groundwater recharge calculations as affected by …...observed water table and precipitation data...
TRANSCRIPT
M.S. Batalha1, M. Th. van Genuchten2,3 and E.M. Pontedeiro3,4
1Department of Civil Engineering, UFRJ, Rio de Janeiro, Brazil
2NIDES Interdisciplinary Center for Social Development, UFRJ, Rio de Janeiro, Brazil
3Department of Earth Sciences, Utrecht University, Netherlands
4Department of Nuclear Engineering, UFRJ, Rio de Janeiro, Brazil
Groundwater Recharge Calculations as Affected by Temporal Averaging of
Meteorological Data
Hydrus Workshop, Prague, 29-30 March 2016
Recharge
Recharge refers to the amount (or flux) of water reaching the groundwater table
Important term in local and larger-scale water resources studies Many different approaches for estimating recharge
Experimental Lysimeter studies, in situ field studies, saturated zone, …
Tracers Environmental (Cl, 14C), historical (3H, 36Cl, CFC, …), heat
Saturated zone studies From water table fluctuations, larger-scale models (MODFLOW)
Unsaturated zone modeling Water budget models, detailed root zone modeling, …
…
Purpose of Study
Recharge estimation and groundwater table fluctuations
Rio Claro Aquifer, Brazil
Effects of temporal averaging of weather data on recharge calculations
Hourly, daily, weekly, monthly, yearly meteorological data
Observed Water Table and Precipitation Data
Tsoft corrected plot of seasonal recharge and drawdown cycles of the Rio Claro aquifer (top), and plot of daily rainfall rates (bottom)
(Rio Claro Aquifer, SP, Brazil; GE Druck water column probe, 5-s data)
Rio Caro Aquifer, Brazil
Root Zone Modeling using HYDRUS-1D
Root Water Uptake:
0
1Tp = 1 mm d-1
Pressure Head, h [L]h1h2h3 highh3 lowh4
Tp = 5 mm d-1
Stre
ss R
espo
nse
Func
tion,
α[-]
0
1Tp = 1 mm d-1
Pressure Head, h [L]h1h2h3 highh3 lowh4
Tp = 5 mm d-1
Stre
ss R
espo
nse
Func
tion,
α[-]
( ) ( ) ( ) pS z,t = h b z Tα
Richards Equation: ( ) 1 - ( )h= K h S ht x xθ∂ ∂ ∂ − ∂ ∂ ∂
Potential Evapotranspiration: Hargreaves (1975)
Grass Cover: Linear Decreasing Root Distribution to 70 cm; LAI=2
Free-Drainage Fixed Water Table Variable flux
/ 0∂ ∂ =h x ( ) ( )= Lh L h t
Atmospheric Surface BCs: Daily rainfall and ETp data
Lower Boundary condition at L = 8 m
( ) exp ( )L GWq t A B h t h= − −
Vadose Zone Hydraulic Properties
Soil Depth (cm)
θr (cm3/cm3)
θs (cm3/cm3)
α (cm-1)
n (-)
Ks
(cm day-1)
0-150 0.039 0.387 0.0334 1.42 53.0
150-250 0.043 0.386 0.0311 1.40 276.
250-350 0.046 0.385 0.0294 1.39 378.
350-450 0.050 0.387 0.0257 1.39 436.
450-550 0.056 0.386 0.0266 1.37 49.8
550-1000 0.057 0.386 0.0263 1.36 99.1
( )( ) 1−− = = −
mnre
s r
hS h + hθ θ αθ θ
( )2
1/( ) 1 1 − mm
e s e eK S = K S - S ( 1 1 / )= −m n
θr = residual water content θs = saturated water content α, n = empirical shape factors Ks = saturated hydraulic conductivity)
40
60
80
100
120
3
4
5
6
7
8
0 10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
Precipitation (1450 mm/y)
Potential ET (1000 mm/y)
Root Water Uptake
Root Zone Modeling (Recharge)
Root Zone Modeling (Recharge)
0∂=
∂hx
Root Zone Modeling (Recharge)
( ) exp ( )L GWq t A B h t h= − − 0∂
=∂hx
Observed and Calculated WT Elevations
( ) exp ( )= − − GWq t A B h t h A = -1.8 cm/day; B = 0.007 cm-1 hGW = 800 cm
Neto, D.C., H.K. Chang, and M.Th. van Genuchten. 2016. A mathematical view of water table fluctuations in a shallow aquifer in Brazil. Ground Water, doi: 10.1111/gwat.12329.
Calculated
Measured
Root Zone Modeling (Recharge)
( ) exp ( )L GWq t A B h t h= − − 0∂
=∂hx
• Different resolutions of
meteorological data
• 1D 2-m deep uniform soil profile
• Richards equation applies
• No root water uptake
• Possible runoff
• Three different soil types
• Three regions of Brazil
Effects of Temporal Averaging on Recharge
Soil θr (-)
θs (-)
α (cm-1)
n (-)
Ks (cm d-1)
Loamy Sand 0.057 0.41 0.124 2.28 350. Loam 0.078 0.43 0.036 1.56 25.0
Clay Loam 0.095 0.41 0.019 1.31 6.24
Soil Hydraulic Properties
( ) ( )1mnr
es r
S h hθ θ
αθ θ
−−= = +
−
( ) ( )2
1/ 1 1ml m
s e eK h K S S = − −
10
100
1000
1000010000
Hydraulic Properties: Head v
C
F
M
Three biomes from Brazil
Daily Precipitation Rates
Penman-Monteith Potential Evaporation Rates
Meteorological Data (Jan 2008 – Dec 2012)
Hourly Data: Hourly P data from Criciúma for 2008 only. The 2008 hourly data were scaled to 2009-2012 to have the same yearly precipitation rates as observed for those years For potential evaporation we downscaled daily data to hourly data using a sinusoidal distribution above a certain nightly minimum:
( )0
0.24 ( 0.264 or 0.736 )
22.75 sin 0.264 0.736 )24 2
p
p
E t d t dE t tE d t dπ π
< >= − ≤ ≤
From Daily to Weekly, Monthly and Yearly Data
p
1
7 for weekly approach (P, E )
30 (28, 29 or 31) for monthly365 (366) for yearly
1 =
=
=
=
= ∑n
ii
n
nn
Y yn
Calculated Recharge Rates (% of Precipitation)
Less recharge with fine-textured soils (soil hydraulic property issue)
Less recharge with averaging over longer times
(numerical problem)
Calculated Recharge Rates (% of Precipitation)
* Runoff depth 2,5 cm
Calculated Recharge Rates (% of Precipitation)
Mean Discharge: Cerrado versus Temperate
Mean Discharge Rates (different soil textures)
Cerrado (distinct wet and dry wet seasons)
Temperate Southern Brazil (no distinct wet and dry seasons)
From Daily to Weekly, Monthly and Yearly Data
Results for Amazonia more like Southern Brazil
From Daily to Weekly, Monthly and Yearly Data
Instantaneous Runoff Runoff for 2.5 ponding depth
Largest differences between hourly and daily data for fine-textured soils, due to instantaneous runoff
Smaller differences when water is allowed to accumulated on soil surface (here up to 2.5 cm)
Also Hourly Data
Runoff increases significantly with hourly data (P > Ks)
Effect of Hourly versus Daily Data on Runoff
Different temporal resolutions give different recharge rates
Daily averaged data give reasonable results, except for very coarse-textured media
Results depend on the distribution of P, T data over year
Recharge rates decrease going from hourly to daily to weekly to monthly to yearly data
Soil type is also important when considering runoff
Conclusions
