Jupyter Notebooks for Oslo Fjord use cases, Aquainfra project
flowchart TB
L0[Level_0 raw satellite images] --Correction --> L1(Level_1 corrected images)--Calculation algorithm--> L2[Level_2 Product]
fb[Ferrybox dataset]
glomma[Glomma logger dataset]
insitu_data[In situ measurements dataset]
fb --> ADC
glomma --> ADC
insitu_data --> ADC
ddos[AquaInfra Data Space]
ADC --Discoverable by --> ddos
L2 -- Limited amount of data uploaded to --> ddos
- Research question: ?
- Parameters that will be analyzed to answer the question: Chl-a, cDOM (example)
- Jupyter Notebook for this question: ?
flowchart TB
fb[Query Ferrybox Chla-f, fDOM]
glomma_dataset[Query glomma fDOM]
niva_db[Query lab analyzed cDOM abs, Chl-a]
function[Apply function that makes it possible to compare values across domains]
analyze[analyze differences?]
fb --> function
niva_db --> function
glomma_dataset --> function
function --> analyze
result
analyze --> result
- Discharge data (Leah)
- Share python script for extracting the NVE data
- Get NVE data for the three rivers
- Water chemistry grab samples (Areti)
- Get from Aquamonitor & Vannmiljø. Same?
- Make generic, shareable script to clean the data if necessary
-
Glomma sensor data
- Data retrieved using app and saved (Leah done)
- Ivana? Make script to access and QC data. Improve on existing QC routines (with Leah/Øyvind K)
- Leah/Areti: Check sensor data
-
Regressions (Leah/Areti)
- Concentration vs sensor: FDOM-DOC, Turb-SPM, Conductivity-NO3, ...?
- Concentration vs discharge
- Seasonally-variable regressions?
- Estimate daily concentrations (Leah/Areti)
- Interpolation
- Stats relationships from regressions
- Estimate daily loads (Leah/Areti)
- Freshwater, DOC, SPM, NO3, TN, TP, ...?