Lecture 4: Monitoring water resources Flashcards
Define the following:
- Monitoring
- Historic Analysis
- Forecasting
- Prediction
- Projections
- Monitoring: assessing current conditions
- Historic analysis: reconstructing or examining the past to
understand resources or risks - Forecasting: making an estimate of what will happen in the
future (exact or deterministic) - Prediction: making an estimate of what might happen in the
future (inexact or probabilistic) - Projections: making an estimate of a plausible future (inexact)
What are different approaches to monitoring water resources?
- in-situ observations
- Remote sensing from satellite and aircraft
- UAVs and other autonomous vehicles
- Paleo-data
- Modelling and prediction
What do we mean by models?
- Remote sensing often uses a retrieval model
to convert/invert the electromagnetic signal
into a hydrologic state/flux - Land Surface and hydrological
Models are used to monitor, forecast
and understand
-Weather and Climate
Models are used to forecast
and project
-Reanalysis and regional climate models
are used to reconstruct global and
regional atmospheric/land variables
-Diagnostic Tools used to
understand specific
processes
What is the uncertainty and error associated with models?
Hydrology and monitoring water resources is inherently uncertain:
-Random processes (e.g. weather); complex process interactions; difficult
to measure properties and states in detail; lack of data
Systematic error or bias
-errors that are consistently high or low over time – e.g. precipitation gauge
has a 10% low bias because of undercatch
Random error
-Errors that are unpredictable in space or time, e.g. hour to hour variations
in water levels
Error propagation
-Errors in a measured value propagate into quantities derived from it, e.g.
model predictions of soil moisture depend on the errors in the precipitation
data used to drive the model
Model errors
- simplified of erroneous representation of the physical processes
- Incorrect or uncertain parameter values
- Errors in forcing data (e.g. precipitation)
What are in-situ measurements?
- precipitation
- snow
- meterology
- evapotranspiration
- streamflow
- soil moisture
- groundwater
how do you measure precipiitation?
Rain gages are most commonly used for the measurement of precipitation,
both in terms of rain fall and snow
There are two main types of rain gages which are used to measure precipitation. These are:
- Non recording rain gages – just provides the daily total
2. Recording rain gages – gives the total but also the distribution within the day 1. Float type rain gages 2. Tipping bucket type rain gages 3. Weighing type rain gages
Also many other types: Optical gauges, capacitance gauges, acoustical,
disdrometers
- there are many types of gauges, which vary in their catch efficiency. Shielded gauges, like the one above, tend to perform better than unsheilded gauges. the liquid water equivalent of snowfall is especially hard to measure
describe snow measurement
Often using standard precipitation gauges • But also sophisticated snow measurement sites
what are the sources of errors in precipitation gages?
Undercatch – due to obstructions, orifice size, orientation, height, wind
shielding
Snowfall undercatch
Splash
Evaporation
Low precipitation
Recording errors
Observing time errors
Changes in gauge location
The last three also apply to measurements of other hydrological
processes
Precipitation measurement errors: undercatch, phase and orographic effects
1) Systematic undercatch of precipitation caused
by wind effects. Correction Factors ~ 1.0 -2.0
2) Undercatch correction dependent on precipitation
phase
3) Underestimation due to orographic effects
describe: estimating precipitation over a catchment
• A problem often faced with monitoring and
assessing water resources is to come up with an
areal average over the region of interest
(catchment, basin) or a gridded field of precipitation
(e.g. to drive a hydrological model)
• This generally requires interpolation of gauge data
between gauges
• Many types interpolation methods: simple
averaging, inverse distance weighting to optimal
interpolation. In general:
• Pinterp = SUM (Pi wi) / SUM (wi)
• NOTE – if the gauge network is dense enough then
all interpolation methods work equally well. If the
network is sparse, none of them work well. How
dense is dense enough?
How do you estimate the weights?
Some examples: • Based on distance -Develop a function based on the distance decay of the correlation in precipitation -This represents how close precipitation values at two different locations are expected to be
• Based on elevation -Precipitation can be strongly affected by elevation (hypsometric method) - Develop a relationship between precipitation and elevation - This relationship can be complex and changeable
what is the station density in the USA; Arctic?
Station density in the US looks very dense, but at the scale of a small catchment it
can be very sparse
Station density in the Arctic is very sparse and leads to large uncertainties in a
region that is experiencing some of the largest changes globally
UK precipitation gauge and Radar networks some of the densest in the world
What are radar measurements of precipitation?
A weather radar is a type of radar used to locate precipitation, calculate
its motion, estimate its type (rain, snow, hail, etc.), and forecast its
future position and intensity. Weather radars are mostly Doppler radars,
capable of detecting the motion of rain droplets in addition to intensity
of the precipitation. Both types of data can be analyzed to determine
the structure of storms and their potential to cause severe weather.
Often radar is merged with gauge data to provide a spatially continuous data product
that is consistent with gauge observations
Global precipitation- a decline in number of gauges leads to increasig uncertainty in global trends and basin averages
Figure. Uncertainties in global land precipitation from several datasets. The lower panel shows decline in the number of contributing gauges since the 1980s
explain gauge density versus uncertainty in spatial precipitation
• The error in areal average precipitation can be estimated (see Dingman) • For different global precipitation datasets, which use different sets of gauges and therefore densities, the error is different and this also depends on the location. • Main errors are from sampling errors (how representative the gauges are of the larger area) • And measurement errors (undercatch, interpolation, orographic) • Simple estimate of error: • E(%) = 40 e-0.05d • d is the gauge density (the number of gauges per 106 square kilometers)
How do you monitor precipiation?
- Direct methods:
a. Pan evaporation – direct measurement, but only free
water evaporation;
b. Weighing lysimeter – direct measurement, but difficult to set up and maintain - Calculation methods – using meteorological data
a. temperature- based
b. radiation based
c. combination methods
How do you estimate actual evaportranspiration (ET)?
- PET methods (Empirical methods with PET/P ratios;
Monthly water balance models; Soil moisture functions;
Complementary methods)
• ET = function(PET, other variables) +
assumptions/empiricism - Water balance approaches (land area budgets; lysimeter
and pan; soil moisture balance; atmospheric water balance;)
• ET = residual of the water balance given available data
for other components; e.g. E = P – R - dS/dt - Turbulent Excahnge/Energy balance approaches (P-M;
Bowen ratio; Eddy correlation; scintillometer)
• ET = derived from measured moisture and energy
fluxes - Water quality approaches (dissolved solids, isotopes)
• ET = derived from concentration of solids or isotopic fractions
equipment:
- scintillometer
- eddy correlation
Estimating actual ET
The Budyko Approach
- look at diagram
Streamflow gauges around the world with readily available data
…
Focus on africa: real-time hydrological monitoring is sparse
in terms of
GRDC streamflow records Real-time precipitation gauges Fluxnet (ET) Soil moisture only from research sites AMMA Network CARBOAFRICA network
How do we estimate water resources in an ungauged basin?
• Some of the places with the most need to understand and predict
water resources are the places with the least capacity to monitor
• This prevents analysis of past water resources variability, and floods
and drought, and thus risk assessment
• Real-time monitoring of water resources and hydrological hazards is
not feasible
• How do we estimate water resources in an ungauged basin?
Local data does exist but not easily useable
- many pentad records on paper
- often not real time
- perhaps the best case scenario in africa
What is involved in remote sensing and modelling
Remote sensing • Vegetation stress • Precipitation • Surface Water Storage • Evapotranspiration • Soil Moisture •Groundwater • Snow Modeling • Global • Regional • National
what is crowd sourcing
The power of the people
Define remote sensing
Remote sensing is the acquisition of information about an object or
phenomenon without making physical contact with the object and thus in contrast
to on-site observation
