Evaluation of Hyper-Resolution Model Derived Water flow ... · CREST and JULES perform consistently...
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➢ CREST and JULES perform consistently well at all three basin scales in terms of ET whereas for streamflow CREST has overestimation at Kessie. There is not any lake module in the model and Kessie is at the downstream of Lake Tana which may cause the overestimation. ➢ Water budget analysis indicates that the water cycle components (ET, streamflow) from CREST balance reasonably well with the observed storage change. These hyper resolution water budget components can be used as inputs in local ground water models for agricultural applications at the farm level. Results: Descriptive Statistics Gilgel Gumara Kessie Time (Months) Terrestrial water storage is an important component of the water cycle. The terrestrial water balance for a prescribed area and period can be written as, = − − Combining remote sensing observations with global/regional hydrologic models have made it possible to estimate different water cycle components at spatial scales where in-situ measurements are sparse or unavailable. These data are critical to support water resources analysis and management. Introduction Distributed Variables Soil Properties Vegetation Library Land Cover Impervious Area Water Balance Energy Balance Atmospheric Layer Canopy Layer Snow Pack Layer Surface Soil Layer Deep Soil Layer River discharge (Q) Interception Evaportranspiration Snow Process Infiltration Fully Distributed Routing Runoff ET Hourly MeteorologIc Variables climatology Precipitation Force T W Calibration Qobs Routing parameters Remotely Sensed Products Leaf Area Index GIS-Terrain elevation flow direction Stream network • CREST-SVAS o A fully distributed hydrological model that Strictly couples energy and water balances and imposes closed energy balance. o For water cycle simulation over large watersheds at a fine spatiotemporal resolution (e.g., 30 m to 1 km and hourly time). • Simulation properties over Blue Nile o Resolution: 500 m and 3 hourly o Duration: 1979 – 2014 o Forcing Data: ✓ Meteorological Forcing: Reanalysis ERA Interim ✓ Precipitation: P version 1 o Static Data: ✓ Soil Texture Map: Soil Grids (3 layers, 0-2 m depth) Rehenuma Lazin, Xinyi Shen, Marika Koukoula, Zoi Dokou, Efthymios Nikolopoulos, and Emmanouil Anagnostou University of Connecticut, Civil and Environmental Engineering, Storrs, CT, United States; email: [email protected] Evaluation of Hyper - Resolution Model Derived Water Budget Components Over the Upper Blue Nile ➢ Simulate the water cycle of the Blue Nile basin at high spatial (500 m) and temporal (hourly) resolution using a hyper-resolution distributed hydrologic model, Coupled Routing and Excess STorage (CREST-SVAS) and evaluate its performance in terms of evapotranspiration (ET) and Streamflow. ➢ Compare the performance of CREST-SVAS with different hydrologic and land surface models in terms of the ET and streamflow at different basin scales. ➢ Evaluate the performance of water budget estimation with respect to the observed and Gravity Recovery and Climate Experiment (GRACE) data. Objective Gumara Boundary Gilgel Boundary Kessie Boundary Basin Outlet Study Area ➢ The Upper Blue Nile basin, the largest basin in Ethiopia (in terms of volume of discharge) o Drainage area: 176,000 km 2 ➢ Evaluation in three sub-basins: o Gumara: 1,394 km 2 o Gilgel: 1,664 km 2 o Kessie: 65,784 km 2 Figure 1: Location of Upper Blue Nile basin and its three sub-basins CREST - SVAS Results: Water Budget Analysis A – C: TWS D – F: Precipitation G – I: ET J – L: Streamflow M: TWS anomalies for Kessie with respect to GRACE Reference Data and Model Forcings Model Validation Figure 2: CREST Framework (Shen and Anagnostou, 2017) Variable Reference Data Temporal Extent Temporal Resolution Precipitation Gauge observed 1984 - 2012 Monthly ET GLEAM model 1980 - 2016 (0.25 o resolution) Daily Streamflow Gumara Gauge observed 1980 - 2002 Daily Gilgel 1980 - 2002 Kessie 1980 - 2012 Terrestrial Water Storage (TWS) Anomaly GRACE 2002 - 2012 Monthly Figure 4: ET map (A) CREST: 500 m (B) GLEAM: 27 km Precipitation Reanalysis vs. Gauges ET Models vs. Calibrated GLEAM Streamflow Models vs. Gauges Figure 5: Relative Error (A - C) and Normalized Taylor Diagram (D - F) of monthly basin averaged precipitation, ET, and streamflow HTESSEL-CaMa JULES WaterGAP LISFLOOD ORCHIDEE NOAH-MP CLMS Reanalysis Era Interim and MSWEP Forcing (0.25 o Resolution) MERRA-2 and CHIRPS Precipitation Forcing (0.25 o Resolution) TWS (mm) A B C Precipitation (mm) D E F ET (mm) G H I Streamflow (mm) J K L Conclusion Figure 6: For Gumara, Gilgel, and Kessie sub-basins M A B A B C D E F Figure 3: CREST vs GLEAM basin averaged daily ET at Upper Blue Nile R = 0.93 NSCE = 0.81 Relative Bias = 4.33%
Transcript of Evaluation of Hyper-Resolution Model Derived Water flow ... · CREST and JULES perform consistently...
➢ CREST and JULES perform consistently well at all three basin scales in terms of ET whereas for
streamflow CREST has overestimation at Kessie. There is not any lake module in the model and
Kessie is at the downstream of Lake Tana which may cause the overestimation.
➢ Water budget analysis indicates that the water cycle components (ET, streamflow) from CREST
balance reasonably well with the observed storage change. These hyper resolution water budget
components can be used as inputs in local ground water models for agricultural applications at the
farm level.
Results: Descriptive Statistics
GilgelGumara Kessie
Time (Months)
Terrestrial water storage is an important component of the water cycle. The terrestrial water
balance for a prescribed area and period can be written as, 𝑇𝑊𝑆 = 𝑃 − 𝐸𝑇 − 𝑄Combining remote sensing observations with global/regional hydrologic models have made
it possible to estimate different water cycle components at spatial scales where in-situ
measurements are sparse or unavailable. These data are critical to support water resources
analysis and management.
Introduction
Distributed Variables
Soil Properties
Vegetation Library
Land Cover
Impervious Area
Water Balance Energy Balance
Atmospheric Layer
Canopy Layer
Snow Pack Layer
Surface Soil Layer
Deep Soil Layer
River
discharge (Q)
Interception
Evaportranspiration
Snow Process
Infiltration
Fully Distributed Routing
RunoffET
Hourly MeteorologIc
Variables
climatology
Precipitation
Fo
rce
T
W
CalibrationQobs
Routing parameters
Remotely Sensed Products
Leaf Area Index
GIS-Terrain
elevation
flow direction
Stream network
• CREST-SVASo A fully distributed hydrological model that
Strictly couples energy and water balances and
imposes closed energy balance.
o For water cycle simulation over large
watersheds at a fine spatiotemporal resolution
(e.g., 30 m to 1 km and hourly time).
• Simulation properties over Blue Nileo Resolution: 500 m and 3 hourly
o Duration: 1979 – 2014
o Forcing Data:
✓ Meteorological Forcing: Reanalysis ERA
Interim
✓ Precipitation: 𝐌𝐒𝐖𝐄P version 1
o Static Data:
✓ Soil Texture Map: Soil Grids (3 layers,
0-2 m depth)
Rehenuma Lazin, Xinyi Shen, Marika Koukoula, Zoi Dokou, Efthymios Nikolopoulos, and Emmanouil AnagnostouUniversity of Connecticut, Civil and Environmental Engineering, Storrs, CT, United States; email: [email protected]
Evaluation of Hyper-Resolution Model Derived Water Budget Components Over the Upper Blue Nile
➢ Simulate the water cycle of the Blue Nile basin at high spatial (500 m) and temporal
(hourly) resolution using a hyper-resolution distributed hydrologic model, Coupled
Routing and Excess STorage (CREST-SVAS) and evaluate its performance in terms of
evapotranspiration (ET) and Streamflow.
➢ Compare the performance of CREST-SVAS with different hydrologic and land surface
models in terms of the ET and streamflow at different basin scales.
➢ Evaluate the performance of water budget estimation with respect to the observed and
Gravity Recovery and Climate Experiment (GRACE) data.
Objective
Gumara Boundary
Gilgel Boundary
Kessie Boundary
Basin Outlet
Study Area➢ The Upper Blue Nile basin, the
largest basin in Ethiopia (in
terms of volume of discharge)
o Drainage area: 176,000
km2
➢ Evaluation in three sub-basins:
o Gumara: 1,394 km2
o Gilgel: 1,664 km2
o Kessie: 65,784 km2 Figure 1: Location of UpperBlue Nile basin and its threesub-basins
CREST-SVAS Results: Water Budget Analysis
A – C: TWS
D – F: Precipitation
G – I: ET
J – L: Streamflow
M: TWS anomalies for Kessie with respect to GRACE
Reference Data and Model Forcings
Model Validation
Figure 2: CREST Framework (Shen and Anagnostou, 2017)
Variable Reference Data Temporal Extent Temporal Resolution
Precipitation Gauge observed 1984 - 2012 Monthly
ET GLEAM model 1980 - 2016 (0.25o resolution) Daily
Streamflow
Gumara
Gauge observed
1980 - 2002
DailyGilgel 1980 - 2002
Kessie 1980 - 2012
Terrestrial Water Storage (TWS) Anomaly GRACE 2002 - 2012 Monthly
Figure 4: ET map (A) CREST: 500 m (B) GLEAM: 27 km
PrecipitationReanalysis vs. Gauges
ETModels vs. Calibrated GLEAM
StreamflowModels vs. Gauges
Figure 5: Relative Error (A - C) and Normalized Taylor Diagram (D - F) of monthly basin averaged precipitation, ET, and streamflow
HTESSEL-CaMa
JULES
WaterGAP
LISFLOOD
ORCHIDEE
NOAH-MP
CLMS
Reanalysis Era Interim and MSWEPForcing (0.25o Resolution)
MERRA-2 and CHIRPS Precipitation Forcing (0.25o Resolution)
TW
S (
mm
)
A B C
Precip
itati
on
(m
m)
D E F
ET (
mm
)
G H I
Str
eam
flow
(m
m) J K L
Conclusion
Figure 6: For Gumara, Gilgel, and Kessie sub-basins
M
A B
A B C
D E F
Figure 3: CREST vs GLEAM basinaveraged daily ET at Upper Blue Nile
R = 0.93
NSCE = 0.81
Relative Bias = 4.33%
