Quantification and Measurement of the Hydrologic Cycle

GEOS 4430 Lecture Notes: Quantiﬁcation and Measurement of the Hydrologic Cycle

Dr. T. Brikowski

Fall 2011

ﬁle:hydro_cycle.tex,v (1.30), printed September 8, 2011 Hydrologic Budget

Misc. information and data sources:

• Texas Regional Water planning homepage

• Region C 2011 water plan Executive Summary

1 Hydrologic Budget

• Hydrologic budget is simply an H2O mass balance

rate of rate of change in − = (1) mass in mass out storage

• usually assume density of water constant, then make a volume balance instead

• estimating these components is a large part of hydrology, and can sometimes be quite diﬃcult

2 Hydrologic Budget (cont.)

• For a watershed (topographic basin) water balance is (Fig. 1):

rate of = P (2) mass in Precipitation|{z} rate of = Q + E + T + R mass out s | {z } |{z} Runoﬀ|{z} Evapotranspiration Recharge

+ Qg (3) Groundwater|{z} Discharge

3 Basin Hydrologic Cycle

Figure 1: Hydrologic cycle for a watershed, after Domenico and Schwartz [Fig. 1.2, 1990]. 4 Evaporation

Misc. information and data sources:

• U.S. Evaporation climatology (calculated)

• U.S. raw evaporation data

• Daily pan (or actual?) evaporation at DFW lakes

• moisture sensor rebate for NTMWD customers

5 Importance of Evapotranspiration

• 2/3 of precipitation in the U.S. returns to the atmosphere by evapotranspiration

• in arid regions ouptput by ET can exceed 90% of basin water inputs

• in humid regions (e.g. Western Washington) ET can be as little as 10% of input

6 Evaporation: Physical Process • endothermic process (requires energy input) (Fig. 2)

• requires relative humidity ≤ 100 (absolute humidity) (relative humidity) = · 100 (saturation humidity) (kg water) humidity = (m3 air)

• absolute humidity is the current moisture content of the air

• saturation humidity is temperature dependent, the dewpoint is the temperature at which saturation humidity becomes equal to the absolute humidity. See Fetter [Table 2.1, 2001] 7 Water Phase Diagram

Figure 2: Phase diagram for H2O, after Tindall and Kunkel [1999]. Energy (e.g. heating) is required to drive water across the two-phase boundary into the vapor ﬁeld (area to right of curve). 8 Evaporation: Measurement

• Direct methods: – pan evaporation (land pan, Figs. 3–4): ∗ observe evaporation from a standard-sized shallow metal pan ∗ best to measure precipitation input separately (i.e. make a quantitative water balance for pan) ∗ apply empirical relationship to estimate lake or plant evaporation (Fig. 6) – lysimeter (Fig. 5) ∗ a cannister containing “natural” soil, installed at ground level ∗ weigh (and perform water balance) to determine moisture content changes due to evaporation 9 • Indirect methods:

cal – Energy budget. 540 gm energy required to transform water to vapor at room temperature. Not all energy recieved by surface water is used for evaporation though:

Qs − Qrs − Qlw − incoming|{z} solar rad. reflected|{z} solar rad. IR radiation|{z} out

Qh − Qe + turbulent|{z} exchange latent heat|{z} of vap.

Qv − Qe = heat brought|{z} in by water flow heat carried|{z} out by vapor

Qθ (4) change in|{z} heat content

– Bowen energy ratio: monitor soil T profile, incoming solar 10 radiation and heat radiated to atmosphere at soil surface (combines Qh & Qe in Eqn. 4, see Hillel [p. 290, 1980] – Eddy correlation method ∗ directly measure water vapor flux using wind speed, humidity measurements, i.e. micro-meteorology ∗ more recently used to measure CO2 fluxes, e.g. ABLE experiment – soil chloride profile (Cl mass balance, e.g. paleoclimate studies)

11 NOAA Evaporation Pan

Figure 3: Example of NOAA standard evaporation pan, from Wikipedia. 12 U.S. Pan Evaporation Contours

Figure 4: U.S. Pan Evaporation Contours, showing general distribution of open-water evaporation. See original data at NWS. 13 Weighing Lysimeter

Figure 5: Example of commercial weighing lysimeter. Note variety of sensors, and monitoring of natural and lysimeter conditions. See UMS for installation details.

14 Transpiration

• Transpiration is evaporation from plants

• underside of leaves contain pores (stoma) which open for photosynthesis during the day

• water drawn into plant by roots to provide support and transport nutrients is lost via stoma

• hence length of day is an important constraint on transpiration

• see animation for a helpful visualization

15 Evapotranspiration: Physical Process

• Transpiration is evaporation from plants

• underside of leaves contain pores (stoma) which open for photosynthesis during the day

• water drawn into plant by roots to provide support and transport nutrients is lost via stoma

• hence length of day is an important constraint on transpiration

• ET is combined bare soil evaporation and plant transpiration 16 • transpiration predominant mechanism for water loss from soil in all but the driest climates [can be 15-80% of basin water losses, Fetter, 2001] (Fig. 6)

• phreatophytes (plants with roots to water table) are generally most important, except in agricultural settings

• for shallow-rooted plants, ET ceases when soil moisture drops below wilting point (plant root suction less than soil suction)

17 ET From Cornﬁeld

Figure 6: ET From Cornﬁeld, showing ratio of ET to open-pan evaporation. Recall that actual evaporation from open water is usually about 0.7 times the pan evaporation. After [Fig. 5-1, Dunne and Leopold, 1978]. 18 Evapotranspiration: Estimation/Measurement

• Measurement – Lysimeters (containing soil and plants) – phytometer - “plant-in-a-box”, airtight transparent enclosure (lab or ﬁeld), monitor humidity of air; unnatural conditions and therefore questionable data

• Estimation – Thornthwaite Method (empirical formula, inputs are T, latitude, season; emphasizes meteorological controls, ignores soil moisture changes, Fig. 7)

10T a E = 1.6 a (5) t I 19 cm where Et is potential evaporation in mo, Ta is mean monthly air temperature in ◦C , I is an annual heat index, and a is a cubic polynomial in I – Blaney-Criddle method, adds a crop factor (empirical estimate of vegetative growth and soil moisture eﬀects); most popular method, calibrated for U.S. only

Et = (0.142Ta + 1.095)(Ta + 17.8)kd (6)

where k is an empirical crop factor (bigger for thirsty crops or fast-growth periods), d is the monthly fraction of daylight hours. – Penman Equation: ∗ use vapor pressure, net radiation, T to calculate ∗ fairly popular, but inaccurte (most parameters estimated) 20 ∗ intended to mimic pan evaporation, so tends to over- estimate ET (e.g. Fig. 9). ∗ Note [Fig. 2.1 Fetter, 2001] is essentially a graphical solution of this equation ∗ see various Ag. schools for free software (e.g. U. Idaho). – Remote sensing: ∗ early eﬀorts developed species-speciﬁc ET rates for a locale, estimate distribution, growth rate, etc. from multi-spectral images, calculate spatially-variable ET rates Czarnecki [e.g. 1990], Owen-Joyce and Raymond [e.g. 1996] ∗ more recently use energy balance approach, e.g. China study comparison with lysimeter data

21 Thornthwaite Method

Figure 7: Graphical solution of Thornthwaite Method, indicating primary dependence on mean air temperature and “heat index” (a U.S.-calibrated indicator of daily temperature range). After [Fig. 5-4, Dunne and Leopold, 1978]. See also online calculator. 22 FAO Penman-Montieth Equation

• worldwide standard method developed by UN Food and Agriculture Organization

• envisions a “reference crop”, accounts for energy balance and “resistance” to ET (i.e. computes reduction from open-water evaporation rate, Fig. 8)

• computes potential evaporation (i.e. maximum possible)

• schematic version of equation:

(net energy flux) + (wind) · (RH) ETo = 23resistances where the energy flux is solar input minus infrared radiation and reflection out, resistances are rs and ra as shown in Fig. 8

24 Setting: FAO Penman-Monteith Equation

Figure 8: Penman-Monteith setting, showing origin of resistance terms. After FAO. 25 ET Method Comparison

Figure 9: Comparison of ET estimation methods. After [Fig. 5-3, Dunne and Leopold, 1978]. See also Casta˜neda-Rao-2005. 26 ET Estimation Review As hydrogeologists, you’ll probably consider the following methods to predict ET, in order of increasing diﬃculty and accuracy (see also FAO Summary) and FAO training manuals:

• Land pan evaporation data: apply appropriate pan coeﬃcients and nearby pan data to estimate reservoir, or even crops (rarely). See Wikipedia summary

• Forms of energy balance – Thornthwaite: meteorology/climate only, ignore vegetation effects. OK for annual average – Blaney-Criddle: adds crop effect. Simple, widely used and broadly inaccurate, better at monthly variations, good when only temperature data is known 27 – Penman: original Penman eqn. mimics pan evaporation curve, accounts for radiation and convective (wind) flux, i.e. most terms in (4) – Penman-Monteith: world standard, assumes realistic “reference crop”. Provides most inter-comparable results.

28 Typical ET Values

mm Figure 10: Typical values for ETo, in day for climate types and temperature range. After UN FAO. See current UTD/TAMU values.

29 ET Example: Colorado River • Colorado River basin (Fig. 11) over-allocated (Fig. 13), so components of water balance there are very important (17.5 Mac−ft Mac−ft yr allocated, actual ﬂow averages 14.5 yr ) • very diﬃcult-to-measure aspect of this is ET

• Tamarisk (salt cedar)

• introduced as decorative plant in 1870’s, has spread through km2 most of watershed (colonization rate 3 yr )

m • individual ET rates 2.5 yr

6 acre−ft • 1984 total consumptive use, Lower Basin 7x10 yr [Owen-Joyce and Raymond, 1996] 30 • of that 15% lost through ET, 6% by natural phreatophytes (primarily tamarisk), 18% exported to AZ, 67% exported to CA

• see USGS biennial consumptive use studies

31 Tamarisk Invasion/Control

• current distribution monitored by USGS

• other organizations organize remediation (e.g. Tamarisk Coalition)

• see TRO Assessment report 2008 for current status of mitigation/impact

32 Colorado River Hydrologic Basin

Figure 11: Colorado River Basin Compact states, and important localities, from [Barnett and Pierce, 2008]. 33 Colorado River Proﬁle

Figure 12: Topographic proﬁle of Colorado River, showing river gradient and major impoundments. After Keller [p. 281, 1996].

34 Colorado River Water Allocation

Figure 13: Colorado River Basin Compact allocation and average discharge. After Keller [p. 282, 1996]. See Wikipedia summary of shortage plans. 35 Evaporation and Climate Change

36 Pan Evaporation Declining

Figure 14: Temporal trends in pan evaporation. Across the US and most of the world pan evaporation rates have declined since the 1940’s. Numbers mm are precipitation trends in decade, [Lawrimore and Peterson, 2000]. 37 Global Humidity Increasing

Figure 15: Temporal trends in speciﬁc humidity: lower atmospheric moisture content has been steadily increasing, upper atmosphere (300mB) moisture decreasing, consistent with brightening. Data from NCDC, based on analysis of GPS satellite signals, this plot from The Blackboard. 38 Evaporation and Global Dimming/Brightening

Figure 16: Observed and modeled global warming and dimming. Light lines show individual IPCC model results. These warming models include dimming eﬀects, and the “evaporation paradox”, after [Schmidt et al., 2007]. See Wild [2009] for good summary of brightening/dimming observations. 39 Climate Forcings

Figure 17: Model results of 20th century climate, with contributions from various forcings. Observed warming best matched by eﬀect of greenhouse gas emissions, moderated through 1990 by particulates (“sulfate”, combined natural and anthropogenic eﬀects). See also Wikipedia summary. 40 Precipitation

Useful data sources:

• National Weather Service ﬂood prediction data

• Intellicast TX-OK 7-day cumulative precip from NEXRAD data

• Intellicast current hourly lightning strikes

41 Precipitation: Physical Process

• condensation caused by cooling of the air mass, usually during lifting – In Texas mostly during frontal storms (“blue norther’s”) (Fig. 18) – See example of March 3, 2000 frontal storm: radar animation, surface weather map, and lightning record

• local climate effects can be important in hydrology – frontal precipitation (most common precip. in winter, see Texas annual precip. distribution, Fig. 19) – convective precipitation (thunderstorms, most common in summer) 42 – e.g. in temperate arid regions snow is predominant recharge contributer, even if not predominant form of precip. – orographic effect: heavier precip. on upwind side of topographic highs, lower than average on downwind side – coastal states often affected by tropical cyclones (e.g. similar effect from upper atmosphere low at DFW 2009, Fig. 20)

43 Frontal Precipitation Model

Figure 18: Cross-section through frontal storm, showing the special case of an occluded front. After Dingman [2002]. 44 North Texas Monthly Normal Climate

Figure 19: North Texas monthly normals (after RSSWeather. See also NOAA Southern Regional Climate Data Center.

45 4-Day Storm Event Cumulative Precipitation

Figure 20: Cumulative precipitation is often highly heterogeneous. 7 day cumulative precipitation from high-level low pressure system in North Texas. Sept. 7-14, 2009 (from Intellicast). 46 Precipitation: Measurement One of the most easily measured hydrologic cycle ﬂuxes

• NOAA uses a variety of automated gauges (Fig. 21)

• see modern summary at Wikipedia and summary of automated airport weather stations, the “gold standard” of weather data worldwide

• Two basic station networks: primary monitoring stations (usually major airports) and cooperative stations (usually not run by NOAA, data quality uncertain). See Fig. 22

• this data accessible for free from .edu IP addresses at National Climate Data Center (NCDC) 47 Rain Gauge Examples

Figure 21: Examples of recording rain gauges, after Dunne and Leopold [1978]. 48 NOAA Weather Station Network

Figure 22: NOAA Weather Station Network, after Dingman [2002]. 49 Treating Precipitation Heterogeneity Precipitation usually extremely variable in space and time. Hard to go from point measurements to regional input, must use:

• arithmetic average, assumes uniform density of precip. or stations

• Theissen polygon method: area-weighted average. Equivalent of natural-neighbor interpolation

• Isohyetal: contouring, includes some concept of local meteorology

• NEXRAD radar: use to estimate areal variability of rainfall, calibrate with ground measurements, 50 – accuracy can be controversial, but now standard for runoﬀ models (see Applied Surface Water Modeling Notes re: NEXRAD) – cumulative estimates avaliable nationwide (intended for ﬂood prediction) at NCDC Hydro Prediction Service

51 Engineering Characterization of Precipitation

See Applied Surface Water Modeling Notes topics:

• Introduction: Design approaches in treating rainfall

• Rainfall data adjustments

• Rainfall data sources (online data)

52 Recharge

53 Recharge

• Physical processes – infiltration - losses = recharge – infiltration = precipitation - runoff – runoff occurs when precip. exceeds infiltration capacity of soil (Hortonian overland flow)

• Measurement – Direct: lysimeters – Indirect ∗ Water table ﬂuctuation · assumes changes in water level in shallow wells reﬂect recharge 54 · see USGS summary · also computer program to develop Master Recession Curve for well water levels ∗ Chemical mass balance: Cl, 3H, δD, δ18O · Cl method (assumes all input is atmospheric, OK if no Cl-sediments in basin; N.B. Cl = 0 in evaporated water) [Dettinger, 1989]

CII + CP P + CQQ = 0 |{z} | {z } Infiltrated mass Precipitation Runoff| {z } PC QC I = P − Q (7) CI CI · Also note that in many desert basins the runoff is 0, simplifying (7) ∗ Determine Baseflow (hydrograph separation) 55 ∗ Use empirical relations based on other basins: e.g. Maxey-Eakin [Watson et al., 1976], uses rainfall and elevation maps to estimate recharge, calibrated to basins of “known” recharge

• see excellent summary of methods and results for desert basins [Hogan et al., 2004] (and online review)

56 Bibliography

57 Tim P. Barnett and David W. Pierce. When will Lake Mead go dry? Water Resour. Res., 44 (W03201), 29 March 2008. doi: 10.1029/2007WR006704. URL http://www.agu.org/ journals/pip/wr/2007WR006704-pip.pdf.

J. B. Czarnecki. Geohydrology and evapotranspiration at franklin lake playa, inyo county, california. Ofr 90-356, Denver, CO, 1990.

M. D. Dettinger. Reconnaissance estimates of natural recharge to desert basins in nevada, u.s. a., by using chloride-balance calculations. J. Hydrol., 106:55–78, 1989.

S. L. Dingman. Physical Hydrology. Prentice Hall, Upper Saddle River, NJ, 07458, 2nd edition, 2002. ISBN 0-13-099695-5.

P. A. Domenico and F. W. Schwartz. Physical and Chemical Hydrogeology. John Wiley & Sons, New York, 1990. ISBN 0-471-50744-X.

T. Dunne and L. B. Leopold. Water in Environmental Planning. W. H. Freeman, New York, 1978. ISBN 0-7167-0079-4.

C. W. Fetter. Applied Hydrogeology. Prentice Hall, Upper Saddle River, NJ, 4th edition, 2001. ISBN 0-13-088239-9.

D. Hillel. Applications of soil physics. Academic Press, New York, 1980. ISBN 0-12-348580-0.

James F. Hogan, Fred M. Phillips, and Bridget R. Scanlon, editors. Groundwater Recharge in a Desert Environment: The Southwestern United States, volume 9 of Water Science and Application. Amer. Geophys. Union, 2004. URL http://www.agu.org/cgi-bin/ agubooks?topic=AL&book=HYWS0093584&search=Scanlon.

E. A. Keller. Environmental Geology. Prentice Hall, Upper Saddle River, NJ, 1996. 7th Ed., ISBN 0-02-363281-X.

Jay H. Lawrimore and Thomas C. Peterson. Pan evaporation trends in dry and humid regions of the united states. Journal of Hydrometeorology, 1(6):543, 2000. ISSN 1525755X. URL http://search.ebscohost.com/login.aspx?direct=true&db= a9h&AN=5716377&site=ehost-live. 58 S. J. Owen-Joyce and L. H. Raymond. An accounting system for water and consumptive use along the colorado river, hoover dam to mexico. Water-supply paper, U.S. Geol. Survey, Washington, D.C., 1996.

G. A. Schmidt, A. Romanou, and B. Liepert. Further comment on ”a perspective on global warming, dimming, and brightening”. EOS, 88(45):473, 11 2007.

J. A. Tindall and J. R. Kunkel. Unsaturated Zone Hydrology for Scientists and Engineers. Prentice-Hall, Upper Saddle River, N.J., 1999. ISBN 0-13-660713-6.

P. Watson, P. Sinclair, and R. Waggoner. Quantitative evaluation of a method for estimating recharge to the desert basins of nevada. J. Hydrol., 31:335–357, 1976.

M. Wild. Global dimming and brightening: A review. J. Geophys. Res., 114, 2009. doi: 10.1029/2008JD011470.