The Hydrological Cycle

Overview

• A very brief history of hydrology• Relevance to training course

– Surface water & groundwater• a single resource

– Climate• Variability• Change

• Oceanic perspective

Typical representation

Not immediately obvious

Rain ?Flow > Rain?

E/T?

Historical developments.

• Greek philosophers (500 BC)– Aware of underground water– Failed to appreciate significance of rain– Relied on reasoning without observation– Hypothesis of “underground condensation”

• Misunderstanding persisted for centuries

Historical developments..

• Leonardo da Vinci (1452-1519)– Made hydrological observations– Developed a concept of a hydrological cycle– Included evaporation and precipitation, but– Still underestimated the role of rainfall &

suggested that rivers were fed from the sea via underground veins

Historical developments…

• Seventeeth century– French scientists produced evidence that

rainfall could account for observed river flow– Edmund Halley (1656-1742) measured

evaporation but still retained belief in underground mechanism

– John Dalton (1760-1844) correctly described the complete hydrological cycle based on his quantitative hydrological and meteorological observations.

The completed cycle

Rain E/T

Flow

Lessons from the brief history

• Though the hydrological cycle now seems an obvious and simple concept this was not always the case

• Development of a complete and rational theory depended on systematic observations and analysis

• Despite developments of complex computer models, data collection, data processing and analysis continue to provide the foundation for the science of hydrology

Back to the present

Strengths of the cycle concept

• Describes spatial variability of flow and storage

• Emphasizes inter-connection of components

• Reinforces that “Everyone lives downstream”

Weaknesses

• Implies a smooth and continuous process

• Suggests a self-contained system

• Does not portray the variability which produces the extremes of flood & drought

But….

• The process is actually highly complex

• The hydrological cycle is driven by climate

• Without variability hydrologists would by now have run out of a job

• Implies a smooth and continuous process

• Suggests a self-contained system

• Does not portray the variability which produces the extremes of flood & drought

Climate drivers

Sept 1997 (El Nino)

Nov 1988 (La Nina)

Sea-surface temperatures

El Niño-Southern Oscillation

• Two components– Tropical ocean– Atmosphere

• Oceanic part (El Niño) revealed by sea-surface temperatures

• Atmospheric part (Southern Oscillation) related to sea-level pressure

• Coupled oscillation is ENSO – a major planetary influence on the hydrological cycle

ENSO• Continuing efforts to observe and understand

the ocean-atmosphere system is likely to lead to improved seasonal climate forecasting in the future

100 200 300 400 500

-4

-2

0

2

Southern Oscillation Index

SOI I

ndex

100 200 300 400 500

-2

0

2

Mar/Apr1992

Apr/May1987

Feb/Mar1983

ME

I Ind

ex

Multivariate ENSO Index

Oceanic viewpoint

Oceanic viewpoint

• Tempting to believe that Pacific people would have been less likely to underestimate the significance of rainfall and evaporation

• Pacific Island country water resources are particularly vulnerable– Small size– In the ENSO “engine room”

Conclusions

• The hydrologic cycle portrays the inter-connectedness of water in its various forms

• Hydrologic variability (floods & droughts) show that the cycle does not behave in a steady and self-contained way

• Climatic variability drives the variability we observe in hydrology

• Observation – data collection, analysis and interpretaion continue to be the foundation of modern hydrology

An exercise with climate data

• Monthly rainfall from Nadi and Suva (1942 to the present)

• Use Excel to compare the seasonal variability of rainfall on either sides of Viti Levu

Mean monthly rainfall

0

50

100

150

200

250

300

350

400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mea

n m

onth

ly ra

infa

ll

SuvaNadi

Suva – mean (33 & 67 percentile)

0

50

100

150

200

250

300

350

400

450

500

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Suva – hyetograph

0

200

400

600

800

1000

1200

Jan-

42

Jan-

45

Jan-

48

Jan-

51

Jan-

54

Jan-

57

Jan-

60

Jan-

63

Jan-

66

Jan-

69

Jan-

72

Jan-

75

Jan-

78

Jan-

81

Jan-

84

Jan-

87

Jan-

90

Jan-

93

Jan-

96

Jan-

99

Jan-

02