tutorials 1,2,3,4 Flashcards
list some typical water quality issues
- pathogen pollution
- pharmaceuticals and EDCs
- excess nutrients and eutrophication
- organic pollution and low DO
- salinity pollution
- chemical pollution
- microbial pollution
affected parties of water quality issues
- agriculture
- aquatic life/animals
- infrastructure (dams)
- people
- nature!
the main contributors of water quality issues
- industry
- agriculture
- domestic users
sources of pollution
- untreated sewage
- domestic waste inputs (are growing)
- open decification
- salt intrusion
- agriculture
- industry
- insecticides
- food industry
what are the water challenges of this century
- increase in water use
- changes in water availability and water quality
both of these result in an increase of water stress
falkenmark indicator
calculates the volume of water available per person
waterstress = water availability/population
water use to availability ratio
focuses on the amount of water that is used to available water resources. can also be used to calculate sector-specific water stress or total water stress. water use/water availability
waterfootprint to availability ratio
waterfootprint/water availability
water scarcity including water quality indicator
calculates the ratio of sectoral water withdrawal of acceptable water quality to the overall water availability
why does water quality matter for water scarcity?
the usability of water depends on sufficient water quantity and quality
water pollutants
- nutrients
- water temperature
- salinity
- pathogens
- chemicals
- EDCs
- heavy metals
- BOD
- emerging pollutants like medicines
why is there a need for a multi pollutant approach
- multiple pollutants may have an impact of multiple sectors
- pollutants can interact (chemically, biologically, physically)
- pollutants can have common sources
- policies for one pollutant may affect other pollutants
- pollutants have multiple impacts on nature and society
- sensitivity to pollution may increase if more pollutants are around
ecosystem services and ecosystem services lakes give to us
ecosystem services are benefits to humans gifted by the environment. the ecosystems we ‘get’ are dependent on the state of the lake. this depends again on the nutrient load, and can either be healthy or unhealthy. examples of ecosystem services are: drinking water, water buffering, flood regulation, filtration, recreation, food, biodiversity, carbon storage, hydropower, education
stressors for lakes
nitrogen, phosphorus, agriculture, sewage systems, fertilizers, insecticides, domestic waste, invasive species
sources of nutrients in lakes
- natural runoff
- agriculture
- urban areas
- industry
- aquaculture
nutrient load
nutrient loss from different sources that ends up into the lakes
PC lake(+)
models the amount of nutrients (N,P,Si) in different compartments of the model. the model includes 3 compartments: air, water and sediment. also includes different water layers, so also stratification
PC lake model inputs
nutrient pollution and temperature. this is measured or other model input
why do we use models
- to formulate hypotheses
- to better understand stressors on ecosystems
- to qualify indicators
- to qualify the impact of these stressors
critical nutrient load and the relationship with temperature
critical nutrient load is the nutrient treshold to keep the lake in a healthy state. this can be used as an indicator for lake water quality. the critical nutrient load is affected by temperature. bc algae do better in warm conditions. higher water temp, bigger change in algae blooms. also stratification when theres a large difference in Temp. disturbs the ecosystem and increases the risk for o2 depletion and P release from the sediment (dead zooplankton sinks)
how can we build trust in models?
- compare model results with observations (validation)
- compare modelled trends over time with observations
- sensitivity analysis: test how sensitive the model outputs are to changes in model parameters
- comparing model inputs with independent datasets
- expert knowledge or local knowledge
- compare model results with the results of similar models
strenghts and weaknesses of model validation
+ easy to understand
+ easy to calculate
+ gives an indicator of how well our models represent reality
+ builds trust
- extreme values can have a large impact
- data are uncertain
- date does not always fit (e.g. global model with no observational data for a continent)
- data is not always comparable
cycle of building trust
compare modelled nutrient fluxes with empirical studies –> compare modelled nutrient trends with empirical studies –> sensitivity analysis –> compare model inputs with other independent datasets –> expert knowledge –> compare model results with other modelling studies
