. It is possible to calculate an average map or a sum map for a specified time period. It is also possible to perform calculations (subtract, sum, multiply, divide) on two different maps or on a single map.
3. Point Value : find a value for a location with given latitude and longitude. Additionally a time graph of pixel values for a given location can be extracted from a series of maps. The time series may then be saved as a comma separated file for further analysis in e.g. Excel.
4. Polygon Averaging : calculation of country, provincial or county averages of EWBMS products. Several secondary maps with the location of countries, zones, provinces or agricultural areas can be used.
5. Classification : changes the digital numbers in EWBMS output products into single values or classes. A spatially continuous map can be transformed into a categorical map or a zoning map.
6. Export : exports the EWBMS products from generic raw binary file to a png, jpg, bmp or gif format and to geographic referenced tif or ASCII grid files. The various formats permit to import EWBMS products easily into other general purpose GIS software like ArcMap, Ilwis, ERDAS Imagine or Idrisi.
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3.4 Catchment drought monitoring system
To provide for a comprehensive and complete evaluation of the drought conditions and water use in the Yellow River basin, three different drought maps may be produced: the CMI map, showing climatic drought, a hydrological water balance map and the agricultural drought index EDI map. Although the different types of drought originate from the same precipitation and water resources deficiency, climatic, hydrological and agricultural drought events may come to expression at different times. For example, rainfall shortages may be immediately noted in the agricultural sector, but the impact on reservoir levels may not affect hydroelectric power production for many months.
A drought monitoring bulletin for the Yellow River has been developed (section 5.2), which may be updated and distributed regularly, thus keeping up with the development of the agricultural, hydrological and climatic drought situation. Information on agricultural drought and a detailed spatial assessment of the water balance in the entire Yellow River basin is to be provided on a monthly basis. Climatic drought is reported yearly.
Figure 3.29: Climatic zones and aridity in the Yellow River basin in 2006 (left) and 2007 (right)
3.4.1 Climatic drought
The climatic moisture index or the aridity index can be used to monitor yearly changes and long term desertification related climatic trends for the entire Yellow River basin. The CMI map for 2006 and 2007 with the classification of climate types of is shown in figure 3.29. It is clear that there are slight differences in aridity between the two years. Note the difference in classification of the large irrigated area in the basin at Yinchuan, which received less rainfall in 2006. Differences are also seen in the southern part of the basin, which was more humid in 2007 than in 2006.
The delineation of the climatic zones will usually be based on more than one year. Programs to combat or prevent desertification, like planting of trees, respond to long term developments. For the design of such programs longer term CMI maps of the Yellow River basin should be created, while in the implementation phase the most recent CMI maps may be useful to adapt or fine tune the activities.
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Figure 3.30 shows the average CMI map for 2006 and 2007. When more satellite data are available in the future, the long term average climatic moisture map or desertification map may be calculated from five or ten years of data. The map defines the arid, semi-arid and dry sub-humid zones, where desertification may occur, and which are indicated for prevention of desertification and land degradation. Every five or ten years, the climatic zone map should be updated with the most recent EWBMS rainfall and evapotranspiration data. Regularly updated CMI maps allow focusing attention and action to those regions that need prevention of desertification the most.
Figure 3.30: Climatic zones of the Yellow River basin (average of 2006 and 2007)
3.4.2 Hydrological drought
Hydrological drought refers to deficiencies in surface and subsurface water supplies and is noted in stream flow amounts, reservoirs and ground water levels. The shortage in water affects reservoir levels for hydropower, water use for irrigation, stream flows and water levels for navigation, recreational water use and ground water levels. Water availability in the Yellow River basin is highly irregular in time and geographically unevenly distributed. Timely and regular assessments of the resources in the entire basin are needed to prevent or reduce impacts from hydrological droughts as much as possible.
The frequency and severity of hydrological drought in the Yellow River basin is quantified by the water balance in the basin and its sub-catchments. The water balance of a catchment is calculated by subtracting the total EWBMS actual evapotranspiration from the total EWBMS precipitation during the hydrological year at sub-basin level. An example of a detailed inventory of water resources in the Yellow River basin is given in figure 3.31. The map shows the water balance for three recent ‘hydrological’ years. The map informs on the amount of water each component of the river network receives. At the same time it indicates the water resources that are available for irrigation, hydropower, urban and industrial use at sub basin level. Note that this hydrological drought indicator is not based on rainfall only, like most classical indicators (SPI, PAI, etc) but also on measured actual evapotranspiration. This information is essential because in the northern part of the basin the evaporation losses can be twice the precipitation, while in the whole basin about 70% of the rainfall is lost by evapotranspiration.
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(a) July 2005 – June 2006
(b) July 2006 – June 2007
(c) July 2007 – June 2008
Figure 3.31: Hydrological drought in Yellow River sub basins: water balance in mm for the last three hydrological years.
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The water balance maps for the three years are clearly different and thus water resources for industry, irrigation, etc. vary considerably from year to year. In the south-east of the basin there was a considerable surplus in 2007/2008 and 2005/2006. But in 2006/2007 evapotranspiration offsets the rainfall. In the northern part of the basin the simultaneous changes are opposite. Also in the eastern part of the basin there are clear differences between the three years. In 2007/2008 the water resources are much higher than during the previous years. However, in the western source area of the river, the changes are opposite and the water budget has decreased during the past three years.
3.4.3 Agricultural drought
Agricultural drought occurs when there is not enough water available to meet the needs of the crops. Agricultural drought leads to reduced crop yields. An analysis of agricultural drought in the Yellow River basin during 2008 has been made using EDI maps, as discussed in section 3.1.5. EDI classified maps have been created for the first and second part of the growing season, i.e. for May-June and July-August of 2008. See figures 3.32 and 3.33.
There is a clear difference between the two maps: water availability for the crops is much higher in July-August than in May-June. This is not unusual as the rainy period starts in May and by July-August more water will be available to the vegetation. Also the crops are more developed than at the beginning of the growing season. The patterns of both maps are similar: drier areas are located in the northern and north- central areas and wetter areas towards the east and the south.
The impact of agricultural drought on crop yield does not only depend on the degree of drought but also on the duration of dry conditions during the entire growing season. It is important to consider the duration of drought because prolonged droughts have a larger impacts on final yields than short dry periods from which some crops can partially recover. Therefore a drought duration map for 2008 was created: the number of dekades with an EDI lower than 45% from March to October. The result is shown in figure 3.32.
The information in the drought duration map is complementary to the EDI maps in figures 3.32 and 3.33 because it informs on the agricultural drought during the entire growing season and not only on a two months period. The drought duration map confirms that the driest regions are located in the northern parts, in and around Ningxia province and north of Lanzhou. The patterns are very similar to the ones in the EDI map of July-August above. The added value of the drought duration map is seen for example in the regions around Zhengzhou and Kaifeng where the drought duration map shows drier conditions than one would expect from the July-August EDI map.
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Figure 3.32: Agricultural drought (EDI) in May-June 2008
Figure 3.33: Agricultural drought(EDI) in July-August 2008
Figure 3.34: Agricultural drought duration from March to October 200, i.e. the number of dekads with EDI < 0.45.
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3.5 Validation of EWBMS products
The purpose of the validation is to provide information on the performance of the EWBMS products in the rainfall-runoff forecasting context. The accuracy of a number of basic products is assessed against independent measurements. Such measurements are usually point data, while the EWBMS product data refer to areas of one satellite pixel of 5x5km sub-satellite. Both data types have their sources of error and do not accurately represent the reality. It is noted that the ground data are samples and not areal averages. In this respect they have considerable sampling errors. Therefore perfect correspondence between field data and EWBMS pixel values cannot be expected.
The precipitation maps are validated by means of the ‘jack-knifing’ method. Results for twenty stations in and around the Weihe and the Upper Yellow River basin are presented in section 3.5.1. In section 3.5.2, the EWBMS 1.5 m air temperature energy is validated against an extensive dataset of readily available GTS data with a dense geographical distribution covering the entire Yellow River basin. Lacking ground measurements, the actual evapotranspiration is validated through the net radiation and sensible heat flux, from which it is derived by subtraction. The EWBMS net radiation is validated by comparison with similar measurements on the ground, the sensible heat flux by comparison with LAS measurements of the same, as earlier discussed in section 3.2. Measuring the sensible heat flux with the LAS is considered the most appropriate approach, since the LAS footprint is of the same order of magnitude as the satellite pixel. But, because of costs, the number of LAS stations is limited to 4. Results of the validation are presented in section 3.5.3 (net radiation) and 3.5.4 (sensible heat flux).
In addition the overall water balance is validated by comparing the yearly net or ‘effective’ precipitation, i.e precipitation minus evapotranspiration, of a basin with the river runoff measured at the outlet of that basin. Results are presented in section 3.5.5.
3.5.1 Validation of precipitation
Validation of EWBMS precipitation maps has been quite difficult because an independent data set is not always available. Therefore the so called jack-knifing method has been used. With this method one precipitation station is left out of the data set used in the rainfall field calculations. The EWBMS rainfall value and the rainfall measured at that location on the ground, provides one validation data pair. The rainfall mapping procedure is then repeated every time taking out another station and putting back the previous one. In this way as many independent rainfall validation data pairs are obtained as there are rainfall stations. This validation data set is then analyzed by means of regression. The results of the jack-knifing validation give a good indication of the quality of the rainfall mapping method. However, the actual mapping results will be better, as the nearest and thus most influential precipitation data point is not used as input in the Jack Knifing run.
As shown in figure 3.37 precipitation measurements are scarce in the Upper Yellow River basin (UYRB), especially in the western areas. Due to the decreasing influence of the summer monsoon in north-western direction, yearly precipitation is decreasing in the same direction. Ten stations in or close to the UYRB are selected for the jack- knifing analysis. These 10 stations are divided into two areas: 5 stations in the wet
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Figure 3.37: Precipitation in the Upper Yellow River Basin 2006, and available precipitation measurement stations (dots).
south-eastern area (red dots) and 5 stations in the dryer northern and western areas (blue dots). Because of the limited number of stations in the upper Yellow river basin, 2 stations, just outside the basin, are also used for the analysis. These stations are considered to be close enough to the basin, to be valuable for the estimation of the EWBMS precipitation performance. Also, since sub-sets of 5 stations are available now, results can be compared with similar sub-sets of the Weihe basin.
In table 3.4 the specifications of the selected stations in the northern and western areas of the upper Yellow river basin are given. The altitude and remoteness of the stations cause difficulties for station maintenance and offer a challenge for satellite precipitation mapping. As shown in table 3.5 the results are very good. Except for 2006, Pearson’s R 2 is well over 0.7 and the relative differences between the yearly reported and estimated rain are well under 10%. In 2006 however the results are a bit worse. For the whole period, from June 2005 to August 2008, the average of 5 stations differs only 4%. The left part of figure 3.38 shows the 5 station averages of reported and estimated values for the whole period. Ideally all dots would be on the 1:1 line. However, errors in both field measurements and satellite derived data result in scatter.
Figure 3.37 shows that in the south-east of the upper Yellow river basin, the station network is a bit denser than in the central and western part, but is still low. Yearly precipitation is higher than in the northern and western areas. Table 3.6 shows the specifications of the 5 stations in the south-east. Table 3.7 shows that the results are excellent. Pearson’s R 2 is close to 0.9 and the relative differences between the yearly reported and estimated rain are very small. For the whole period, from June 2005 to July 2008, the average of 5 stations differs only by 1%. The right scatter plot of figure 3.38 shows the related scatter plot. Compared to the same plot of the northern and western stations (figure 3.40, left) the points are closer to the 1:1 line, resulting in a higher R 2, and indicating better performance of the EWBMS precipitation module in the south-eastern area. From a rainfall-runoff modeling point of view, this is preferable, since precipitation in the southeastern area is higher.
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Table 3.4: Stations used for jack-knifing in the north and west part of the UYRB WMO nr. Name Latitude Longitude Altitude (m) 56029 Yushu 33.02 97.02 3682 56033 Maduo 34.92 98.22 4273 56043 Guolouo 34.80 100.30 3720 56046 Dari 33.75 99.65 3968 56065 Henan 34.73 101.60 3501
Table 3.5: Jack-knifing results for the average of the northern and western stations Year Pearson’s Reported Estimated Difference R2 rain (mm) rain (mm) (%) 2005 (from 6/20) 0.76 402 397 -1 2006 0.66 444 528 19 2007 0.85 587 552 -6 2008 (until 7/31) 0.75 311 330 6 Total 0.76 1744 1807 4
Table 3.6: Stations used for the jack-knifing analysis in the SE area of the UYRB WMO nr. Name Latitude Longitude Altitude (m) 56067 Jiuzhi 33.40 101.50 3630 56074 Maqu 34.00 102.10 3473 56079 Ruoergai 33.58 102.97 3441 56151 Banma 32.90 100.80 3530 56173 Hongyuan 32.80 102.60 3493
Table 3.7: Jack-knifing results for the average of the SE stations of the UYRB Year Pearson’s Reported Etimated Difference R2 rain (mm) rain (mm) (%) 2005 (from 6/20) 0.87 546 503 - 8 2006 0.86 661 685 4 2007 0.89 649 650 0 2008 (until 7/31) 0.90 338 335 - 1 Total 0.88 2194 2174 - 1
Daily Rainfall Scatter Plot, Average of 5 northern and Daily Rainfall Scatter Plot, Average of 5 southeastern western UYRB stations UYRB stations 16 16 1:1, R2=0.76 1:1, R2=0.88 14 14
12 12
10 10
8 8
6 6 Estimated rainfall (mm) 4 Estimated rainfall4 (mm)
2 2
0 0 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 Reported rainfall (mm) Reported rainfall (mm)
Figure 3.38: Daily rainfall scatter plots of observed versus estimated rainfall average of 5 stations in the north and west UYRB (left) and south-east (right).
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Figure 3.39: Precipitation in the Weihe Basin 2006, and available precipitation measuring stations (dots).
Figure 3.39 shows that the station network in the Weihe basin is much denser than in the upper Yellow river basin. But also here, stations are scarce in the northern part. But this is less serious whereas, due to the decreasing influence of the summer monsoon, yearly precipitation is decreasing to the north. Ten stations in the Weihe basin are selected for the jack-knifing analysis. These are divided in two sets: 5 stations in the wet south-eastern and downstream area (white dots) and 5 stations in the dryer northern and western upstream areas (blue dots).
In table 3.8 the specifications of the selected stations in the upstream areas of the Weihe basin are given. The jack-knifing analysis is done in the same way as in the upper Yellow river basin, discussed earlier in this section. Table 3.9 shows that the results are very good, except for 2008. Pearson’s R2 is well over 0.7 and the relative differences between the yearly reported and estimated rain are less than 5%. For the year 2008 however, the results are a bit worse. But, since in 2008 the time period was shorter and the precipitation lower, this outcome is less representative. For the whole period, from June 2005 to August 2008, the average between the measured and satellite derived rainfall values of the 5 stations differs only 4%. The left scatter plot of figure 3.40 shows the 5 stations averages of all daily reported and daily estimated values for the whole period.
Figure 3.39 shows that in the south-eastern downstream areas of the Weihe basin, the station network is well distributed and relatively dense. Yearly precipitation is higher than in the northern and western areas. Table 3.10 shows the specifications of the 5 stations of the south-eastern downstream areas that are used in the jack-knifing analysis. Table 3.11 shows that the jack-knifing results are good, though differences between the years are considerable. Pearson’s R 2 varies between 0.77 and 0.92 and the difference between the yearly reported and estimated rain between -16% and 6%. For the whole period, from June 2005 to August 2008, the average between the measured and satellite derived rainfall differ -6%. The right part of figure 3.40 shows the corresponding scatter plot. Compared to the scatter plot of the upstream areas (figure 3.40, left) the values are much closer to the ideal line, resulting in a higher R 2 and indicating a better performance of the EWBMS precipitation module in the south-eastern high precipitation area, as desirable for rainfall-runoff forecasting.
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Table 3.8: Stations used for jack-knifing in the upstream area of the Weihe basin. WMO nr. Name Latitude Longitude Altitude (m) 53738 Wuqi 36.90 108.20 1331 53915 Pingliang 35.55 106.67 1348 53923 Xifengzhen 35.73 107.63 1423 56092 Longxi 35.00 104.70 1729 57014 Beidao 34.50 105.90 1085
Table 3.9: Jack-knifing results for the average of upstream stations of the Weihe. Year Pearson’s Reported rain Estimated rain Difference R2 (mm) (mm) (%) 2005 (from 6/20) 0.72 410 397 - 3 2006 0.83 501 511 2 2007 0.74 518 541 4 2008 (until 7/31) 0.68 202 242 20 total 0.71 1631 1690 4
Table 3.10: Stations used for jack-knifing in the downstream area of the Weihe basin. WMO nr. Name Latitude Longitude 53929 Changwu 35.20 107.80 53942 Luochuan 35.80 109.50 57025 Fengxiang 34.50 107.40 57037 Yiaxian 34.90 109.00 57134 Foping 33.90 108.00
Table 3.11: Jack-knifing results for the average of downstreamstations of the Weihe. Year Pearson’s Reported rain Estimated rain Difference R 2 (mm) (mm) (%) 2005 (from 6/20) 0.89 484 515 6 2006 0.92 674 652 - 3 2007 0.77 736 658 -11 2008 (until 7/31) 0.78 353 297 -16 total 0.82 2248 2121 - 6
Daily Rainfall Scatter Plot, Average of 5 Weihe upstream Daily Rainfall Scatter Plot, Average of 5 Weihe stations downstream stations
16 16 1:1, R2=0.71 1:1, R2=0.82 14 14
12 12
10 10
8 8
6 6 Estimated rainfall (mm) Estimated rainfall (mm) 4 4
2 2
0 0 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 Reported rainfall (mm) Reported rainfall (mm) Figure 3.40: Daily rainfall scatter plots of observed versus estimated rainfall average of 5 stations in upstream (left) and downstream parts (right) of the Weihe basin.
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Considering all the areas of the Upper Yellow River basin and the Weihe basin, during the whole validation period (June 20, 2005 – July 31, 2008), the jack-knifing validation results can be considered good. Since over- and underestimations are well balanced between the different areas and years, systematic errors in the EWBMS precipitation are not expected. Moreover Pearson’s R 2 is higher in the areas with more precipitation, which reduces the effects of random errors on the rainfall-runoff simulation.
3.5.2 Validation of air temperature
Daily averaged 1.5 m air temperature ( T1.5m ) data from the WMO-GTS are compared to corresponding EWBMS values with a grid size of 0.05° by 0.05° (about 5.6 km by 4.5 km in the Yellow River basin ). Figure 3.41 shows the location of the GTS stations involved.
Figure 3.41: Location of the GTS 1.5 m air temperature stations used for validation
The WMO-GTS temperatures are measured with a sensor that is placed at 1.5 m above the surface and is protected from precipitation and sunlight by a white painted louvered box. The EWBMS daily average 1.5m temperatures are determined with a linear regression between the daily average surface temperature T o and the boundary layer temperature T a, as discussed in section 3.1.3. These are compared with the measured air temperatures at the 25 GTS stations. In 2006, the resulting average difference on a daily basis is 0.49 °C, the RMSE is 5.23°C and the correlation coefficient is 0.89. For ten daily averaged temperatures, the difference of 2006 reduces to 0.00 °C, the RMSE is 3.25 and the correlation coefficient increases to 0.96. Similar results are found for the data of 2007. Table 3.5.1 presents the summary statistics of the comparison.
The results show that the 10-daily GTS and EWBMS temperatures are closer than the daily data. For this reason it was decided to use the ten daily moving averages of the EWBMS temperature, instead of the daily values, as input for the freeze/thaw module (see section 3.1.4). The results in the table also indicates that EWBMS temperatures seem to perform evenly well in all seasons. The RMSE is smallest in winter and the correlation coefficient slightly higher in spring and autumn than in summer, but there is no large difference between the seasons. Examples of these time series are presented in figure 3.42. The agreement between GTS and EWBMS measured 1.5m air temperature is good. The presence of clouds could affect the accuracy of the
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40 53614 - 2006 30 GTS EWBMS 20
10
0
-10 1.5 m Air Temperature (°C) -20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
40 54823 - 2007 GTS 30 EWBMS 20
10
0
-10 1.5 m Air Temperature (°C) -20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
Figure 3.42: Yearly course of ten daily average 1.5 m air temperatures in 2006 and 2007 for two stations in the Yellow River basin.
EWBMS measured temperature. To investigate this influence, days with high cloud cover were omitted, retaining consecutively only those days in the data set where cloud cover from 9am until 3pm is less than 50%. Figure 3.43 shows the scatter plots of 10 daily average EWBMS versus GTS temperatures for 2007 and the influence of cloud cover on the results. When the cloudy days are eliminated minor improvement in temperature is seen in 2007: the correlation coefficient increases to 0.97 and the RMSE reduces to 2.85°C. But for the data of 2006, no improvement is obtained: the RMSE increases to 3.25°C and the correlation decreases to 0.94. So there is no clear influence of cloud cover on the accuracy of the EWBMS 1.5 m air temperature data.
Table 3.12: Average difference, RMSE and correlation of T1.5m (2006-2007) Daily temperature 10 Daily average temperature 2006 ∆ RMSE R ∆ RMSE R Year 0.46 5.22 0.89 -0.03 3.20 0.96 Winter 2.07 5.18 0.69 0.17 2.66 0.86 Spring -0.45 5.57 0.74 -0.21 3.29 0.89 Summer -0.99 5.10 0.65 -0.34 3.61 0.78 Autumn 1.47 5.07 0.84 0.39 3.34 0.92 Year -1.00 5.77 0.89 -0.20 3.11 0.96 Winter -2.51 5.30 0.66 -0.43 2.61 0.88 Spring -0.80 6.20 0.78 -0.72 3.09 0.92 Summer -0.01 6.31 0.65 -0.49 3.00 0.86 Autumn -0.55 5.19 0.70 0.92 3.45 0.88
∆ = average difference T EWBMS -TGTS .
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40 40
30 30
20 20
10 10 EWBMS (°C) EWBMS (°C)
0 0 1.5 m 1.5 m 1.5
T T -10 -10 Cloud Cover < 50% -20 -20 -20 -10 0 10 20 30 40 -20 -10 0 10 20 30 40
T 1.5 m GTS (°C) T 1.5 m GTS (°C)
Figure 3.43: Influence of cloud cover on 1.5m air temperature. The left side shows the entire dataset, the right side shows data with cloud cover lower than 50% .
The geographical distribution of errors is shown in figure 3.44. The deviations between ground and EWBMS temperatures are smaller in the northern and western part of the Yellow River basin. Higher annual rainfall in the south and the east can partially explain the slightly larger deviations. The overall agreement between the ground data and EWBMS temperatures is good. The difference with ground data is smaller for 10 daily average temperatures and cloud cover has only a moderate effect on the results. EWBMS temperatures are more precise in the northern and eastern parts of the Yellow River basin.
Figure 3.44: Geographical distribution of differences between the GTS and EWBMS 1.5m temperature in 2007. Red areas indicate larger, green areas indicate smaller deviations.
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200 ) -2 150
100
50
0 EWBMS Net Radiation (W.m
-50 -50 0 50 100 150 200 NR-Lite Net Radiation (W.m -2 )
Figure 3.45: Daily averaged net radiation determined by the NR-Lite versus EWMBS net radiation. Days on which less than 80% of data was logged are excluded.
3.5.3 Validation of net radiation
Daily averaged net radiation data from all LAS sites were compared to corresponding EWBMS values with a grid size of 0.1° by 0.1° or 11 km by 9 km. Details on the net radiation measurements and the location of the LAS sites was already presented in section 3.2.3. Figure 3.45 shows net radiation measured with the NR-Lite net radiometers in the field plotted against EWBMS net radiation. In the analysis the data measured at the four LAS stations for the period August 2005 until December 2007 are used. Data were quality checked and only days where with more than 80% of the 10 minutes values available, were included. The final dataset consists of 1066 data points.
The comparison EWBMS and NR-Lite net radiation shows that the satellite derived values and the values measured in the field are quite consistent. The relationship exhibits a Pearson’s correlation coefficient of 0.80, an average difference of 2.5 Wm -2 and a standard error of 27 Wm -2. The discrepancies between the observed and modeled values are minor with absolute differences in magnitude larger than 50 Wm -2 in only 4% of the cases.
Table 3.13: Average difference, RMSE and correlation of daily and 10 daily average net radiation (2005-2007) daily 10 daily average Nr ∆ RMSE R ∆ RMSE R (-) (W.m -2) (W.m -2) (-) (W.m -2) (W.m -2) (-) Jingchuan 597 10 24 0.88 10 15 0.93 Xinghai 192 -2 23 0.81 -2 8 0.87 Maqin 208 -11 29 0.76 -11 16 0.84 Tangke 69 -12 29 0.84 -13 14 0.86
∆: average difference RN EWBMS - R N NR-Lite Nr : number of data points used (quality controlled data with more than 80% of 10minutes logging)
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200 Jingchuan- 2006
) GTS -2 150 EWBMS
100
50
0 Net Net Radiation (W.m
-50 Mar Apr May Jun Jul Aug Sep Oct
200 Xinghai - 2007 )
-2 150
100
50
0 GTS Net Radiation (W.m EWBMS -50 Apr May Jun Jul Aug Sep Figure 3.46: EWBMS (red) and ground measured (blue) net radiation time series at Jingchuan and Xinghai
200 200 Jingchuan - 2006 Xinghai - 2007 LAS )
-2 EWBMS 150 150 Difference
100 100
50 50
0 0 Sensible Heat Flux (W. m
-50 -50 Mar May Jul Sep May Jun Jul Aug Sep
Figure 3.47: EWBMS (red) and ground measured (blue) 10-daily net radiation time series at Jingchuan and Xinghai. The difference is shown in orange.
Table 3.13 shows the yearly average differences, correlations and RMSE for daily and ten daily data at each of the four LAS sites. The relationship for the entire dataset exhibits an overall correlation coefficient of 0.85, an average difference of 2 W.m -2 and a RMSE of 25 W.m -2 for daily data. The errors are random and observed differences on a daily basis are balanced equally with 56% of the differences being positive and 44% of the differences being negative. On a ten daily basis the RSME decreases to 14 Wm -2 and the correlation coefficient increases to 0.90. The average difference remains the same.
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200 ) -2 150
100
50
0
EWBMS Net Radiation (W.m Precitation < 1mm -50 -50 0 50 100 150 200 NR-Lite Net Radiation (W.m -2 )
Figure 3.48: Daily averaged net radiation determined by the NR-Lite versus EWMBS net radiation. Data on rainy days and on days where less than 80% of the daily data were logged are excluded.
In figures 3.46 and 3.47 examples are given of a time series comparison of daily and 10-daily net radiation at Jingchuan 2006 and Xinghai 2007. The peak values of EWBMS data are less extreme than those observed with the NR-Lite. This may be due to the difference in scale of the measurements. Another potential cause for differences between the two measurements is precipitation, which may leave residual moisture on the NR-Lite instrument. Therefore all data collected during days or nights when precipitation was recorded were excluded from the analysis. Figure 3.48 shows the resulting scatter plot (656 data points). There is no notable change in the correlation coefficient or RMSE. The agreement between EWBMS and ground measured net radiation is not influenced by rainfall.
Net radiation remains among the most difficult atmospheric parameters to measure accurately on the ground. The NR-Lite is not perfect. The instrument is less sensitive to long-wave radiation than to solar radiation (Cobos and Baker, 2003). Errors can be caused by wind and precipitation. In this evaluation, only the precipitation effect has been considered. The reference cited suggests that the effect of wind is minor compared to the precipitation effects. It is likely that the differences between the EWBMS and the ground data are caused the difference in scale. The NR-Lite is measuring a small surface area of a few square meters, while the EWBMS net radiation pertains to and area of about 99 km 2.
In conclusion the net radiation values from the EWBMS correspond well with the NR-Lite measurements on the ground. The differences are small compared to the absolute values and are evenly distributed. Best results are obtained for the ten daily time scale. The data show no influence of rainfall.
3.5.4 Validation of sensible heat flux
The LAS sensible heat fluxes were averaged by day and compared to corresponding daily EWBMS values. Grid size of the EWBMS data is 0.1° by 0.1° or 11 km by 9 km. Figure 3.49 shows two examples of time series of the daily sensible heat flux
79 Satellite Water Monitoring and Flow Forecasting System for the Yellow River derived from the EWBMS and measured by LAS. In the detailed analysis, data of all four LAS stations where used for the period august 2005 until august 2008. The LAS data were quality checked and only days where more than 80% of the 10-minute readings were available, have been evaluated. The final dataset consists of 755 data points. The evaluation has shown EWBMS and LAS derived sensible heat fluxes are consistent. Fore the entire dataset the relationship exhibits a correlation coefficient of 0.64, an average difference of -1 W.m -2 and a RMSE of 16 W.m -2. The errors are random and observed differences on a daily basis are balanced well with 47% of the differences being positive and 53% of the differences being negative. On a ten daily average basis the RSME decreases to 10 W.m -2 and the correlation increases to 0.75. The average difference remains the same. Table 3.14 shows the results for each LAS site separately. The 10 daily correlation coefficients range from 0.63 to 0.83 and the RMSE’s from 8 to 13 W.m -2. The agreement is a bit better in Xinghai than at the other stations, but all stations show fair results.
A potential source for discrepancy between the EWBMS and LAS derived sensible heat fluxes is precipitation; the scintillometer beam may be interrupted by rainfall. Another potential influence is cloud cover. On cloudy days, the EBWMS results make use of the Bowen ratio of the previous day, which may not always be appropriate. Hence, the EWBMS sensible heat flux may deviate from the real sensible heat flux when cloud cover is high. For this reason, rainy days and days with high cloud cover were omitted from the dataset, retaining only those days where precipitation is zero (385 data points) and those days where cloud cover is less than 40% (325 data points). However, precipitation does not seem to affect the results. The RMSE is the same (16 W.m -2) and the correlation even decreases to 0.60. Also cloud cover does not influence the results, RMSE stays the same and correlation decreases to 0.57 on days with low cloud cover.
Although the scale of the LAS sensible heat flux measurements is of similar order of magnitude as the EWMBS results, the surface area over which the LAS is measuring is still much smaller than the EWBMS pixel. This explains why considerable differences may occur between both measurements. However, the analysis that the EWBMS sensible heat flux data are consistent with and similar to those measured with the LAS. The differences are small compared to the absolute values and distributed evenly. Better results are obtained for ten daily average values than for daily values and the presence of rainfall or cloud cover seems not to influence the results.
Table 3.14: Average difference, RMSE and correlation of daily and 10 daily average sensible heat flux (2005-2008) daily 10 daily average Nr ∆ RMSE R ∆ RMSE R (-) (W.m -2) (W.m -2) (-) (W.m -2) (W.m -2) (-) Jingchuan 337 1 16 0.615 1 9 0.796 Xinghai 207 -3 14 0.717 -3 8 0.824 Maqin 126 0 15 0.606 -3 13 0.633 Tangke 69 -2 18 0.693 -1 12 0.826
∆ : average difference H EWBMS – H LAS Nr : number of data points used (quality controlled data with more than 80% of 10minutes logging)
80 Chapter 3 – Energy and Water Balance Monitoring System
200 )
-2 Jingchuan - 2006 LAS 150 EWBMS
100
50
0 Sensible Heat Flux (W. m -50 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
200 )
-2 Xinghai - 2007 LAS 150 EWBMS
100
50
0 Sensible Heat Flux (W. m -50 May Jun Jul Aug Sep Oct Figure 3.49: Daily EWBMS (red) sensible heat flux versus LAS data (blue) at Jingchuan in 2006 and Xinghai in 2007.
200 200 )
-2 Jingchuan - 2006 Xinghai - 2007 LAS 150 150 EWBMS Difference 100 100
50 50
0 0 Sensible Heat Flux (W. m -50 -50 Feb Mar Apr May Jun Jul Aug Sep Oct May Jun Jul Aug Sep Oct Figure 3.50: Comparison of 10 daily averaged LAS and EWBMS sensible heat flux at Jingchuan in 2006 and Xinghai in 2007.
3.5.5 Validation of catchment water budget
Assuming that there is no other loss of water than evapotranspiration and that changes in catchment water storage can be neglected, the yearly net precipitation (precipitation minus evapotranspiration) in a catchment should be equal to the river discharge at the outlet of that catchment. In this section, the EWBMS net precipitation for both the Upper Yellow River basin and the Weihe basin is compared with the measured discharge. Of course, the comparison is not completely ‘waterproof’, since changes in water storage, both in the ground and as snow on the surface, may occur. Nevertheless the exercise will give a valuable indication of the suitability of the EWBMS products to be used as input for the dedicated rainfall-river runoff model, discussed in chapter 4.
81 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Upper Yellow River basin
The outlet of the Upper Yellow River basin (UYRB) is situated at the Tangnaihai hydraulic station. River discharges are derived from water levels and a historical discharge-water level relation. In figure 3.51, daily net precipitation values averaged for the entire basin are plotted with the daily averaged river discharge at Tangnaihai for the period July 2005 to June 2008. As expected, the discharge values follow the net precipitation trend with a little delay. Clearly shown are the monsoon periods from June to October, with a lot of (net) precipitation and river discharge and the dry winters in between, with small negative net precipitation values and hardly any river discharge. In between, and just before the rainy periods in summer, evapotranspiration can be quite high, because incoming radiation in these months is quite high.
In figure 3.52 the river discharge and net precipitation are plotted in a cumulative way. Also from this figure it is clear that there is hardly effective precipitation during winter, where the graphs proceed almost horizontally. However, evapotranspiration is already increasing in spring, while precipitation only starts in early summer. This means that water is stored during the winter. The cumulative river discharge follows the cumulative net precipitation very well, indicating that the water balance of the basin is derived correctly from the satellite data.
Comparing yearly discharges with yearly net precipitation is a suitable evaluation method provided that year to year changes in water storage are small. From this point of view the calendar year may not be the most suitable evaluation period. Ideally an evaluation year would starts just before the rainy season. Since most rain falls from July on, and since we have satellite data available from July 2005, we also evaluate the water balance of the catchment for yearly periods starting on July 1 st . Table 3.14 shows the yearly discharge, net precipitation, precipitation and evapotranspiration for both the calendar and the evaluation years. The results are very good; differences between yearly discharge and yearly net precipitation are always below 30% and for the entire period, the difference is less than 10%.
Weihe basin
The outlet of the Weihe basin is situated at Huaying, but the last hydrological station before the outlet is at Huaxian. The area upstream of Huaxian covers about 80% of the entire Weihe basin. River discharges measured here are derived from water levels, using a historical discharge-water level relation. Figure 3.53 shows the daily net precipitation values averaged for the Weihe basin upstream of Huaxian and the daily river discharge at Huaxian, for the period July 2005 to June 2008. Also here the discharge follows the net precipitation with some delay. However, compared to the net precipitation, the river discharge seems to be very low. Clearly shown are the monsoon periods from June to October, with a lot of (net) precipitation and relatively high river discharge and the dryer winters in between, with small negative net precipitation values and hardly any river discharge. In between and just before the rainy season, evapotranspiration is very high (negative net precipitation), because incoming radiation in these months is high.
82 Chapter 3 – Energy and Water Balance Monitoring System
12 Net precipitation 5 days-floating average 6 River discharge at Tangnaihai 10 5 8 4 6 3 4 2 2 1 River Discharge (mm) Net Precipitation(mm) 0 0 -2 -1 Jul-05 Jul-06 Jul-07 Jul-08
-4 Jan-06 Mar-06 Jan-07 Mar-07 Jan-08 Mar-08 -2 Nov-05 May-06 Nov-06 May-07 Nov-07 May-08 Sep-05 Sep-06 Sep-07
Figure 3.51: Daily net precipitation and discharge in the upper Yellow River basin.
1800 1600 Cum . evapotranspiration 1400 Cum . net precipitation 1200 Cum . precipitation 1000 Cum . river dis charge 800 600 400 200 Water(mm) 0 Jul-08 Jul-07 Jul-06 Jul-05 Jan-08 Mar-08 Jan-07 Mar-07 Jan-06 Mar-06 Nov-07 May-08 Nov-06 May-07 Nov-05 May-06 Sep-07 Sep-06 Sep-05 Figure 3.52: Cumulative net precipitation and discharge in upper Yellow River basin.
Table 3.15: Yearly net precipitation and river discharge in Upper Yellow River basin. Period Tangnaihai Net Precipitation Evapo- discharge Precipitation (mm) transpiration (mm) (mm) (mm) July '05- June '06 193 165 551 385 2006 110 80 496 416 July '06- June '07 117 151 555 404 2007 145 146 549 403 July '07- June '08 135 99 500 401 2006-2007 256 226 1045 819 Total period 444 416 1606 1191
Figure 3.54 presents the cumulative components of the EWBMS water budget and the river discharge at Huaxian. In the winter period the lines are almost horizontal whereas there is almost no effective precipitation. But, evapotranspiration is already increasing in early spring, causing a decrease in cumulative net precipitation in spring. This implies that water is withdrawn from the soil. The cumulative river discharge is smaller than the cumulative net precipitation, indicating that water balances does not completely fit: there is more net precipitation than river discharge. This is also clear from table 3.16. Here several remarks could me made.
83 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
12 Net precipitation 5 days-floating average 6 River dis charge at Huaxian 10 5 8 4 6 3 4 2 2 1 NetPrecipitation (mm) 0 0 RiverDischarge (mm) -2 -1 Jul-05 Jul-06 Jul-07 Jul-08 Jan-06 Mar-06 Jan-07 Mar-07 Jan-08 Mar-08
-4 Nov-05 May-06 Nov-06 May-07 Nov-07 May-08 -2 Sep-05 Sep-06 Sep-07
Figure 3.53: Daily net precipitation and river discharge in the Weihe basin.
1800 1600 Cum. evapotranspiration 1400 Cum. net precipitation 1200 Cum. precipitation 1000 Cum. river discharge 800 600 400 200 Water (mm) Water 0 Jul-08 Jul-07 Jul-06 Jul-05 Jan-08 Jan-07 Jan-06 Nov-07 Nov-06 Nov-05 Mar-08 Mar-07 Mar-06 Sep-07 Sep-06 Sep-05 May-08 May-07 May-06 Figure 3.54: Cumulative net precipitation and river discharge in the Weihe basin.
Table 3.16: Yearly net precipitation and river discharge in the Weihe basin. Period Huaxian Net Precipitation Evapo- discharge Precipitation (mm) transpiration (mm) (mm) (mm) July '05- June '06 66 108 562 454 2006 35 58 541 483 July '06- June '07 29 13 511 498 2007 38 69 568 499 July '07- June '08 37 93 584 491 2006-2007 73 127 1109 982 Total period 133 214 1657 1443
First the assumption that no changes in water storage take place, may not me right. In the Weihe basin there are a many reservoirs, where water is stored in wet and released in dry periods. In addition we may not be able to exclude the possibility that actual discharge may be somewhat different due to unknown leakage from the basin. Finally it should ne noted that the water balance in the Weihe is quite delicate, as runoff is only 10% of the precipitation. Therefore small errors in the precipitation and evaporation lead to much larger errors in the run-off. From this point of view the satellite based results can be considered good and certainly above our original expectations.
84 Chapter 3 – Energy and Water Balance Monitoring System
3.6 References
Andreas, E.L. (1990) "Two-Wavelength Method of Measuring Path-Averaged Turbulent Surface Heat Fluxes", J. Atmos. Ocean. Tech . 6, 280-292.
Cobos D.R. and Baker J.M. (2003) ‘Evaluation and Modification of a Domeless Net Radiometer’, Agronomy Journal , 95, p. 177–183.
Hill, R.J., S.F. Clifford and R.S. Lawrence (1980) "Refractive Index and Absorption Fluctuations in the Infrared Caused by Temperature, Humidity and Pressure Fluctuations", J. Opt. Soc. Am 70, 1192-1205.
Kohsiek, W. (1982b) "Optical and In Situ Measuring of Structure Parameters Relevant to Temperature and Humidity and Their Application to the Measuring of Sensible and latent Heat Flux" NOAA Tech. Memor. ERL WPL-96, NOAA Environmental Research Laboratories, Boulder, CO, USA, 64 pp.
Meijninger, W.M.L. (2003). Surface fluxes over natural landscapes using scintillometry, Wageningen University, 164 p.
Panofsky, H.A. and J.A. Dutton (1984) "Atmospheric Turbulence: Models and Methods for Engineering Applications", John Wiley and Sons, New York, 397 p.
Kondratyev, K.Y.(1969) ‘Radiation in the Atmosphere’, Academic Press, New York, London.
Rosema A., Snel J.F.H, Zahn H., Buurmeijer W.F., Hove van L.W.A.(1998) ‘The relation between Laser-Induced chlorophyll fluorescence and photosynthesis’, Remote Sensing of Environment, 65, p. 143-153.
Valk, P.de, Feijt A., Roozekrans H., Roebeling R., Rosema A. (1998) "Operationalisation of an algorithm for the automatic detection and characterisation of clouds in Meteosat imagery”
UNEP (1992). World Atlas of Desertification. Edward Arnold. London.
85 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
86 Chapter 4 – Large Scale Hydrological Model
4 LARGE SCALE HYDROLOGICAL MODEL
Within the Hydrological Bureau (HB) of the Yellow River Conservancy Commission (YRCC), the LSHM has been implemented for two selected areas. The implementation for the Upper Yellow River is referred to as the Water Resources Forecasting System (WRFS), which simulates the rainfall-runoff processes up to Tangnaihai station in the upper reach of the Yellow River. A second implementation has been set up for the Weihe sub-basin and is referred to as the Flood Forecasting System (HWFS), which specifically allows monitoring and short-range forecasting of high discharges during the flood season in the lower reaches of the Weihe up to the confluence with the main branch of the Yellow River.
Both implementations are based on the same model code, but are slightly adapted to specific details of the target areas. The operation and data requirements are also mostly identical.
A technical description is presented in the next two sections. Details about the temporal and spatial data for the models are described in sections 3 and 4. Evaluation of the validation results and the model performance is detailed in the last section of this chapter, which followed by a list of references cited in the text.
4.1 Technical reference
The terrain is represented on a regular two-dimensional grid for which the spacing between the grid nodes, or cell centres, may differ between the x and y directions. In the vertical direction, each grid node is characterized by a surface elevation and an elevation to an imperious base level at some depth below the surface. The main river and major tributaries form a schematic stream network that is coupled to the land cells in the topographic valleys or river pathways. Figure 4.1 shows a cross-sectional representation of the model domain geometry.
precipitation evapotranspiration
infiltration surface runoff river flow TS
subsurface flow IB z zb
grid node spacing datum
Figure 4.1: Schematic cross-section through the terrain illustrating the model processes and the model grid discretization. TS: terrain surface; IB: impervious base.
87 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The model is forced by precipitation and actual evapotranspiration, which are input as gridded fields in the spatial resolution of the model grid for each time interval. When applicable snowmelt may be added to the precipitation can be replaced by snowmelt.
Since the evapotranspiration is computed offline, there is no explicit representation of soil hydrological processes. Water infiltrating the surface (that is from precipitation minus evaporation, or snowmelt) is assumed to directly recharge a subsurface lateral flow storage that eventually drains into the stream network through which the water is transported towards the outlet as river flow. The part of the net input that does not infiltrate is routed down slope as surface runoff. The latter process is implemented to allow for sub grid parameterization of flow in the stream network that is too fine to be incorporated by an explicit river channel specification. This typically comprises the small streams and creeks in headwater areas and slopes where the topographic relief cannot be represented at the resolvable grid scale. Figure 4.1 also shows the overall connection between the processes.
The theoretical background and implementation of the processes are separately described in the subsequent sections. For a full account of the numerical solutions, the reader is referred to the user manual (Maskey and Venneker, 2008).
4.1.1 Land component transport
The surface runoff routing is carried out by draining part of the ponded water from a cell to its steepest decent neighbour cell, based on eight possible flow directions. A similar approach was applied by Arora and Boer (1999) to a variable velocity land surface water routing scheme in a general circulation model. The Manning equation for uniform flow with the wide-rectangular channel assumption is applied to parameterize the surface water discharge, i.e.:
1 q = H 3/5 S 2/1 (4.1) N
where, q is the surface discharge per unit width (L2T−1) from the cell, H is the grid cell average depth of ponded water (L), S is the topographic slope (L 1L−1) to the steepest decent neighbour cell and N is the effective Manning roughness coefficient (L −1/3 T) of the surface. A range of roughness coefficient values for various flood plain conditions are given in Arcement and Schneider (1989).
The surface storage water balance for a unit surface area is described by a non-linear ordinary differential equation, i.e.
dH 1 1 = − H 3/5 S 2/1 + q + r − I (4.2) dt ∆L N I
where ∆L is the distance between the centres of the cells in the flow direction (L), q I is the cumulative inflow from the contributing upslope cells (LT −1), r is the net rainfall or snowmelt rate (LT −1), and I is the net rate of infiltration (LT −1) into the soil surface recharging the subsurface storage.
In the absence of a soil water accounting scheme, because evaporation is obtained from the EWBMS, a subgrid parameterization for the infiltration is formulated as:
88 Chapter 4 – Large Scale Hydrological Model
* = H I min I, max (4.3) ∆t in which H* is the amount of ponded water remaining after updating for surface runoff and Imax is a prescribed maximum infiltration rate. The latter is generally depending on soil type, and is generally smaller for clayey soils than for sandy soils.
The computations are carried out from upstream to downstream to accumulate the surface runoff over all cells on the basis of pre-defined drainage direction map, which is derived from the basin topography (Jenson and Domingue, 1988). An explicit finite difference formulation of the water balance, as given in (4.2), is used to first update the head due to surface runoff, after which the infiltration is accounted for.
For subsurface flow each cell in the terrain is regarded as a conceptual storage reservoir that can be characterized by the properties and generalized flow behaviour of porous media. In order to apply a general flow formulation in view of the sparse or lacking subsurface data, it is necessary to make rigorous simplifications with respect to the geometric structure and hydraulic parameters of the storage. For each cell, the reservoir extends to a certain average depth below surface, which acts as an impervious base. Parameters for the reservoir are considered effective parameters representing average values that are valid for the full cell extents. As such, the reservoir can be considered as an idealized homogeneous and isotropic unconfined aquifer, which is being forced by the net infiltration flux at the surface and drains laterally towards its neighbouring cells. A conceptual representation of the subsurface storage geometry, and the associated hydraulic parameters and fluxes is shown in figure 4.2. The subsurface storage water balance for a unit surface area is expressed by the continuity equation:
∂ ∂ ∂ ∂ ∂ h + h = − h − D x D y n e I (4.4) ∂x ∂x ∂y ∂y ∂t
2 −1 where h is the hydraulic head (L), D is the saturated hydraulic diffusivity (L T ), n e is the effective porosity or specific yield (L 3L−3), and I is the infiltration rate (LT −1) as defined above that is recharging the reservoir. The hydraulic head is the sum of the impervious base elevation z b and the (phreatic) storage head above that base elevation η (L) i.e.
= + η h z b (4.5)
The base elevation is parameterized by a simple linear relationship with the surface elevation z, viz.
= − ( − ) z b z 10000 z s b (4.6) in which s b is a scaling parameter that can be fitted from comparison with runoff records on a (sub)catchment basis or, if available, field observations. The lateral flow per unit cross-sectional area (LT −1) is proportional to the hydraulic gradient as described by Darcy's law, that is:
∂h q = −k x e ∂x (4.7) ∂h q = −k y e ∂y
89 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
∆x
I
qx η qx
z h zb
datum
Figure 4.2:. Schematic cross-section of the grid cell subsurface storage geometry.
−1 for both directions, and in which k e is the effective hydraulic conductivity (LT ). The saturated hydraulic diffusivity, or transmissivity, is dependent on saturated thickness η (L) of the storage aquifer (e.g. Brutsaert, 2005), i.e.
= η D k e (4.8)
The flow formulation of (4.4) is discretized in time and space using an explicit finite difference scheme on a two-dimensional computational grid (e.g. Press et al., 1992). It is possible that during a time step, water may have exfiltrated at the surface if saturation excess has occurred. In such situations, the ponded water head on the surface and the subsurface hydraulic head need to be adjusted accordingly to their final values at t + ∆t.
4.1.2 River routing
The one-dimensional river flow component is based on the Muskingum-Cunge routing method (Cunge 1969) with lateral inflow. This model routes the flow through a discrete channel network from upstream to downstream points over specified time intervals ∆t. The flow propagation from time step n to n+1 between points j to j+1 in a segment of the channel network is given by:
n+1 = n+1 + n + n + Q j+1 C0Q j C1Q j C2Q j+1 C3Ql (4.9)
where, Q(j, n) is the discharge (L 3T−1) at a point j along the channel reach and time step n and Ql is the lateral inflow contribution of the land component to the river network as described in the next section. Following Ponce (1986), the coefficients in Eqn (4.8) are given by:
90 Chapter 4 – Large Scale Hydrological Model
−1+ Co + Re C = 0 1+ Co + Re 1+ Co − Re C = 1 1+ Co + Re (4.10) 1− Co + Re C = 2 1+ Co + Re 2Co C = 3 1+ Co + Re
Here, C0 is the Courant number and Re is the Reynolds number, which are obtained from
c∆t Co = (4.11) ∆s Q Re = (4.12) ∆ BS 0c s
−1 where c is the wave celerity (LT ), B is the channel top width (L), S0 is the channel bed slope and ∆s is the channel segment length (L). The wave celerity is defined as (Lighthill and Whitham, 1955)
∂Q c = (4.13) ∂ A s
in which A is the cross-section flow area (L 2), which is also a function of the discharge Q. At any time step, the values of B, A, and the water depth h (L) are dependent on the discharge, which is to be computed. Therefore, the variable parameters Co and Re are commonly evaluated from a reference discharge Q ref , which is estimated using a three-point average (Tang et al., 1999), plus the contribution by lateral inflow. With the estimated Q ref , the cross-section parameters such as water depth, top width and wet cross-section area are determined iteratively using the normal depth condition, i.e. Manning’s flow equation (e.g. Chow, 1959):
1 Q = AR 3/2 S 2/1 (4.14) n 0
where n is the Manning roughness coefficient and R is the hydraulic radius (L).
4.1.3 Land-river coupling
Following Prudic et al. (2004), the volumetric flow from the subsurface storage into the stream channel is described using Darcy’s equation
h − h Q = BLk s (4.15) l b b
where B is a representative width (i.e., the channel top width) of the stream (L), L is the effective length of the stream segment inside the grid cell (L), k b is the hydraulic
91 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
B
hr η h hs zr
datum Figure 4.3: River cell geometry and illustration of the land-river flow exchange.
−1 conductivity of the stream bed (LT ), h s is the total head (L) in the stream channel, and b is the effective thickness of the river bed material (L). If Q l is negative, the flux is removed from the river flow and adds to the head increase of the subsurface storage. Figure 4.3 shows the geometry and flow relations of a land cell that is connected to the river network. The total stream head is defined as
= + h s z r h r (4.16)
in which z r is the elevation of the stream bed (L). This is taken as
1 z = ()z + z (4.17) r 2 b
The effective thickness of the river bed material is estimated as
1 b = ()z − z (4.18) 2 r b
The above relations ensure the geometrical consistency of the three-dimensional land- channel model structure (see figure 4.3).
4.1.4 Forecasting of river flows
The large scale hydrological model, described in the previous sections, is used to setup a flow forecasting scheme. In the forecasting mode the hydrological model uses observed flows at a number of upstream hydrological stations, which are referred to as forced boundary flows, and estimated rainfall for the forecast period. The estimates of the forced boundary flows for the forecast period are based on a statistical model, whereas the estimate of the future rainfall is either based on a number of rainfall scenarios or is obtained from other rainfall forecasts. The model predicted flows at the downstream forecast locations are updated using a simple assimilation scheme of observed flows for the estimation of forecast errors. The forecasting algorithm can be described by a set of equations as following. The flow at the upstream boundary points is obtained from
3 = α + β qˆ t ∑ iq t−i (4.19) i=1
92 Chapter 4 – Large Scale Hydrological Model
where qˆ t is the estimated forced boundary flow at one time step ahead and q t−i are
the observed flows at the previous time steps. The model parameters α and βi are determined using regression analysis on daily time series of observed discharges (e.g. Alt et al., 1989). At the downstream forecast points, both a forecast value of the flow and an estimate of the prediction error are obtained through
f = M + Q t Q t eˆ t (4.20) 2 = θ + φ − M eˆ t ∑ i (Q t−i Q t−i ) (4.21) i=1
f M where Q t is the forecast flow at one time step ahead, Q t is the model predicted flow M at one time step ahead, eˆt is the estimated error on the model predicted flow ( Q t ) M and Q t−i and Q t−i are observed and model simulated flows, respectively, at previous
time steps. The parameters of the error model θ and φi are determined using regression analysis on historic forecasts at that point (e.g. Alt et al., 1989).
4.2 System implementation
4.2.1 Software components
A schematic overview of the major system components is shown in figure 4.4. The structural core of the LSHM is a distributed hydrological process model that requires time-varying input of rainfall and actual evapotranspiration. The parameters for the model are related to the hydraulic properties and configuration of the topography, soils, land cover and stream channels. These data are typically obtained from global data sets available through internet and published maps, such as those found in YRCC (1987). The model parameters generally require calibration adjustment through evaluation of simulation output against observation data, in order to produce favourable results for the resolvable grid scale (see figure 4.4). For further details on hydrological models and hydrological modelling practice, the reader is referred to the general literature (e.g. Beven, 2001).
Figure 4.4. Schematic representation of the major components of the river monitoring system .
93 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The rainfall and evaporation inputs for the model are produced by the EWBMS from satellite remote sensing and ground observation data (see figure 4.4). The EWBMS produces near-real-time grid fields that are used as input for the LSHM, which is structured on a two-dimensional grid with a spatial resolution matching that of the input (approximately 5 by 5 km). From the storages on and below the land surface, the water is transported towards a one-dimensional stream network representation, which subsequently routes the water downstream along the river channels towards the outlet of the model basin. At the end of a simulation run, the internal storage can be saved in a file that can be used to specify the initial conditions at the start of continuation run. Alternatively, the initial storage can be specified for example as a constant value for the entire model grid.
Simulations can also be carried in forced boundary mode. When running in this mode the observed discharge at some upstream station in the river network is used as specified boundary condition for the period of simulation. The part of the basin that is upstream of the boundary inflow point is then effectively cut off from the model area. Figure 4.5 shows an overview of the system when running in forecast mode. In this case the flow data of the station for which the forecast is to be made are assimilated into the model from the real time observation data base. Then, the model is run in combination with a user-specified rainfall scenario to produce a forecast of the discharge for the next time interval. While running in operational forecast mode, the model must be optimally calibrated and initialized.
Figure 4.5. Schematic representation of the forecast mode extension components.
4.2.2 User interface
The model is specifically designed to be used within an operational environment consisting of a data base infrastructure connected with client machines through a local area network. Details of the systems integration in the Bureau of Hydrology data base and flood forecasting environment are outlined in chapter 5 of this report. A full account of the user interface is presented in the User Manual (Maskey and Venneker, 2008). Here, the description is restricted to an overview of the general characteristics. The user interface is created in such a way that the model can also be run as a stand-alone implementation, provided that the input data is available locally. From a user point of view, this makes no difference. Besides the start-up screen, there is no difference between the menu structure of the WRFS and the HWFS.
At the top level, a distinction is made between general file utilities, simulation and graphical visualization, each with its own pull down menu. Simulation and graphic
94 Chapter 4 – Large Scale Hydrological Model view facilities have a hierarchical structure that precludes actions to be performed if the previous step has not been made first. Submenus within the simulation tree allow to: • specify the simulation control, such as timing and initial state • prepare the simulation, i.e. read and preprocess the input data • run the simulation, i.e. execute the model • save the simulation result output for analysis
The latter action, in particular, will also save a large number of internal state data that can be analyzed separately if required, but generally not as part of the operational model running. The preparation and simulation itself will notify the user when ready, and during model execution the progress is shown in the status bar section of the main screen. Optionally, precipitation input can be processed separately for sub- basins for later water balance comparison.
The graphic view menu has options to view the results itself or to compare the results against observed discharge data. Further selections allow to choose a station from an internally stored list or to choose independent points along the river channel network and will show the hydrograph for the whole simulation period in a separate graphics window. When output is displayed for a station at which an observational record is available, a result summary is printed below the hydrograph to enable the operator to carry out an objective analysis of the simulation results. The summary includes the mean observed and simulated discharges, the mean error (model bias), the root mean squared error and the coefficient of efficiency, as well as the simulated and observed volumes and their percentage difference.
The forecast mode options are presented in a separate popup form in which the operator selects the timing parameters and chooses from one or more likely rainfall scenarios (see also figure 4.5). As with running in simulation mode, the forecast results can be displayed graphically in a result screen.
It is noted that the graphics facilities are only intended for an initial assessment by the operator, and when running as stand-alone model. Since the results are also transferred to the data base, a much more detailed selection for visualization is generally made from within the Yellow River Flood Forecasting System that is directly connected with the data base infrastructure. This allows for a uniform presentation of all data, both observed and simulated and is the preferred platform to select and compile results for further assessment.
A full account of the user-changeable parameters and their values as set upon delivery is given in the User Manual (Maskey and Venneker, 2008). These parameters can be adjusted through a carefully designed and executed re-calibration process, when more data have become available, or alternatively, when conditions have significantly changed to justify the attempt to optimize the settings in order to warrant an improved performance.
95 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
4.3 Upper Yellow River Water Resources Forecasting System
4.3.1 Description of the data requirements
Atmospheric input data for WRFS are required on a daily time interval:
1. EWBMS daily precipitation fields 2. EWBMS daily actual evaporation fields
The projection of the satellite-derived input forcing data to the model grid specification for the upper Yellow River model is carried out by the EWBMS. It is noted that only liquid precipitation is to be provided as input. In cases where there is a melting snow cover present in the area, the snowmelt is aggregated into the precipitation fields by the EWBMS. For the WRFS, this is simply treated as a water mass influx to the system.
In order to produce the output required for visualization and to evaluate performance statistics, the WRFS needs daily average river discharges from one or more of the following stations (see figure 4.6), with latitude and longitude in decimal degrees and elevation in metres:
Tangnaihai 100.15 E, 35.50 N, 2665 Jungong 100.65 E, 34.70 N, 3079 Maqu 102.08 E, 33.97 N, 3400 Dashui 102.27 E, 33.98 N, 3400 Tanke 102.47 E, 33.42 N, 3410 Jimai 99.65 E, 33.77 N, 3948 Huangheyan 98.17 E, 34.88 N, 4215
Users can add or remove stations as required. It is not problematic if discharge data for a station are not available. The model will run as usual and output results, but it is not able produce the statistical comparison with measured data. If part of a station time-series record is missing, it will not be used to evaluate the comparison statistics.
4.3.2 Description of the terrain data
The model grid uses a conformal conic projection with the following specifications:
Projection: Lambert conformal conic (Beijing 1954 parameters) Ellipsoid: Krasovsky 1940 Central meridian: 108 E Reference latitude: 28 N Standard parallel 1 34 N Standard parallel 2 39 N False easting: 500,000 m False northing: 0 m
Upper Y boundary: 898,770 m Lower Y boundary: 473,770 m Right X boundary: 76,000 m Left X boundary: −609,000 m Grid rows: 85; node spacing: 5000 m Grid columns: 137; node spacing: 5000 m
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Note that grid boundaries refer to the cell edges. Grid rows are counted along the Y dimension, grid columns are counted along the X dimension. The X and Y directions for the projected grid are not parallel to the E-W and N-S directions, respectively. See Snyder (1987) for further details on the applied map projection.
The elevation data were obtained from the Shuttle Radar Topography Mission data set with a resolution of 30 arc seconds (SRTM30), see Farr and Kobrick (2000). The data were obtained from URL ftp://e0srp01u.ecs.nasa.gov , using tiles E60N40 and E100N40 from the version 2 distribution. Re-projection and aggregation of the raw data into the target grid specification above was carried out using standard GIS software.
The stream network (see Fig. 4.6) and (sub)basin boundaries extracted from the DEM using proprietary software that enables to specify the network start and end points as required, and follows largely the overall procedure described by Jenson and Domingue (1998), which entails the following steps:
1. Removal of spurious pits and other artefacts that would otherwise disrupt the flow pattern. 2. Creation of a flow direction grid, which is used for subsequent analysis and processing steps. 3. Delineation of drainage accumulation area (upslope catchment area) and (sub)basin boundaries, using the method by Marks et al. (1984). 4. Extraction of the drainage channel network by automatically following the flow direction grid down slope from the assigned stream start points to the outlet or higher-order stream (see also O’Callaghan and Mark, 1984).
The pit removal was carried out manually in an iterative fashion to ensure that the resulting drainage pattern and delineated (sub)basin boundaries (figure 4.6) are consistent with those found in the printed maps (YRCC, 1987).
850000 Tangnaihai
800000 Huangheyan
750000 Jungong
700000
Jimai Maqu Dashui 650000
Tanke 600000
550000
500000
-600000 -550000 -500000 -450000 -400000 -350000 -300000 -250000 -200000 -150000 -100000 -50000 0 50000
Figure 4.6: Layout of the WRFS catchment and stream network geometry .
97 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Auxiliary terrain data used to obtain first-guess estimates of model parameters are soil maps (FAO-UNESCO, 2003) and land cover data from the Global Land Cover Characteristics data set, obtained from URL http://eros.usgs.gov (see Loveland et al. 2000, for details). For river cross-section geometry, use was made of survey data from YRCC where available.
4.4 Weihe basin High Water Forecasting System
4.4.1 Description of the data requirements
Atmospheric input data for HWFS are required on a daily time interval:
1. EWBMS daily precipitation fields 2. EWBMS daily actual evaporation fields
The EWBMS outputs for the Wei River are matched to the model grid by the HWFS LSHM using bilinear interpolation. The EWBMS fields for the Weihe model are different depending on the satellite (GMS or FY2C) resolution and geo-reference, and are slightly larger than the model grid extents. Snow accumulation and ablation are dealt with in a similar fashion as is done for the WRFS (see above).
Daily average river discharge data can be provided for the following stations (see Fig. 4.7), with latitude and longitude in decimal degrees and elevation in meters:
Huaxian 109.77 E, 34.58 N, 346.2 Zhuangtou 109.83 E, 35.05 N, 375.0 Lintong 109.20 E, 34.43 N, 357.3 Maduawang 109.15 E, 34.23 N, 438.9 Zhangjiashan 108.60 E, 34.63 N, 476.3 Xianyang 108.70 E, 34.32 N, 390.4
Users can add or remove stations as required. The treatment of missing river flow observation data is similar to that of the WRFS as described above.
4.4.2 Description of the terrain data
The model grid is specified for a latitude-longitude coordinate system as follows:
Projection: Geographic latitude-longitude North boundary: 37.55 N South boundary: 33.45 N East boundary: 110.55 E West boundary: 103.45 E Grid rows: 82; node spacing: 0.05 deg (5547.5 m, av.) Grid columns: 142; node spacing: 0.05 deg (4535.3 m, av.)
Note that grid boundaries refer to the cell edges. Grid rows are counted along the N-S dimension, grid columns are counted along the W-E dimension. Metric node distances for the Wei River grid are latitudinal range averages for the WGS 84 ellipsoid. In any case, node spacing is measured with respect to the reference ellipsoid and changes with altitude of the terrain. See Snyder (1987) for further details on map projections.
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The elevation data were obtained from the Shuttle Radar Topography Mission data set with a resolution of 30 arc seconds (SRTM30), see Farr and Kobrick (2000). The data were obtained from URL ftp://e0srp01u.ecs.nasa.gov , using tile E100N40 in the version 2 distribution. Since the raw data are in a latitude-longitude grid, the only processing step carried was to aggregate the elevations to the target resolution by averaging. The extraction of the stream network and the basin delineation were carried out as described for the upper Yellow River above. The stream network layout is depicted in figure 4.7. Auxiliary terrain data used to obtain first-guess estimates of model parameters are soil maps (FAO-UNESCO, 2003) and land cover data from the Global Land Cover Characteristics data set, obtained from URL http://eros.usgs.gov (see Loveland et al. 2000, for details). For river cross-section geometry, use was made of survey data from YRCC where available.
Weihe LSHM Stream Network Grid G55
Rev 2006-05-24 37.5
25 7 24 8 37 6 23 23 7 24 20 22 22 6 36.5
21 5 15 21
13 20 14 4 36 40 4 39 19 14 19 5 13 18 3 3 12 2 39 18 35.5 12 38 35 2 15 37 10 17 46 45 16 1 1 41 10 11 3738 17 33 32 Liulin Zhuangtou 45 34 11 35 16 Yaoxian32 47 34 36 46 41 Chunhua 44 42 44 43 40 847 Zhangjiashan 9 Huaxian 42 36 9 Luofubu 43 Taoyuan 33 34.5 25 31 Lintong 35 Xianyang29 26 28 Maduwang 27 30 30 29 Luolicun 26 QinduzhenGoaqiao Dayu 31 34 28 27
33.5 103.5 104 104.5 105 105.5 106 106.5 107 107.5 108 108.5 109 109.5 110 110.5
Figure 4.7: Representation the Weihe basin HWRF stream network layout.
4.5 Evaluation of simulation results
4.5.1 Validation data
Input data for validation were produced by the EWBMS, delivering daily values of precipitation and evaporation. The period of time is from 2006-01-01 to 2008-07-31, covering a little more than 2.5 years (equivalent to 943 days), and almost three rainfall seasons. All input data used for validation are derived by the EWBMS from FY2C satellite imagery obtained during the demonstration phase of the project. This choice to use exclusively FY2C data for validation is prompted by (i) the fact that both the models and the EWBMS have been progressively adapted and optimized for this input type during the latter parts of the testing phase, and (ii) that this particular satellite will be used for further operation. Although FY2C data are available since approximately mid-2005, the starting point for validation is deliberately chosen at the beginning of the next calendar year, i.e. during the low flow period, in order to minimize the uncertainty related to establishing realistic initial storage conditions of the model. Given the relatively short period for which data are available, sensitivity to initial conditions could adversely affect the validation outcome.
99 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Validation data used for the Upper Yellow River WRFS model consisted of mean daily discharge, measured over 24 h intervals starting from 08:00 local time, obtained at four hydrological stations. The discharge stations are located in the main branch of the Upper Yellow river as indicated in Table 4.1 (see also Fig. 4.6). The station of Jungong has a relatively short period of missing observation data, approximately 100 days in the first four months of 2006. Otherwise, the station records are complete for all four stations during the entire validation period.
Validation data used for the Weihe Sub-basin HWFS consisted of mean daily discharge, measured over 24 h intervals starting from 08:00 local time, obtained at the hydrological stations Huaxian and Lintong. These stations are located relatively close to each other. Note that the Huaxian hydrological station does not measure the flow contributed by the Beiluohe tributary (the last major tributary in the in eastern part of the basin, see figure 4.7). Data from this and the other major northern tributaries are only sparsely available or are not representing daily mean values. Except for the lower Weihe valley, stations in most of the Weihe basin fall outside the responsibility of the YRCC. Due to the unstable bed conditions in the outlet reach of the Weihe River, there is no discharge measurement station capturing the flow of the entire Weihe sub-basin at the confluence with the Yellow River.
The performance of the models is assessed by several objective criteria. The squared correlation coefficient (in the statistical sense: the coefficient of determination), is indicated by R2, which is determined from a non-weighted linear regression between the observed and the simulated discharge series. The value ranges from [−1;+1] with the positive limit indicating perfect fit. A similar measure of fit is the coefficient of (model) efficiency (COE) as defined by Nash and Sutcliffe (1970), viz.
N ( − )2 ∑ Qi,obs Qi sim, = − i=1 COE 1 N (4.22) ()Q − Q 2 ∑i=1 i,obs avg
Table 4.1: Location characteristics of discharge stations used for model validation Upper Yellow River Weihe Sub-basin Station location Drainage area (km 2) Station location Drainage area (km 2) Tangnaihai 118,725 Huaxian 106,300 Jungong 97,825 Lintong 93,700 Maqu 86,725 Jimai 45,800
Table 4.2: Spatial variation ranges of model parameters used for validation runs . Parameters Upper Yellow River Weihe River Maximum infiltration rate (mm/day) 4-50 50-200 Hydraulic conductivity (mm/day) 1000 50-200 Effective porosity 0.25 0.25 Scaling parameter for base elevation 200 500 River bed thickness factor 1 2 Limit on land grid cell to river bank slope Maximum 1:500 1:100 Minimum 1:2000 1:2500 Manning’s roughness coefficient River flow 0.05 0.04 Surface flow 1 0.5
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where the subscripts denote observed, simulated and average of the discharge, and the index i refers to the individual data points from a series of length N data. The COE value ranges from minus infinity to +1 (exact fit). As a special case, a value of zero indicates that the model behaves no better than assuming that the average discharge is the best predictor. The model bias is given by:
= − BIAS Qsim, avg Qobs, avg (4.23)
which indicates whether there is a general tendency towards over- or underestimation of the model. The bias is also reported as a percentage error of the cumulative discharge volume during the simulation period. Furthermore, the root mean squared error (RMSE) and its normalized version, the relative root mean squared error (RRMSE) are computed by
N ()− 2 ∑ Qi,obs Qi sim, RMSE = i=1 N (4.24) RMSE RRMSE = Qavg
respectively, where the symbols are as defined above. The RMSE and RRMSE are expressed in the same units as the discharge. Table 4.2 lists the spatial range of variation in the model parameters for both implementations as determined by successive calibration runs. The values are probably not optimal and may be revised when more data are available.
4.5.2 WRFS validation results
The initial state of the Upper Yellow River WRFS has been setup by using an independent spin-up series for 11 years of input data derived from interpolated GTS observations for the period 1991-2001 (obtained from NOAA-NCDC), in combination with evaporation computed by a land surface model processed on a half- degree grid. On average some ten GTS gauges are reporting on a daily basis inside or in the proximity of the Upper Yellow River drainage basin. Although the reliability of the input and the accuracy of the evaporation computations have not been verified except roughly for annual water balance checks, the spin-up procedure is assumed to result in reasonably well-behaved initial storage conditions for low-flow situations, compared to a no-knowledge scenario. The observed and simulated hydrographs resulting from the validation run for each of the four stations are shown in figure 4.8.
Table 4.3: Model performance for the Upper Yellow River WRFS. Criterion Hydrological station Jimai Maqu Jungong Tangnaihai forecast *) R2 0.80 0.82 0.80 0.80 0.93 COE 0.77 0.82 0.80 0.80 0.84 RMSE (m 3/s) 55.5 128.2 162.3 189.3 161 RRMSE 0.45 0.38 0.37 0.39 0.17 BIAS (m 3/s) 21.9 −2.1 2.6 −3.24 −78 % volume error 17.9 −0.61 0.6 −0.67 Drainage area (km 2) 45800 86725 97825 118725 *) 24 h forecast results.
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Figure 4.8: Simulation results for the Upper Yellow River WRFS during the validation period.
Figure 4.9: Results of daily 24 h forecast runs for Tangnaihai from June 1 st to November 27 th , 2007.
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Figure 4.10: Scatter plots of the validation run for the Upper Yellow River WRFS.
Figure 4.11: Histograms of the simulation error distributions from the Upper Yellow River WRFS validation run. There are 20 bins over the full scale on the x-axis.
103 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Regression lines for the validation run at the same stations are presented in figure 4.10. Furthermore, figure 4.11 shows station histograms of the simulation error at the four stations. Figure 4.9 shows the hydrograph for daily 24 h forecasts at Tangnaihai during the 2007 rainfall season. The overall model performance results using the criteria described in section 4.5.1 are presented in table 4.3 below.
4.5.3 HWFS validation results
Due to lack of long-term auxiliary rainfall and runoff data for the Weihe sub-basin, the initial state of the HWFS could only be established by using the available data for 2006 and 2007. Starting from an arbitrary state, the two-year input has been recycled through sequential model runs whereby the resulting state at the end of each run was used as initial conditions for the subsequent run. This procedure has been repeated four times to cover a total spin-up period of eight years. A procedure such as this should be considered as a last resort, only to be used if nothing else is available and does not permit to make statements about the reliability of the initial conditions.
Because YRCC does not have access to stream information from stations outside the lower Weihe valley, and because of the numerous hydraulic operations in the Weihe basin at large, it was decided to carry out the validation by running the model in forced boundary mode, driven at Xianjiang in the main river branch, at Zhangjiashan upstream of the outlet of the Jinghe, and for the smaller southern tributaries at Qinduzhan and Maduwan. Note however, that for the last three stations, large parts of the annual record data are not available. Particularly for the Zhangjiashan station this means that part of the flow was driven from normal simulations in the Jinghe basin, which has more than 80 reservoirs in its 42,000 km 2 catchment area.
The observed and simulated hydrographs resulting from the validation run for the stations are shown in figure 4.12. Note that the station at Lintong, as many other stations in the lower Weihe region, is only operated during the flood season, generally from June to September. As a result, a simulated-observed flow comparison for the base flow periods cannot be made. Regression lines for the validation run at the same stations are presented in figure 4.14. Furthermore, figure 4.15 shows station histograms of the simulation error at the validation stations. Figure 4.13 shows the hydrograph for daily 24 h forecasts at Huaxian during the 2007 rainfall season. The overall model performance results using the criteria described in section 4.5.1 are presented in table 4.4 below.
Table 4.4: Model performance for the Weihe sub-basin HWFS Criterion Hydrological station Lintong Huaxian forecast *) R2 0.75 0.80 0.79 COE 0.71 0.79 0.75 RMSE (m 3/s) 97.0 63.5 110 RRMSE 0.46 0.50 0.37 BIAS (m 3/s) −9.1 −14.1 -47 % volume error −4.4 −11.1 Drainage area (km 2) 93700 106300 *) 24 h forecast results.
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4.5.4 Discussion
The hydrograph results for the Upper Yellow River WRFS show a consistent result for all four stations (figure 4.8). The peak discharges appear to be over-estimated in all stations except at the beginning of the simulation period. This may to some extent be explained by a slight over-estimation in the area upstream of Jimai, covering about one-third of the basin, although the effect is somewhat further enhanced between Jimai and Maqu. Precipitation in the upper part of the basin is sparsely monitored,
Figure 4.12. Simulation results for the Weihe sub-basin HWFS during the validation period.
Figure 4.13: Results of daily 24 h forecast runs for Huaxian from June 1st to November 27th, 2007.
105 Satellite Water Monitoring and Flow Forecasting System for the Yellow River and is characterized by very high altitudes, which makes a proper water balance analysis impossible. Minor deviations between observed and simulated discharges appear in the beginning of the runoff seasons at the onset of snowmelt, which may differ for altitudinal ranges.
The performance and error indicators for the upper Yellow River (table 4.4) show values that can be considered generally good, considering the extent of the area. The average error between observed and simulated discharge is small compared to the range of flow, always less than 10% of the maximum peak flow during the simulation period. With the exception of Jimai, simulations for the stations Tangnaihai, Jungong and Maqu show a negligible bias and water balance error. The slightly larger values for Jimai, with a positive bias indicating over-estimation of discharge are probably related to the explanation above. It is noted that the base flow simulations for Jimai are not deviating from the observations (figures 4.8 and 4.10). Moreover, the scatter plots of figure 4.10 show little spread in the higher discharge regime, which is not commonly seen in hydrological modeling studies. This is also apparent from the error distributions of figure 4.11. The vast majority of the simulation errors are located in the centre. Both figure 4.10 and 4.11 also show the over-estimation of peak flows, which results in slightly skewed error distributions.
Figure 4.14: Scatter plots of the validation run for the Weihe sub-basin HWFS.
Figure 4.15: Histograms of the simulation error distributions from the Weihe sub- basin HWFS validation run. There are 20 bins depicted over the full scale on the x- axis.
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The daily 24 h forecasts for Tangnaihai during the 2007 rainfall season compare well with the observed discharges, as is shown in figure 4.9. Moreover, from table 4.3 it is seen that the fits have improved with respect to the simulation runs. The bias error, however, has reduced but is still relatively small compared to the magnitude of the discharge. This may possibly be improved by a more elaborate data assimilation scheme as well as using alternative rainfall forecasts instead of simple scenarios.
The situation for the Weihe HWFS is more difficult to assess. As can be seen from the hydrographs (Fig. 4.12), the response to rainfall is very fast, in reality often rising to peak discharge in much less time than the 24 hour observation and simulation intervals. Moreover, the area is characterized by a very large number of hydraulic operations. There are hundreds of reservoirs plus a large number of diversions present in the area, mostly for, but not limited to, irrigation purposes. In general, details of the operations are unknown, but from comparing mean daily and instantaneous discharge data, and rainfall data for a small number of situations, it appears that at least some reservoirs are releasing water very quickly, resulting in flow characteristics that cannot be explained from the rainfall data.
Nevertheless, the performance indicators for the Weihe sub-basin HWFS summarized in table 4.4 show reasonable model behavior, with only the R2 and COE for Lintong being slightly less than those for the stations in the upper Yellow River. The overall root mean squared error is less than 10% of the higher peak flows during the simulation period (see figure 4.12). The bias and volume error are negligible compared to the observed range of variation. Contrary to the simulations for the upper Yellow River, the scatter plots for the Weihe simulations do show a larger variation in the higher flow regimes (figure 4.14). This can probably be explained by the reservoir operations taking place during high water periods. The simulation error distributions presented in figure 4.15 show that the errors are located in a narrow band around the zero point and in terms of frequency fall off rapidly for the larger error ranges. This is in line with the rapid response of the area resulting in narrow rainfall-runoff response peaks with a considerable peak flow-base flow range.
The daily 24 h forecasts for Huaxian during the 2007 rainfall season compare favorably to the observed discharges, as is shown in figure 4.13. Contrary to the upper Yellow River, the fits are slightly less with respect to the simulation runs (table 4.4). The major problem for forecasting flows in the Weihe area are probably related to the reservoir operations. Although difficult to achieve, inclusion of reservoir outflow data in the model scheme will be required to obtain improved forecast results.
It is obvious that a period of only two-and-half years is short for a comprehensive model performance assessment. The reasons for this are largely related to the availability of data, with the FY2C imagery becoming available only in mid 2005. It is therefore recommended to keep monitoring the model output during the coming years in order to collect a more complete set of data that can be used for further calibration and validation at a later stage. Furthermore, it would be useful for the Weihe in particular to increase the monitoring frequency to four or six synoptic times per day, and to investigate the possibility of acquiring (real-time) information of the major hydraulic operations. This would also enable to identify and improve shortcomings with the outlook on further optimizing the performance, which would in turn improve the quality of the services that can be delivered from the results.
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4.6 References
Alt, F.B., Hung, K., and Wun, L.-M. (1989) Time Series Analysis, In: Wadsworth, Jr., H.M. (ed.) Handbook of Statistical Methods for Engineers and Scientists , Chapter 18, McGraw-Hill.
Arcement, G.J., Jr., and Schneider, V.R. (1989) Guide for Selecting Manning’s Roughness Coefficients for Natural Channels and Flood Plains, USGS Wat. Supply Pap. 2339, U.S. Geological Survey, Denver, CO.
Arora, V.K. and Boer, G.J. (1999). A variable velocity flow routing algorithm for GCMs, J. Geophys. Res. , 104(D24):30965-39979.
Beven, K.J. (2001), Rainfall-Runoff Modelling, The Primer , John Wiley and Sons.
Brutsaert, W. (2005) Hydrology, an Introduction , Cambridge Univ. Press.
Chow, V.T. (1959), Open-Channel Hydraulics , McGraw-Hill Book Company, Inc.
Cunge, J.A. (1969), On the subject of a flood propagation method (Muskingum method), J. Hydraul. Res. , 7:205-230.
EARS (2005) Manual for the EARS Energy and Water Balance Monitoring System (EWBMS), EARS, Delft, the Netherlands.
FAO-UNESCO (2003) The Digital Soil Map of the World, Version 3.6 , Food and Agriculture Organization of the United Nations, Rome, Italy.
Farr, T.G., and Kobrick, M. (2000) Shuttle Radar Topography Mission produces a wealth of data. Eos, Trans. Am. Geophys. Union , 81:583-585.
Goode, D.J., and Appel, C.A. (1992) Finite-difference Interblock Transmissivity for Unconfined Acquifers and for Acquifers Having a Smoothly Varying Transmissivity, USGS Wat. Resour. Investigations Report 92-4124. U.S. Geological Survey, Denver, CO.
Harbaugh, A.W. (2005) MODFLOW-2005, the U.S. Geological Survey Modular Ground-water Model, the Ground-water Flow Process, U.S. Geological Survey Techniques and Methods 6-A 16. U.S. Geological Survey, Denver, CO.
Jenson, S.K., and Domingue, J.O. (1988) Extracting topographic structure from digital elevation data for geographic information system analysis, Photogramm. Eng. Remote Sens. , 54:1593-1600.
Lighthill, M.J., and Whitham, G.B. (1955) On kinematic waves, I. flood movement in long rivers, Proc. Roy. Soc. London , 229A:281-316.
Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, J, Yang, L., and Merchant, J.W. (2000) Development of a Global Land Cover Characteristics database and IGBP DISCover from 1-km AVHRR data: Int. J. Remote Sensing , 21:1303-1330.
Marks, D., Dozier, J., and Frew, J. (1984) Automated basin delineation from digital elevation data, GeoProcess. , 2:299-311.
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Maskey, S., and Venneker, R. (2008) Large-Scale Hydrological Model for the Satellite-based Water Monitoring and Flow Forecasting System in the Yellow River Basin, User manual, Version 3, UNESCO-IHE Institute for Water Education, Delft, Delft, the Netherlands.
Nash, J.E., and Sutcliffe, J.V. (1970) River flow forecasting through conceptual models. Part I – A discussion of principles, J. Hydrol. , 10:282-290.
O’Callaghan, J.F. and Mark, D.O. (1984) The extraction of drainage networks from digital elevation data, Comput. Vision Graphics Image Process. , 28:323-344.
Ponce, V.M. (1986), Diffusion wave modeling of catchment dynamics, J. Hydr. Engrg. , 112(8), 716–727.
Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. (1992), Numerical Recipes in C. The Art of Scientific Computing , Cambridge Univ. Press.
Prudic, D.E., Konikow, L.F., and Banta, E.R. (2004) A New Streamflow-routing (SFR1) Package to Simulate Stream-Aquifer Interaction with MODFLOW-2000, USGS OFR 2004-1042, U.S. Geological Survey, Denver, CO.
Snyder, J.P. (1987) Map Projections – A Working Manual , USGS Professional Paper 1395.
Tang, X.-N., Knight, D.W., and Samuels, P.G. (1999), Volume conservation in variable parameter Muskingum-Cunge method, J. Hydr. Engrg. , 125:610-620.
YRCC (1987), Huanghe River Valley Atlas . Yellow River Conservancy Commission Publishing, Zhengzhou, China.
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110 Chapter 5 – System Implementation at YRCC
5 SYSTEM IMPLEMENTATION AT YRCC
5.1 System set-up
5.1.1 Satellite data receiving and processing system
The meteorological satellite data reception and processing system, is a PC-based system, developed by Shinetek Satellite Application System Engineering Co. Ltd in Beijing to receive, process and display FY-2c (or MTSAT) S-VISSR data. FY-2c is a Chinese geostationary meteorological satellite, located at 105 o E. The system has been installed at the YRCC office in Zhengzhou and Lanzhou in April 2005. Since then the YRCC Hydrology Bureau receives and processes hourly meteorological satellite images for the use with the EWBMS. This includes, after processing and projection, the creation of the VIS and IR band image formats that serve as input to the EWBMS. All the files are put on the project FTP server.
Signal processing procedure
The high-frequency satellite signal (FY-2c: 1687.5 MHz, MTSAT: 1687.1 MHz) is received by a parabolic antenna with a diameter of 3m. The satellite signal is amplified and converted in the high-frequency unit to the first intermediate-frequency of 137.5 MHz. This signal is sent to the receiver by cable, where it is converted by filtering and amplifying to the second intermediate-frequency (10.7 MHz). After demodulation, the fundamental signal of 660 kbps is generated. This signal is send to a bit synchronizer for clock extraction and code conversion, and then to a frame synchronizer for frame synchronous signal detection, channel separation and data format conversion. Finally the data are transmitted to a computer for storage and later processing by the EWBMS system to various products.
Figure 5.1: Satellite receiving antenna in Zhengzhou (middle).
111 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 5.2: The satellite receiving computer and receiver in Zhengzhou.
System configuration
The Meteorological satellite data reception and processing system is a PC-based system (see figure 5.2) that consists of the following components: • Parabolic antenna of 3 m diameter • Feed horn • Low noise amplifier with LNA down converter (high-frequency unit) • High-frequency cable • PCI ingestor card • 2 PC’s • 2 monitors • Software
Receiving computer
The Geostationary meteorological satellite receiving software (GeoReceive) is an important component of the front-end of the entire system. The software is permanently running and managing the data reception according to a timetable set by the user. The software interface will hide during idle time and pop-up and enter in receiving mode on time of data reception. The software automatically adds year, month, day, hours, minutes, seconds and milliseconds, scanning line number, and automatically superimposes a latitude-longitude grid, as well as provincial boundaries, rivers and lakes. Figure 5.5 shows the interface of the GeoReceive software. Automatic projection software (AutoProject) allows automatic projection of the received data according to parameters set by the user. The software also transmits the projected files to the back-end and external servers. Figure 5.6 show the software interface. The data product is automatically shown after reception.
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Figure 5.3: Satellite data receiver hardware configuration.
Processing computer
The main processing software is on the processing computer and consists of software for image processing, product generation, product storage in a database and the catchment monitoring website. The image processing and product generation software processes the satellite data to cloud products, which includes geographic overlays, moving clouds animation, and automatic storage in the MYSQL database.
5.1.2 Computer network
The LAN at the YRCC Hydrology Bureau has a double star topology. There are two Cisco 6500 switches in the network core, and many Cisco 3500 switches, which together with the 1000 Mb web backbone make up the LAN. The server is placed in the Hydrology Bureau network room, which realizes the network mutual connections in the fastest and the most reliable way. Figure 5.4 shows the structure of the Hydrology Bureau LAN.
Figure 5.4: Structure of the Hydrology Bureau LAN .
113 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 5.5: The interface of the GeoReceive software .
Figure 5.6: The interface of AutoProject software.
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FY-2c Satellite Receiving PC Server / Database WEB site
Energy and Water Balance WMO-GTS monitoring PC
Present Hydro models Hydro-info Runoff and flood Server forecasting PC
Figure 5.7: System component integration structure
For the Sino-Dutch Project, all the systems are running and connecting to the LAN, in particular the satellite receiving and processing system, the EWBMS, the runoff forecasting system for the Yellow river upper reach, and the flood forecasting system for the lower Weihe, as well as the catchment monitoring website system that publishes the data on the internet. The detailed integration of the system hardware components with the LAN is shown in figure 5.7.
5.1.3 Data base
The real-time water information of both the source area of the Yellow River and the Weihe River are available through the Real-time Water Information Database (RWDB) of the Yellow River. These data are loaded into the Sino-Dutch Database by a special program. The RWDB is built as an individual system. There are three main steps in the data reporting system: the reporting of the hydrological stations, the transmission by the sub-centres, and the reception, translation and storage by the centre in Zhengzhou. The RWDB is based upon the Sybase DB management system. There 13 types of real-time data in the data base, including precipitation, water level, discharge and evaporation.
According to the requirements of Sino-Dutch project, a special program was developed, which can automatically read the real-time rainfall data (hourly, daily and dekadly) and hydrological data (water level, discharge, etc.) from RWDB and write these data into the rainfall table and hydrological table in Sino-Dutch DB for use with the WRFS, HWFS and Sino-Dutch website. The Sino-Dutch DB (in Chinese and English) is based upon the SQL Server, and there are 15 tables including the SD_SPRPR, a table for rainfall duration at the hydrological stations, SD_SDPR, a table for the daily rainfall at the hydrological stations, and SD_SHYDR, a table containing the basic hydrological data. A flow chart of the data exchange is shown in figure 5.8
115 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Begin
Open RWDB
Retrieving related today’ water information
Data format conver sion
Open Sino -Dutch DB
Writing above data into Sino -Dut ch DB
Close all DB
End
Figure 5.8 Sino-Dutch DB data flow chart
The data base server is currently a DELL 6600 PC, with PIII900 CPU, 1G memory and 250G SCSI HD, running under a Windows 2000 operating system. The data base management system is SQL Server 2000. Tables have been designed and created, basic data have been loaded, and programs have been developed for loading the real- time flow data, the runoff and flood forecasting results, as well as for compiling and loading the LAS station observations. At present, a Chinese and an English version of the data base are operative on the SQL server. All basic station data are loaded in the relevant tables. Real-time rainfall and flow data are loaded every day automatically. Runoff and flood forecasting results and also LAS results are loaded manually. These data can be inquired through the Chinese and English website. The Sino-Dutch data base is composed of 15 tables in 3 sub-databases. They are: • the basic information database (4 tables), • the input information database (7 tables) and • the processing results data base (4 tables). The data base structure in presented in table 5.1.
There are 4 tables in the basic information database. The table for the basin code provides district information. It includes 19 records, one for each district. The table for the basic meteorological stations provides the basic information of the relevant meteorological stations, but there is no record in this table. The table for the basic hydrological station contains the basic information of the relevant hydrological stations. In this table there are 11 records. The table for the LAS stations provides the basic information of the LAS stations in Tangke, Maqin, Xinghai and Jingchuan.
Program for loading the real-time regime into database automatically
The main function of this program is to automatically read the real-time rainfall data (period, day and dekad) and hydrological data (water level and discharge) from the real-time hydrological regime database and write these data into the rainfall table and hydrological table in the database.
116 Chapter 5 – System Implementation at YRCC
Table 5.1 Sino-Dutch data base table structure Sub- Table name Table identifier database Basic Table for basin code SD_BASINCD informatio Table for basic meteorological station SD_QSTATION n database Table for basic hydrological station SD_SSTATION Table for basic LAS station SD_LASSTATION Input Table for period rainfall of meteorology station SD_QPRPR Informa- Table for daily rainfall of weather station SD_QDPR tion Table for decadal rainfall of weather station SD_QTPR database Table for period rainfall of hydrologic station SD_SPRPR Table for daily rainfall of hydrologic station SD_SDPR Table for decadal rainfall and monthly rainfall of SD_STPR hydrologic station Table for Basic Hydrology data SD_SHYDR Processing Table for runoff forecasting result SD_RUNOFF_FORE results Table for flood forecasting result SD_FLOOD_FORE database Table for LAS operation result (10-minute) SD_LASOUTPUT1 Table for LAS operation result (daily) SD_LASOUTPUT2
Program for loading the runoff and flood forecasting results into databases
This program’s main function is to convert the data format of the runoff and flood forecasting results from the LSHM, and load these data into the database. As the creation date of forecasting results is uncertain, this model is run manually.
Compiling and loading LAS station final results into database
There are two kinds of LAS final data, one is at 10-minute interval, and the other is daily data. Because the data quality needs to be checked and analyzed, these data are loaded into the database manually.
5.1.4 Organization and operation
For the Sino-Dutch Cooperative Project a steering group has been set up in April 2004 by the Hydrology Bureau of YRCC, lead by Gu Yuanze, deputy director of the Hydrology Bureau and group leader, and Zhao Weimin, Chief engineer of Hydrology Bureau as deputy group leader. Secretaries are: Dai Dong and Qiu Shuhui.
The system implemented in the framework of this project includes 6 subsystems, which are: • the LAS and automatic meteorological station observation system, • the satellite images and GTS data receiving and processing system, • the Energy and Water Balance Monitoring System, • the upper Yellow river runoff forecasting system, • the lower Weihe flood forecasting system and • the achievement publishing web site system.
In order to guarantee the system’s operation, the Hydrology and Water Resources Information Centre of the Hydrology Bureau has set up a subsystem operational management regulation , and a group for management and operation . The regulation contains the rules for running the mentioned subsystems and includes information on
117 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
(a) how to run, (b) when to run, (c) who is responsible, (d) result analysis, and (e) reporting of problems. For the monitoring of satellite data reception and processing operations a form is used that is to be completed by the operator every day and week. Similar forms are used for the LSHM and LAS processing.
5.1.5 LAS station, data collection and processing
As part of the project four Large Aperture Scintillometers (LAS) have been installed in Yellow River basin: one in Jingchuan on the Loess Plateau in the Weihe catchment, and three on the Qinghai-Tibeta plateau at Xinghai, Maqin and Tangke. The sites were selected during a field trip from April 10 to 24 2005, during which 5000 km was covered. More information on the LAS measurements is given in section 3.2.3. Detailed information on each LAS site is presented in Annex 1.
The Upper Hydrology Bureau is responsible for the three LAS stations in the Qinghai-Tibet Plateau area. The Sanmenxia Hydrology Bureau is responsible for the Jingchuan LAS station. Local workers at the sites are charged with the LAS operation and maintenance. The data are collected every month on the 1 st and 16 th . The local workers take out the PCI card and download the data to a laptop and report any malfunctioning. Finally they fill out the LAS log form. The LAS data is transmitted to EARS as a part of the data transfer process. The LAS data and other data measured at the site are processed with EVATION software to derive the sensible heat flux and actual evapotranspiration. The most import outputs of the system and algorithm are: air temperature, wind speed, net radiation, sensible heat flux and actual evapotranspiration at 10 minute intervals. Detail on the LAS system and processing are given in section 3.2.
5.2 Catchment monitoring bulletin
5.2.1 Reporting flood and drought information
A catchment monitoring bulletin has been developed in which flood or drought situations in the Yellow River basin are summarized and reported. In the bulletin, the energy and water balance situation is evaluated, a degree of deviation from the normal situation is given and when appropriate and special advices or warnings for certain authorities are made.
To determine the needed contents of the bulletin, a group of experts worked on two case studies. The first case is a flood situation in the Weihe basin at the beginning of October 2005 and the second case a period of mild drought beginning of April 2006. In each case study, 10 days of data from the EWBMS were analyzed, information was extracted and evaluated. During the decision making process of the group the format and contents were determined and two template bulletins were produced.
118 Chapter 5 – System Implementation at YRCC
5.2.2 Bulletin contents
Effective rainfall map: shows the distribution of effective rainfall, i.e. rainfall minus actual evapotranspiration for a 10 day period in the Yellow River basin.
Local information
Tabulated detailed information and time series on rainfall and actual evapotranspiration are presented for six important locations in Yellow River basin, Zhengzhou, Xi’an, Yinchuan, Lanzhou, Jinan and Darlag.
Water balances
For each sub-catchment of the basin, the water balance is determined and tabulated. The balance indicates the net amount of water added by rainfall or removed by evapotranspiration from each component of the river network and informs on the water resources that are available for irrigation, hydropower, urban and industrial use at sub basin level.
Agricultural drought
Agricultural drought is evaluated by analysis of the EDI map and its development in time. The severity of the drought and possible reduced crop yields are tabulated by sub-catchment.
An example of the Yellow River Satellite Monitoring Bulletin is presented in Annex 2.
5.3 Catchment monitoring website
A catchment monitoring website has been developed to present the monitoring results to YRCC and to other end users of the system. Furthermore, the website also serves as a general information platform for the Sino-Dutch Project.
5.3.1 Target users
The users of the catchment monitoring website belong to the following categories: • First-level users. These users can browse all the system information, and are the technical personnel who operate and apply this system in the Sino-Dutch project at the Hydrology Bureau of YRCC. • Second-level user . This user level can browse the majority of the information through a personal user name and password. These users are the technical personnel who operate the system. Other users may browse the information through a default user name and password.
119 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
5.3.2 Website design and structure
From a technical point of view, the web server generates both static and dynamic content. Static content is composed of general descriptive pages, most often providing background information. Dynamic content is generated upon user request. For example: measurements or processing results at a specific location and for a specific period of time. In this case, the web server connects to the data base, retrieves the data, and converts the results into HTML code for a suitable presentation form, such as for example a graph or map. Fig. 5.9 shows the connection between user and system platforms. Attention is also paid to security and integrity issues, carried out from within a user account and permissions management sub-system.
From a functional point of view the web site is divided into several sections, each starting from the home page. These sections are: • Project description including achievements and products; • FTP access; • Catchment monitoring bulletin archive section. The first section constitutes the largest part of the website and is subdivided in sections for the upper Yellow River reach and Weihe sub-basin. The material in the section includes satellite images, LAS data, processed rainfall and actual evaporation, hydrological and agricultural drought information, and runoff simulation and forecasting results.
From a user point of view, the web site presents a uniform page structure to all users in a layout that is self-explanatory. Furthermore, it aims at presenting the requested information rapidly in a clear and understandable format. Users can request information from a selection interface that has a similar appearance, for different types of data. The selection involves specifying a data sub-type, a location or region, and a fixed time or period of time, depending on the kind of data requested. In order to optimize the information retrieval process for the user, it is possible to use shortcut key strokes for frequently used functions. Figures 5.10 and 5.11 present the browser homepage as seen by the users. The website is available in both Chinese and English language.
. Figure 5.9: System structure architecture
120 Chapter 5 – System Implementation at YRCC
Figure 5.10: Layout of the catchment monitoring website homepage in Chinese
Figure 5.11: Layout of the catchment monitoring website homepage in English
121 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 5.12: Example of an upper Yellow river flow forecast as presented on the project website
122 Chapter 6 – Conclusion, Outlook and Recommendations
6 CONCLUSIONS, OUTLOOK AND RECOMMENDATIONS
The Sino-Dutch project partners have successfully developed the first satellite based water monitoring and river flow forecasting system in the world. This system has been operationally implemented at the Hydrology Bureau of Yellow River Conservancy Commission in Zhengzhou. The system is a combination of an innovative satellite based climatic monitoring system, known as the Energy and Water Balance Monitoring System (EWBMS), developed by EARS, and a dedicated Large Scale Hydrological Model (LSHM), developed by UNESCO-IHE.
A most significant innovation is that the EWBMS does not only produce rainfall data fields, the type of input that is traditionally used in rainfall-river runoff modeling, but also generates data fields of the actual evapotranspiration. Such information was never available from routine surface observations before. The latter data are crucial for an accurate determination of the catchment’s water balance, particularly because evapotranspiration amounts to 70 or 80 % of the precipitation. These data, which in the past could only be estimated, can now be determined from space. The whole EWBMS-LSHM system is almost independent of real time data, except that rainfall point data, already available through the WMO-GTS system, are used to calibrate the rainfall data fields in real time, and that the LSHM assimilates station discharge observations when run in forecast mode.
Given the distributed data fields of temperature, radiation, evapotranspiration and rainfall, generated for the entire basin on a daily basis, the system is very useful for drought monitoring. The catchment drought monitoring system, that has been implemented, produces climatic, hydrological and agricultural drought maps for the whole basin, and aggregates tabulated data for each sub-catchment within the basin. These data are an important part of the catchment monitoring bulletin and website that have been developed. In principle the drought monitoring system is not restricted to the Yellow River basin but may be used to monitor the entire territory. The system would be very suitable and could immediately be used to monitor and assess the drought currently occurring in the northern part of China.
The river runoff forecasting system has been implemented in two major sub-basins: the upper reach of the Yellow river, upstream of Tangnaihai, and the Weihe tributary. For both basins the LSHM can be run in a simulation and a forecasting mode. For forecasting the user may introduce fixed boundaries for which measured discharge data can be entered, and he can also extend the EWBMS effective precipitation with simple scenario’s for the coming days. In this way the LSHM may be used to anticipate high water in the lower Weihe.
In the upper Yellow river, the EWBMS has done better than expected in calculating the water budget and simulating river runoff, particularly when considering the fact that rainfall stations are scarce, and that the physical conditions on the plateau are very different from these in the low land. To reach this result we have succeeded in adapting the EWBMS so as to work well at high altitude. It was necessary to adapt the heat exchange calculations for decreasing air density and increasing aerodynamic roughness with height. For a proper estimation of rainfall it appeared essential to account for the reduced height of the precipitable water column at higher altitude. Another innovative element is that we have adapted the system to take account of snow fall in winter and snow melt in spring.
Extensive work has been done on the validation of the data generated by the EWBMS-LSHM system. EWBMS air temperature data fields have been validated with temperature data from meteorological stations. Rain gauge data from such
123 Satellite Water Monitoring and Flow Forecasting System for the Yellow River stations were used to evaluate the performance of the EWBMS rainfall module. To serve the validation of the EWBMS energy balance components, generated by the EWBMS, four Large Aperture Scintillometer (LAS) systems have been implemented in the basin, of which three on the Qinghai plateau and one on the Loess plateau, The LAS is an innovative instrument that is capable of measuring the sensible heat flux along a horizontal path of several kilometers. The LAS stations were also equipped with net radiometers and air temperature sensors. With the measurements from these advanced measuring sites, operated and maintained by the YRCC Bureau for the Upper Yellow River in Lanzhou, and by the Sanmenxia Hydrological Bureau, it has been possible to successfully validate the energy balance products of the system. In addition the overall water budget, generated by the EWBMS, has been validated by comparing the yearly effective precipitation (precipitation - actual evapotranspiration) of the sub-basin with the corresponding river discharge. The results are very good.
Finally the EWBMS data have been used as input into the LSHM to generate river flow. LSHM performance has been validated by comparing simulated with observed flow. All these validation activities have produced very good results, certainly if it is recognized that the validation data are usually measured at different scale, are imperfect, and cannot be considered the truth when large scale areal estimates are considered.
In the framework of the project a dedicated catchment monitoring bulletin has been developed, which may contain drought, water budget at pixel, sub-catchment and catchment level as well as river flow information. Also a website has been developed that is instrumental in presenting the information to the YRCC departments and services involved, to related government organizations as well as to a larger public. The website is directly connected to the data base in which all information and system processing results are stored, and which serves simultaneously as an operational visualization platform. At the YRCC Hydrological Bureau, a team of experts has been formed that is operating the system on a daily basis and will diffuse the information through regular issues of the bulletin and updates of the website.
The EWBMS, after its development, is a cost-effective system. It provides a lot of information for the whole river basin at very moderate costs. The satellite data are free. The PC technology used is low cost. Highest operating costs are in the manpower, but compared to the investments and number of people that would be required to collect such data in the traditional way, the overall operating costs are also very low.
Given the successful operational implementation of the EWBMS-LSHM system it may be wise to look forward and consider how the system could further improve the cost-effectiveness of YRCC operations. A follow-up project could be considered in which the run-off forecasting is implemented in the whole basin. Also its potential functionality in operating the various dams could be addressed. In addition a further extension of monitoring scale could be considered in relation to the S-N diversion, when large amounts of water will be transported across the watershed, which will have a considerable impact on both the source and destination areas.
Another possibility for further extending the utility of the system includes its adaptation to use rainfall forecasts generated by meteorological models so as to extend its forecasting capability from 1 to 3 days and possibly longer, up to a medium range forecast of 10 days.
124 Chapter 6 – Conclusion, Outlook and Recommendations
Recommendations
Based on the project results obtained and the considerations in this chapter, the project partners come to the following recommendations:
(1) Given the good results of the EWBMS-LSHM and its successful operational implementation in the Yellow River Upper Reach and in the Wei River, it is proposed to extend the implementation of the system to the entire Yellow River basin. (2) It is also proposed to extend the forecasting range of the current system by integrating future rainfall and evapotranspiration scenario’s using available numerical weather forecasts. (3) It is proposed to investigate the utility of the system for dam operations and to develop the corresponding water management tools. (4) It is proposed to develop a permanent hydrological, agricultural and climatological drought monitoring facility for China, so as to document and learn from the current drought episode and to prepare for similar events in the future. (5) Given the available water resources on the one, and the agricultural water needs on the other side, for which the information is both provided by the system, it is proposed to develop a water allocation decision support system. Such methodology will be beneficial to decision making in relation to the S-N water diversion and for water allocation to alleviate and overcome drought. (6) It is recommended that Dutch and Chinese government establish a lasting cooperation in the field of satellite hydrology with the objective to draw the full benefits of this new technology and to add a new and challenging perspective to their existing relationship in the water domain.
125 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
126 Annex 1 – LAS Stations Information
ANNEX 1: LAS STATIONS INFORMATION
Jingchuan LAS station
Location Transmitter Receiver Latitude Longitude Latitude Longitude 107 o 21’ 35 o 20’ Altitude 1061 m Altitude 1039 m Height lower temperature measurement 1 m Height upper temperature measurement 2 m Height wind speed measurement 4 m Path length 1343 m Roughness length 0.1 m Zero displacement height 0.7 m Measuring period all year Start of measurements 1/1/2006 Description : The system is installed over a rough, but open cultivated landscape with maize fields, grasslands and a single isolated tree in the measurement area. The LAS receiver is mounted at the local hydrological station near the riverside. The transmitter is installed on the top of a hill in a village. The LAS path direction is 30° NE. The instruments are working on current grid both at the receiver and the transmitter site. Receiver and transmitter are mounted on a steel construction of supporting piles. The LAS is measuring at an effective height of 23.7 m over a heterogeneous but representative area. With low crops and small dispersed obstacles present, the roughness length z0 of the terrain is estimated at 0.1m.
Transmitter site at Jingchuan station Path of Jingchuan LAS
127 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Xinghai LAS station
Location Transmitter Receiver Latitude Longitude Latitude Longitude 35 ゜35 ′34.7 ″ 99 ゜59 ′03.7 ″ 35 ゜36 ′36.0 ″ 99 ゜58 ′40.7 ″ Altitude 3302 m Altitude 3328 m Height lower temperature measurement 1 m Height upper temperature measurement 3 m Height wind speed measurement 4.2 m Path length 1871 m Roughness length 0.1 m Zero displacement height 0.7 m Measuring period 15/3 – 15/11 Start of measurements 15/3/2006 Description: The instruments are installed in Xinghai county, Qinghai province. The site is an open landscape of hilly grasslands with weak slopes (<5%). No mountains peaks were visible around the area. Yaks and sheep are regularly grazing on the site and in the surrounding areas. One road is crossing the LAS measurement path. The LAS transmitter is installed on a local government building. The receiver and the meteorological station are mounted on a steel platform mounted by two concrete pillars, near the house of a Tibetan family. The instruments are working on solar power at the receiver site and on current grid at the transmitter site. The LAS is measuring over a relatively homogeneous area of grassland and very low vegetation. Roughness elements are not densely packed and therefore the roughness length z0 of the terrain is taken to bee very small. The effect of z0 on H is minimized by installing the LAS relatively high; the scintillometer beam is measuring at an effective height of 24.4 m above the surface.
Transmitter at Xinghai LAS station Path of Xinghai LAS
128 Annex 1 – LAS Stations Information
Maqin LAS station
Location Transmitter Receiver Latitude Longitude Latitude Longitude 34 ゜28 ′14.7 ″ 100 ゜14 ′05.4 ″ 34 ゜27 ′44.2 ″ 100 ゜14 ′28.8 ″ Altitude 3721 m Altitude 3727 m Height lower temperature measurement 1 m Height upper temperature measurement 3.9 m Height wind speed measurement 5.2 m Path length 110 m Roughness length 0.03 m Zero displacement height 0.21 m Measuring period 15/3 – 15/11 Start of measurements 15/3/2006 Description : The system is operating in Maqin county in Qinghai province. The site is an open flat grassland with a very weak slope (<0.6%). The valley is surrounded by mountain peaks that are oriented in a north-south direction. Yaks are regularly grazing on the site and in the surrounding areas. The LAS transmitter is installed near a housing block. The receiver and the meteorological station are mounted near the house of a living Buddha. The instruments are working on solar power at the receiver site and on current grid at the transmitter site. Receiver and transmitter are mounted on a steel platform supported by two robust concrete piles. The LAS is measuring at an effective height of 5.8m over a homogeneous area of grassland. With no or very little roughness elements present, the roughness length z0 of the terrain is estimated at 0.03m.
Working at Maqin LAS station Path of Maqin LAS
129 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Tangke LAS station
Location Transmitter Receiver Latitude Longitude Latitude Longitude 33 ゜23 ′51.4 ″ 102 ゜27 ′47.6 ″ 33 ゜24 ′10.3 ″ 102 ゜27 ′47.7 ″ Altitude 3438 m Altitude 3430 m Height lower temperature measurement 1 m Height upper temperature measurement 3.7 m Height wind speed measurement 5.1 m Path length 586 m Roughness length 0.03 m Zero displacement height 0.21 m Measuring period 15/3 – 15/11 Start of measurements 15/3/2006 Description : The fourth LAS system is operating near Tangke in Ruoergai county of Sichuan province. The site is located in the more humid part of the Qinghai-Tibetan plateau, with wet grasslands and peat lands. The site consists of a flat open landscape with grassland and a very weak slope (<1%). Mountains peaks are visible on the horizon. Yaks are regularly grazing on the measuring site and in the surrounding areas. Two small loam houses near the transmitter site are located below the path of the LAS beam. The instruments are working on solar power at the transmitter site and on current grid at the receiver site. Receiver and transmitter are mounted on a steel platform supported by two robust concrete piles at about 7 m above the surface. The LAS is measuring at an effective height of 23.7 m over a heterogeneous but representative area. With uniform grassland and very low vegetation, the roughness length z0 of the terrain is estimated at 0.03m
Receiver site at Tangke station Path of Tangke LAS
130 Annex 2: Catchment Monitoring Bulletin
ANNEX 2: CATCHMENT MONITORING BULLETIN
E-mail: [email protected] Issuing date: 30 September 2005 Internet: http://218.28.41.1.4/zhweb
INTRODUCTION
The present document provides the following information: • An overview of water availability in the Yellow River basin during the current dekad • An overview of agricultural drought conditions during the past four months
In Annex 3 the most important terms are explained.
DATA AND METHOD
EWBMS is the acronym of Energy and Water Balance Monitoring System , a FY-2C based water resources monitoring system operated by the Hydrology Bureau of YRCC. The assessment of water availability and drought in the basin is based on visible and thermal infrared hourly data from the geostationary meteorological satellite FY-2C.
(1) Hourly satellite data are processed to daily average values of surface temperature, air temperature, net radiation, potential and actual evapotranspiration.
2) Rainfall data are processed based on cloud frequency data derived from hourly satellite data and rainfall measurements from WMO-GTS stations.
(3) Using GIS, evapotranspiration and rainfall results are integrated for sub- catchment areas and water balance and drought monitoring information are generated.
The information is available on daily basis and is spatially continuous with a pixel resolution of 5 km.
131 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Summary and highlights
Like the previous days, the amounts of rainfall were very high between 21 and 30 of September, especially in the WeiHe basin, the middle and the lower reaches of the Yellow River. The south-eastern part of the Yellow River basin suffers from continued heavy rains that are much higher than normal. During only ten days, at some locations in the WeiHe basin up to 200 mm of rain has fallen.
Figure 1: Total effective rainfall (mm) during the period between 21 and 30 September 2005
During the entire period of ten days, the WeiHe basin, the middle and the lower reaches of the Yellow River basin suffered form unusual high rainfalls. Decadal precipitation topped even 200 mm at some locations in the WeiHe basin. Also parts of the lower Yellow River basin and of Shandong province suffered from heavy rainfalls. At some locations in this area precipitation amounts exceeded 150 mm water. Daily rainfall at Xi’an for 5 consecutive days was more than 15 mm, with a maximum of 23 mm at 29 September 2005.
Actual evapotranspiration was lowest in the plateau areas and towards the north. During the last ten days on average 10 mm of water has evaporated, with relatively more water evaporating in the southern and eastern parts of the basin. The ten-daily sum of actual evapotranspiration is ranging from 5 to 20 mm in the basin.
Some sub-catchments have been receiving rainfall amounts that were 15 times higher than the amount of water evaporating from its surface. Effective rainfall ranged from 50 mm to 150 mm in the WeiHe basin and from 25 mm to 90 mm in the Upper Yellow River basin.
132 Annex 2: Catchment Monitoring Bulletin
A c t u a l a l v a e u t A c
35 3 35 3 Jinan 30 Darlag 30 2.5 2.5
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5 5 5 05 05 05 005 005 005 005 005 005 005 /20 /20 /2 /2 /200 2 /200 2 2 2 20 2 9/ / 9/ 9/ 9 /2005 9 /2005 0 /09 0 /09 /09 09 0 09 0 0 / r i p r s n a t o p 7/ 9/ 3/ 6/ 0/ 09/ 21 22/ 23 24/09 /2005 25 26/09 /2005 2 28/09 2 30/0 9 21/09 /2005 22/ 09/2005 2 24/ 25/ 09/2005 2 27/ 09/ 28/09/ 200 29 3
35 3 3 5 3 Lanzhou Yinchuan 30 3 0 2.5 2.5
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[ m] m Xi’an Zhengzhou 30 4 0 2. 5 2. 5 3 5 25 2 3 0 2
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R a iR fn a l l
Figure 2: Daily rainfall (bar) and actual evapotranspiration (line), both in mm, for selected locations from 21 to 30 September 2005.
133 Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Table 1: Rainfall and actual evapotranspiration summary information for 6 selected locations .
Station Total Total [mm] days 10 Highest 10 in days Total since Sep01 01 Total since 2005* July Total [mm] days 10 10 Highest in days Total since Sep01 01 Total since 2005* July Rainfall Actual Evapotranspiration Darlag 51 11 131 606 8 1,1 31 143 Jinan 118 32 199 710 16 2,5 62 274 Lanzhou 46 14 145 416 5 1,4 18 124 Yinchuan 19 10 86 215 12 1,8 27 193 Xi’an 143 23 196 881 13 2,3 62 233 Zhengzhou 150 44 197 759 16 2,8 66 268
Runoff and Water Balance
The water balance is fundamental for the hydrological cycle. It gives information on the amount of water the river network will receive. At the same time it indicates the water resources available for agriculture, industry or residential use. A detailed inventory of the water resources in the Yellow River basin is given below. The map shows the water balance in the Yellow River for each of the sub-catchments. Water balance is defined by subtracting evapotranspiration from precipitation and averaging the value for each sub- catchment. The water balance is given in mm for each sub-catchment.
Effective rainfall increases from west to east and from north to south. Water balance is positive in nearly every sub-catchment and extremely high in most parts of the Weihe basin.
Figure 3: Water balance at sub-catchment level, i.e. the total amount of water (mm) added to or leaving the sub-catchment during the dekad 21-30 September 2005
134 Annex 2: Catchment Monitoring Bulletin
In the sub-catchments of the Jinghe River, Dawenhe River, Manghe Basin, Qinhe River, Dongpinghu Lake, Jishui and Tingshuihe the Weihe basin, the situation is critical. The latter sub-catchments all saw their amount of water increased by more than 120 mm in the period of only 10 days. The Dawenhe Basin suffered most from the heavy rainfall, and average effective rainfall over the basin is 139 mm. In all these areas, there is a high risk for flooding.
The sub-catchments with the smallest amounts of water are found in the north of the Yellow River basin. In Maobulakongdui basin in the north, actual evapotranspiration is a little higher than rainfall over the 10-day period, and consequently this basin has a negative water balance.
The effective precipitation values (mm) are multiplied by the surface area for each of the sub-catchments. The corresponding total amount of water in millions of m 3 water added or removed during the 10-day period is given in the table below. This is the amount of water the river network will receive and it gives an indication on the water resources availability.
The effective precipitation values (mm) are multiplied by the surface area for each of the sub-catchments. The corresponding total amount of water in millions of m 3 water added or removed during the 10-day period is given in the table below. This is the amount of water the river network will receive and it gives an indication on the water resources availability.
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Table 2: Effective precipitation, area and total water volume by sub-catchment Eff Precip. Area Volume Nr Sub-catchment (mm) (km2) (10 6 m3) 1 Jiaqu Basin 42 2195 93 2 Baihe Basin 37 5482 202 3 Shaqu Basin 33 1602 52 4 Zhanganhe Basin 31 1040 32 5 Jimaihe Basin 33 1856 61 6 Darilequ Basin 27 3400 93 7 Heihe Basin 41 7931 327 8 Kequ Basin 25 2439 61 9 Xikehe Basin 61 1002 62 10 HongnongjianheBasin 97 2037 197 11 Dongkequ Basin 55 3437 187 12 Xikequ Basin 49 2658 131 13 Luohe Basin 78 18864 1463 14 Youerqu Basin 65 1903 124 15 Requ Basin 48 6599 315 16 Dongqu Basin 64 1301 83 17 Lenaqu Basin 51 1536 79 18 Qiemuqu Basin 69 5552 381 19 Manghe Basin 122 1907 232 20 Duoqu Basin 49 5823 283 21 Zequ Basin 74 4745 349 22 Haoqinghe Basin 111 572 64 23 DuoqinankelangBasin 63 1183 74 24 Wenyanqu Basin 115 2396 276 25 Kariqu Basin 42 3106 130 26 Sushuihe Basin 120 5595 673 27 Baqu Basin 78 4247 331 28 Jishui Basin 126 1068 135 29 Jinghe Basin 128 18670 2388 30 Daixahe Basin 65 7160 463 31 Longwuhe Basin 75 4955 373 32 Manglahe Basin 76 2918 221 33 Qushianhe Basin 62 5874 365 34 Taohe Basin 53 25438 1351 35 Wanchuanhe Basin 39 1875 73 36 Xiaqu Basin 67 1411 94 37 Gaohongyahe Basin 64 1127 73 38 Shiwangchuan Basin 111 2351 262 39 Jingdihe Basin 125 4941 616 40 Daheba Basin 61 3955 242 41 Dongpinghu Basin 137 761 105 42 Puhe Basin 105 7491 784
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Eff Precip. Area Volume Nr Sub-catchment (mm) (km2) (106 m3) 43 Yunyanhe Basin 119 1779 212 44 Zulihe Basin 51 10752 548 45 Dawenhe Basin 139 8557 1190 46 Xinshuihe Basin 124 4293 532 47 Qinhe Basin 135 12935 1748 48 Chuchanhe Basin 86 1220 105 49 Yanhe Basin 95 7671 726 50 Qingjianhe Basin 75 4068 306 51 Malianhe Basin 97 19061 1852 52 Zhuanglanghe Basin 38 4083 156 53 Qingshuihe Basin 46 14472 660 54 Hongliugou Basin 36 1186 43 55 Wulonghe Basin 35 375 13 56 Kushuihe Basin 40 7515 297 57 Qingliangshigou Basin 46 286 13 58 Sanchuanhe Basin 55 4082 226 59 Qiushuihe Basin 43 1985 86 60 Jialuhe Basin 29 1137 33 61 Huangshui Basin 36 32453 1152 62 Weifenhe Basin 21 1478 31 63 Lanyihe Basin 19 2178 40 64 Tuweihe Basin 17 3199 56 66 Fenhe Basin 82 39624 3260 67 Wudinghe Basin 35 30232 1051 68 Zhujiachuan Basin 19 2900 56 69 Gushanchuan Basin 10 1280 13 70 Xianchuanhe Basin 28 1488 42 71 Dusitu Basin 1 8840 10 72 Qingshuichuan Basin 15 884 13 73 Pianguanhe Basin 27 2085 57 74 Kuyehe Basin 7 8708 58 75 Yangjiachuan Basin 26 996 26 76 Huangpuchuan Basin 12 3253 40 77 Xiliugou Basin 2 1298 2 78 Hashilachuan Basin 3 1194 4 79 Erdous Basin 10 42415 438 80 Mabulakongdui Basin -2 1324 -3 81 Hunhe Basin 24 5540 134 82 Main stream 43 167578 7226 83 Kundulun Basin 9 2800 26 84 Daheihe Basin 17 17726 295 85 Wuliangsuhai Basin 5 29558 149 86 Beiluohe Basin 109 27025 2945
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Agricultural Drought Monitoring The agricultural drought indicator gives information on water availability for crops and vegetation. The agricultural drought indicator is strongly related to soil moisture and the actual drought conditions of the ground. Moisture availability is the most important factor influencing the conditions of crops and plants. The agricultural drought is evaluated over a two months period, a suitable time period to evaluate the growth conditions of the crops and to estimate possible crop yield losses during the growing season.
The map below shows the agricultural drought indicator EDI in the Yellow River for each sub-catchment. The map gives the spatial distribution of drought that was experienced by the vegetation over the past two months. Note that EDI informs on conditions of the crop at the present dekad, but that these conditions are mainly determined by the water availability during the past two months. More information on agricultural drought and EDI is given in Annex 1.
In the lower reaches and in the Weihe basin, crops and vegetation water availability conditions are optimal to near optimal. Only light droughts, from which crops can quickly recover are found in some sub-catchments of this area. South of Shaanxi province and in the surroundings of Zhengzhou and Xi’an, the sub-catchments all have an EDI that is higher than 78%.
Continued severe droughts are found in the North of the Yellow River basin. Droughts are most pronounced in the provinces Inner Mongolia, Gansu, Ningxia Huizu, the northern part of Shannxi and some parts in the source area of the Yellow River. The EDI values for each sub-catchment are given in the table below. EDI values of the previous dekad are also given, to indicate the change of agricultural drought in time. Dekad Dn is the current dekad, D n-1 the previous dekad, etc.
Figure 4: Agricultural drought at sub-catchment level in EDI (%)
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Table 3: Agricultural drought development in current and past five dekads in the sub-catchments. Evapotranspiration Drought Index
(EDI) Sub Drought class catchment Dn-5 Dn-4 Dn-3 Dn-2 Dn-1 Dn 1 Jiaqu Basin 0.64 0.65 0.66 0.69 0.68 0.69 light drought 2 Baihe Basin 0.69 0.70 0.73 0.76 0.76 0.76 light drought 3 Shaqu Basin 0.55 0.55 0.56 0.57 0.54 0.55 moderate drought 4 Zhanganhe Basin 0.61 0.62 0.62 0.64 0.62 0.60 light drought 5 Jimaihe Basin 0.60 0.58 0.56 0.57 0.56 0.58 moderate drought 6 Darilequ Basin 0.60 0.58 0.55 0.56 0.56 0.58 moderate drought 7 Heihe Basin 0.64 0.65 0.67 0.70 0.69 0.69 light drought 8 Kequ Basin 0.62 0.59 0.55 0.55 0.55 0.54 moderate drought 9 Xikehe Basin 0.61 0.60 0.59 0.59 0.55 0.54 moderate drought 10 Hongnongj. Basin 0.96 0.97 0.97 0.98 0.97 0.96 optimal WA *) 11 Dongkequ Basin 0.63 0.62 0.60 0.59 0.56 0.56 moderate drought 12 Xikequ Basin 0.60 0.58 0.56 0.55 0.53 0.54 moderate drought 13 Luohe Basin 0.88 0.91 0.92 0.93 0.93 0.92 optimal WA 14 Youerqu Basin 0.60 0.59 0.57 0.56 0.52 0.50 severe drought 15 Requ Basin 0.59 0.57 0.53 0.52 0.51 0.50 severe drought 16 Dongqu Basin 0.64 0.62 0.60 0.57 0.54 0.51 moderate drought 17 Lenaqu Basin 0.63 0.61 0.60 0.59 0.58 0.54 moderate drought 18 Qiemuqu Basin 0.64 0.62 0.60 0.58 0.54 0.53 moderate drought 19 Manghe Basin 0.86 0.86 0.86 0.86 0.86 0.86 near optimal WA 20 Duoqu Basin 0.61 0.60 0.60 0.60 0.60 0.58 moderate drought 21 Zequ Basin 0.64 0.63 0.62 0.62 0.60 0.59 moderate drought 22 Haoqinghe Basin 0.83 0.84 0.85 0.85 0.85 0.85 near optimal WA 23 Duoqinank. Basin 0.62 0.62 0.60 0.56 0.54 0.49 severe drought 24 Wenyanqu Basin 0.87 0.87 0.89 0.90 0.90 0.87 near optimal WA 25 Kariqu Basin 0.58 0.56 0.57 0.59 0.55 0.53 moderate drought 26 Sushuihe Basin 0.81 0.81 0.83 0.84 0.84 0.84 near optimal WA 27 Baqu Basin 0.67 0.66 0.65 0.65 0.62 0.60 light drought 28 Jishui Basin 0.72 0.73 0.76 0.78 0.77 0.77 light drought 29 Jinghe Basin 0.66 0.69 0.74 0.79 0.79 0.79 light drought 30 Daixahe Basin 0.73 0.74 0.73 0.71 0.69 0.65 light drought 31 Longwuhe Basin 0.71 0.71 0.70 0.70 0.68 0.63 light drought 32 Manglahe Basin 0.66 0.66 0.67 0.67 0.65 0.60 light drought 33 Qushianhe Basin 0.56 0.55 0.54 0.55 0.53 0.49 severe drought 34 Taohe Basin 0.70 0.71 0.70 0.69 0.67 0.67 light drought 35 Wanchuanhe Basin 0.57 0.59 0.58 0.56 0.54 0.52 moderate drought 36 Xiaqu Basin 0.57 0.57 0.56 0.56 0.55 0.53 moderate drought 37 Gaohongyahe Basin 0.61 0.61 0.58 0.58 0.57 0.53 moderate drought 38 Shiwangchuan Basin 0.75 0.77 0.78 0.80 0.80 0.81 near optimal WA 39 Jingdihe Basin 0.83 0.83 0.84 0.86 0.86 0.83 near optimal WA 40 Daheba Basin 0.47 0.46 0.46 0.50 0.51 0.49 severe drought 41 Dongpinghu Basin 0.80 0.84 0.86 0.89 0.89 0.84 near optimal WA 42 Puhe Basin 0.62 0.66 0.70 0.71 0.69 0.67 light drought 43 Yunyanhe Basin 0.70 0.73 0.76 0.79 0.79 0.80 light drought 44 Zulihe Basin 0.58 0.60 0.61 0.62 0.59 0.54 moderate drought
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45 Dawenhe Basin 0.81 0.83 0.84 0.87 0.87 0.86 near optimal WA 46 Xinshuihe Basin 0.73 0.75 0.78 0.80 0.80 0.80 near optimal WA 47 Qinhe Basin 0.83 0.85 0.86 0.87 0.86 0.86 near optimal WA 48 Chuchanhe Basin 0.76 0.79 0.81 0.88 0.86 0.84 near optimal WA 49 Yanhe Basin 0.66 0.68 0.72 0.74 0.71 0.70 light drought 50 Qingjianhe Basin 0.60 0.63 0.67 0.69 0.67 0.67 light drought 51 Malianhe Basin 0.65 0.66 0.69 0.70 0.69 0.66 light drought 52 Zhuanglanghe Basin 0.47 0.49 0.52 0.51 0.49 0.45 severe drought 53 Qingshuihe Basin 0.52 0.55 0.55 0.54 0.52 0.48 severe drought 54 Hongliugou Basin 0.44 0.47 0.46 0.43 0.39 0.37 severe drought 55 Wulonghe Basin 0.62 0.67 0.67 0.71 0.73 0.74 light drought 56 Kushuihe Basin 0.50 0.53 0.52 0.50 0.47 0.43 severe drought 57 Qingliangshigou Basin 0.65 0.69 0.73 0.77 0.78 0.78 light drought 58 Sanchuanhe Basin 0.77 0.79 0.81 0.86 0.85 0.82 near optimal WA 59 Qiushuihe Basin 0.72 0.74 0.77 0.83 0.83 0.82 near optimal WA 60 Jialuhe Basin 0.54 0.58 0.63 0.67 0.66 0.66 light drought 61 Huangshui Basin 0.65 0.66 0.67 0.67 0.64 0.60 light drought 62 Weifenhe Basin 0.70 0.73 0.76 0.80 0.78 0.76 light drought 63 Lanyihe Basin 0.65 0.67 0.70 0.75 0.74 0.73 light drought 64 Tuweihe Basin 0.54 0.57 0.61 0.66 0.66 0.65 light drought 66 Fenhe Basin 0.73 0.75 0.77 0.81 0.80 0.79 light drought 67 Wudinghe Basin 0.52 0.55 0.59 0.62 0.62 0.62 light drought 68 Zhujiachuan Basin 0.63 0.66 0.68 0.71 0.71 0.69 light drought 69 Gushanchuan Basin 0.52 0.54 0.59 0.64 0.63 0.58 moderate drought 70 Xianchuanhe Basin 0.62 0.65 0.69 0.73 0.73 0.70 light drought 71 Dusitu Basin 0.39 0.42 0.45 0.48 0.47 0.46 severe drought 72 Qingshuichuan Basin 0.51 0.53 0.57 0.61 0.60 0.56 moderate drought 73 Pianguanhe Basin 0.58 0.61 0.65 0.69 0.68 0.65 light drought 74 Kuyehe Basin 0.54 0.56 0.61 0.66 0.63 0.61 light drought 75 Yangjiachuan Basin 0.56 0.59 0.64 0.69 0.69 0.66 light drought 76 Huangpuchuan Basin 0.58 0.59 0.62 0.65 0.63 0.59 moderate drought 77 Xiliugou Basin 0.48 0.49 0.55 0.58 0.56 0.55 moderate drought 78 Hashilachuan Basin 0.57 0.58 0.63 0.67 0.66 0.64 light drought 79 Erdous Basin 0.47 0.50 0.53 0.56 0.54 0.51 moderate drought 80 Mabulakongdui Basin 0.52 0.54 0.57 0.58 0.55 0.51 moderate drought 81 Hunhe Basin 0.73 0.75 0.77 0.81 0.80 0.79 light drought 82 Main stream 0.55 0.57 0.57 0.58 0.56 0.54 moderate drought 83 Kundulun Basin 0.43 0.42 0.42 0.42 0.40 0.39 severe drought 84 Daheihe Basin 0.47 0.49 0.52 0.49 0.49 0.49 severe drought 85 Wuliangsuhai Basin 0.39 0.40 0.39 0.39 0.37 0.36 severe drought 86 Beiluohe Basin 0.73 0.75 0.78 0.80 0.80 0.79 light drought 87 Weihe Basin 0.80 0.81 0.83 0.84 0.82 0.80 light drought *) WA= water availability
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Summary and conclusions
The south-eastern area of the Yellow River basin suffers from continued heavy rains that are much higher than normal. It is very likely that the river network can not receive the increased amounts of water in time, enhancing the risk of flooding in these areas.
Even in the areas with very high rainfall during this dekad, light agricultural drought is present and may influence crop yields. In the source area, drought conditions are more pronounced, effective rainfall is lower than in the east, and droughts are light to severe. The northern part of the Yellow River basin suffers from continued severe drought with only small effective rainfall amounts, making it very difficult or impossible to grow crops.
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Annex A: Agricultural Drought
Agricultural drought is defined as drought that occurs when there is not enough moisture available to meet the needs of the vegetation. EDI, the Evapotranspiration Drought Index indicates the availability of moisture for crop/vegetation growth. The EDI is an agricultural drought indicator. This means that EDI is more than an indicator of the actual drought state of the ground surface. Not only it gives information on the amount of soil water present. It also gives information on the physical and biological properties of the soil and on crop conditions. Crop/vegetation conditions and photosynthesis are directly related to the amount of water that is available for the plants. EDI also includes influences from stage of growth, biological characteristics of the plant, cattle grazing and weather conditions.
The EDI value is defined as the average of relative evapotranspiration for a two month period:
EDI = Σ (RE ) / n = Σ (E / E P ) / n where
EDI : Evapotranspiration Drought Index E : Actual Evapotranspiration EP : Potential Evapotranspiration RE : Relative Evapotranspiration n : Number of days in 2 months
The agricultural drought classification according to the EDI value is as follows:
EDI value Agricultural Drought Classification 0.9 - 1 Optimal water availability 0.8 – 0.9 Near optimal water availability 0.6 – 0.8 Light agricultural drought 0.5 – 0.6 Moderate agricultural drought 0.3 – 0.5 Severe agricultural drought 0.0 – 0.3 Extreme agricultural drought
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Annex B: Sub-catchments of the Yellow River basin
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Annex C: Retrieval methodology and explanation of terms
Term Explanation Origin of data
Rainfall (R) The rainfall mapping is based on rainfall Satellite and data gathered by WMO-GTS rainfall GTS stations and cloud durations data for 5 cloud levels generated from FY-2C satellite data. A multiple regression between satellite derived cloud data and the GTS rain data gives pixel by pixel rainfall estimations. The procedure has a build-in quality control by means of the jack knifing method.
Actual Actual evapotranspiration represents the Satellite evapotranspi- latent heat flux exchanged between the ration (E) land surface and the atmospheric boundary layer. The latent heat flux is obtained as the difference between net radiation and sensible heat flux. It is given as the amount of water in mm/day that actually evaporates from the surface (soil and plants).
Potential The amount of water in mm/day that Satellite evapotranspi- would evaporate from the ground ration (E P) surface (soil and plants) in case of unrestricted water availability. Relative The ratio of actual over potential Satellite evapotranspi- evapotranspiration: RE = E / E P ration (RE)
Effective Rainfall Rainfall minus actual evapotranspiration. Satellite and (ER) ER = R – E GTS
Evapotranspi- Explained in Appendix A. Satellite ration Drought Index (EDI)
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