A tightly coupled GIS and distributed hydrologic modeling framework

Bhatt, G.; Kumar, Mukesh; Duffy, Christopher

doi:10.1016/j.envsoft.2014.08.003

Cited by 115 publications

(67 citation statements)

References 40 publications

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“…Further work could perhaps focus on identifying a threshold slope, or range of aspect values above which the output variables become sensitive, thus restricting application of the model to a particular range of landscapes for which the model is robust. These results underline the potential complementarity of integrating GIS, EO and modelling approaches to involve spatial data handling and analysis support for land surface paramaterisation [74,75]. However, the development of several advanced GIS tools are needed to further progress the coupling of GIS functions within a modelling framework and exploit the full functionality of such data assimilation methods.…”

Section: Simsphere As a Tool For Environmental Managementmentioning

confidence: 99%

“…The capability of GIS functions related to efficient data conversion algorithms, exploratory spatial data analysis tools, spatial interpolation, modelling languages, and temporal and three-dimensional data analysis capabilities are some highlighted areas where GIS has the potential to advance modelling applications related to land surface processes. Such advancements would allow for a host of spatial analysis methods to be incorporated within an environmental modelling framework, ranging from the integration of the interpolation of geostatistics which would allow researchers to include discontinuities or weight observations of quality, to a convenient source for the display and visualisation of results [74,75]. An understanding of the underlying GIS tools for environmental modelling is a potential avenue for future works.…”

Section: Simsphere As a Tool For Environmental Managementmentioning

confidence: 99%

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Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community

Petropoulos

Ireland

Griffiths

et al. 2015

Water

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In today's changing climate, the development of robust, accurate and globally applicable models is imperative for a wider understanding of Earth's terrestrial biosphere. Moreover, an understanding of the representation, sensitivity and coherence of such models are vital for the operationalisation of any physically based model. A Global Sensitivity Analysis (GSA) was conducted on the SimSphere land biosphere model in which a meta-modelling method adopting Bayesian theory was implemented. Initially, effects of assuming uniform probability distribution functions (PDFs) for the model inputs, when examining sensitivity of key quantities simulated by SimSphere at different output times, were examined. The development of topographic model input parameters (e.g., slope, aspect, and elevation) were derived within a Geographic Information System (GIS) before implementation within the model. The effect of time of the simulation on the sensitivity of previously examined outputs was also analysed. Results showed that simulated outputs were significantly influenced by changes in topographic input parameters, fractional vegetation cover, vegetation height and surface moisture availability in agreement with previous studies. Time of model output simulation had a significant influence on the absolute values OPEN ACCESSWater 2015, 7 2102 of the output variance decomposition, but it did not seem to change the relative importance of each input parameter. Sensitivity Analysis (SA) results of the newly modelled outputs allowed identification of the most responsive model inputs and interactions. Our study presents an important step forward in SimSphere verification given the increasing interest in its use both as an independent modelling and educational tool. Furthermore, this study is very timely given on-going efforts towards the development of operational products based on the synergy of SimSphere with Earth Observation (EO) data. In this context, results also provide additional support for the potential applicability of the assimilation of spatial analysis data derived from GIS and EO data into an accurate modelling framework.

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Section: Simsphere As a Tool For Environmental Managementmentioning

confidence: 99%

Section: Simsphere As a Tool For Environmental Managementmentioning

confidence: 99%

Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community

Petropoulos

Ireland

Griffiths

et al. 2015

Water

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“…Depending on the level of interaction between participating models, three forms of coupling are commonly reported in the literature: loose, tight, and embedded coupling (Bhatt et al, 2014;Brandmeyer and Karimi, 2000). Loose coupling involves exchange of results between two or more models with no need for modification within the participating models.…”

Section: Technical Details Of the Couplingmentioning

confidence: 99%

Integrated Modeling of Land and Water Resources in two African Catchments

Yalew¹

2018

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Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers, the author nor UNESCO-IHE for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. A pdf version of this work will be made available as Open Access via http://repository.tudelft.nl/ihe This version is licensed under the Creative Commons SUMMARYLand and water are two of the most important and interacting natural resources that are critical for human survival and development. Growing population and global economic expansion are accelerating the demand for land and water for uses such as agriculture, urbanization, irrigation, hydropower, and industrialization. The land surface changes dynamically due to these demands and other socio-economic drivers. Biophysical factors such as topographic suitability, climate change, and rainfall variability further influence landuse changes and land-use change decisions. Water resources are likewise experiencing pressure from overuse, pollution, and changes in hydrologic processes as a result of both socio-economic and biophysical factors.Land and water resources are considered to have strong interactions. Although the science of this interaction is not new, the two are typically managed under separate governance systems (Le Maitre et al., 2014). As a result, land-use change and hydrology or water resources are studied usually separately. Modelers and model developers reproduced the existing separation in governance of land and water resources in their respective models, which gave rise to mild treatment of one of the resources when analyzing the other. The result is that hydrology and water resources are often considered to be processes and resources affected only by biophysical components, ignoring anthropogenic contributions that may actually influence such characteristics through direct effects on land-use, for instance.The need to understand and explicitly represent the interaction between water resources on the one hand and land-use changes with its drivers on the other is imperative for sustainable management of integrated natural resources management in general and land and water resources management in particular. In recent years, interesting sub-disciplines such as 'socio-hydrology' are emerging which recognize and emphasize the importance of socioeconomic and anthropogenic effects on hydrologic and water resources analysis. Interaction and feedback between land-use and water resources is still not explicitly and dynamically represented in most scientific modeling tools related to land and water, however.Furthermore, often due to limitations of access and computing resources, frameworks for communicating integrated assessment and modeling in a way that can be operational to resource managers and decision makers related to such resources is limited. This is especially evident in developing regions. After the land-use models calibrated and...

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“…Land cover/soil classification data were also downloadable from the geospatial data gateway of Natural Resources Conservation Service (NRCS) webpage (http://datagateway.nrcs.usda.gov/). All spatially distributed data sets were then converted into raster format for loading into PIHMgis (Bhatt et al, 2014), an open-source 20 GIS-hydrologic model framework that automatically extracts hydrogeological data sets and meteorological forcings, and maps them onto model grids. …”

Section: Hydroclimatic Data and Watershed Detailsmentioning

confidence: 99%

Assessing the resiliency of surface water and groundwater systems under groundwater pumping

Seo

Mahinthakumar

Sankarasubramanian

et al. 2017

Preprint

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Abstract:Since surface water and groundwater systems are fully coupled and integrated systems, increased groundwater withdrawal during drought may reduce groundwater discharges into the stream, thereby prolonging both systems' recovery from drought. To analyze watershed response to basin-level groundwater pumping, we propose an uncertainty framework to understand the 5 resiliency of groundwater and surface water systems using a fully-coupled hydrologic model under transient pumping. The proposed framework incorporates uncertainties in initial conditions to develop robust estimates of restoration times of both surface water and groundwater systems and quantifies how pumping impacts state variables such as soil moisture. Groundwater pumping impacts over a watershed were also analyzed under different pumping volumes and different 10 potential climate scenarios. Our analyses show that groundwater restoration time is more sensitive to variability in climate forcings as opposed to changes in pumping volumes. After the cessation of pumping, streamflow recovers quickly in comparison to groundwater, which has higher persistence. Pumping impacts on various hydrologic variables were also discussed. Given that surface water and groundwater are inter-connected, optimal management of the both 15 resources should be considered to improve the watershed resiliency under drought. Potential for developing optimal conjunctive management plans using seasonal-to-interannual climate forecasts is also discussed.Hydrol. Earth Syst. Sci. Discuss., https://doi

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A tightly coupled GIS and distributed hydrologic modeling framework

Cited by 115 publications

References 40 publications

Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community

Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community

Integrated Modeling of Land and Water Resources in two African Catchments

Assessing the resiliency of surface water and groundwater systems under groundwater pumping

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