Transcript Slajd 1 - ISAC

Institute of Meteorology and Water Management, POLAND,
Krakow
EUMETSAT H-SAF – Satellite Application Facility in Support
to Operational Hydrology and Water Management
VALIDATION OF SATELLITE
PRECIPITATION PRODUCTS WITH USE
OF HYDROLOGICAL MODELS –
EUMETSAT H-SAF ACTIVITIES
Jerzy Niedbała
Institute of Meteorology and Water Management
Hydrological Forecasting Office
Jan Sadoń
Institute of Meteorology and Water Management
Piotr Struzik
Institute of Meteorology and Water Management
Satellite Research Department
Whole H-SAF Team contributed
Presentation outline:
1. EUMETSAT SAFs – already presented by Thomas Heinemann.
2. H-SAF overview – already presented by Bizzaro Bizzari.
Remained:
3. IMWM Poland - data sources for hydrological models.
4. Hydrological cycle and information needed for modelling of processes.
5. Hydrological models, thier requirements and possible use of satellite
data – outcome of H-SAF (Cluster 4) activities.
6. H-SAF Validation programme (precipitation products):
- Conventional validation,
- Hydrological impact studies.
7. Solving the problem of spatial and temporal resolution.
8. Conclusions
Institute of Meteorology and Water Management, Kraków, Poland
Hydrological Forecasting Office
Satellite Research Department
EUMETSAT Satellite Application Facility in Support to Operational
Hydrology and Water Management (H-SAF)
Activities:
•
Operational hydrology – forecasting and warning
•
Operational receiving, processing and distribution of satellite products to the
users in IMWM network.
•
Research and implementation of satellite products in meteorology, hydrology,
agrometeorology…
•
H-SAF activities officially started (15 Sept.2005) – development phase 20052010, 12 European countries involved. Poland coordinates Hydrological
Validation and implementation cluster.
Lightning
detection
SAFIR
METEOSAT
(7,8,9)
NOAA (all)
FengYun 1D
Ready for Metop
60 Synop
152 Climate
978 Raingauges
NWP:
LM-COSMO,
ALADIN
IMWM
196 Snow obs. posts
Poland
8 radars
Fig. 28 - Composite image from all Polish radars.
989 telemetric
posts
Operational Hydrology and modelling
Main activities of operational hydrology is closely related with use of
forecasting hydrological models, which convert meteorological inputs,
hydrological inputs and parameters characterising cachment to discharges in
streams and rivers.
Use of such a models is also part of flood warning systems, which determine risk
of flood according to forecasted hydrograms.
Generally most of hydrological models are deterministic, based on physical
equations describing water fluxes and energy exchange. Many models used in
operational practise are still conceptual, semiempirical or empirical.
Well-designed, distributed network that measure temperature, precipitation
(rainfall and snowfall), snowpack, soil moisture, vegetation properties,
radiation, wind, evaporative flux and humidity contribute to the quality of
hydrological forecasts.
Hydrological
processes are
frequently rapid and
dangerous
Development of flash
flood as a result of
severe storm
(Switzerland)
Proper information and warning is highly required
Well informed people do not panic even in extreme conditions
Development in operational hydrology and resulted
demands for better input data
• Classic hydrological models have been optimised for use
with point observations (such as precipitation and
streamflow) and were inadequate for extension to data
assimilation, which is distributed in space.
• We observe consequent exchange of hydrological models
used operationally from models, which can accept only point
data to the models based on gridded information.
• An improved higher resolution of observed data will decrease
the uncertainty of hydrological model predictions.
• Remote sensing data should bring new type of information
(both qualitative and quantitative) accepted by hydrological
models.
Space structure of model levels.
Preprocessing of data is required to fit input variables to model
resolution.
- grid based
A model structure based on regular fine
grids, matching the resolution of the
other spatial data, is much to be preferred.
-based on HRU’s (Hydrological
Response Units) Data are spatially
aggregated giving mean values for
Hydrological Responce Units (HRU).
-lumped subcatchment is
another alternative frequently
used in operational hydrology.
Different scales of data processing in typical levels of
hydrological model
Satellite information (depending of instrument used) has frequently not
adequate spatial and temporal resolutions – down/up scaling and merging
with other data sources is required
Hydrological cycle vs. satellite
products
EUMETSAT Satellite Application Facility in Support
to Operational Hydrology and Water Management
(H-SAF)
H-SAF bottom-up aproach
Requirements driven by operational hydrology needs.
Creation of operational satellite products for:
-Better spatialisation of conventional measurements,
-To complement ground observations on the areas with sparse ground
networks and/or not covered by radars,
- Merging satellite products with other data sources,
- Redundancy of information - useful in case of disaster situation (damage of
measuring posts or data links)
Final assessment of satellite products to be done by Hydrological
Impact Studies.
Demonstration and training on satellite products use, in real operational
environment of State Hydrological Services
Logic of the incremental development scheme
Current
instruments
Initial
databases
Baseline
algorithms
Prototyping
Augmented
New
databases
instruments
Cal/val
Advanced
programme
algorithms
Version-1
Version-2
Final Version
2007
Inter
Consortium
beta product
delivering
Operational End-users and Hydrological
End-user
feedback
validation programme
H-SAF activities on satellite products validation
Cluster 1
Cluster 2
Cluster 3
Precipitation
products
Soil Moisture
products
Snow products
Classical
Validation.
Comparison
to ground
measurements.
Hydrological
impact
studies
Belgium
Germany
Hungary
Italy
Poland
Slovakia
Turkey
Austria
France
ECMWF
Finland
Germany
Poland
Turkey
Romania
Belgium, France, Germany, Italy, Poland, Slovakia, Turkey
At least 24 catchments, 14 operational models
The purpose of hydrological validation plan
• to establish the plans of the hydrological institutes for
performing the impact studies soon after start of the regular
products distribution.
• The objective of the Hydrological validation programme is to
independently assess the benefit of the novel satellite-derived
data on practical hydrological applications.
Elements of hydrological validation plan
• elaboration of the requirements (i.e., what is needed to
perform the impact studies),
• selection and/or development of the algorithm/modelling
tools to perform the impact studies,
• describtion of the test sites, their equipment and the
experiment planned to be carried out,
•Performance of the impact studies on the base of all test
sites using all available satellite data,
•structure of the Education and Training (E&T) activities.
Structure of the WP-5000 (Hydrovalidation)
WP - 5000
Hydrological validation
Poland (IM WM)
WP-5100
Products training
Poland + Several
WP-5200
Developments
Poland + Several
WP-5300
Impact study 1
Belgium
WP- 5400
Impact study 2
France
WP- 5110
Soil moisture
Austria
WP- 5210
Development
Belgium
WP- 531
Scheldt 0river
WP- 5410
Grand/Petit
Morin
WP- 5510
Sieg
catchment
WP- 5610
Tanaro river
WP- 5120
Snow
Finland
WP- 5220
Development
France
WP- 5320
Meuse river
WP- 5420
Beauce
region
WP- 5520
Ammer
catchment
WP-5620
Arno river
WP- 5130
Precipitation
Italy
WP- 5230
Development
Germany
WP- 5700
Impact stu dy 5
Poland
WP- 5430
AdourGaronne
basin
WP- 5530
Dill
catchment
WP-5630
Basento river
WP-5800
Impact study 6
Slovakia
WP-5500
Impact study 3
Germany
WP-5600
Impact study 4
Italy
WP- 5240
Development
Italy
WP- 5710
Sola river
WP- 5250
Development
Poland
WP- 5720
Skawa river
WP- 5820
Nitra river
WP- 5260
Development
Slovakia
WP- 5730
Prosna river
WP- 5830
Kysuca river
WP-5920
Manavgat
WP- 5930
Upper
Euphrates
WP- 5270
Development
Turkey
WP- 5840
Hron river
WP- 5850
Topla river
WP- 5940
Upper Karasu
WP- 5950
Kırkgöze
basin
WP- 5810
Myjava river
WP- 5900
Impac t study 7
Turkey
WP- 5910
Sakarya river
Catchment characteristics
Climatological criterion - classifies rivers depending on the
climatological zone the river is located in.
Within area covered by EUMETSAT member and cooperating states,
three major zones can be distinguished: warm temperate zone
(subzones: Mediterranean and marine), cold temperate zone (subzones:
continental and subpolar) and mountainous zone,
Geographical and high-altitude criterion – describes localization of the
river including location in a specific geographical environment.
According to this criterion rivers are categorized as for example
mountainous rivers, littoral rivers, etc.,
Catchment management criterion – allows to group the rivers, or more
precisely their catchments, regarding predominate spatial management
type. (e.g. urban, agricultural or sylvan catchments, etc.),
Catchment size criterion – classifies river systems taking into account
the total area of their catchments.
Hydrological regimes
Pluvial, oceanic – occurs in temperate climate. Distribution of precipitation is
homogeneous throughout the whole year. The annual river flow is high. In the
summer time, due to evaporation, water level in rivers is lower comparing to the
winter. Western Europe rivers
Pluvial, Mediterranean – maximum water level appears in winter, because of the
strongest recharge. Rivers can periodically or even totally dry up in summer.
Rivers in Mediterranean Countries
Nival – the rivers are frozen through the most part of the year. The highest flows
appear in spring due to snowmelt. Minimum water levels are observed in autumn
and winter. Rivers in Eastern Europe and Scandinavia,
Pluvio-nival – two periods of high water levels are observed: the first in spring,
caused by the snow cover melting as well as rainfalls and the second one in
summer, caused by rainfalls Rivers in Eastern and partly Central Europe
Glacial – the rivers have their headwaters in the glaciated area. Maximum water
flows are observed in summer Central Europe rivers.
Operational hydrological models and selected testbeds
Belgium: SCHEME grid cell conceptual model,
2 test sites  Scheldt, Meuse
France: SAFRAN-ISBA-MODCOU set of models,
3 test sites  Grand/ Petit Morin, Beauce, Adour-Garonne,
Germany: PRMS, HBV-BfG, MMS/MHMS models,
3 test sites  Sieg river,
Italy: ARTU’, NASH, DRiFT models,
3 test sites  Arno, Basento, Tanaro rivers,
Poland: SH system (SMA, conceptual),
3 test sites  Soła, Skawa, Prosna,
Slovakia: MIKE11-NAM, Hron rainfall-runoff,
5 test sites  Myjava, Kysuca, Nitra, Hron, Topľa,
Turkey: Snowmelt Runoff Model SRM, HBV models,
5 test sites  Upper Euphrates, Upper Karasu, Kırkgöze , Manavgat,
Sakarya,
Finland: to be defined.
Test catchments location
?
•7 (8) countries
•24 test sites
•Variety of climatological conditions
•Variety of terrain conditions
•Variety of land cover
•Different hydrological regimes
•Catchment size: 242 – 102000 km2
• 902 raingauges, 21 radars
Input data for hydrological models (analysis performed by H-SAF Cluster 4)
PARAMETER
REQUIRED SPATIAL RESOLUTION
REQUIRED TEMPORAL
RESOLUTION
Precipitation
Regular grid 50m, 100m, 500 m, 1
km, 7 km, 8 km
HRU, subcatchment
10 min, 15 min, 1h, 3h, 6h, daily
Snowfall
Regular grid 1 km, 8 km,
HRU
6 h - daily
Air temperature
8 km, 25 km, point, HRU
10 min, 15 min, 1h, 2h, daily
Soil moisture
point, catchment
daily
Snow covered area
50m, catchment
daily
Solar radiation
8 km, 50 km, point
15 min,1h, 6h, daily
Snow water
equivalent
50m, catchment
daily
Long wave radiation
8 km
1h
Sun duration
50 km
6h
Land use, vegetation
type
30m, 250 m, 1km
10 days, season
Wind speed
7 km, 8 km, 25 km
10 min, 1h, 2h
Humidity
8 km, 50 km, point
1h, 2h, 6 h
Lesson learnt
• The analysis of the questionnaires from the hydrological modellers do not allow to
common definition of the parameters expected from the satellite products at this
point.
• It is necessary to analyse the available (now or in near future) SAF’s products that
could be useful for application in hydrological models  inter-SAF activities.
• Large variety of models regarding spatial domain – from detailed grids (100-500 m),
through HRU based model to catchment based models,
• Large variety of requirements regarding temporal domain – from detailed 10-15 min
data to daily means,
• Most important inputs: precipitation (including snow), temperature, radiation
components (mainly solar).
• Less frequently used parameters: detailed radiation budget (short/long wave),
evapotranspiration, vegetation type and actual status.
• Data needed (lack of adequate ground measurements), expected from satellite
information: soil moisture, snow water content.
• We predict constant grow of user requirements during H-SAF development phase
due to modernisation of measuring tools and models themselves.
General hydrological validation algorithm (1)
Preparation of tool verification - the hydrological model
simulated mode - calibration and the verification of hydrological model
output from
hydrological model
(simulated hydrograph)
hydrological
model
comparing simulated
and observed
hydrographs
• average square error
149190100 P00050B
149190100 P00050B 149190100 B00050B
• average square relative error
160,00
120,00
• time relative 100,00
error
80,00
60,00
40,00
20,00
0,00
30-08-06 6:00 30-08-06 18:00 31-08-06 6:00 31-08-06 18:00 01-09-06 6:00 01-09-06 18:00
160,00
O ś w artości - w sz ystkie patrametry serii
140,00
• maximum relative
error
Oś wartości - wsz ystkie patrametry serii
input data for hydrological
models from manual and
automatic ground stations and
experimental resarch
140,00
120,00
100,00
80,00
60,00
40,00
20,00
0,00
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input data for hydrological models
from radar system (now-casting)
and meteorological model
(forecasting)
Hydrologicalvalidation
model in operating
mode(2)
General hydrological
algorithm
operating mode - starting hydrological model in operating mode
output from
hydrological model
(forecasted
hydrograph)
hydrological
model
comparing forecasting
and observed
hydrographs in nonoperating time
• average square error
149190100 P00050B
149190100 P00050B 149190100 B00050B
160,00
• average square relative
error
140,00
• maximum relative
error
120,00
100,00
• time relative error
80,00
60,00
40,00
20,00
0,00
30-08-06 6:00 30-08-06 18:00 31-08-06 6:00 31-08-06 18:00 01-09-06 6:00 01-09-06 18:00
160,00
O ś w artości - w sz ystkie patrametry serii
Oś wartości - wsz ystkie patrametry serii
input data for hydrological
models from manul and
automatic ground stations and
experimental resarch
140,00
120,00
100,00
80,00
60,00
40,00
20,00
0,00
30-08-06 6:00 30-08-06 18:00 31-08-06 6:00 31-08-06 18:00 01-09-06 6:00 01-09-06 18:00
input data for hydrological
models from radar system (nowcasting) and meteorological
model (forecasting)
rainfall and snow
satellite data
General
hydrological validation algorithm (3)
Hydrological model in operating mode using satellite data
satellite data
operating mode - starting hydrological model in
operating mode
input data for hydrological
models from manul and
automatic ground stations and
experimental resarch
comparing two
forecasted
hydrographs
(computed on base
standard or satellite
data) with observed
hydrograph in nonoperating time
output from
hydrological model
(standard forecasted
hydrograph and
forecasted hydrograph
computed using
satellite data)
hydrological
model
• average square error
149190100 P00050B 149190100 B00050B
149190100 P00051B
• average square relative error
• maximum relative error
140,00
120,00
100,00
• time relative error
soil moisture
temperature,
rainfall and snow
80,00
60,00
40,00
20,00
0,00
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149190100 B00050B
160,00
O ś w artości - w sz ystkie patrametry serii
O ś w artości - w sz ystkie patrametry serii
160,00
149190100 P00050B
140,00
120,00
100,00
80,00
60,00
40,00
20,00
0,00
30-08-06 6:00 30-08-06 18:00 31-08-06 6:00 31-08-06 18:00 01-09-06 6:00 01-09-06 18:00
General hydrological validation
(4)
Hydrologicalalgorithm
validation plan
criteria of choice
comparing two
forecasting
hydrographs
(computed on base of
standard or satellite
data) with observed
hydrograph in nonoperating time
use of the satellite data
increases the quality of
hydrological
forecasting
statistical analyses
YES
NEITHER
„YES” NOR
„NO”
NO
149190100 P00051B 149190100 P00050B 149190100 B00050B
O ś w artości - w sz ystkie patrametry serii
160,00
• average square
error
140,00
120,00
100,00
• average square
relative error
we recommend satellite
data as an input to
hydrological
forecasting model
we recommend
standard data as an
input to hydrological
forecasting model
80,00
60,00
40,00
20,00
0,00
30-08-06 6:00 30-08-06 18:00 31-08-06 6:00 31-08-06 18:00 01-09-06 6:00 01-09-06 18:00
• maximum relative
error
• time relative error
Further research must be
done: when, where and why
use of satellite data gives
negative results. Satellite data
could be used in case when
other data are not available
Feedback to Clusters 1,2,3
Solving the problems with spatial and temporal resolution
(satellite products bias ?).
1. H-SAF additional tasks:
- up/down scaling methods and algorithms,
- Merging sateliite products with ground observations,
- Adapting satellite products to hydrological models
inputs (interfaces).
2. Tools included in hydrological models (also considered):
- Data processors,
- Embedded GIS tools.
Preprocessing Tasks
Temporal domain - to reach the calculation time step of the
hydrological model
 Temporal disaggregation of measurements
Spatial domain – data regionalisation/scaling
 Spatial interpolation of data (measured or forecasted) to a
reference grid or center points of HRUs
 Vertical dependence of parameters to be taken into account
 Filling of data gaps
Preprocessor: Precipitation
Observed Station Data
Forecasted Grid Data
TemporalDisaggregation
Disaggregation
Temporal
SpatialInterpolation
Interpolation
Spatial
(fromStation
StationtotoReference
ReferenceGrid)
Grid)
(from
SpatialInterpolation
Interpolation
Spatial
(fromNWP
NWPGrid
GridtotoReference
ReferenceGrid)
Grid)
(from
SpatialAggregation
Aggregation
Spatial
fromReference
ReferenceGrid
GridtotoHRU
HRU
from
SpatialAggregation
Aggregation
Spatial
fromReference
ReferenceGrid
GridtotoHRU
HRU
from
Observed station data transformed
into mean HRU values
Forecasted grid data transformed
into mean HRU values
Satellite data are available in defined time slots (variable for polar sat.) and not
in regular grid (resolution depend on viewing angle)
Conclusions:
1. H-SAF is preparing operational structure for hydrology. Without
acceptation of products and their quality (at least among
EUMETSAT Member and Cooperationg States), this activity will be
useless.
2. Operational structure must include not only products creation but
also creation of communication links to the users (GTN-H, WIS ?).
3. Strong need for closer links between satellite data providers and
hydrological users – H-SAF consortium cosists of both.
4. We do not forsee to re-invent the wheel – large part of activities
based on well known and partially tested algorithms and
methods. Hydrological component of H-SAF useful not only for
internal purposes.
5. First H-SAF products already available – soil moisture.