We explore how to address the challenges of adaptation of water resources systems under changing conditions by supporting flexible, resilient and low-regret solutions, coupled with on-going monitoring and evaluation. This will require improved understanding of the linkages between biophysical and social aspects in order to better anticipate the possible future co-evolution of water systems and society. We also present a call to enhance the dialogue and foster the actions of governments, the international scientific community, research funding agencies and additional stakeholders in order to develop effective solutions to support water resources systems adaptation. Finally, we call the scientific community to a renewed and unified effort to deliver an innovative message to stakeholders. Water science is essential to resolve the water crisis, but the effectiveness of solutions depends inter alia on the capability of scientists to deliver a new, coherent and technical vision for the future development of water systems
This paper explores Natural Language Generation techniques for online river information tailoring. To solve the problem of unknown users, we propose 'latent models', which relate typical visitors to river web pages, river data types, and river related activities. A hierarchy is used to integrate domain knowledge and latent user knowledge, and serves as the search space for content selection, which triggers user-oriented selection rules when they visit a page. Initial feedback received from user groups indicates that the latent models deserve further research efforts.
<p class="paragraph">Scotland is increasingly vulnerable to periods of dry weather, impacting water users and the natural environment. In 2022, large parts of Scotland have experienced water scarcity, resulting in Scotland Environmental Protection Act (SEPA) suspending water abstractions for abstraction licence holders in some Scottish catchments. To understand and manage these water scarcity events in Scotland, we need to monitor and model the drought processes. This research is a part of a Scottish Government funded project &#8216;Understanding the vulnerabilities of Scotland&#8217;s water resources to drought&#8217; which has been co-constructed with a range of national level stakeholders and aims to understand what the specific impacts of droughts are and what are the vulnerabilities that may apply to Scotland under future change. This includes the understanding of the spatial variability and characteristics of future hydrological drought events and short-term forecasting of drought duration to inform adaptive catchment management, while considering water resources requirements of different user sectors. As a first step towards constructing a national short-term drought forecasting framework, w<span xml:lang="EN-GB" data-contrast="none">e</span><span data-ccp-charstyle="normaltextrun"> have</span> reviewed the <span data-ccp-charstyle="normaltextrun">state-of-the art </span><span data-ccp-charstyle="normaltextrun">hydrological modelling approaches</span> <span data-ccp-charstyle="normaltextrun">currently</span> <span data-ccp-charstyle="normaltextrun">applied in</span><span data-ccp-charstyle="normaltextrun"> the UK</span>. <span data-ccp-charstyle="normaltextrun">Our review suggests</span> a lumped conceptual model, <span data-ccp-charstyle="normaltextrun">GR</span>6J and <span data-ccp-charstyle="normaltextrun">a </span><span data-ccp-charstyle="normaltextrun">distributed hydrological response unit-based model, </span><span data-ccp-charstyle="normaltextrun">HYPE,</span> <span data-ccp-charstyle="normaltextrun">are </span><span data-ccp-charstyle="normaltextrun">the most appropriate hydrological models for</span><span data-ccp-charstyle="normaltextrun"> both</span><span data-ccp-charstyle="normaltextrun"> simulating </span>and<span data-ccp-charstyle="normaltextrun"> short-term</span> forecasting of <span data-ccp-charstyle="normaltextrun">droughts</span>, <span data-ccp-charstyle="normaltextrun">based on </span><span data-ccp-charstyle="normaltextrun">the following </span><span data-ccp-charstyle="normaltextrun">criteri</span>a<span data-ccp-charstyle="normaltextrun">: </span><span data-ccp-charstyle="normaltextrun">openly</span> <span data-ccp-charstyle="normaltextrun">available</span> model code, <span data-ccp-charstyle="normaltextrun">proven ability at </span><span data-ccp-charstyle="normaltextrun">simulating and forecasting low flows</span><span data-ccp-charstyle="normaltextrun">, </span><span data-ccp-charstyle="normaltextrun">and widely</span> used and supported <span data-ccp-charstyle="normaltextrun">m</span>odel. <span data-ccp-charstyle="normaltextrun">In </span>next steps, <span data-ccp-charstyle="normaltextrun">w</span>e <span data-ccp-charstyle="normaltextrun">will </span><span data-ccp-charstyle="normaltextrun">design</span> <span data-ccp-charstyle="normaltextrun">a common modelling framework </span><span data-ccp-charstyle="normaltextrun">for drought simulation and forecasti</span><span data-ccp-charstyle="normaltextrun">ng </span><span data-ccp-charstyle="normaltextrun">in </span>Scotland. <span data-ccp-charstyle="normaltextrun">Using both H</span><span data-ccp-charstyle="normaltextrun">YPE and GR6J, </span><span data-ccp-charstyle="normaltextrun">w</span>e <span data-ccp-charstyle="normaltextrun">will </span><span data-ccp-charstyle="normaltextrun">set up and test</span> both<span data-ccp-charstyle="normaltextrun"> models</span> in <span data-ccp-charstyle="normaltextrun">a</span> <span data-ccp-charstyle="normaltextrun">medium size </span><span data-ccp-charstyle="normaltextrun">long-term monitoring </span><span data-ccp-charstyle="normaltextrun">test catchment in</span> Tarland <span data-ccp-charstyle="normaltextrun">in northeast Scotland (~70km</span><span xml:lang="EN-GB" data-contrast="none"><sup>2</sup></span><span data-ccp-charstyle="normaltextrun">) where we&#8239;have good process understanding and recent </span>hydro climatological datasets<span data-ccp-charstyle="normaltextrun">.</span> <span xml:lang="EN-GB" data-contrast="auto">C</span><span data-ccp-charstyle="normaltextrun">omparison of </span><span xml:lang="EN-GB" data-contrast="auto">the model performances</span> of HYPE and GR6J will guide us to take a decision on which model to move forward with for upscaling in Scotland. Machi<span xml:lang="EN-GB" data-contrast="none"><span data-ccp-charstyle="normaltextrun">ne learning approaches </span><span data-ccp-charstyle="normaltextrun">for low-flow forecasting using </span><span data-ccp-charstyle="normaltextrun">long-short-memory networks will also be explored in </span><span data-ccp-charstyle="normaltextrun">develop</span>ing</span><span data-ccp-charstyle="normaltextrun"> a multi-model </span><span data-ccp-charstyle="normaltextrun">drought </span>forecasting ensemble. <span data-ccp-props="{">&#160;</span></p> <p class="paragraph"><span data-ccp-props="{">Keywords: Drought, water scarcity, modelling, HYPE, GR6J, forecasting&#160;</span></p>
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