Application of soil moisture information for operational water management

Research output: ThesisPhD Thesis - Research UT, graduation UT

Abstract

Water in the unsaturated part of the soil subsurface is referred to as soil moisture. Soil moisture and related processes are often considered as key components of the hydrological cycle, affecting hydrological, meteorological, biological, and biogeochemical processes. The dry period in the summer of the year 2018 highlighted the necessity of understanding soil moisture dynamics and integrating related information in water management approaches. However, the current application of soil moisture information in operational water resources management is limited. One of the reasons is the lack of measurement data. Recently, the increasing availability of high-resolution soil moisture data retrieved using remote sensing methods has led to new possibilities for utilization in water management.

The research aim of this work was to show the use of high-resolution soil moisture information for operational water resources management. First, the needs of water managers were identified. We recommend that decision-makers should focus on the development of structured methodologies for integrating both evidence-based and experiential information in decision support systems. In addition, we focused on retrieving accurate soil moisture information on both regional and local spatial scales using both a data assimilation scheme and a novel data-driven modelling method. We expect that the increasing availability of high-resolution remotely sensed soil moisture data and developments in data storage and computational environments will lead to an increase in the application of data assimilation schemes and other data-driven modelling methods in operational water resources management. Last, we discussed several applications to integrate the research findings in operational water management. Finally, we challenge both researchers and water managers to continue to invest in these approaches, as the call for optimized, consistent, and sustainable water management becomes increasingly important in the future.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Hulscher, Suzanne J.M.H., Supervisor
  • Augustijn, Dionysius C.M., Supervisor
  • Hendriks, Dimmie M.D., Co-Supervisor
Thesis sponsors
Award date30 Jan 2020
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-4948-6
Electronic ISBNs978-90-365-4948-6
DOIs
Publication statusPublished - 30 Jan 2020

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water management
soil moisture
data assimilation
hydrological cycle
decision support system
water
modeling
remote sensing
methodology
summer
method
water resources management
soil

Keywords

  • Soil moisture
  • Operational water management
  • Remote Sensing

Cite this

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title = "Application of soil moisture information for operational water management",
abstract = "Water in the unsaturated part of the soil subsurface is referred to as soil moisture. Soil moisture and related processes are often considered as key components of the hydrological cycle, affecting hydrological, meteorological, biological, and biogeochemical processes. The dry period in the summer of the year 2018 highlighted the necessity of understanding soil moisture dynamics and integrating related information in water management approaches. However, the current application of soil moisture information in operational water resources management is limited. One of the reasons is the lack of measurement data. Recently, the increasing availability of high-resolution soil moisture data retrieved using remote sensing methods has led to new possibilities for utilization in water management.The research aim of this work was to show the use of high-resolution soil moisture information for operational water resources management. First, the needs of water managers were identified. We recommend that decision-makers should focus on the development of structured methodologies for integrating both evidence-based and experiential information in decision support systems. In addition, we focused on retrieving accurate soil moisture information on both regional and local spatial scales using both a data assimilation scheme and a novel data-driven modelling method. We expect that the increasing availability of high-resolution remotely sensed soil moisture data and developments in data storage and computational environments will lead to an increase in the application of data assimilation schemes and other data-driven modelling methods in operational water resources management. Last, we discussed several applications to integrate the research findings in operational water management. Finally, we challenge both researchers and water managers to continue to invest in these approaches, as the call for optimized, consistent, and sustainable water management becomes increasingly important in the future.",
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Application of soil moisture information for operational water management. / Pezij, Michiel .

Enschede : University of Twente, 2020. 175 p.

Research output: ThesisPhD Thesis - Research UT, graduation UT

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AU - Pezij, Michiel

PY - 2020/1/30

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N2 - Water in the unsaturated part of the soil subsurface is referred to as soil moisture. Soil moisture and related processes are often considered as key components of the hydrological cycle, affecting hydrological, meteorological, biological, and biogeochemical processes. The dry period in the summer of the year 2018 highlighted the necessity of understanding soil moisture dynamics and integrating related information in water management approaches. However, the current application of soil moisture information in operational water resources management is limited. One of the reasons is the lack of measurement data. Recently, the increasing availability of high-resolution soil moisture data retrieved using remote sensing methods has led to new possibilities for utilization in water management.The research aim of this work was to show the use of high-resolution soil moisture information for operational water resources management. First, the needs of water managers were identified. We recommend that decision-makers should focus on the development of structured methodologies for integrating both evidence-based and experiential information in decision support systems. In addition, we focused on retrieving accurate soil moisture information on both regional and local spatial scales using both a data assimilation scheme and a novel data-driven modelling method. We expect that the increasing availability of high-resolution remotely sensed soil moisture data and developments in data storage and computational environments will lead to an increase in the application of data assimilation schemes and other data-driven modelling methods in operational water resources management. Last, we discussed several applications to integrate the research findings in operational water management. Finally, we challenge both researchers and water managers to continue to invest in these approaches, as the call for optimized, consistent, and sustainable water management becomes increasingly important in the future.

AB - Water in the unsaturated part of the soil subsurface is referred to as soil moisture. Soil moisture and related processes are often considered as key components of the hydrological cycle, affecting hydrological, meteorological, biological, and biogeochemical processes. The dry period in the summer of the year 2018 highlighted the necessity of understanding soil moisture dynamics and integrating related information in water management approaches. However, the current application of soil moisture information in operational water resources management is limited. One of the reasons is the lack of measurement data. Recently, the increasing availability of high-resolution soil moisture data retrieved using remote sensing methods has led to new possibilities for utilization in water management.The research aim of this work was to show the use of high-resolution soil moisture information for operational water resources management. First, the needs of water managers were identified. We recommend that decision-makers should focus on the development of structured methodologies for integrating both evidence-based and experiential information in decision support systems. In addition, we focused on retrieving accurate soil moisture information on both regional and local spatial scales using both a data assimilation scheme and a novel data-driven modelling method. We expect that the increasing availability of high-resolution remotely sensed soil moisture data and developments in data storage and computational environments will lead to an increase in the application of data assimilation schemes and other data-driven modelling methods in operational water resources management. Last, we discussed several applications to integrate the research findings in operational water management. Finally, we challenge both researchers and water managers to continue to invest in these approaches, as the call for optimized, consistent, and sustainable water management becomes increasingly important in the future.

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