LANDSCAPE ECOLOGY SREM 3011 LECTURE 12 Dr Brendan Mackey Department of Geography The Australian National University
LANDSCAPE ECOLOGY SREM 3011 LECTURE 12 Dr Brendan Mackey Department of Geography The Australian National University
Conceptual framework: model response of ecological criteria' phenomena to the primary environmental regimes At a meso scale, climatic variables based on long term, mean monthly rainfall, maximum& minimum temperatures provide fundamental inputs to light, heat, moisture regimes Nutrient regime?? Discuss next week Next few lectures: role of topography in defining PERs at a topo scale moisture regime radiation regime climate(meso) vs topo-scaled topographic effects
• Conceptual framework : model response of ecological ‘criteria’/phenomena to the primary environmental regimes • At a meso scale, climatic variables based on long term, mean monthly rainfall, maximum & minimum temperatures provide fundamental inputs to light, heat, moisture regimes • Nutrient regime?? Discuss next week Next few lectures: - role of topography in defining PERs at a topo scale moisture regime radiation regime - climate (meso) vs topo-scaled topographic effects
Next 2 weeks lectures A. Topo-scaled modelling of PERs B. Other statistical models for analyzing site data C. Ecological classification regionalization
Next 2 weeks lectures: A. Topo-scaled modelling of PERs B. Other statistical models for analyzing site data C. Ecological classification & regionalization
If species distributions are a function of the moisture, radiation thermal and nutrient regimes then the more accurately we'track,them(ie the PERs), the better we can ' track'plant and animal response Moisture regime is only coarsely approximated by precipitation precipitation is a useful index of moisture regime at regional scales, but limited/problematic at both continental and smaller scales Problem: estimate moisture regime across entire landscape, factoring in (1)evaporation topography and soil profile how to generate gridded estimates in an analogous manner to our climate models?
• If species distributions are a function of the moisture, radiation, thermal and nutrient regimes, then the more accurately we ‘track’ them (ie. the PERs), the better we can ‘track’ plant and animal response • Moisture regime is only coarsely approximated by precipitation - precipitation is a useful index of moisture regime at regional scales, but limited/problematic at both continental and smaller scales • Problem: estimate moisture regime across entire landscape, factoring in (1) evaporation (2) topography and (3) soil profile : how to generate gridded estimates in an analogous manner to our climate models?
Mean annual precipitation(mm) within a region, evapo -'constant P-E& available moisture (AM) As E>P must to have same amt of am Therefore important for continent. wide analysis
Mean annual precipitation (mm) • within a region, evapo ~ ‘constant’ • P- E& available moisture (AM) • As E>, P must > to have same amt of AM Therefore important for continentwide analysis
Average evaporation from Australian Standard Tanks, in units of mm/year EVAPORATION 和系
Average evaporation from Australian Standard Tanks, in units of mm/year
Potential evaporation is a function of: 1 Solar radiation 2. Temperature(surface 3. Atmospheric moisture 4. Turbulencelaerodynamic roughness US Weather Bureau Class A evaporation pans with screen in the foreground and without screen in the background
Potential evaporation is a function of: 1. Solar radiation4 2. Temperature (surface) 3. Atmospheric moisture 4. Turbulence/aerodynamic roughness US Weather Bureau Class A evaporation pans with screen in the foreground and without screen in the background
ANUSPLIN surfaces have been fitted to australian network of 'class a pan data thereby enabling gridded estimates of potential evaporation to be generated. Therefore, can calculate(P-Eor(P: 3) But PE*actual evaporation(AE) AE is limited by soil moisture Therefore need to factor in soil water availability and calculate a moisture index as a function of 1. Precipitation 2. PE 3. Soil water status
• ANUSPLIN surfaces have been fitted to Australian network of ‘class A’ pan data thereby enabling gridded estimates of potential evaporation to be generated. Therefore, can calculate (P-E) or (P:E) • But PE actual evaporation (AE) • AE is limited by soil moisture Therefore need to factor in soil water availability and calculate a moisture index as a function of 1. Precipitation 2. P.E. 3. Soil water status
So-called water-balance can be calculated on a daily, weekly or monthly time step 25mm 50mm 25mm 50mm 25mm 50m 1.0 0.75 0.75 0.5 0.25 0.0 JAN FEB MAR Available water capacity f(soil depth, soil texture) in this example, awc 100mm Moisture index =1 if 'bucket is full 0 if bucket'empty
• So-called water-balance can be calculated on a daily, weekly or monthly time step: • Available water capacity = f (soil depth, soil texture) • in this example, AWC = 100mm • Moisture index = 1 if ‘bucket’ is full = 0 if ‘bucket’ empty 25mm 50mm 25mm 50mm 25mm 50mm 1.0 0.75 0.0 0.75 0.5 0.25 JAN FEB MAR
Can generate prediction of MI at any location at which you know: 1.XYZ Precipitation, potential evaporation 2. Soil depth texture AWc Ultimately, what's important for a plant is not how much rain falls, but how much water is in the soil where its roots are! But how to generate gridded Mi values across entire landscapes? need 'micro scaled soil maps most soil maps do not map profile depth depth more important to AWc calculation than texture
• Can generate prediction of MI at any location at which you know: 1. XYZ Precipitation, potential evaporation 2. Soil depth + texture AWC Ultimately, what’s important for a plant is not how much rain falls, but how much water is in the soil where its roots are! • But how to generate gridded MI values across entire landscapes? - need ‘micro’ scaled soil maps - most soil maps do not map profile depth - depth more important to AWC calculation than texture
