US Long-Term Ecological Research Network

WSC 2006 Spatial interactions among ecosystem services in the Yahara Watershed

Abstract
Understanding spatial distributions, synergies and tradeoffs of multiple ecosystem services (benefits people derive from ecosystems) remains challenging. We analyzed the supply of 10 ecosystem services for 2006 across a large urbanizing agricultural watershed in the Upper Midwest of the United States, and asked: (i) Where are areas of high and low supply of individual ecosystem services, and are these areas spatially concordant across services? (ii) Where on the landscape are the strongest tradeoffs and synergies among ecosystem services located? (iii) For ecosystem service pairs that experience tradeoffs, what distinguishes locations that are win win exceptions from other locations? Spatial patterns of high supply for multiple ecosystem services often were not coincident locations where six or more services were produced at high levels (upper 20th percentile) occupied only 3.3 percent of the landscape. Most relationships among ecosystem services were synergies, but tradeoffs occurred between crop production and water quality. Ecosystem services related to water quality and quantity separated into three different groups, indicating that management to sustain freshwater services along with other ecosystem services will not be simple. Despite overall tradeoffs between crop production and water quality, some locations were positive for both, suggesting that tradeoffs are not inevitable everywhere and might be ameliorated in some locations. Overall, we found that different areas of the landscape supplied different suites of ecosystem services, and their lack of spatial concordance suggests the importance of managing over large areas to sustain multiple ecosystem services. <u>Documentation</u>: Refer to the supporting information of the follwing paper for full details on data sources, methods and accuracy assessment: Qiu, Jiangxiao, and Monica G. Turner. &quot;Spatial interactions among ecosystem services in an urbanizing agricultural watershed.&quot; <em>Proceedings of the National Academy of Sciences</em> 110.29 (2013): 12149-12154.
Contact
Dataset ID
290
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Each ecosystem service was quantified and mapped by using empirical estimates and spatially explicit model for the terrestrial landscape of the Yahara Watershed for 2006. Crop production (expected annual crop yield, bu per yr) Crop yield was estimated for the four major crop types (corn, soybean, winter wheat and oats) that account for 98.5 percent of the cultivated land in the watershed by overlaying maps of crop types and soil-specific crop yield estimates. The spatial distribution of each crop was obtained from the 2006 Cropland Data Layer (CDL) from the National Agricultural Statistics Service (NASS) and soil productivity data were extracted from Soil Survey Geographic (SSURGO) database. Crop and soil data were converted to 30 m resolution and the two maps were overlain to estimate crop yield in each cell. For each crop-soil combination, crop area was multiplied by the estimated yield per unit area. Estimates for each crop type were summed to map estimated crop yield for 2006. Pasture production (expected annual forage yield, animal-unit-month per year ) As for crop production, forage yield was estimated by overlaying the distribution of all forage crops (alfalfa, hay and pasture/grass) and soil specific yield estimates. The spatial distribution of each forage crop was also derived from 2006 CDL, and rescaled to 30 m grid prior to calculation. The SSURGO soil productivity layer provided estimates of potential annual yield per unit area for each forage crop. Overlay analyses were performed for each forage-soil combination, as done for crops, and summed to obtain the total expected forage yield in the watershed for 2006. Freshwater supply (annual groundwater recharge, cm per year) . Groundwater recharge was quantified and mapped using the modified Thornthwaite-Mather Soil-Water-Balance (SWB) model. SWB is a deterministic, physically based and quasi three-dimensional model that accounts for precipitation, evaporation, interception, surface runoff, soil moisture storage and snowmelt. Groundwater recharge was calculated on a grid cell basis at a daily step with the following mass balance equation<p align="center">Recharge= (precipitation + snowmelt + inflow) &ndash;<p align="center">(interception + outflow + evapotranspiration) &ndash; delta soil moisture<p align="center"> We ran the model for three years (2004 to 2006) at 30m resolution, with the first two years as spin up of antecedent conditions (e.g. soil moisture and snow cover) that influence groundwater recharge for the focal year of 2006. Carbon storage (metric tons<sup> </sup>per ha) We estimated the amount of carbon stored in each 30 m cell in the Yahara Watershed by summing four major carbon pools: aboveground biomass, belowground biomass, soil carbon and deadwood/litter. Our quantification for each pool was based mainly on carbon estimates from the IPCC tier-I approach and other published field studies of carbon density and was estimated by land-use/cover type.Groundwater quality (probability of groundwater nitrate concentration greater than 3.0 mg per liter, unitless 0 to1) Groundwater nitrate data were obtained from Groundwater Retrieve Network (GRN), Wisconsin Department of Natural Resources (DNR). A total of 528 shallow groundwater well (well depth less than the depth from surface to Eau Claire shale) nitrate samples collected in 2006 were used for our study. We performed kriging analysis to interpolate the spatial distribution of the probability of groundwater nitrate concentration greater than 3 mg<sup> </sup>per liter. We mapped the interpolation results at a 30m spatial resolution using Geostatistical Analyst extension in ArcGIS (ESRI). In this map, areas with lower probability values provided more groundwater quality service, and vice versa. Surface water quality (annual phosphorus loading, kg per hectare). We adapted a spatially explicit, scenario-driven modeling tool, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) to simulate discharge of nonpoint-source phosphorus. A grid cells phosphorus contribution was quantified as a function of water yield index, land use/cover, export coefficient, and downslope retention ability with the following equation:Expx = ALVx * sum of the products from y=x+1 to X for (1-Ey)where ALVx is the adjusted phosphorus export from pixel x , Ey is the filtration efficiency of each downstream pixel y , and X represents phosphorus transport route from where it originated to the downstream water bodies. Filtration efficiency was assigned by cover type: natural vegetation was assigned a high value, semi-natural vegetation an intermediate value, and developed or impervious covers were assigned low values. We ran the model for 2006 and mapped estimated phosphorus loading across the watershed. The ecosystem service of providing high quality surface water was the inverse of phosphorus loading. Therefore, areas with lower phosphorus loading values delivered more surface water quality, and areas with higher phosphorus loading values supplied less surface water quality.Soil retention (annual sediment yield, metric tons per hectare). We quantified annual sediment yield as the (inverse) indicator for soil retention by using the Modified Universal Soil Loss Equation (MUSLE). MUSLE is a storm event based model that estimates sediment yield as a function of runoff factor, soil erodibility, geomorphology, land use/cover and land management. Specifically, a grid cells contribution of sediment for a given storm event is calculated as:Sed= 11.8*(Q*q<sub>p</sub>)<sup>0.56</sup> * K * LS * C * Pwhere Sed represents the amount of sediment that is transported downstream network (metric tons), Q is the surface runoff volume (m<sup>3</sup>), q<sub>p </sub>is the peak flow rate (cubic meters per s), K is soil erodibility which is based on organic matter content, soil texture, permeability and profiles, LS is combined slope and steepness factor, and C* P is the product of plant cover and its associated management practice factor. We used the ArcSWAT interface of the Soil and Water Assessment Tool (SWAT) to perform all the simulations. We ran this model at a daily time step from 2004 to 2006, with the first two years as spin up , then mapped total sediment yield for 2006 across the watershed. Similar to surface water quality, the ecosystem service of soil retention was the inverse of sediment yield. In this map, areas with lower sediment yield provided more of this service, and areas with higher sediment yield delivered less. Flood regulation (flooding regulation capacity, unitless, 0 to 100) We used the capacity assessment approach to quantify the flood regulation service based on four hydrological parameters: interception, infiltration, surface runoff and peak flow. We first applied the Kinematic Runoff and Erosion (KINEROS) model to derive estimates of three parameters (infiltration, surface runoff and peak flow) for six sampled sub basins in this watershed. KINEROS is an event-oriented, physically based, distribution model that simulates interception, infiltration, surface runoff and erosion at sub-basin scales. In each simulation, a sub basin was first divided into smaller hydrological units. For the given pre-defined storm event, the model then calculated the amount of infiltration, surface runoff and peak flow for each unit. Second, we classified these estimates into 10 discrete capacity classes with range from 0 to 10 (0 indicates no capacity and 10 indicates the highest capacity) and united units with the same capacity values and overlaid with land cover map. Third, we calculated the distribution of all land use/cover classes within every spatial unit (with a particular capacity). We then assigned each land use/cover a capacity parameter based on its dominance (in percentage) within all capacity classes. As a result, every land use/cover was assigned a 0 to 10 capacity value for infiltration, surface runoff and peak flow. This procedure was repeated for six sub basins, and derived capacity values were averaged by cover type. We applied the same procedure to soil data and derived averaged capacity values for each soil type with the same set of three parameters. In addition, we obtained interceptions from published studies for each land use/cover and standardized to the same 0 to 10 range. Finally, the flood regulation capacity (FRC) for each 30m cell was calculated with the equation below:FRC= for each land use and land cover class the sum of (interception + infilitration + runoff + peakflow) + for each soil class the sum of (infiltration + runoff + peakflow).To simplify interpretation, we rescaled original flood regulation capacity values to a range of 0 to100, with 0 representing the lowest regulation capacity and 100 the highest. Forest recreation (recreation score, unitless, 0 to 100). We quantified the forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area for each 30m grid cell with the equation below:FRSi= Ai * sum of (Oppti + Popi + Roadi)where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5200) to a range of 0 to 100, with 0 representing no forest recreation service and 100 representing highest service. Several assumptions were made for this assessment approach. Larger areas and places with more recreational opportunities would provide more recreational service, areas near large population centers would be visited and used more than remote areas, and proximity to major roads would increase access and thus recreational use of an area. Hunting recreation (recreation score, unitless 0 to100) We applied the same procedure used for forest recreation to quantify hunting service. Due to limited access to information regarding private land used for hunting, we only included public lands, mainly state parks, for this assessment. The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility for each 30m grid cell with the following equation:<br />HRSi= Ai * sum of (Spei + Popi + Roadi)where HRS is hunting recreation score, A is the area of public wild areas open for hunting and fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28000) to a range of 0 to100, with 0 representing no hunting recreation service and 100 representing highest service. Similar assumptions were made for this assessment. Larger areas and places with more game species would support more hunting, and areas closer to large population centers would be used more than remote areas. Finally, proximity to major roads would increase access and use of an area.
Short Name
Ecosystem services in the Yahara Watershed
Version Number
20

North Temperate Lakes LTER: Groundwater Chemistry 1984 - current

Abstract
Water chemistry is measured annually in 11 monitoring wells to characterize regional groundwater chemistry in the Trout Lake area. The chemical parameters measured include pH, conductivity, total alkalinity, disolved inorganic and organic carbon, total nitrogen, nitrae, ammonia, total phosphorus, calcium, magnesium, soium, potassium, chloride, sulfate, iron, manganese, total silica and dissolved reactive silica. Chemical data areavailable at a quarterly sampling frequency for some years. In addition (see related data set - Groundwater Level), water levels in 37 monitoring wells are measured several times per year. The wells are scattered throughout the Trout Lake hydrological basin and the data are used to calibrate and test regional groundwater flow models. Sampling Frequency: annually - with some earlier data from quarterly sampling Number of sites: 11
Dataset ID
10
Date Range
-
Maintenance
ongoing
Metadata Provider
Methods
Ammonium, Nitrate, Nitrit Samples for ammonium and nitrate or nitrite are collected together with a peristaltic pump and tubing and in-line filtered (through a 0.40 micron polycarbonate filter) into new, 20 ml HDPE plastic containers with conical caps. The samples are stored frozen until analysis, which should occur within 6 months. The samples are analyzed for ammonium (and nitrateornitrite) simultaneously by automated colorimetric spectrophotometry, using a segmented flow autoanalyzer. Ammonium is determined by utilizing the Berthelot Reaction, producing a blue colored indophenol compound, where the absorption is monitored at 660 nm. The detection limit for ammonium is approximately 3 ppb and the analytical range for the method extends to 4000 ppb. The detection limit for nitrateornitrite is approximately 2 ppb and the analytical range for the method extends to 4000 ppb. Method Log: Prior to January 2006 samples, ammonium was determined on a Technicon segmented flow autoanalyzer. From 2006 to present, ammonium is determined by an Astoria-Pacific Astoria II segmented flow autoanalyzer. Chloride, Sulfate Samples for chloride and sulfate are collected together with a peristaltic pump and tubing and in-line filtered (through a 0.40 micron polycarbonate filter) into new, 20 ml HDPE plastic containers with conical caps. The samples are stored refrigerated at 4 degrees Celsius until analysis, which should occur within 6 months. The samples are analyzed for chloride (and sulfate) simultaneously by Ion Chromatography, using a hydroxide eluent. The detection limit for chloride is approximately 0.01 ppm and the analytical range for the method extends to 100 ppm. The detection limit for sulfate is approximately 0.01 ppm and the analytical range for the method extends to 60 ppm. Method Log: Prior to January 1998 samples, chloride was determined on a Dionex DX10 Ion Chromatograph, using a chemical fiber suppressor. From 1998 to 2011, chloride was determined by a Dionex model DX500, using an electro-chemical suppressor. From January 2011 until present, chloride is determined by a Dionex model ICS 2100 using an electro-chemical suppressor. Calcium, magnesium, sodium, potassium, iron, and manganese Samples for calcium analysis (as well as dissolved nitrogen and phosphorus, magnesium, sodium, potassium, iron, and manganese) are collected together with a peristaltic pump and tubing and in-line filtered (through a 40 micron polycarbonate filter) into 120 ml LDPE bottles and acidified to a 1percent HCl matrix by adding 1 ml of ultra pure concentrated HCl to 100 mls of sample. For every sample acidification event, three acid blanks are created by adding the same acid used on the samples to 100 mls of ultra pure water supplied from the lab. Once acidified, the samples are stable at room temperature until analysis, which should occur within one year. Until acidification, the samples should be refrigerated at 4 degrees Celsius. Calcium, as well as magnesium, sodium, potassium, iron, and manganese are analyzed simultaneously on an optical inductively-coupled plasma emission spectrophotometer (ICP-OES). The acidified samples are directly aspirated into the instrument without a digestion. Calcium is analyzed at 317.933 nm and at 315.887 nm and viewed axially for low-level analysis and radially for high level analysis. The detection limit for calcium is 0.06 ppm with an analytical range of the method extends to 50 ppm. The detection limit for iron is 0.02 ppm with an analytical range of the method extends to 20 ppm. The detection limit for magnesium is 0.03 ppm with an analytical range of the method extends to 50 ppm. The detection limit for manganese is 0.01 ppm with an analytical range of the method extends to 2 ppm. The detection limit for potassium is 0.06 ppm with an analytical range of the method extends to 10 ppm. The detection limit for sodium is 0.06 ppm with an analytical range of the method extends to 50 ppm. Method Log: Prior to January 2002, calcium, magnesium, sodium, potassium, iron, and manganese were determined on a Perkin-Elmer model 503 Atomic Absorption Spectrophotometer. Lanthanum at a 0.8percent concentration was added as a matrix modifier to suppress chemical interferences. From January 2002 to present, samples are analyzed for calcium on a Perkin-Elmer model 4300 DV ICP. Inorganic and organic carbon Samples for inorganic and organic carbon are collected together with a peristaltic pump and tubing and in-line filtered, if necessary, (through a 0.40 micron polycarbonate filter) into glass, 24 ml vials (that are compatible with the carbon analyzer autosampler), and capped with septa, leaving no head space. The samples are stored refrigerated at 4 degrees Celsius until analysis, which should occur within 2-3 weeks. The detection limit for inorganic carbon is 0.15 ppm, and the analytical range for the method is 60 ppm. The detection limit for organic carbon is 0.30 ppm and the analytical range for the method is 30 ppm. Method Log: Prior to May 2006 samples, inorganic carbon was analyzed by phosphoric acid addition on an OI Model 700 Carbon Analyzer. From May 2006 to present, inorganic carbon is still analyzed by phosphoric acid addition, but on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer. Method Log: Prior to May 2006 samples, organic carbon was analyzed by heated persulfate digestion on an OI Model 700 Carbon Analyzer. From May 2006 to present, Organic carbon is analyzed by combustion, on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer. Dissolved reactive silicon Samples for silicon are collected with a peristaltic pump and tubing and in-line filtered (through a 40 micron polycarbonate filter) into 120 ml LDPE bottles and acidified to a 1percent HCl matrix by adding 1 ml of ultra pure concentrated HCl to 100 mls of sample. For every sample acidification event, three acid blanks are created by adding the same acid used on the samples to 100 mls of ultra pure water supplied from the lab. Once acidified, the samples are stable at room temperature until analysis, which should occur within one year. Until acidification, the samples should be refrigerated at 4 degrees Celsius. Dissolved reactive silica is determined by the Heteropoly Blue Method and the absorption is measured at 820 nm. The detection limit for silicon is 6 ppb and the analytical range is 15000 ppb. Method Log These determinations were performed manually using a Bausch and Lomb Spectrophotometer from the beginning of the project until April 1984. From 1984 through 2005, dissolved reactive silicon was determined on a Technicon Auto Analyzer II. From January 2006 to present, samples are run on an Astoria-Pacific Astoria II Autoanalyzer. total and dissolved nitrogen and phosphorus Samples for total and dissolved nitrogen and phosphorus analysis are collected together with a peristaltic pump and tubing and in-line filtered, when necessary, (through a 40 micron polycarbonate filter) into 120 ml LDPE bottles and acidified to a 1percent HCl matrix by adding 1 mL of ultra pure concentrated HCl to 100 mls of sample. For every sample acidification event, three acid blanks are created by adding the same acid used on the samples to 100 mls of ultra pure water supplied from the lab. Once acidified, the samples are stable at room temperature until analysis, which should occur within one year. Until acidification, the samples should be refrigerated at 4 degrees Celsius. The samples must first be prepared for analysis by adding an NaOH–Persulfate digestion reagent and heated for an hour at 120 degrees C and 18-20 psi in an autoclave. The samples are analyzed for total nitrogen and total phosphorus simultaneously by automated colorimetric spectrophotometry, using a segmented flow autoanalyzer. Total nitrogen is determined by utilizing the automated cadmium reduction method, as described in Standard Methods, where the absorption is monitored at 520 nm. The detection limit for total and dissolved nitrogen is approximately 21 ppb and the analytical range for the method extends to 2500 ppb. The detection limit for total phosphorus is approximately 3 ppb and the analytical range for the method extends to 800 ppb. Method Log: Prior to January 2006 samples, total nitrogen was determined on a Technicon segmented flow autoanalyzer. From 2006 to present, total nitrogen is determined by an Astoria-Pacific Astoria II segmented flow autoanalyzer. pH We sample at the deepest part of the lake using a peristaltic pump and tubing, monthly during open water and approximately every five weeks during ice cover. We collect two types of pH samples at each sampling depth: one in 20ml vials with cone cap inserts to exclude all air from the vial, and one in 125ml bottles to be air equilibrated before analysis. The depths for sample collection are based on thermal stratification: top and bottom of the epilimnion, mid thermocline, and top, middle,and bottom of the hypolimnion. During mixis we sample at the surface, mid water column, and bottom. We analyze for pH the same day that samples are collected, keeping them cold and dark until just before analysis. Samples are warmed to room temperature in a dark container, and the air equilibrated samples are bubbled with outside air for at least 15 minutes prior to measurement. We measure pH using a Radiometer combination pH electrode and Orion 4Star pH meter. Protocol Log: 1981-1988 -- used a PHM84 Research pH meter. 1986 -- began analyzing air equilibrated pH. 1988 - July 2010 -- used an Orion model 720 pH meter.</p>
Short Name
NTLGW02
Version Number
23

North Temperate Lakes LTER: Groundwater Levels 1984 - current

Abstract
Water levels in monitoring wells are measured several times throughout the year. The number of monitored wells has ranged over the study period from 19 to 44 wells. Currently, 37 wells are being monitored 4 - 5 times per year. The wells are scattered throughout the Trout Lake hydrological basin and the data are used to calibrate and test regional groundwater flow models. In addition (see related data set - Groundwater Chemistry), water chemistry is measured annually in a subset of 11 of these wells to characterize regional groundwater chemistry in the Trout Lake area. Sampling Frequency: varies - generally from 4 - 9 times a year Number of sites: 44
Dataset ID
9
Date Range
-
LTER Keywords
Maintenance
ongoing
Metadata Provider
Methods
Standard water level measurements in ground water wells. See abstract for more methods descriptions.
Short Name
NTLGW01
Version Number
22
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