Spatial and temporal processes of the hydrologic cycle are simulated commonly using mathematical models, so as to gain insights into the system and to facilitate information of management decisions. Advancements in spatial and temporal data acquisition through remote sensing and other environmental monitoring technologies, together with exponential increase in computing power, pose several opportunities as well as challenges for modelling and simulation of hydrologic and hydrogeological systems for water resources management.
Significant advances over the last decade have revolutionised the way in which mathematical tools are used for model parameter estimation and optimisation, data assimilation, and coupled simulation-optimisation for water resources management. Data-driven approaches are becoming increasingly popular in hydrological modelling and management, with a plethora of applications such as process simulators, for uncertainty quantification or as fast-running surrogate models for physically based hydrologic models. Global optimisation algorithms are also being implemented increasingly for a wide array of purposes, including parameter estimation, uncertainty quantification, and identification of solutions for multi-objective water resources management problems. This Research Topic is aimed at featuring a cross section of the latest developments in these research domains.
This Research Topic invites submissions from studies that use advanced computational tools for hydrologic and hydrogeological modelling and water resources optimisation and management. Topics of particular interest include, but are not limited to:
Local and global optimisation approaches for model calibration and parameter estimation;
Data assimilation for hydrological models;
Data-driven models aided by Artificial Intelligence/Machine Learning;
Simulation-optimisation models for surface water and groundwater management;
Uncertainty quantification of model predictions;
Optimisation under prediction uncertainty; and,
Data-worth analysis and optimisation of monitoring networks.
Spatial and temporal processes of the hydrologic cycle are simulated commonly using mathematical models, so as to gain insights into the system and to facilitate information of management decisions. Advancements in spatial and temporal data acquisition through remote sensing and other environmental monitoring technologies, together with exponential increase in computing power, pose several opportunities as well as challenges for modelling and simulation of hydrologic and hydrogeological systems for water resources management.
Significant advances over the last decade have revolutionised the way in which mathematical tools are used for model parameter estimation and optimisation, data assimilation, and coupled simulation-optimisation for water resources management. Data-driven approaches are becoming increasingly popular in hydrological modelling and management, with a plethora of applications such as process simulators, for uncertainty quantification or as fast-running surrogate models for physically based hydrologic models. Global optimisation algorithms are also being implemented increasingly for a wide array of purposes, including parameter estimation, uncertainty quantification, and identification of solutions for multi-objective water resources management problems. This Research Topic is aimed at featuring a cross section of the latest developments in these research domains.
This Research Topic invites submissions from studies that use advanced computational tools for hydrologic and hydrogeological modelling and water resources optimisation and management. Topics of particular interest include, but are not limited to:
Local and global optimisation approaches for model calibration and parameter estimation;
Data assimilation for hydrological models;
Data-driven models aided by Artificial Intelligence/Machine Learning;
Simulation-optimisation models for surface water and groundwater management;
Uncertainty quantification of model predictions;
Optimisation under prediction uncertainty; and,
Data-worth analysis and optimisation of monitoring networks.