Advances in global hydrology–crop modelling to support the UN’s Sustainable Development Goals in South Asia

Biemans, Hester; Siderius, Christian

doi:10.1016/j.cosust.2019.10.005

Cited by 12 publications

(10 citation statements)

References 72 publications

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“…Below are a number of concepts that illustrate the definition of knowledge and related matters: Knowledge: Knowledge is the product of data, intuition, and experience [61]. As a result, knowledge is information that has been organized, digested, and structured in such a way that it may be applied [36]. Organizational routines, rules, processes, documents, and practices involve a mix of contextual information, values, expertise, new infor mation, and new expertise that exist in knowing minds, organizational routines, rules, processes, documents, and practices [4].…”

Section: Electronic Information Burdenmentioning

confidence: 99%

Impact of Technological Burden on Knowledge Management Functions in Jordanian Industrial Companies

Al-Ani¹,

Jawad²,

Abdelal³

2022

UHD J SCI TECH

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The goal of this study is to see how electronic information overload affects knowledge management functions in Jordanian businesses. All Jordanian industrial enterprises registered on the Amman Stock Exchange were included in the study’s sample. Three hundred and seventy-three people were chosen at random from a simple random sample of 30% of the study population of 1242 senior and intermediate managers in the research community. Following the retrieval of the surveys, 206 questionnaires were found to be valid for analysis. It was used to do descriptive and heuristic statistical procedures, like simple and multiple regression analysis. The SPSS.16 application was used to do this. The study ends with the following findings: Electronic information overload (technological overload) has a statistically significant influence on knowledge management functions (acquisition, generation, transmission, exchange, and application) in Jordanian industrial companies. This work made a number of recommendations as a result of its findings, including: Adopting an organizational aspect that suits the nature of the tasks that industrial companies in Jordan perform, as well as providing technical capabilities to reduce the electronic information overload that these companies face while performing their tasks.

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Section: Electronic Information Burdenmentioning

confidence: 99%

Impact of Technological Burden on Knowledge Management Functions in Jordanian Industrial Companies

Al-Ani¹,

Jawad²,

Abdelal³

2022

UHD J SCI TECH

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“…These models have been applied to many questions of both basic and applied hydrology, such as climate change and other anthropogenic impacts on global river systems (Bosmans et al, 2017;Döll et al, 2012;Haddeland et al, 2014;Hanasaki et al, 2008b;Vörösmarty et al, 2000aVörösmarty et al, , 2010Wada et al, 2011), groundwater depletion (Döll et al, 2014;Gleeson et al, 2012;Grogan et al, 2017;Wada et al, 2012), and the role of water extractions in sealevel change (Gleeson et al, 2012;Konikow, 2011;Pokhrel et al, 2012). The GHMs have also been used extensively in the study of food security and agricultural yields (Biemans and Siderius, 2019;Döll and Siebert, 2002;Elliott et al, 2014;Haqiqi et al, 2021;Liu et al, 2017; as well as formed the foundation for water quality models (Mineau et al, 2015;Stewart et al, 2011;de Wit, 2001;Wollheim et al, 2008a, b;Zuidema et al, 2018) and the inputs for flood inundation models (e.g., Yamazaki et al, 2011). Recently, GHMs have been employed in interdisciplinary studies to evaluate human-hydrologic systems and the food-energy-water nexus, such as human and economic impacts of flooding (Dottori et al, 2018), hydropower (Mishra et al, 2020;Turner et al, 2019), power-plant cooling capacity Stewart et al, 2013;Webster et al, 2022), water markets (Rimsaite et al, 2021), irrigation decision-making under climate change (Zaveri et al, 2016), and virtual water trade (Dalin et al, 2017;…”

Section: Introductionmentioning

confidence: 99%

Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality

et al. 2022

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Abstract. This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. The WBM was first published in 1989; here, we describe the first fully open-source WBM version (v.1.0.0). Earlier descriptions of WBM methods provide the foundation for the most recent model version that is detailed here. We present an overview of the model functionality, utility, and evaluation of simulated global river discharge and irrigation water use. This new version adds a novel suite of water source tracking modules that enable the analysis of flow-path histories on water supply. A key feature of WBM v.1.0.0 is the ability to identify the partitioning of sources for each stock or flux within the model. Three different categories of tracking are available: (1) primary inputs of water to the surface of the terrestrial hydrologic cycle (liquid precipitation, snowmelt, glacier melt, and unsustainable groundwater); (2) water that has been extracted for human use and returned to the terrestrial hydrologic system; and (3) runoff originating from user-defined spatial land units. Such component tracking provides a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior. We find that WBM v.1.0.0 simulates global river discharge and irrigation water withdrawals well, even with default parameter settings, and for the first time, we are able to show how the simulation arrives at these fluxes by using the novel tracking functions.

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“…These models have been applied to many questions of both basic and applied hydrology, such as climate change and other anthropogenic impacts on global river systems (Bosmans et al, 2017;Döll et al, 2012;Haddeland et al, 2014;Hanasaki et al, 2008b;Vörösmarty et al, 2000aWada et al, 2011), groundwater depletion (Petra Döll et al, 2014;Gleeson et al, 2012;Wada et al, 2012), and the role of water extractions in sea level change (Gleeson et al, 2012;Konikow, 2011;. GHMs have also been used extensively in the study of food security and agricultural yields (Biemans & Siderius, 2019;Elliott et al, 2014;Haqiqi et al, 2020;Liu et al, 2017;Schewe et al, 2014) as well as formed the foundation for water quality models (Mineau et al, 2015;Stewart et al, 2011a;Wit, 2001;Wollheim et al, 2008a,b;Zuidema et al, 2018) and the inputs for flood inundation models (e.g., Yamazaki et al, 2011). Recently, GHMs have been employed in interdisciplinary studies to evaluate human-hydrologic systems and the food-energy-water nexus, e.g., human and economic impacts of flooding (Dottori et al, 2018), hydropower (Mishra et al, 2020;Turner et al, 2019) and powerplant cooling capacity (van Beek et al, 2012;Stewart et al, 2013;Webster et al, 2022), water markets (Rimsaite, 2021), irrigation decision-making under climate change (Zaveri et al, 2016), and virtual water trade (Dalin et al, 2017;Konar et al, 2013).…”

mentioning

confidence: 99%

WBM: A scalable gridded global hydrologic model with water tracking functionality

Grogan

Zuidema

Prusevich

et al. 2022

Preprint

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Abstract. This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. WBM has a long publication history; here we describe the first fully open source WBM version. This version includes a suite of water source tracking modules that enable analysis of flow-path histories on water supply. Earlier descriptions of WBM methods provide the foundation of the most recent model version detailed here. WBM is available here: https://github.com/wsag/WBM. WBM is written in the perl data programming language (PDL), making use of several open-source perl libraries. As a convenience we also provide a Singularity container that simplifies installation of dependencies. We present an overview of the model functionality, utility, and validation of global river discharge and irrigation water use using data from the Global Runoff Data Centre and FAO statistics. A key feature of WBM is the ability to identify the partitioning of sources for each stock or flux within the model. Therefore, users can determine what proportion of any flux consists of each of the primary inputs of water to the surface of the terrestrial hydrologic cycle, previously extracted water for human uses, or runoff generated from any place on the Earth’s surface. Such component tracking provides both a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior, as well as perform model experiments in new ways.

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Advances in global hydrology–crop modelling to support the UN’s Sustainable Development Goals in South Asia

Cited by 12 publications

References 72 publications

Impact of Technological Burden on Knowledge Management Functions in Jordanian Industrial Companies

Impact of Technological Burden on Knowledge Management Functions in Jordanian Industrial Companies

Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality

WBM: A scalable gridded global hydrologic model with water tracking functionality

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