Modeling of hydrological processes and water use across Northern - - PowerPoint PPT Presentation
Modeling of hydrological processes and water use across Northern Eurasia Alexander Shiklomanov 1,2,3 and Alexander Prusevich 1 1-Water Systems Analysis Group, EOS, University of New Hampshire, USA 2-Laboratory of Hydrological Cycle, Shirshov
http://www.wsag.unh.edu
1-Water Systems Analysis Group, EOS, University of New Hampshire, USA 2-Laboratory of Hydrological Cycle, Shirshov Institute of Oceanology RAS, Moscow, Russia 3-Arctic and Antarctic Research Institute, Saint Petersburg, Russia
ENVIROMIS-2016
Tomsk, July 11-16, 2016
– physically based macroscale hydrological model (Vörösmarty, 1998) – WTM Routing based on river network (STN)
– WBM + irrigation + reservoirs+ diversions+ water withdrawal (industrial, domestic, livestocks) + multiple (dynamic) land cover/use ; glacier discharge, hourly/daily/monthly time step (2 routing models, 3 ET functions), coupled with permafrost GPL model (under development)
Input Parameters from File, Spreadsheet
Model Code
Core version, Branched versions Output Variables and Datasets
RIMS (the Magic Table)
Datasets, Datasets Metadata, Visualization, Analysis, Data Aggregation, Data Manipulation Fundamental Model Components
WB Processes to generate runoff Water Routing by river network Water Use for human needs
stream and/or ground water
6) data interpolation and shading tools; 7) point/station data list with clickable symbols that open station pages in a separate browser window; 8) fold-out section to run the Data Calculator application to perform mathematical and logical functions over gridded or vector datasets;.
Regional Integrated Mapping and Analysis System
http://neespi.sr.unh.edu/maps 1) data search/selection, spatial navigation, metadata link, etc.; 2) coordinate and map data value reader; 3) pixel query tool (i-tool) gets coordinates, country, watershed, and map data value; 4) time series navigation tool; 5) map size and base layer choices;
Earth System Science Data Category Key Sources Examples of Major Parameters Current Dataset Count
Source Source + DataCube
Hydrology UNH, CCNY Discharge, runoff, river networks, irrigation, dams 200 250 Past and Present Climate NASA, NOAA, UDel, Princeton U. Temperature, precipitation, evapotranspiration (ET), heat radiation, pressure, wind 70 210 NCEP, MERRA 62 160 Future Climate and Hydrology IPCC , UNH Temperature, precipitation, ET, snow, runoff, discharge 680 4100 Remote Sensing MODIS, UNH, UOklahoma Vegetation indices, soil moisture, clouds, snow, fires 48 60 Physical Geography NASA, USGS, UNH Elevation, bathymetry, Blue Marble, Lon/Lat 28 22 Oceanography NOAA, NCOF SST, sea ice 3 4 Land Cover UM, NASA, USGS Land cover, vegetation, permafrost, freeze/thaw 60 80 Sociology and Economics CIESIN, World Bank, US CIA, UNH Population, GDP, industry, mortality/birth/malnutrition rates 30 60 Agriculture UWisc, Various Crop land, crops, fertilizer loads, greenhouse emissions 160 200 Polygon Masks UNH Watershed, sea/ocean catchments, continents, countries, administrative units 18 18 Station Data UNH, AGS Hydrology, climate, public health 8 8 Total ~1400 ~5100
WBM-TrANS works with any topological river networks in any projections and in any basin subsets. Some common TRNs-
* Can be downscaled to coarser resolutions (Fekete et al., 2001)
Network parameters-
GRanD database- Extent: Global Total: 6,885 dams Russia: 52 dams Params: 56
NID database- Extent: USA Total: 83,987 dams Params: 65
Both can be used in WBM-TrANS
0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 Discharge release (Q/Qmean over 5 past years) Reservoir Level (Storage/Capacity)
Reservoir Operating Rule
Optimal reservoir level Average Annual Discharge (5 yrs) Logarithmic Release Rule Exponential Release Rule
𝐸 = 𝐸$%&𝑇( + 𝑚𝑜 𝑙𝑇-/ /0
𝐸 = 𝑓𝑦𝑞 𝑐 𝑇 − 𝑇678%$9:
@ 𝑏𝑢 𝑇 ≥ 𝑇678%$9:
where 𝑙 =
B /CDEFGHIJ/ K0
and 0.1<SR<1 is regulatory capacity (a ratio between annual cumulative flow volume and the reservoir capacity). Dam Purpose b α Comment Generic 0.2 0.8 10 2/3
Works for most of dams
Hydropower 0.2 0.9 40 1
Steeper curve to keep high water level
Irrigation 0.1 0.8 10 1/2
Flatter curve to have more even discharge
Natural Lake 0.0 0.5 1.6 2/3
Smooth curve, release is smoothed inflow
Table of suggested parameters for Equation (1) – As of now, only Generic rule is coded in WBM.
Old Mill & Dam, Durham, NH
100 200 300 400 500 600 700 800 900 1979 1980 1981 1982 1983 1984
Year
Merrimack River (12,005 km2)
GRDC, observed WBM, with dams. Nash-Sutcliffe Coef. = 0.79 WBM, no dams. Nash-Sutcliffe Coef. = 0.72 200 400 600 800 1000 1200 1400 1600 1800 1979 1980 1981 1982 1983 1984
Year
Connecticut River (25,020 km2)
GRDC, observed WBM, with dams. Nash-Sutcliffe Coef. = 0.82 WBM, no dams. Nash-Sutcliffe Coef. = 0.72
Global Inventory of Glaciers (GIG) Database (UAF/UNH) Extent: Global Total: 200,302 glaciers Russia: 4299 glaciers Params: unknown
Glacier Area
Melt water release point from the Glacier
In WBM-TrANS Glacier area gets subtracted from grid cell area and melt water from GIG DB is added to its runoff
Grid Cell Area
Gridded glacier discharge mean yearly climatology based on ERA-40 climate data and UAF glacier model simulation
Ø Snow pack size depends non-linearly on altitude, via precipitation and temperature. Ø In temperate mountainous regions, with seasonally- varying snow line, using the average elevation, temperature, and precipitation over the entire grid cell gives wrong answer. Ø How it works:
bands (we use 200 m intervals from 0 to 5 km elev.).
from grid cell average elevation to band's elevation.
bands are not considered; WBM lumps all areas of same elevation range into 1 band.
together (weighted by area fraction) to give grid-cell average for writing to output files.
Ø Use of elevation and snow bands is less necessary when running WBM at high resolution (e.g. 6 min network or higher). Ø When snow bands are used on 30 min network, the spring snow melting season extends from about 10 days (no snow bands) to at least 4 weeks (with snow bands) in mountainous regions.
the soil reaches a crop-dependent threshold
Evergreen Needleleaf Forest Mixed Forest Urban Land Grasslands Corn Wheat Rye Fallow Land Vine Rice Corn Fallow Land Vine Natural and Anthropogenic (non-crop) Landcovers-
Can be a Time Series dataset. Presently we use 14 MODIS LC
WBM parameters (e.g. root depth) that results in LC dependent runoff.
Rainfed Croplands-
Can be a Time Series dataset. Presently we use 64 MIRCA crop
Irrigation Equipped Area-
Can be a Time Series dataset. Presently we use 32 MIRCA crop
All crops allow crop rotation, and
Fractional Partitioning of Landcovers by type Fractional Partitioning of Rainfed Crops Fractional Partitioning of Irrigated Crops Example of a Grid Cell
Harvested irrigation area Irrigation Water Demand
High 280,000 Low 0.00 High 800 Low 0
High 680 Low 0
Drainage basin image from Wikimedia.org
Inter-basin Transfer Inter-”sub”-basin Transfer
Inter-Basin Hydrological Transfers Database
(Google Earth, Reservoir database (GRanD), Canals, …)
(remove, merge, split into segments, use river)
to handle new process
✔ ✔ ✔ ✔ ✔ ✔ Objective: Global and Continental Scale Earth System Science (also regional scale for Northeast USA) Method:
Inter-basin Hydrological Database – Data Summary
Continent Count Africa 6 Australia 3 Eurasia 78 North America 54 South America 9 Total 150 Status Count Completed 61 Under Construction & Proposed 89 Total 150
Lammers et al, in prep.
Proposed South-to-North Water Transfer Schemes - China Under Construction & Planned: Western, Central and Eastern Routes Possible future transfers: Brahmaputra à Salween à Mekong à Yangtze Rivers
Source: modified from futuretimeline.net
? ? ?
Diversion Rules – A Simplified Method to Move Water Currently using PercentFlow and MaxPercent Methods
Lammers et al, in prep.
For each diversion we now need a Minimum flow value, a Maximum flow value and a percentage. 1) Diversions do not start until the donor river has at least the minimum flow; 2) The amount diverted is a percentage of the flow above the minimum flow; 3) The diversion amount never exceeds the maximum;.
River Discharge Differences: With Diversion - Without Diversions Diversion
CCSM4 RCP 8.5 Year 2050-2059 Loss Gain
Discharge Difference (m3/s)
Donor Basin Receiving Basin James Bay Project Québec, Canada Donor Basin
Unsustainable groundwater: groundwater extracted in excess of total groundwater recharge
impacts, including water withdrawal for irrigation, domestic, industrial and livestock needs, effects of reservoirs and large water transfers.
upstream downstream Figure above demonstrates change in long-term mean annual discharge along the main stem of Syr Darya river (blue line) from inlet (right) to outlet (left). Brown line characterizes changes in water availability per capita along the river based on cumulative aggregation of population living in upstream watershed area.
0" 0.1" 0.2" 0.3" 0.4" 0.5" 0.6" 0.7" 0.8" 0.9" 1" 0000-01-01" 0000-01-19" 0000-02-06" 0000-02-24" 0000-03-14" 0000-04-01" 0000-04-19" 0000-05-07" 0000-05-25" 0000-06-12" 0000-06-30" 0000-07-18" 0000-08-05" 0000-08-23" 0000-09-10" 0000-09-28" 0000-10-16" 0000-11-03" 0000-11-21" 0000-12-09" 0000-12-27"
Mean%frac)on%of%countries%in%daily%discharge%of% Syr5Darya%River%at%mouth%
Uzbekistan" Kyrgyzstan" Kazakhstan"
0" 200" 400" 600" 800" 1000" 1200" 1980) 1981) 1982) 1983) 1984) 1985) 1986) 1987) 1988) 1989) 1990) 1991) 1993) 1994) 1995) 1996) 1997) 1998) 1999) 2000) 2001) 2002) 2003) 2004) 2006) Discharge**m3/s*
Monthly*discharge*of*Syr7Darya*river*at*estuary*by*country*
Kazakhstan" Uzbekistan" Kyrgyzstan"
Qyzylorda,%Kazakhstan%
0" 100" 200" 300" 400" 500" 600" 1980" 1982" 1984" 1986" 1988" 1990" 1992" 1994" 1996" 1998" 2000" 2002" 2004" 2006" 2008" 2010" 2012" Area%of%irrigated%land%(thous.%Ha)% Year%Tashauz,%Turkmenistan%
0" 100" 200" 300" 400" 500" 600" 1980" 1982" 1984" 1986" 1988" 1990" 1992" 1994" 1996" 1998" 2000" 2002" 2004" 2006" 2008" 2010" 2012" Area%of%irrigated%land%(thous.%Ha)% Year%Karakalpakstan,%Uzbekistan%
0.0# 50.0# 100.0# 150.0# 200.0# 250.0# 300.0# 350.0# 1980# 1982# 1984# 1986# 1988# 1990# 1992# 1994# 1996# 1998# 2000# 2002# 2004# 2006# 2008# 2010# 2012# Area%of%irrigated%land%(thous.%Ha)% Year%Leninabad,%Tajikistan%
0" 50" 100" 150" 200" 250" 300" 1 9 8 " 1 9 8 2 " 1 9 8 4 " 1 9 8 6 " 1 9 8 8 " 1 9 9 " 1 9 9 2 " 1 9 9 4 " 1 9 9 6 " 1 9 9 8 " 2 " 2 2 " 2 4 " 2 6 " 2 8 " 2 1 " 2 1 2 " Area%of%irrigated%land%(thous.%Ha)% Year%Osh,%Kyrgystan%
250$ 260$ 270$ 280$ 290$ 300$ 310$ 320$ 330$ 340$ 1980$ 1982$ 1984$ 1986$ 1988$ 1990$ 1992$ 1994$ 1996$ 1998$ 2000$ 2002$ 2004$ 2006$ 2008$ 2010$ 2012$ Area%of%irrigated%land%(thous.%Ha)% Year%Bishkek,%Kyrgystan%
300# 310# 320# 330# 340# 350# 360# 1980# 1982# 1984# 1986# 1988# 1990# 1992# 1994# 1996# 1998# 2000# 2002# 2004# 2006# 2008# 2010# 2012# Area%of%irrigated%land%(thous.%Ha)% Year%Fergana,%Uzbekistan%
Uzbekistan Tajikistan Kyrgystan
Turkmenistan
The map shows Central Asian countries and sub-country administrative units. The census data about land use, crops and irrigated area from 1980 to 2013 for the administrative units have been combined with gridded MIRCA2000 rainfed and irrigated crop data to provide the dynamic of land use in WBM-TrANS simulations.
Water demand for irrigation in 2014 Change in water demand for irrigation between 2010- 2014 and 1988-1992 The water demand for irrigation has increased in CA since 1990, however the spatial changes have been heterogeneous due to political and social transformations
Water Scarcity Index (WSI) combines information about water abstractions and water availability: WSI=W/Q where W are the freshwater abstractions and Q is the available water. The calculations of WSI were made simulations using only locally generated water resources (left plots) and total available water resources (including inflow). The results were aggregated for administrative units.
Water Availability Index (WAI)
compares all available water resources to the water demands (i.e. domestic, industrial and agricultural)
Water availability per capita (total) Water availability per capita (local) Water availability per crop (total) Irrigation water demand maps
Map of water temperature for Merrimack river watershed on June 16, 2013 based on simulation with MERRA climate data
Water temperature if greatly influenced by presence of river hydro-infrastructure and primarily by dams. Shallow reservoirs cause the water to be warmer than unobstructed river channel (see example above of lake Winnipesaukee at the NE part of the Merrimack river watershed). Water release from deep reservoirs cause downstream water to be colder (see tributary below Franklin Falls river dam)..
Day
Real−Time River Discharge − Lena at Kusur MERRA shifted by 1 month
Discharge (m3/s) 2009 2010 2011 2012 50000 100000 150000
Data courtesy of AARI
Observed River Discharge WBM w/ MERRA Climate
Day
Real−Time River Discharge − Yenisey at Igarka MERRA shifted by 1 month
Discharge (m3/s) 2009 2010 2011 2012 50000 100000 150000
Data courtesy of AARI
Observed River Discharge WBM w/ MERRA Climate
Deviation of simulated mean annual river runoff over 2040-2060 relative to the long-term observed mean (1959-1999).
Observed and WBM simulated daily river discharge for Lena and Yenisey
Deviation of August, 2013 precipitation from long term mean
derived from MERRA Deviation of August, 2013 runoff from long term mean over (1979-2013) based on WBM simulation
0" 5000" 10000" 15000" 20000" 25000" 30000" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" m3/s% Month%
Daily%discharge%of%Amur%river%above%Komsomolsk%na%Amure%
2013" AVERAGE"(197922013)" 2013_pris9ne"
All GCM’s inputs (AR4 and AR5) and WBM-TraNS outputs can be viewed, analyzed and download through RIMS
http://earthatlas.sr.unh.edu/maps http://neespi.sr.unh.edu/maps http://nh-rims.sr.unh.edu/maps http://www.riverthreat.net/maps http://riceghg.sr.unh.edu/maps
Most of WBM-TRANS outputs can be found, viewed and analyzed through one of UNH RIMS websites. Use the search engine to find and analyze necessary data (runoff, discharge, AR5, etc)