An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years to create valid end-to-end simulations as different models need to be configured in consistent ways and generate data that is usable by other models. MINT is a novel framework for model integration that captures extensive knowledge about models and data and aims to automatically compose them together. MINT guides a user to pose a well-formed modeling question, select and configure appropriate models, find and prepare appropriate datasets, compose data and models into end-to-end workflows, run the simulations, and visualize the results. MINT currently includes hydrology, agriculture, and socioeconomic models.Office of the VP for Researc

Approaches to analyse and model changes in impacts:reply to discussions of “How to improve attribution of changes in drought and flood impacts”^*

We thank the authors, Brunella Bonaccorso and Karsten Arnbjerg-Nielsen for their constructive contributions to the discussion about the attribution of changes in drought and flood impacts. We appreciate that they support our opinion, but in particular their additional new ideas on how to better understand changes in impacts. It is great that they challenge us to think a step further on how to foster the collection of long time series of data and how to use these to model and project changes. Here, we elaborate on the possibility to collect time series of data on hazard, exposure, vulnerability and impacts and how these could be used to improve e.g. socio-hydrological models for the development of future risk scenarios.</p

Introduction to Remote Sensing: Science for Society

Science for Society: Extending Earth Science Research Results into Decision Support Tool

Dynamic Bayesian Networks as a Decision Support Tool for assessing Climate Change impacts on highly stressed groundwater systems

Bayesian Networks (BNs) are powerful tools for assessing and predicting consequences of water management scenarios and uncertain drivers like climate change, integrating available scientific knowledge with the interests of the multiple stakeholders. However, among their major limitations, the non-transient treatment of the cause-effect relationship stands out. A Decision Support System (DSS) based on Dynamic Bayesian Networks (DBNs) is proposed here aimed to palliate that limitation through time slicing technique. The DSS comprises several classes (Object-Oriented BN networks), especially designed for future 5 years length time steps (time slices), covering a total control period of 30 years (2070-2100). The DSS has been developed for assessing impacts generated by different Climate Change (CC) scenarios (generated from several Regional Climatic Models (RCMs) under two emission scenarios, A1B and A2) in an aquifer system (Serral-Salinas) affected by intensive groundwater use over the last 30 years. A calibrated continuous water balance model was used to generate hydrological CC scenarios, and then a groundwater flow model (MODFLOW) was employed in order to analyze the aquifer behavior under CC conditions. Results obtained from both models were used as input for the DSS, considering rainfall, aquifer recharge, variation of piezometric levels and temporal evolution of aquifer storage as the main hydrological components of the aquifer system. Results show the evolution of the aquifer storage for each future time step under different climate change conditions and under controlled water management interventions. This type of applications would allow establishing potential adaptation strategies for aquifer systems as the CC comes into effectThis study has been partially supported by the European Community 7th Framework Project GENESIS (226536) on groundwater systems and from the subprogram Juan de la Cierva (2010) of the Spanish Ministry of Science and Innovation as well as from the Plan Nacional I+D+i 2008-2011 of the Spanish Ministry of Science and Innovation (Subprojects CGL2009-13238-C02-01 and CGL2009-13238-C02-02). T. Finally, the authors want to thank the Segura River Basin Agency (Confederacion Hidrografica del Segura) for the data and information facilitated, and to all the stakeholders who have collaborated in this research.Molina, JL.; Pulido Velázquez, D.; García-Arostegui, J.; Pulido-Velazquez, M. (2013). Dynamic Bayesian Networks as a Decision Support Tool for assessing Climate Change impacts on highly stressed groundwater systems. Journal of Hydrology. 479:113-129. https://doi.org/10.1016/j.jhydrol.2012.11.038S11312947

Decision support systems for large dam planning and operation in Africa

Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects

A Typology for Characterizing Human Action in MultiSector Dynamics Models

The role of individual and collective human action is increasingly recognized as a prominent and arguably paramount determinant in shaping the behavior, trajectory, and vulnerability of multisector systems. This human influence operates at multiple scales: from short-term (hourly to daily) to long-term (annually to centennial) timescales, and from the local to the global, pushing systems toward either desirable or undesirable outcomes. However, the effort to represent human systems in multisector models has been fragmented across philosophical, methodological, and disciplinary lines. To cohere insights across diverse modeling approaches, we present a new typology for classifying how human actors are represented in the broad suite of coupled human-natural system models that are applied in MultiSector Dynamics (MSD) research. The typology conceptualizes a “sector” as a system-of-systems that includes a diverse group of human actors, defined across individual to collective social levels, involved in governing, provisioning, and utilizing products, goods, or services toward some human end. We trace the salient features of modeled representations of human systems by organizing the typology around two key questions: (a) Who are the actors in MSD systems and what are their actions? (b) How and for what purpose are these actors and actions operationalized in a computational model? We use this typology to critically examine existing models and chart the frontier of human systems modeling for MSD research