A presentation that overviews existing modeling frameworks to analyze water system sensitivity to climate change and other stress factors

Poff, L.

Vörösmarty, C.J.

Wolheim, W.

UNT Digital Library

C.J. Vörösmarty, L. Poff, W. Wollheim, with             B. Fekete, W. Clements,  R. Stewart, and P. Green Impact of Climate Change and Variability                 on the Nation's Water Quality                                      and Ecosystem State!EPA Program Review: Consequences of Global Climate Change: Water Quality Impacts, Ecological Impacts & Nonlinear Responses                                               Washington DC            22 September 2011 Overall Goal •  To expand an existing modeling framework to analyze water system sensitivity to climate change and other stress factors •  Four component efforts: – FrAMES – Pollution applications: Thermal and N – Water Management: Reservoirs and Water Transfers  – Links to habitat and biodiversity Non-point Loads Point Loads Land Micro-aquatic Macro-aquatic Coastal Processing     and/or     Metabolism Local Runoff Air Temp Local Q Reservoirs Parameters Soil Tau Export Export Export h and v Hydraulic equations Hydraulic Load micro macro Length/Sinuosity Geomorphology Vf - accumulated grids - upstream average grids - basin means - basin profiles - biome means - sample @ station locations Summary stats Graphics Local Precip Retention R = 1 – e(-Vf/HL) micro macro Local Routed Retention FrAMES: Framework for     Aquatic Models of the Earth System: Multi-constituent  Modules Inland Satellite Remote Sensing             CZ Remote Sensing LTER & CUAHSI Regional – Continental – Global Systems Conceptual Model - AEM  (Aquatic Ecosystem Model) DIN POC PON DON DOC Auto-C Auto-N (C:N) Detr-C Detr-N Water  Column Stream Bottom CO2, N2 Leaf Litter DOM Land DIP Courtesy: W. Wollheim Merging Macro-Microscale Rivers in Continental Models River Network Characteristics number length Mean length Mean drainage area Figure	  1.	  	  Network	  sta1s1cs	  in	  FrAMES	  assuming	  geomorphic	  and	  hydraulic	  characteris1cs	  in	  Table	  1.	  A)	  mean	  drainage	  area	  and	  mean	  lengths	  for	  each	  river	  order,	  B)	  global	  total	  length	  and	  number,	  and	  C)	  global	  sum	  of	  benthic	  surface	  area	  for	  each	  water	  Dynamic Watershed Zone of Influence on Land-to-Coastal Fluxes --Dependent on hydraulics, biogeochemical processing &  temperature Water Temperature (Tw)	  = ƒ 	  Cloud Cover Solar Radiation (SR) Wind Speed Initial Water Temp. (To) Air Temp. (Ta) Te = Ta + [ (SR – QO) / q ]	  Tw = (To – Te) exp	  -q * L  ρ * Cp * d * v Water Temperature (Tw) In-­‐Stream	  Temperature	  Equilibrium	  Model	  	  	  (Dingman,	  1972)	  Energy Exchange Function (q) River Geometry Flow Rate Relative Humidity Missouri R. (1991) USA Yangtze R. (1980) China Parana R. (1983) Argentina NS = 0.24 Selenga R. (1991) Russia Modeled	  	  Observa1on	  Legend 1	  -­‐	  	  	  Σ	  (Oi	  –	  Pi)2	  Σ	  (Oi	  –	  Ō)2	  Nash-­‐Sutcliffe	  Coefficient	  (NS)	  =	  Danube R. (1986) Hungary Water	  Temperature	  (oC)	  NS = 0.83 NS = 0.76 NS = 0.91 NS = 0.68 Murray R. (1985) Australia NS = -0.60 Mekong R. (1991) Laos Nile R. (1980) Egypt NS = -2.6 Sao Francisco (1990) Brazil NS = -1.01 NS = -0.28 Modeled	  	  Observa1on	  Legend 1	  -­‐	  	  	  Σ	  (Oi	  –	  Pi)2	  Σ	  (Oi	  –	  Ō)2	  Nash-­‐Sutcliffe	  Coefficient	  (NS)	  =	   Krishna R. (1990) India NS = -8.50 Water	  Temperature	  (oC)	  Reservoir Effect Average Observed Daily Water Temperature (oC) Average Predicted Daily Water Temperature (oC) Mean Absolute Error = 4.0 oC Comparison of Average Daily Observed and Predicted Temperatures (MAE) Degree Latitude n = 172 1 : 1 line Change in Mean Decadal Water Temperatures in Large Rivers [1970-1979] to [1990-1999] (Q > 30 m3 s-1) Temperature Change (oC) First National Geospatial Synthesis      (based on EPA Clean Watershed Needs Assessment) n =11,996 n =13,307 IMPACT OF $80-100B* IN INVESTMENTS IN SEWAGE TREATMENT PLANT UPGRADES (1984-2004)….a job half-done?    ~Half-Trillon $ needed through 2030  M. Rychtecka, C.J. Vörösmarty / City University of New York * provisional est. New and Retrofitted Facilities  Yield Substantial Gains in  Treatment Efficiencies  But Wastewater Effluent Loads Show Substantial Increases Net Impact: One-Half of Receiving Watersheds (HUC-8 units)  Show Load Increases RELATIVE CHANGE IN TOTAL N LOAD REMOVAL EFFICIENCY CHANGE ONLY (SC1)"WASTEWATER FLOW CHANGE ONLY "(SC2)"EFFICIENCY & WASTEWATER FLOW CHANGE  (SC3) History of US Dam and Reservoir Construction Source : National Inventory of Dams …emblematic of water development globally How and why did hydraulic engineering evolve? And what is its likely trajectory in the future? UPPER COLORADO LOWER COLORADO MISSOURI        539 Mm3/yr GREAT BASIN  99 Mm3/yr CALIFORNIA   4697 Mm3/yr ARKANSAS-WHITE - RED    216 Mm3/yr     RIO  GRANDE  161 Mm3/yr   MEXICO   315 Mm3/yr # Database IBWT within UPPER COLORADO Subregions m3/year 65 Sarvis Ditch 146,784 66 Stillwater Ditch 5,254,633 67 Dome Creek Ditch 822,732 60 Divide Creek Highline feeder ditch 1,307,491 61 Main Canal 1 82,088,216 62 Main Canal 2 79,263,543 63 Summit Reservoir outlet canal 9,732,172 # Database IBWT within LOWER COLORADO Subregions m3/year 86 Pipeline from pumping plant on Black River 6,882,829 87 Eastern Canal (at head) 92,363,120 88 East Branch Consolidated Canal 109,656,535 89 Roosevelt Water conservation District main canal 47,612,399 80 Pipeline from blue Ridge Reservoir 11,520,720 81 Cila Graviti Main Canal 632,381,468 Biological Data • Macroinvertebrates • Fish • Algae Vorosmarty Lab Derive Watershed Boundaries 1879 EMAP and 1536 NAQWA Sites Carlisle Lab Watershed-Scale Env. Variables (e.g., land use, geology, soil drainage area, etc.) Vorosmarty Lab Flow Time Series Under Different Scenarios Vorosmarty Lab Temperature Time Series Under Different Scenarios Poff Lab Temperature Variables (e.g., Mean Annual Temp., Mean July Temp., Temp. CV) Poff Lab Flow Variables (e.g., Mean Annual Runoff, Flood Interval, Base Flow,  High Flow Duration) Fish Functional Traits (e.g., position in water column, trophic guild, spawning frequency, substrate preference, body length, etc.) Macroinvertebrate Functional Traits (e.g., functional feeding group, habit, voltinism, occurrence in drift, respiration, etc.) Taxonomic Metrics (e.g., richness, abundance, EPT richness, etc.) Tolerance Metrics (e.g., IBIs, % tolerant taxa, etc.) In Conclusion • FrAMES (Framework for Aquatic Models of the Earth System) provides computational backbone to multi-stressor analysis • Major advance: In-basin, time-varying fluxes • Core data sets include: thermal loading and impact, nutrients, water engineering works • Next steps: integration of component models, downscaled climate forcings; links to habitat, taxonomic groups, impact assessment 

EPA Program Review: Consequences of Global Climate Change: Water Quality Impacts, Ecological Impacts & Nonlinear Responses

https://digital.library.unt.edu/ark:/67531/metadc949178/m2/1/high_res_d/vorosmarty.pdf

EPA Program Review: Consequences of Global Climate Change: Water Quality Impacts, Ecological Impacts & Nonlinear Responses

Abstract

Similar works

Full text

Available Versions

UNT Digital Library