Common use of Citizen Science Clause in Contracts

Citizen Science. Citizen Science (CS) has long been recognised as an inexpensive way of gathering large amount of environmental data in many locations, potentially also at high frequency. Studies have evaluated how this data can be used complementarily in environmental research and agency monitoring (▇▇▇▇- ▇▇▇▇▇▇ et al. 2017; ▇▇▇▇▇▇▇▇ et al. 2017) as well as for achieving specific monitoring and assessment goals e.g. the United Nations sustainable development goal SDG 6.3.2 (▇▇▇▇▇▇▇▇▇ et al. 2020a; ▇▇▇▇▇▇▇▇▇ et al. 2020b). In this chapter we review the challenges and benefits of using CS data for monitoring of water quality in general, and specifically related to those water quality parameters considered the greatest relevance to the calibration and validation of EO products i.e. chlorophyll-a, algal blooms, water colour, dissolved organic carbon and water clarity / turbidity. We also review CS data on nutrients which are highly complementary to satellite EO data on water quality, particularly as a driver of chlorophyll-a and cyanobacterial blooms. Where available, we sought any CS data explicitly gathered in relation to water quality monitoring by satellite Earth Observation (EO). The chapter includes a data review of existing CS schemes and projects used in monitoring of the key EO-sensed water quality parameters listed above. This review of existing schemes is used to identify potential sources of data for calibration and validation of EO water quality monitoring services, or potentially schemes that could be encouraged to collect data to support development of EO water quality products. This overview of challenges, benefits and data availability concludes with final recommendations related to the use of CS data to validate, calibrate and complement EO services for water quality monitoring. An initial literature review was carried out using Web of Science (WoS Advanced Search) with the following scope: English language, all documents, Dates: 1970-2021, all collections. The search string was for titles (TS) with the following terms: TS = (“citizen Science” or “community science” or “volunteer monitoring” or “crowd-source” or “public engagement”) AND TS = (nutrients or turbidity or “water clarity” or DOC or Chlorophyll-a or chlorophyll-a or cyanobacteria or “trophic state index” or “surface reflectance” or DOC or CDOM or “optical properties” or “suspended sediments”) The number of relevant papers identified in the WoS search (dated 21/07/2021) are summarised in Error! Reference source not found.. The title and abstracts of the 104 records where both search terms were present were reviewed to produce a shortlist of publications for more detailed review (advantages, disadvantages, etc.) of CS methods for collecting water quality data. Set Results Search string #1 9,795 TS=(“citizen Science” or “community science” or “volunteer monitoring” or “crowd-source” or “public engagement”) #2 744,866 TS=(nutrients or turbidity or “water clarity” or DOC or chlorophyll-a or Chlorophyll-a or cyanobacteria or “trophic state index” or “surface reflectance” or DOC or CDOM or “optical properties” or “suspended sediments”) #3 104 #1 AND #2 A structured google search was also carried out (e.g. “citizen science” + “water quality” + “chlorophyll”) to produce a catalogue of potentially relevant existing schemes/data sets. In addition to this, we were provided with an inventory of schemes identified in a similar review carried out for GEO AquaWatch (▇▇▇▇▇▇▇ ▇▇▇▇▇, pers. comm). Based on the Web of Science and Google searches, the following information was gathered on data available from the catalogue of relevant citizen science schemes: o Campaign-based or continuous? o Period available o Region of interest: regional to global o No. of records/samplings o No. of sites – easily available or not? o Frequency of observations – easily available or not? o Accessibility of data – especially sources with APIs The review of existing CS schemes identified 25 different global, regional or national schemes relating to lake data from one, or more, of the EO-related water quality parameters (nutrients, chlorophyll, algal blooms, water clarity / turbidity and water colour / DOC) (Table 7). Most additional sub-national schemes identified in the USA, or schemes for rivers, streams, or coastlines were not reviewed further in this study. The monitoring parameters most associated with citizen science data collection were algal blooms

Citizen Science. Citizen Science (CS) has long been recognised as an inexpensive way of gathering large amount of environmental data in many locations, potentially also at high frequency. Studies have evaluated how this data can be used complementarily in environmental research and agency monitoring (▇▇▇▇- ▇▇▇▇▇▇ Hadj- Hammou et al. 2017; ▇▇▇▇▇▇▇▇ et al. 2017) as well as for achieving specific monitoring and assessment goals e.g. the United Nations sustainable development goal SDG 6.3.2 (▇▇▇▇▇▇▇▇▇ et al. 2020a; ▇▇▇▇▇▇▇▇▇ et al. 2020b). In this chapter we review the challenges and benefits of using CS data for monitoring of water quality in general, and specifically related to those water quality parameters considered the greatest relevance to the calibration and validation of EO products i.e. chlorophyll-a, algal blooms, water colour, dissolved organic carbon and water clarity / turbidity. We also review CS data on nutrients which are highly complementary to satellite EO data on water quality, particularly as a driver of chlorophyll-a and cyanobacterial blooms. Where available, we sought any CS data explicitly gathered in relation to water quality monitoring by satellite Earth Observation (EO). The chapter includes a data review of existing CS schemes and projects used in monitoring of the key EO-sensed water quality parameters listed above. This review of existing schemes is used to identify potential sources of data for calibration and validation of EO water quality monitoring services, or potentially schemes that could be encouraged to collect data to support development of EO water quality products. This overview of challenges, benefits and data availability concludes with final recommendations related to the use of CS data to validate, calibrate and complement EO services for water quality monitoring. An initial literature review was carried out using Web of Science (WoS Advanced Search) with the following scope: English language, all documents, Dates: 1970-2021, all collections. The search string was for titles (TS) with the following terms: TS = (“citizen Science” or “community science” or “volunteer monitoring” or “crowd-source” or “public engagement”) AND TS = (nutrients or turbidity or “water clarity” or DOC or Chlorophyll-a or chlorophyll-a or cyanobacteria or “trophic state index” or “surface reflectance” or DOC or CDOM or “optical properties” or “suspended sediments”) The number of relevant papers identified in the WoS search (dated 21/07/2021) are summarised in Error! Reference source not found.. The title and abstracts of the 104 records where both search terms were present were reviewed to produce a shortlist of publications for more detailed review (advantages, disadvantages, etc.) of CS methods for collecting water quality data. Set Results Search string #1 9,795 TS=(“citizen Science” or “community science” or “volunteer monitoring” or “crowd-source” or “public engagement”) #2 744,866 TS=(nutrients or turbidity or “water clarity” or DOC or chlorophyll-a or Chlorophyll-a or cyanobacteria or “trophic state index” or “surface reflectance” or DOC or CDOM or “optical properties” or “suspended sediments”) #3 104 #1 AND #2 A structured google search was also carried out (e.g. “citizen science” + “water quality” + “chlorophyll”) to produce a catalogue of potentially relevant existing schemes/data sets. In addition to this, we were provided with an inventory of schemes identified in a similar review carried out for GEO AquaWatch (▇▇▇▇▇▇▇ ▇▇▇▇▇, pers. comm). Based on the Web of Science and Google searches, the following information was gathered on data available from the catalogue of relevant citizen science schemes: o Campaign-based or continuous? o Period available o Region of interest: regional to global o No. of records/samplings o No. of sites – easily available or not? o Frequency of observations – easily available or not? o Accessibility of data – especially sources with APIs The review of existing CS schemes identified 25 different global, regional or national schemes relating to lake data from one, or more, of the EO-related water quality parameters (nutrients, chlorophyll, algal blooms, water clarity / turbidity and water colour / DOC) (Table 7). Most additional sub-national schemes identified in the USA, or schemes for rivers, streams, or coastlines were not reviewed further in this study. The monitoring parameters most associated with citizen science data collection were algal blooms