Numerical Modeling of Ecohydrological Processes and Contaminant Transport
Using Microsoft Azure Cloud
Chunmiao ZhengCenter for Water Research
Peking University(http://hydro.pku.edu.cn)
Three Grand Challenges in Hydrological Sciences
• Water Cycle: An Agent of Change
• Water and Life• Clean Water for People
and Ecosystems
− National Research Council (2012)
China Water Crisis:Water Scarcity & Water Pollution
用于污水灌溉的河流
Water Pollution Control Action Plan~2 Trillion RMB
Science 340May 17, 2013
C. Zheng & J. Liu
Case Study 1: Numerical Modeling of Ecohydrological Processes in Heihe River Basin
Heihe River Basin
Total Area: 130,000 km2
The second largest inland river basin In China
Gobi Desert
Qilian Mountains
Hexi Corridor Oases
Gobi DesertMountain: 34%Oasis: 9%Desert:57% Main River Channel
Landscape of Heihe River Basin
Qilian Country
Qilian Country
Ejina Basin
Terminal Lake
Zhangye BasinField Study
Heihe Research Program
• An on-going major research programme of National Natural Science Foundation of China (2011-2018)
• Conduct an integrative study of ecological and hydrologic processes in Heihe River Basin toward more sustainable water resources management
• Led by Prof. Cheng Guodong of Chinese Academy of Sciences and advised by an expert panel of multidisciplinary scientists
• 200 million RMB core funding (~32 million USD)
Overall Objectives
Integrate observation, experimentation, and modeling
Improve predictive capability
Increase water use efficiency
Toward more sustainable water resources in arid ecosystems
Decision maker
Decision support system
Future forecast
Hydrological Ecological Economic
Scientist
HEIFLOW (Hydrological-Ecological Integrated watershed-scale FLOW model)
• Physically-based distributed-parameter 3D numerical model
• Include all key components of the hydrological cycle
• 2D overland flow and river channel hydrodynamics
• Saturated/unsaturated zones• 3D solute and heat transport• Modules for agricultural
crops and desert vegetation
Heihe River Basin Geodatabase
Simulation
Raster series
Surface water :(2-D)Waterbody (Polygon feature class): lake, ponds, (swamps)Waterline (Line feature class): streams, riversWaterPoint (Point feature class): springs, water withdrawal/discharge locationsWatershed (Polygen feature class): drainage areas
Groundwater: (2-D , 3-D)Aquifer (Polygon feature class):confined and unconfinedWell (Point feature class): monitoring, water supply, and irrigation wellsBorehole ( Boreholelog Table): vertical dataBoreline and BorePoint: 3D (z-enbled) line and point feature classGeoArea, GeoPoint
Framework
Subsurface
Georaster
Hydrogeological map
AquiferLandcover
TM
Geology control points
“Digital Heihe” Datasets
Foundation data DEM,Topographic, Hydrological map
Earth observation dataLandsat, ASTER, QuickBird——Thematic dataGeology, Hydrology, Vegetation——
Observation dataMeteorology、Hydrology、Groundwater——
Experimental dataField Survey, test——
Model dataRadiation,Land assimilation,SWAT——
http://westdc.westgis.ac.cn/
DIGITAL RIVER BASIN
3D View of Subsurface with Cross Sections
Observation well
Meteorological Station
River
Glacier and snow
Quaternary aquifer
Tertiary aquifer
Bedrock
LegendsHydrogeological Units
Middle & Lower Heihe River Basin Model Domain:~100,000 km2
Grid spacing:1 km by 1 kmRows: 548Columns: 404Layers: 5
Surface Water Model for Middle/Lower Heihe
Discretization and Parameter Zonation for Subsurface Model
Layer 1 2 3 4 5
Unconfined aquifer
Aquitard 1 Shallow confined
Aquitard 2 Deep confined
HEIFLOW on the Cloud
Conceptual Model
Model Execution
Model Calibration
Uncertainty Analysis
Optimized Decision
Compute Nodes
(Worker Role)Front End
Node
(Web
Role)
Azure Storage
(Blob/Table/SQL)
Element Model Execution Calibration
Parameterization Design Execution
Integration Execution Calibration
Head Node
(Worker Role)
Client Applications
ArcGISMatlab GMS
Blob Upload & Download
Hpcpack
Cluster & jobmanagement
Jobscheduling
Microsoft Azure
HPC on Microsoft Azure for ecohydrological modeling
Deployment of HPC on Azure for ecohydrological modeling
Managing NodesInstalling HPC Uploading Model Verification Application
Deployment of HPC on Azure for ecohydrological modeling
Managing NodesInstalling HPC Uploading Model Verification Application
Deployment of HPC on Azure for ecohydrological modeling
Managing NodesInstalling HPC Uploading Model Verification Application
Deployment of HPC on Azure for ecohydrological modeling
Managing NodesInstalling HPC Uploading Model Verification Application
Deployment of HPC on Azure for ecohydrological modeling
Managing NodesInstalling HPC Uploading Model Verification Application
Compute NodesFront End Node
Azure Storage
(Blob/Table/SQL)
Head Node
Client Applications
ArcGISMatlab GMS
Blob Upload
Hpcpack
Clustermanagement
Jobscheduling
Microsoft Azure
Ecohydrological model execution in HPC on Azure
Data Preparing & Uploading Job Submitting
Blob Down
Status Monitoring
Result Getting
Compute NodesFront End Node
Azure Storage
(Blob/Table/SQL)
Head Node
Client Applications
ArcGISMatlab GMS
Blob Upload
Hpcpack
Clustermanagement
Jobscheduling
Microsoft Azure
Ecohydrological model execution in HPC on Azure
Blob Down
Data Preparing & Uploading Job SubmittingStatus
MonitoringResult Getting
Compute NodesFront End Node
Azure Storage
(Blob/Table/SQL)
Head Node
Client Applications
ArcGISMatlab GMS
Blob Upload
Hpcpack
Clustermanagement
Jobscheduling
Microsoft Azure
Ecohydrological model execution in HPC on Azure
Blob Down
Data Preparing & Uploading Job SubmittingStatus
MonitoringResult Getting
Compute NodesFront End Node
Azure Storage
(Blob/Table/SQL)
Head Node
Client Applications
ArcGISMatlab GMS
Blob Upload
Hpcpack
Clustermanagement
Jobscheduling
Microsoft Azure
Ecohydrological model execution in HPC on Azure
Blob Down
Data Preparing & Uploading Job SubmittingStatus
MonitoringResult Getting
(a)
800 1000 1200 1400 1600 1800800
1000
1200
1400
1600
1800
Co
mp
ute
d (
m)
Observed (m)
Computed vs. Observed Head
Middle HRB
Lower HRB
(b)
Model Calibration:
(a) Comparison between contour maps of computed
and observed groundwater levels;
(b) Comparison between computed and observed
heads at monitoring wells;
(c) Comparison of computed and observed
streamflows and evapotranspiration
0
20
40
60
80
100
120
140
160
180
Str
eam
flo
w (
bil
lio
n m
3/
s) 高崖 (NS=0.92) Simulated
Observed
0
20
40
60
80
100
120
140
160
180
200
Str
eam
flow
m
3/
s)
正义峡(NS=0.85)Simulated
Observed
Gaging Station 1
Gaging Station 2
(c)
Simulated Evapotranspiration Dynamic Patterns (2000)
Element Model - Calibration
Comparison of parameter estimation runtimesobtained from a dedicated local desktop array and virtual machines run on the cloud
PEST
Results of sensitivity analysis on Cloud
00.5
11.5
22.5
3
rch
_71
rch
_64
rch
_77
rch
_76
rch
_63
rch
_74
rch
_78
rch
_82
rch
_91
rch
_79
rch
_83
rch
_84
rch
_81
rch
_80
rch
_73
rch
_65
rch
_86
rch
_75
rch
_90
rch
_88
rch
_89
rch
_69
rch
_72
rch
_70
rch
_61
rch
_85
rch
_67
rch
_62
rch
_68
rch
_60
rch
_66
Sensitivity_recharge
0
0.05
0.1
0.15
0.2
0.25
hk_
27
hk_
54
hk_
29
hk_
18
hk_
42
hk_
11
hk_
3
hk_
25
hk_
13
hk_
14
hk_
1
hk_
9
hk_
22
hk_
5
hk_
17
hk_
49
hk_
41
hk_
30
hk_
19
hk_
33
hk_
35
hk_
38
hk_
48
hk_
39
hk_
51
hk_
31
hk_
44
hk_
52
Sensitivity_conductivity
629
43
0
100
200
300
400
500
600
700
Local Execution with 1 core HPC Execution with 24 cores on Azure
Computational performance of PEST for sensitivity analysis to
HPC on Azure
Tim
e in
Ho
urs
Case Study 2:Thorium Reactive Transport Modeling
Baotou tailings pond, one of the largest
tailings in China piled above ground
surfaces.
Of greatest concern is the potential for
radioactive pollution of the Yellow River
nearby which is the primary water
source for 150 million people.
Most productive “secondary mines”,
approximately 11 floors high!
Modeling Contaminant Transport and Remediation
Zheng 1990Zheng and Wang 1999Zheng 2010Zheng et al. 2013
MT3DMS
RT3D
MGOSOMOS
SEAM3DSEAWAT
PHT3D
MT3DMS-Based Transport Modeling Tools
Seawater
Intrusion
Biodegradation
kinetics +
transport
Geochemical
reactions +
transport
Bio-chemical
reactions +
transport
Management
Optimization
Management
optimization
and monitoring
network design
Tailings pond leakage
(Henning Prommer, 2013)
Numerical model Transported chemical undergoes
surface complexation, with mineral
dissolution/precipitation.
R
Governing equation::
Single species transport
model in MT3DMS with
advection, multiple
species reactive modeling
in PHREEQC.
Architecture of Modeling Application on Azure
Simulation Results
Conservative transport plumes
Thorium reactive transport plumes
Sensitivities of thorium mass-fluxes into collection-trench
to 24 hydrogeological parameters
FD, TVD, split-operator algorithm
72 layers, 745 columns
Execution Time and Costs
Acknowledgements
National Natural Science Foundation of China (grant no. 91225301)
Microsoft Research Asia(grant no. CNIC-MSR2013)
Our cloud modeling research is supported by
PKU: Guangjun Zhang; Yingying Yao; Xiang Huang; Yong TianMicrosoft Resxearch Asia: Lily Sun; Xin Ma
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