ORCID Profile
0000-0002-7885-8248
Current Organisation
University of Aberdeen
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Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-11027
Abstract: & & Over recent decades major advances have been made in global hydrological modelling underpinned by progress in high-resolution data availability, as well as in computational and data storage capabilities. These advances have provided hydrologists with opportunities to develop high-resolution large-scale hydrological models (LHMs) designed to represent and study the global hydrological cycle. However, with the aim of answering relevant questions for water resources policy and management, LHMs have recently been used in a number of regional applications. This has been enabled by their increasing spatial resolution which makes it possible to zoom-in on specific regions, essentially removing the barriers between global and regional models.& & & & Notwithstanding their growing sophistication, the current generation of LHMs still fall short in their ability to represent dynamic trade-offs in the water-food-energy-environment nexus, and water competition between upstream and downstream users. These limitations hinder the ability of LHMs to provide reliable insights at any scale other than the global, leaving the task of incorporating human water management activities within these models as one of the grand challenges for the hydrologic research community.& & & & Catchment-scale water management models (CWMMs) adopt a holistic systems approach to comprehensively address water availability, use, infrastructure, and policy aspects within multi-sectoral water allocation. The coupling of these models with LHMs can enhance their representation of human interventions in the natural water cycle (e.g., management of reservoirs, intra- and inter-basin water transfers) and improve the accuracy of water demand estimations such as irrigation requirements by including irrigation schemes. The inclusion of this local knowledge into LHMs& #8217 modelling process can, therefore, increase their capacity to support rigorous nexus analyses to inform water policy and management decisions.& & & & This work represents the preliminary outcome of a project with the overall research objective of developing and providing a & #8220 roof-of-concept& #8221 to explore and design an approach for integrating CWMMs with LHMs, and to assess its potential and limitations to enhance the quality of information LHMs provide at regional scale. This work will present the initial efforts to compare the outcomes of LHMs from the Inter-Sectoral Impact Model Intercomparison Project and the CWMM AQUATOOL in the Ebro River basin, a heavily managed catchment in Spain with multiple competing water uses. This comparison will provide an estimate of the capacity of LHMs to provide useful information for decision making, as well as to identify knowledge gaps to be filled with management models.& &
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-14991
Abstract: Future water security will be determined by climate change along with socio-economic changes, driving water availability, water demands and catchment conditions. Over recent decades, hydrological models have evolved to incorporate the effect of anthropic activities that allow them to explore the main challenges and opportunities regarding global water security. These advances have been underpinned by progress in high-resolution and large-scale data availability, as well as in computational and data storage capabilities. Hydrologists are currently capable of developing high-resolution large-scale hydrological models designed to represent and study the global hydrological cycle, and even to zoom-in on specific regions essentially removing the barriers between global and regional models. However, the use of these modelling approaches is often seen with suspicion by end-users, be it regional water managers or water users, who may consider that their personal knowledge and understanding of the catchments where they carry out their activities is disregarded in favour of novel technologies. Indeed, despite their growing sophistication, the current generation of LHMs is not yet exempt of limitations in their ability to represent dynamic trade-offs in the water-food-energy-environment nexus, and water competition between upstream and downstream users in complex water resources systems. These limitations hinder the ability of LHMs to provide reliable insights at the regional or local levels, leaving the task of incorporating human water management activities within these models as one of the grand challenges for the hydrologic research community. The inclusion of this local knowledge into LHMs& #8217 modelling process can, therefore, increase their capacity to support rigorous nexus analyses to inform water policy and management decisions. Unfortunately, the access to these data may be limited by several inconveniences such as overprotective water authorities, language access barriers, or simply not existing at all. This work explores to what extent the inclusion of local knowledge can improve the performance of globally formulated models as well as their reliability to support decision making on the ground. We discuss what type of data might be more relevant and what should be the priorities in data acquisition to maximise the output of modellers efforts. To demonstrate this, we built a CWatM model of the Ebro River catchment in Spain using large-scale datasets to later substitute or enhance such datasets with data obtained from local and regional specific datasets available from local authorities and water users. The additional data/enhancements were included in separate and cumulative steps. The model improvements were assessed comparing the model results against gauged flows, reservoir storage, water demand and supply, and the system& #8217 s drought indicator. The findings of this study will assist in the transition of globally formulated models to being applied locally by identifying the priorities in data gathering and advances in modelling capabilities, ensuring that they provide reliable outputs to inform decision making.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for David Haro-Monteagudo.