Study: Managing Data From models Fernando Aguilar IFCA-CSIC - - PowerPoint PPT Presentation

Algae Bloom Case Study: Managing Data From models

Fernando Aguilar IFCA-CSIC aguilarf@ifca.unican.es e-Research Summer Hackfest

RIA-653549

• LifeWatch is an ESFRI oriented to Biodiversity and Ecosystems research.
• Under LifeWatch umbrella many different projects will be supported, both on basic and applied

research.

• This Case Study on the Modeling of a Water Reservoir & Prediction of Algae Bloom is a collaboration of

IFCA and Ecohydros SL, an Spanish SME working for CHD (Duero river water authorities).

• It is a long standing collaboration (>10 years), and many steps have been given:
• Deployment and integration of complex remote instrumentation
• Data taking and visualization
• Modeling of the whole system, including hydrological and biological model (DELFT-3D)
• Validation of the model and predictive system

ALGAE BLOOM ARE DUE TO EUTROPHICATION PROBLEMS, AND POINT TO PROBLEMS IN WATER QUALITY MANAGEMENT

• Community interest: authorities (local, regional, national, European level), researchers on fresh-water

systems (as a basic framework for modeling and simulation of ecological processes)

• Deployment of new monitoring stations in lakes and water reservoirs

Research Community

Introduction

• Framework: Collaboration within European LIFE+ project (ROEM+). SME

Ecohydros.

• Reservoir hydrodinamic and Water Quality modelling. Cuerda del Pozo: water

supply, water activities.

• Previous work
• Platform takes data from water: physical, chemical, biological, etc. Allows to know

water status (data taken since 2010 aprox.)

• Data visualization tool. Aims to alert authorities when the water quality is under

the limits.

INDIGO-DataCloud RIA-653549

Scientific problem

• One more step: knowing before an event happens the status of the

water using modelling tools (Delft3D used in cloud).

• Goal: alert authorities not only in real time but before.
• The main problem is eutrophication:

INDIGO-DataCloud RIA-653549

Scientific problem

• Within 5 years of continuous monitoring in CdP Reservoir, this is the

cyanobacterias concentration close to the dam:

INDIGO-DataCloud RIA-653549 2010 - 2011 Dolichospermum planctonicum Aphanizomenon flos-aquae 2013 Dolichospermum crassum Colonias Woronichinia naegeliana 2014 Colonias Microcystis novacekii Dolichospermum crassum

Computing and data model

• Delft3D-FLOW: Different resolutions (Bathymetry, 5-40m horizontally,

0.5-3m vertically, 35 layers). Z-model.

• Number of input parameters:
• Tributaries/Initial conditions: flow, temperature, salinity.
• Meteo: Rain, air temp, humidity, solar radiation, wind.
• Interrelationship between parameters.

Integrating distributed data infrastructures with INDIGO-DataCloud

6

Computing and data model

• Goal: Reproduce thermocline and water level.
• Not easy, but very good results.

Integrating distributed data infrastructures with INDIGO-DataCloud

7

Computing and data model

• Water Quality: More complex model due to the number of processes

involved.

• Goal: reproduce algae bloom.
• Input: hydrodynamic output, nutrient concentrations

(initial/tributaries), initial algae concentrations, sediments, other coefficients/ratios (mortality, growth, ratio chl/C, etc.).

Integrating distributed data infrastructures with INDIGO-DataCloud

8

Computing and data model

• We tried to model it increasingly, adding parameters one by one:

Continuity>Oxygen>Nutrients…

• The model is not validated yet. Problems found.
• Oxigen is not well distributed in the water column.
• Nutrients (Phosphatus, Nitrates) decreasing (and dissapear).
• Algae trend to die.
• Tests in forced scenarios (sweep):
• +50% nutrients
• +90% nutrients
• Analyze Results: Ophidia?

Integrating distributed data infrastructures with INDIGO-DataCloud

9

Implementation strategy

• OneData: Key component!
• We need a storage distribution solution: link inputs-computing-outputs.

Sharing capabilities.

• Input – Output files up to 50Gb. Problem?
• Schedule:
• Docker: Delft3D (Done)
• Create OneData Space.
• Upload input files.
• Mount OneData client with Delft3D
• Test With 1 model.
• Add Parameter sweep.
• Test with N models.
• Check Outputs.

Integrating distributed data infrastructures with INDIGO-DataCloud

10

Implementation strategy

• Ophidia: Very interesting.
• Model outputs: N parameters in time and space.
• Parameter interrelationship: Nutrients Vs. Algae, Temperature Vs.

Oxygen, etc. In time + In Space/time.

• Schedule:
• Test 1 param in time
• Test 2 params in time
• Test 5 params in time
• Test 1 param in space/time (2D and 3D)
• Test 2, Test 5…

Integrating distributed data infrastructures with INDIGO-DataCloud

11

Implementation strategy

• Kepler: interesting.
• Model outputs must be analyze after completing.
• We have an R script to analyze thermoclines: Model Vs. Real Data.
• Kepler could fit on this.
• Schedule:
• Workflow to run script (R) with an input file.
• Workflow to compare input file with real data (from MySQL dataset).
• Very ambitious: Link Delft3D output (in docker) with Kepler.

Integrating distributed data infrastructures with INDIGO-DataCloud

12

Summary

Integrating distributed data infrastructures with INDIGO-DataCloud

13 OneData Output

Ophidia

Kepler

Bonus: TRUFA

Integrating distributed data infrastructures with INDIGO-DataCloud

14 OneData