Front. Archit. Civ. Eng. China 2010, 4(1): 47–55 DOI 10.1007/s11709-010-0001-x
RESEARCH ARTICLE
Lei WANG, Cho Thanda NYUNT, Toshio KOIKE, Oliver SAAVEDRA, Lan Chau NGUYEN, Tran van SAP
Development of an integrated modeling system for improved multi-objective reservoir operation
© Higher Education Press and Springer-Verlag 2009
Abstract Reservoir is an efficient way for flood control and improving all sectors related to water resources in the integrated water resources management. Moreover, multiobjective reservoir plays a significant role in the development of a country’s economy especially in developing countries. All multi-objective reservoirs have conflicts and disputes in flood control and water use, which makes the operator a great challenge in the decision of reservoir operation. For improved multi-objective reservoir operation, an integrated modeling system has been developed by incorporating a global optimization system (SCE-UA) into a distributed biosphere hydrological model (WEB-DHM) coupled with the reservoir routing module. The new integrated modeling system has been tested in the Da River subbasin of the Red River and showed the capability of reproducing observed reservoir inflows and optimizing the multi-objective reservoir operation. First, the WEB-DHM was calibrated for the inflows to the Hoa Binh Reservoir in the Da River. Second, the WEB-DHM coupled with the reservoir routing module was tested by simulating the reservoir water level, when using the observed dam outflows as the reservoir release. Third, the new integrated modeling system was evaluated by optimizing the operation rule of the Hoa Binh Reservoir from 1 June to 28 July 2006, which covered the annual largest flood peak. By using the optimal rule for the reservoir operation, the annual largest flood peak at downstream control point (Ben Ngoc station) was successfully reduced (by about 2.4 m for water level and 2500 m3$s–1 for discharge); while after the simulation periods, the reservoir water level was increased by about 20 m that could supply future water use. Received July 17, 2009; accepted October 4, 2009
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Lei WANG ( ), Cho Thanda NYUNT, Toshio KOIKE, Oliver SAAVEDRA Department of Civil Engineering, the University of Tokyo, Japan E-mail: wang@hydra.t.u-tokyo.ac.jp Lan Chau NGUYEN, Tran van SAP National Hydro-Meteorological Service, Ministry of Natural Resources and Environment, Vietnam
Keywords distributed biosphere hydrological model (WEB-DHM), optimization, multi-objective reservoir, the Red River basin
1
Introduction
In recent years, climate change has been one of the greatest environmental, social, and economic threats to the Earth. The Intergovernmental Panel on Climate Change (IPCC) reported that the changes in precipitation and temperature will lead to the changes in runoff and water availability, and the occurring frequencies of extreme events (e.g., floods and drought) are likely to increase in the coming 50 years [1]. The climate change-induced variability in hydrological cycles directly affects regional water resources management. Integrated water resources management (IWRM) is a systematic process for sustainable development, allocation, and monitoring of water resources use in the context of social, economic, and environmental objectives. There are various ways for implementing IWRM, e.g., flood management using structural and non structural measures. The regional IWRM can be actually applied in the real world by demonstrating the improvements of existing water resources management practices. By promoting IWRM, safe and stable water supply, enhancement of flood control to protect lives and properties, and conservation of water environment can be achieved. Reservoir is one of the effective ways for flood control and improving all sectors related to water resources in IWRM. It has a lot of water linkages control together to solve water related problems, such as flood damage, water shortage, and water usage. Moreover, multi-objective reservoir plays a significant role in the development of a country’s economy especially in developing countries. All multi-objective reservoirs have conflicts and disputes in the flood control and water use. The multi-objective reservoir operation is a complicated problem involving many decision variables, multiple objectives together with
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considerable risk and uncertainty [2]. Traditional reservoir operation is based on heuristic procedures, embracing rule curves, and subjective judgments by the operator. Moreover, conflicts of multiple objectives give the operator a great challenge in the decision of reservoir operation. Operator should consider the flood control for vulnerability of people and also water use for various socio-economic values. Operator decides the reservoir release based on the present water level, water demands, inflows to the dam, and the time of year. To increase the reservoir efficiency and balance the various demands from the different users, a systematic approach for reservoir operation should be based on not only the traditional probabilistic analysis but also the information and prediction of extreme hydrological events and advanced computational technology [3]. In this study, an integrated modeling system has been developed for improved multi-objective reservoir operation. The new integrated modeling system has been tested in the Da River subbasin of the Red River, with a focus on checking its capability of reproducing observed reservoir inflows and optimizing multi-objective reservoir operation.
2
Integrated modeling system
The section describes the integrated modeling system developed by incorporation of a global optimization system [4] into a distributed biosphere hydrological model [5,6] coupled with the reservoir routing module [7,8]. In Section 2.1, the distributed biosphere hydrological model is briefly reviewed. The reservoir routing module and the global optimization scheme are described in Sections 2.2 and 2.3, respectively. In Section 2.4, the definition of the objective function for optimizing multipurpose reservoir operation is discussed. Finally, the whole framework of the integrated modeling system is presented in Section 2.5. 2.1
Distributed biosphere hydrological model
The water and energy budget-based distributed hydrological model (WEB-DHM) [5,6] is a distributed biosphere hydrological model that enabled consistent descriptions of water, energy, and CO2 fluxes at a basin scale and in a spatially distributed manner. The overall model structure is shown in Fig. 1 and can be described as follows: 1) A digital elevation model (DEM) is used to define the target area, and then, the target basin is divided into subbasins (see Fig. 1(a)). 2) Within a given subbasin, a number of flow intervals are specified to represent time lag and accumulating processes in the river network according to the distance to the outlet of the subbasin. Each flow interval includes several model grids (Fig. 1(b)). 3) For each model grid with one combination of land use type and soil type, the land surface submodel is used to
calculate turbulent fluxes between the atmosphere and land surface independently (see Figs. 1(b) and (d)). 4) The hydrological submodel is used to calculate the runoff from a model grid with a subgrid parameterization. Each model grid is subdivided into a number of geometrically symmetrical hillslopes (Fig. 1(c)), which are the basic hydrological units (BHUs) of the WEBDHM. For each BHU, the hydrological submodel is used to simulate lateral water redistributions and calculate runoff (see Figs. 1(c) and (d)). The runoff for a model grid is the total response of all BHUs in it. 5) For simplicity, the streams located in one flow interval are lumped into a single virtual channel in the shape of trapezoid. All the flow intervals are linked by the river network generated from the DEM. All the runoff from the model grids in the given flow interval is accumulated into the virtual channel and led to the outlet of the river basin. It should be mentioned that, for simplicity and reducing computation costs, the interactions of groundwater between flow intervals are not considered in the model. Furthermore, within a flow interval, the lateral moisture exchanges between model grids are not formulated. Therefore, the model can maintain high efficiency for simulations of large-scale river basins while incorporating subgrid topography (see Ref. [5]). 2.2
Reservoir routing
A reservoir is formed by damming the river flow at somewhere of the river network and control the water release according to the reservoir operation rule. 2.2.1
Governing equation
Reservoir routing uses a simple storage function, and the continuity equation is dV ¼ I – O, dt
(1)
where V is the storage, I is the inflow, and O is the outflow. The equation can be rewritten as the finite difference form Vtþ1 – Vt I þ Itþ1 Ot þ Otþ1 ¼ t – , Δt 2 2
(2)
where t and t + 1 indicates current and the next time step, respectively; Δt is time interval. By assuming little changes between the outflows in current and the next time step, Vt+ 1 can be approximately derived as It þ Itþ1 (3) – Ot Δt: Vtþ1 ¼ Vt þ 2 Figure 2 shows the flow chart of reservoir routing. The volume and water level of the current time step (Vt and Ht) are defined as the initial conditions for the reservoir routing. The inflows to reservoir (It and It+ 1) can be
Lei WANG et al. Development of an integrated modeling system for improved multi-objective reservoir operation
Fig. 1 Overall structure of the WEB-DHM. (a) Division from a basin to subbasins; (b) subdivision from a subbasin to flow intervals comprising several model grids; (c) discretization from a model grid to a number of geometrically symmetrical hillslopes; (d) process descriptions of water moisture transfer from the atmosphere to river. Here, the land surface submodel is used to describe the transfer of the turbulent fluxes (energy, water, and CO2 fluxes) between the atmosphere and land surface for each model grid, where Rsw and Rlw are downward solar radiation and long wave radiation, H is the sensible heat flux, and l is the latent heat of vaporization. The hydrological submodel simulates both surface and subsurface runoff using grid-hillslope discretization and then simulates flow routing in the river network
simulated by the WEB-DHM. The outflow of current time step (Ot) is defined by the reservoir operation rule. Therefore, the storage of the next time step (Vt+ 1) can be obtained. The water level of the reservoir for the next time step (Ht+ 1) can be estimated from Vt+ 1 using reservoir characteristics (e.g., V–H curve). According the value of Ht+ 1, the reservoir operation rule for the next time step can be determined. 2.2.2
Reservoir operation rule
In the study, for simplicity, the reservoir water release is assumed as proportional to the inflow [8]: Ot ¼ kIt ,
(4)
Fig. 2 Flow chart for reservoir routing
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where k is the coefficient. If k = 0, all of the gates are closed and no outflows go to the downstream; if k = 1, all the simulated inflows will be released from the reservoir. 2.3
Global optimization scheme
The shuffle complex evolution method developed at the University of Arizona (SCE-UA) [4] is a global optimization scheme. The SCE-UA method is based on a synthesis of four concepts: 1) combination of deterministic and probabilistic approaches; 2) systematic evolution of a “complex” of points spanning the parameter space, in the direction of global improvement; 3) competitive evolution; and 4) complex shuffling. Details about the optimization procedure can be found in Ref. [4]. The SCE-UA scheme is expected to obtain the parameter set that can produce the global optimum of the objective function. 2.4 Objective function for optimizing multi-purpose reservoir operation
The techniques to solve multi-objective optimization can be classified into two groups: 1) the aggregation approach and 2) the Pareto domination approach. In the first approach, the priorities of objectives are established beforehand; while in the second approach, no preference information is considered or is available before the optimization [9]. The aggregation approach combines the different objectives into one aggregated scalar to be optimized; while the Pareto domination approach is based on whether one solution is dominated by another [10]. In this study, with the first approach, different weighted objectives are combined together and used for the optimization of the aggregated objective function. This method transforms the various objective functions into a single scalar objective function. The weighted objective function is given as follows: Min FðX Þ ¼
N X
wi gi ðfi ðX ÞÞ,
(5)
i¼1
where fi(X) is the ith objective function; X is the parameter set; wi is the weight assigned to the ith objective, and XN w ¼ 1; and gi($) is the transforma0£wi£1 and i¼1 i tion function assigned to the ith objective function. The priority of each objective can be specified by using different weights. 2.5
Integrated modeling system
The whole framework of the integrated modeling system, which has incorporated the SCE-UA into the WEB-DHM coupled with reservoir routing module, is shown in Fig. 3. First of all, the optimal parameter and its feasible parameter
space under the desired objectives are necessary to be defined. The flow chart for running the integrated modeling system can be described as follows: 1) WEB-DHM is carefully calibrated for the reservoir inflows, and the calibrated model is then used to simulate the reservoir inflows during the optimization procedure. 2) For each optimization run, the reservoir releases are defined by using the new optimal parameter (k, the ratio of inflow and outflow) generated by the SCE-UA method towards the global optimum of the objective function. However, for the first optimization run, the reservoir releases are initialized by an initially given value of k. 3) After each optimization run (in which the WEB-DHM coupled with reservoir routing module is carried out for a number of time steps in the simulation period), the integrated objective value is estimated from the defined objective function. 4) Steps 2) and 3) are repeated until the predefined criteria (related to flood reduction and hydropower generation) are satisfied.
3
Datasets
3.1
Study area
The Red River originates from the mountainous region in Yunnan Province of China, at an elevation of nearly 2500 m. Then, it runs through Vietnamese provinces and flows into the Gulf of Tonkin (part of the South China Sea). The Red River basin is located in the tropical monsoon region, with the latitude from 20°N to 26°N and the longitude from 100°E to 106.5°E. The total area of the Red River basin is about 169000 km2, including 48% in China, 1% in Laos, and 51% in Vietnam. In Vietnam territory, the Red River basin covers surface lands of 26 provinces and cities including the Hanoi capital and Red River delta. The Red River comprises of three main upstream tributaries Da, Thao, and Lo rivers, and its delta forms near the capital Hanoi. The river delta has a triangular form with the apex near to Viet Tri, which is the junction of three main tributaries [3]. The climate of the Red River basin is subtropical, and it is mainly affected by the eastern Asia monsoon wind. Only 10% to 20% of the annual rainfall occurs in the dry season (November to April), while the other 80% to 90% of total annual rainfall concentrates in the rainy season (May to October). The mean annual rainfall in the upper basin (China territory) is approximately 1100 mm per year, while about 1900 mm per year in the Vietnam territory. The annual river runoff of the basin is around 130 billion m3, representing an average discharge of about 3700 m3$s–1 [3]. The Hoa Binh Reservoir (see Fig. 4) is the largest reservoir in Vietnam. It plays an important role in flood
Lei WANG et al. Development of an integrated modeling system for improved multi-objective reservoir operation
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Fig. 3 Flow Chart of integrated modeling system for optimized multi-objective reservoir operation
Fig. 4 Red River basin
control for the Red River basin delta and hydropower generation. It is a multi-objective reservoir. It was completed in 1989 and started in operation since 1990. It
is designed to control the largest historical flood peak and to produce on the average 7.8 billion kWh per year electricity corresponding to 40% of Vietnam’s electricity [11]. The Hoa Binh Reservoir, 70 km from Hanoi, is located on the Da River, the largest tributary of the Red River that contributes with more than 50% of the discharges including flood peaks to the lower regions. Therefore, the floods in Da River play a key role in downstream flooding, and the Hoa Binh Reservoir is the key measure for controlling flood in the Red River basin. The storage volume of the reservoir is quite big and satisfies both of the two conflicting purposes: flood control and hydropower generation. Besides the flood control, the requirement of power generation from the Hoa Binh Reservoir for Vietnam’s socio-economic development is also very strong. In recent years, the problems of inefficient operation of current reservoirs in Vietnam caused too much water during the flood season, threatening the reservoir safety, and little water during the dry season, causing the decreases in hydropower generation and thereby negative effects on the economy [3]. The problems have shown the urgency of efficient reservoir operation by considering the future goals of water uses and changes of hydrological
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conditions. Therefore, the application of optimization technique in reservoir operation is expected to provide a balanced solution between conflicting and unbalanced objectives. 3.2
Data preparation
Spatial data inputs for WEB-DHM include the topography, the land use and the soil. The DEM with 90 m resolution was obtained from http://srtm.csi.cgiar.org/. For reducing computation cost, 90 m DEM was aggregated into 4 km model grid for the model simulation (see Fig. 5(a)), while the sub-rid topography was described by the 100 m DEM (resampled from 90 m DEM). A digital map of 1-kmresolution SiB2 land use type was extracted from USGS Earth Resources Observation and Science Center (EROS). It was also aggregated into the 4 km model grids (see Fig. 5(b)). The agriculture/C3 grassland and forest were the dominant vegetations of the Da River subbasin. The soil type for the basin was obtained from the Food and Agriculture Organization [12] together with the soil hydraulic parameters, including the van Genuchten parameters (α and n) [13], the saturated soil moisture content (θs), and the residual soil moisture content (θr), as well as the saturated hydraulic conductivity for soil surface (Ks). The six hourly precipitation data from 94 rain gauges (see Fig. 4) in the Red River basin were provided by the National Hydro-Meteorological Service of Vietnam (NHMS) and National Center for Hydro-Meteorological Forecasting (NCHMF). The available rain gauges were well distributed in the lower area but sparse in the upper regions. The other surface meteorological data including air temperature, relative humidity, air pressure, wind speed, downward solar and long wave radiation, and cloud fraction were obtained from the JRA-25 reanalysis [14]. The lat-long grid resolution was 110 km, and it is available every 6 h. The dynamic vegetation parameters, leaf area index
Fig. 5
(LAI), and the fraction of photosynthetically active radiation absorbed by the green vegetation canopy (FPAR) were obtained from satellite data. Global LAI and FPAR MOD15_BU 1 km datasets [15] were used in this study, which are eight-daily composites of MOD15A2 products, and were provided from EOS Data Gateway of NASA. All the inputs were interpolated to a 4 km grid and hourly time step for model simulations.
4
Results and discussion
The aim of this study is to test the new integrated modeling system for optimizing the operation of the multi-objective reservoir (Hoa Binh; see Fig. 4). First, the WEB-DHM was calibrated for the inflows to the Hoa Binh Reservoir. Second, the WEB-DHM coupled with the reservoir routing module was tested by simulating the reservoir water level, when using the observed dam outflows as the reservoir release. Third, the new integrated modeling system (in which SCE-UA was introduced into the WEB-DHM coupled with reservoir routing module) was evaluated by optimizing the operation rule of the Hoa Binh Reservoir. During the flood period July 2006, the flood peak at Hoa Binh Reservooir inlet was about 14000 m3$s–1, and this was the largest flood peak in 2006. Therefore, considering the available datasets, this paper will focus on the period from 1 June to 28 July 2006, which covered the annual largest flood peak. 4.1
WEB-DHM model calibration
Figure 6 shows the observed and simulated six hourly inflows to the Hao Binh Reservoir from 1 June to 28 July 2006. After calibration, the WEB-DHM reproduced the dam inflows very well with Nash-Sutcliffe model efficiency coefficient (Nash) [16] equal to 0.87, and the bias error 5%, where Nash is defined as
Spatial distribution of DEM and land use in the Da River subbasin. (a) Spatial distribution; (b) land use
Lei WANG et al. Development of an integrated modeling system for improved multi-objective reservoir operation
53
Fig. 6 Observed and simulated six hourly inflows to the Hoa Binh Reservoir in 2006
Nash ¼ 1 –
n X
ðQoi – Qsi Þ2 =
i¼1
n X
ðQoi – Qo Þ2 ,
(6)
i¼1
where Qoi is observed discharge; Qsi is simulated discharge; n is the total number of time series for comparison; and Qo is the mean value of the discharge observed over the simulation period. The higher Nash is, the better the model performs. A perfect fit should have a Nash value equal to one. 4.2 Test of WEB-DHM coupled with reservoir routing module
Figure 7(a) displays the volume and water level (V–H) relation curve of the Hoa Binh Reservoir. Figure 7(b) illustrates the observed and simulated hourly water level of the reservoir from 1 June to 28 July 2006 with the observed dam outflows as the reservoir release. It was found that the calibrated WEB-DHM coupled with the reservoir routing module generally performed well in simulating the reservoir water level, as compared to the observed one. 4.3 Optimization of multi-objective reservoir (Hoa Binh) operation
The main purpose of this study is to deal with the trade-off between flood control and hydropower generation in the Hoa Binh Reservoir. Generally, flood control and hydropower generation conflicts with each other since the reservoir should keep storage space for flood control, while high water level is necessary to provide hydraulic head to the turbines [3]. For the Hoa Binh Reservoir, the objective function considered for reducing the flood peak at downstream is the first priority, and the hydropower generation is the second priority. ! T X 1 Min F ¼ w1 ðHds_sim – Hds_opt Þ2 T t¼1 þ w2
T X 1 t¼1
T
! ðRdam_sim – Rmax Þ
2
,
(7)
Fig. 7 Volume and water level (V–H) relation curve of the Hoa Binh Reservoir and the observed and simulated hourly water level of the reservoir from 1 June to 28 July 2006 with the observed dam outflows as the reservoir releases. (a) V–H relation curve; (b) observed and simulated hourly water level
where Hds_sim is the simulated water level at Ben Ngoc, which is 1 km far from the release point of the Hoa Binh Reservoir; Hds_opt is the optimal water level at downstream flood control point; Rdam_sim is the simulated water level of the Hoa Binh Reservoir; Rmax is the maximum water level of the reservoir; T is the total number of time steps; wi is the weight normalized by standard deviation of ith objective function, which means f ðX Þ wi ðf ðX ÞÞ ¼ i is used as the transformation function i here. For simplicity, w1= w2= 0.5 is adopted in this study. After defining the objective function for the Hoa Binh Reservoir, the ratio k of the inflow and outflow will be optimized to satisfy the integrated objective. The optimization of the reservoir operation was initialized as the observed reservoir water level and inflows to the reservoir on 1 June 2006. By using the integrated modeling system, the optimal operation rule for the Hoa Binh Reservoir from 1 June to 28 July 2006 has been achieved. Figure 8 shows the simulated hourly water level at the Hoa Binh Reservoir and the downstream station (Ben Ngoc) from 1 June to 28 July 2006 by using the optimized release schedule for the Hoa Binh Reservoir. It can be seen that by using the optimized reservoir operation rule, after
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Fig. 9 simulated discharges at Ben Ngoc from 1 June to 28 July 2006 by using the optimized release for the Hoa Binh Reservoir. Here, the initial water level of the reservoir is taken as the observed water level
Fig. 8 Simulated hourly water level at the Hoa Binh Reservoir and the downstream Ben Ngoc from 1 June to 28 July 2006 by using the optimized release from the reservoir. Here, the initial reservoir water level is taken as the observed water level. (a) Hoa Binh Reservoir; (b) downstream Ben Ngoc
the operation period from 1 June to 28 July 2006, the reservoir water level was increased by about 20 m (see Fig. 8(a)); and the highest water level at Ben Ngoc was decreased by about 2.4 m (Fig. 8(b)) comparing to their water levels resulted from the present release guide line. It means that more water could be saved in the reservoir for the future hydropower generation (or other water use), and the flood risk in the downstream control point (Ben Ngoc) could be significantly reduced with the water level less than alarming level. Figure 9 shows the simulated and observed discharges at Ben Ngoc by using optimized reservoir operation rule, which further confirmed the validity of the new modeling system for optimizing the multiobjective reservoir (Hoa Binh). The annual largest flood peak of 2006 at Ben Ngoc station was successfully reduced by about 2500 m3$s–1.
5
Concluding remarks
An integrated modeling system for optimized multiobjective reservoir operation has been developed and
tested at a subbasin of the Red River. Through the application of the new system to the Hoa Binh Reservoir in the Da River subbasin from 1 June to 28 July 2006, the optimized reservoir release rule has been obtained. By using the optimal rule for the operation of Hoa Binh Reservoir in the same period, the annual largest flood peak at the downstream control point (Ben Ngoc station) was successfully reduced (by about 2.4 m for water level and 2500 m3$s–1 for discharge); while after the simulation periods, the water level in the reservoir was increased by about 20 m that can supply future water use. The integrated modeling system has demonstrated the ability to achieve the optimal operation rule for the multiobjective reservoir operation (Hao Binh). In the future, for the operational purpose, there are still some issues need to be further addressed. First, in the current test, the optimized reservoir operation rule was obtained through only the optimization with two-month floods that covered the annual largest flood peak. In the next step, the integrated modeling system should be applied to the whole flood seasons (preflood season, main flood season, and postflood season) for more reliable reservoir operation. Second, in the study, the observed in-situ rainfall was used to simulate the inflows to the Hoa Binh Reservoir. However, for the practical reservoir operation, it is rather difficult to define the release rule according to the simulated reservoir inflows with observed rainfall. To achieve enough leading time (during which floods will travel from upstream to downstream), the forecasted rainfall should be considered for the simulation of dam inflows. Third, in the reservoir operation, the present study emphasized the release schedule for considering the flood control and hydropower generation. In the future, the objective function can be modified according to the priorities of the different aspects for societal and economical purposes, as well as the current features of
Lei WANG et al. Development of an integrated modeling system for improved multi-objective reservoir operation
the reservoir (e.g., maximum discharges of overflow gate, number of gates, and the maximum level of spillway). In this way, reservoir operation for flood control will be more feasible and effective. Finally, for effective IWRM of the whole Red River basin, allied multiple-reservoir operation should be considered, although the operation of the Hoa Binh Reservoir is of the first importance. This is because that multiple reservoirs exist in the upper area of Red River delta, where the capital Hanoi and other economic and industrial developments are located. Therefore, the new integrated modeling system should be extended to the whole Red River basin while considering the optimization of multiple multi-objective reservoirs’ operation.
7.
8.
9.
10. Acknowledgements This study was funded by the Japan Aerospace Exploration Agency. The second author is also supported by grants from the Asian Development Bank (ADB). Global 8-daily MODIS Terra LAI/ FPAR 1 km datasets are from the EOS Data Gateway of NASA.
11.
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