Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad; Link, Robert; Huang, Zhongwei; Liu, Lu; Feng, Leyang

doi:10.5334/jors.197

Cited by 17 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Historical water supplies are calculated by a global hydrologic model, Xanthos (Li et al, , Li et al, ), for the 1975–2010 period and are held constant at individual basin level values from 2015 onward. Xanthos calculates basin‐level accessible water for historical periods, using climate reanalysis temperature and precipitation data from WATCH (Weedon et al, ) to simulate monthly gridded total runoff products.…”

Section: Methodsmentioning

confidence: 99%

Water Sector Assumptions for the Shared Socioeconomic Pathways in an Integrated Modeling Framework

Graham

Davies

Hejazi

et al. 2018

Water Resources Research

Self Cite

View full text Add to dashboard Cite

The Shared Socioeconomic Pathways (SSPs) were developed without explicit assumptions for the future of the water sector; therefore, projections of future water demands based on the SSPs often lack a treatment of water technology assumptions that is consistent with the SSP storylines. This study has developed a set of qualitative and quantitative assumptions for future water sector technological advancements in the agricultural, electricity, manufacturing, and municipal sectors within the SSPs and then applied the resulting scenarios to an integrated assessment model to permit analysis of future water demand in a water‐constrained world. These scenarios are then compared to another set that excludes the adoption of water‐efficient technologies. Water demand impacts of individual SSP assumption categories are analyzed to determine scenario‐by‐scenario changes. By 2100, global annual water demands range from 3,560 to 6,600 km3. The results show that (1) technological change in the water sector can act to reduce water demand in a water limited world by up to 32% in 2100 in the SSP scenarios, (2) the most sustainable scenario produces end‐of‐century water withdrawals lower than 2010 values, (3) low‐income regions will likely be one of the largest drivers of future water demands and exhibit the greatest sensitivity to highly‐efficient water technologies, and (4) nonwater sector SSP assumptions have significant and differing impacts on demands across SSP scenarios that act to alter global water demands.

show abstract

Section: Methodsmentioning

confidence: 99%

Water Sector Assumptions for the Shared Socioeconomic Pathways in an Integrated Modeling Framework

Graham

Davies

Hejazi

et al. 2018

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…The scales determine the spatial or temporal scope within the modeling work. Up to a certain extent, temporal and spatial scales are often correlated Li et al (2018). For instance, it is unlikely for a model representing CLEW dynamics in an urban district to have a ten-year time scale because the uncertainties related to boundary conditions (what happen at the interface of the modeled system with the outside) and future projections would become extremely high.…”

Section: Modeling Scale and Resolutionmentioning

confidence: 99%

Climate-Land-Energy-Water Nexus Models Across Scales: Progress, Gaps and Best Accessibility Practices

Vinca

Riahi

Rowe

et al. 2021

Front. Environ. Sci.

View full text Add to dashboard Cite

Approaches that integrate feedback between climate, land, energy and water (CLEW) have progressed significantly in scope and complexity. The so-called nexus approaches have shown their usefulness in assessing strategies to achieve the Sustainable Development Goals in the contexts of increasing demands, resource scarcity, and climate change. However, most nexus analyses omit some important inter-linkages that could actually be addressed. The omissions often stem from technical and practical considerations, but also from limited dissemination of new open-source frameworks incorporating recent advances. We review and present a set of models that can meet the needs of decision makers for analysis tools capable of addressing a broad range of nexus questions. Particular attention is given to model accessibility, usability and community support. The other objective of this review is to discuss research gaps, and critical needs and opportunities for further model development from a scientific viewpoint. We explore at different scales where and why some nexus interactions are most relevant. We find that both very small scale and global models tend to neglect some CLEW interactions, but for different reasons. The former rarely include climate impacts, which are often marginal at the local level, while the latter mostly lack some aspects because of the complexity of large full CLEW systems at the global level.

show abstract

“…A global-scale study related to water scarcity and its regional socioeconomic implications can serve as a testbed for the data fusion development. For example, the effort to collect and harmonize data to support global-scale water scarcity studies which have been conducted using GCAM (Global Change Analysis Model) and the suite of models that provide data to, and downscale from, GCAM [1][2][3][4][5] have exposed the value of this type of analysis and have invoked a flurry of community data requests for those seeking to conduct their own analysis. For global-scale water models, large data sources range from observations or simulations such as, population (density), country/region distribution, water basin and geopolitical region distribution, irrigated areas, livestock density, land area, soil moisture, average temperature, precipitation, and sectoral water withdrawals.…”

Section: Narrativementioning

confidence: 99%

A Self-Evolution Data Fusion Platform for Large-Scale Water Models

2021

Self Cite

View full text Add to dashboard Cite

Data acquisition and assimilation enabled by machine learning (ML), artificial intelligence (AI), and advanced methods including experimental/network design/optimization, unsupervised learning (deep learning), leveraging advanced hardware (e.g., edge computing). This development is to enable solving the science questions regarding the human and climatic factors that interact with and drive global water scarcity. Science Challenge:Data-model integration in the context of complex high dimensional hierarchical nonlinear structure. A self-evolution, comprehensive data fusion platform; building data fusion approaches from the ground up into a web-based, globally accessible platform. Rationale:AI's superpower is fueled by data. The successes of applying AI for building integrative predictive models rely on data accessibility, adequacy, and quality, which are generally promoted for data managed in adherence to FAIR (findable, accessible, interoperable, and reusable) guiding principles. Even considering the ability to employ FAIR data, it is still non-trivial to integrate data for scientific discovery (data fusion) given the usually high-dimensional and hierarchical nonlinear cross-dependence structure nature of some domains (e.g., complex large-scale water modeling systems). Recent advances in computational resources, observation and monitoring methods, and sensing tools, bring unprecedented opportunities in data-model integration or data fusion but introduce new challenges that require a systematic approach to provide heuristic value.

show abstract

Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

Cited by 17 publications

References 15 publications

Water Sector Assumptions for the Shared Socioeconomic Pathways in an Integrated Modeling Framework

Water Sector Assumptions for the Shared Socioeconomic Pathways in an Integrated Modeling Framework

Climate-Land-Energy-Water Nexus Models Across Scales: Progress, Gaps and Best Accessibility Practices

A Self-Evolution Data Fusion Platform for Large-Scale Water Models

Contact Info

Product

Resources

About