groundwater recharge calculations as affected by …...observed water table and precipitation data...

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

Thanks!!

Rien van Genuchten

rvangenuchten@hotmail.com