Your search found 7 records
1 Otazo-Sanchez, E. M.; Navarro-Frometa, A. E.; Singh, V. P. (Eds.) 2020. Water availability and management in Mexico. Cham, Switzerland: Springer. 516p. [doi: https://doi.org/10.1007/978-3-030-24962-5]
Water availability ; Water management ; Water resources ; Water allocation ; Water demand ; Water use ; Water supply ; Water security ; Drinking water ; Water quality ; Runoff ; Forecasting ; Water governance ; Climate change ; Water pollution ; Public health ; Health hazards ; Sanitation ; Aquaculture ; Wastewater irrigation ; Drainage systems ; Groundwater extraction ; Oil and gas industries ; Hydraulic fracturing ; Environmental impact ; Hydrology ; Ecology ; Wetlands ; Data mining ; Legal aspects ; Urban areas ; River basins ; Lakes ; Valleys ; Models ; Uncertainty ; Case studies / Mexico / Candelaria River / Nexapa River / Tulancingo Aquifer / Puebla Aquifer / Mezquital Valley / Lake Cajititlan / Hidalgo / Zacatecas / Nuevo Leon / Sinaloa / San Luis Potosi / Cuernavaca
(Location: IWMI HQ Call no: e-copy SF Record No: H049347)
2 Jacobs-Mata, Inga; Mukuyu, Patience. 2020. Knowledge review and agenda setting for future investments in research on water governance in South Africa. Pretoria, South Africa: Water Research Commission (WRC). 43p. (WRC Report No. 2911/1/20)
Water governance ; Research and development ; Investment ; Knowledge level ; Assessment ; Integrated management ; Water resources ; Water management ; Water policy ; Water law ; Data mining ; Trends ; Research projects ; Funding ; Stakeholders ; Institutions ; Government / South Africa
(Location: IWMI HQ Call no: e-copy only Record No: H049797)
http://wrcwebsite.azurewebsites.net/wp-content/uploads/mdocs/2911_final.pdf
https://vlibrary.iwmi.org/pdf/H049797.pdf
https://vlibrary.iwmi.org/pdf/H049797.pdf
(1.06 MB) (1.06 MB)
3 Gaffoor, Z.; Pietersen, K.; Jovanovic, N.; Bagula, A.; Kanyerere, T. 2020. Big data analytics and its role to support groundwater management in the Southern African development community. Water, 12(10):2796. (Special issue: The Application of Artificial Intelligent in Hydrology) [doi: https://doi.org/10.3390/w12102796]
Groundwater management ; Data analysis ; SADC countries ; International waters ; Aquifers ; Data mining ; Machine learning ; Remote sensing ; Monitoring ; Technology ; Hydrological data ; Water levels ; Water storage ; Uncertainty ; Precipitation ; Social media ; Models / Southern Africa
(Location: IWMI HQ Call no: e-copy only Record No: H050040)
(1.58 MB) (1.58 MB)
Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of heterogeneous data through advanced analytics to drive information discovery. This paper aims to highlight the potential role BDA can play to improve groundwater management in the Southern African Development Community (SADC) region in Africa. Through a review of the literature, this paper defines the concepts of big data, big data sources in groundwater, big data analytics, big data platforms and framework and how they can be used to support groundwater management in the SADC region. BDA may support groundwater management in SADC region by filling in data gaps and transforming these data into useful information. In recent times, machine learning and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big data from collection to information delivery requires critical application of selected tools, techniques and methods. Hence, in this paper we present a conceptual framework that can be used to manage the implementation of BDA in a groundwater management context. Then, we highlight challenges limiting the application of BDA which included technological constraints and institutional barriers. In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA exists to be used in groundwater sciences thereby providing the basis to further explore data-driven sciences in groundwater management.
4 Jacobs-Mata, Inga; Mukuyu, Patience; Dini, J. 2021. A review of trends in scientific coverage of water governance in South Africa and what this means for agenda-setting of public investment in water governance R&D. Water SA, 47(1):10-23. [doi: https://doi.org/10.17159/wsa/2021.v47.i1.9441]
Water governance ; Public investment ; Research and development ; Bibliometric analysis ; Integrated management ; Water resources ; Water management ; Water policy ; Stakeholders ; Research projects ; Funding ; Trends ; Government ; Political aspects ; Institutions ; Data mining / South Africa
(Location: IWMI HQ Call no: e-copy only Record No: H050260)
(1.25 MB) (1.25 MB)
A review of global trends in water governance reveals a paradigm dominated by political and institutional change which becomes increasingly aligned with global shifts towards sustainability and also a rapid decline in the hydraulic mission. Closely aligned to these trends, but distinct in its own trajectory, South Africa’s water governance dynamics have evolved through a period of considerable socio-political change marked by inequitable resource allocation and water scarcity. This paper presents the results of a review of water governance research and development (R&D) trends in South Africa, aimed at informing the national funding agency – the Water Research Commission (WRC) – in its agenda-setting process for future water governance research. Through a bibliometric analysis, a data-mining exercise, and stakeholder consultations, this paper distils four key areas of focus for the future of water governance research in South Africa: (i) that future water governance research needs to be more needs-based, solution-oriented and embedded within real-life contexts; (ii) the need for a paradigm shift in water governance research to a constructive, adaptive and rapid response research agenda in an environment of increasing change and uncertainty; (iii) the need for the enabling environment to be strengthened, including acknowledgement of the role of individuals as agents of change, and the role of WRC in establishing a community of practice for water governance experts that can respond to issues with agility; and (iv) a consolidation of fragmented project-based knowledge to a programmatic approach that builds the pipeline of expertise in the water governance R&D domain.
5 Tague, C.; Frew, J. 2021. Visualization and ecohydrologic models: opening the box. Hydrological Processes, 35(1):e13991. [doi: https://doi.org/10.1002/hyp.13991]
Hydrology ; Models ; Data mining ; Machine learning ; Techniques ; Vegetation ; Evapotranspiration ; Stream flow ; Communication
(Location: IWMI HQ Call no: e-copy only Record No: H050200)
(15.10 MB)
Earth system models synthesize the science of interactions amongst multiple biophysical and, increasingly, human processes across a wide range of scales. Ecohydrologic models are a subset of earth system models that focus particularly on the complex interactions between ecosystem processes and the storage and flux of water. Ecohydrologic models often focus at scales where direct observations occur: plots, hillslopes, streams, and watersheds, as well as where land and resource management decisions are implemented. These models complement field-based and data-driven science by combining theory, empirical relationships derived from observation and new data to create virtual laboratories. Ecohydrologic models are tools that managers can use to ask “what if” questions and domain scientists can use to explore the implications of new theory or measurements. Recent decades have seen substantial advances in ecohydrologic models, building on both new domain science and advances in software engineering and data availability. The increasing sophistication of ecohydrologic models however, presents a barrier to their widespread use and credibility. Their complexity, often encoding 100s of relationships, means that they are effectively “black boxes,” at least for most users, sometimes even to the teams of researchers that contribute to their design. This opacity complicates the interpretation of model results. For models to effectively advance our understanding of how plants and water interact, we must improve how we visualize not only model outputs, but also the underlying theories that are encoded within the models. In this paper, we outline a framework for increasing the usefulness of ecohydrologic models through better visualization. We outline four complementary approaches, ranging from simple best practices that leverage existing technologies, to ideas that would engage novel software engineering and cutting edge human–computer interface design. Our goal is to open the ecohydrologic model black box in ways that will engage multiple audiences, from novices to model developers, and support learning, new discovery, and environmental problem solving.
6 Garibay, V. M.; Gitau, M. W.; Kongo, V.; Kisekka, J.; Moriasi, D. 2022. Comparative evaluation of water resource data policy inventories towards the improvement of East African climate and water data infrastructure. Water Resources Management, 36(11):4019-4038. [doi: https://doi.org/10.1007/s11269-022-03231-z]
Water resources ; Databases ; Policies ; Comparative analysis ; Data mining ; Strategies ; Infrastructure ; Water quality ; Meteorological factors / East Africa / Kenya / United Republic of Tanzania / Uganda / Rwanda / Burundi / Ethiopia / South Africa / Canada / United States of America / Germany
(Location: IWMI HQ Call no: e-copy only Record No: H051364)
https://link.springer.com/content/pdf/10.1007/s11269-022-03231-z.pdf
https://vlibrary.iwmi.org/pdf/H051364.pdf
https://vlibrary.iwmi.org/pdf/H051364.pdf
(1.66 MB) (1.66 MB)
The recognized challenge of freely accessing climate and water data in East Africa poses a problem in undertaking relevant analytical studies and making informed water resources management decisions in the region. This study seeks to understand the defining characteristics of policies and distribution infrastructure, in the context of meteorological, water quantity, and water quality data, that determine whether or not a user will be able to freely and readily access existing data. An analysis was developed to quantify the information contained in legislation, official documents and websites, and similar textual resources from the study region and elsewhere to establish commonalities, potential trends, and patterns in the documentation behind data streams culminating successfully in a portal or database accessible by the public. A quantitative analysis was applied to discern overall patterns in what constitutes effective policy and to diagnose where there may be impediments in the path between data collection and its application. Generally, the foundational elements present in the documentation pertaining to most accessible data streams represented are: (1) known organization in charge of that data type; (2) known location where this data would be stored; (3) defined data collection format; and (4) commitment to a plan for making data available to potential users. Examination of overlap between elements absent in unsuccessful data streams and present in successful data streams suggests that those without a documented commitment to making data available online rarely result in a functioning, accessible portal and vice versa. Amongst other findings, this knowledge has the potential to contribute towards the development and refinement of policies so that more emphasis is placed on openness and access, leading to informed decision-making and management of water resources.
7 Jie, Y.; Xiaoshu, C.; Jun, Y.; Zhewen, K.; Jianxia, C.; Yimin, W. 2024. Geographical big data and data mining: a new opportunity for “water-energy-food” nexus analysis. Journal of Geographical Sciences, 34(2):203-228. [doi: https://doi.org/10.1007/s11442-024-2202-6]
Food security ; Water resources ; Water quality ; Energy ; Climate change ; Nexus approaches ; Big data ; Data mining ; Interactions ; Sustainability ; Sustainable development ; Urbanization ; Land use ; Socioeconomic aspects ; Policies ; Artificial intelligence ; Machine learning ; Models ; Decision-support systems
(Location: IWMI HQ Call no: e-copy only Record No: H052609)
(2.54 MB)
Since the Bonn 2011 conference, the “water-energy-food” (WEF) nexus has aroused global concern to promote sustainable development. The WEF nexus is a complex, dynamic, and open system containing interrelated and interdependent elements. However, the nexus studies have mainly focused on natural elements based on massive earth observation data. Human elements (e.g., society, economy, politics, culture) are described insufficiently, because traditional earth observation technologies cannot effectively perceive socioeconomic characteristics, especially human feelings, emotions, and experiences. Thus, it is difficult to simulate the complex WEF nexus. With the development of earth observation sensor technologies and human activity perception methods, geographical big data covering both human activities and natural elements offers a new opportunity for in-depth WEF nexus analysis. This study proposes a five-step framework by leveraging geographical big data mining to dig for the hidden value in the data of various natural and human elements. This framework can enable a thorough and comprehensive analysis of the WEF nexus. Some application examples of the framework, major challenges, and possible solutions are discussed. Geographical big data mining is a promising approach to enhance the analysis of the WEF nexus, strengthen the coordinated management of resources and sectors, and facilitate the progress toward sustainable development.
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