Geothermal Water Use: Life Cycle Water Consumption, Water Resource Assessment, and Water Policy Framework

The uneven distribution of water resources availability globally puts pressure on environmental and human or socio-economic systems and has complex implications for the interactions within these systems. The natural environment and water resources are increasingly threatened by development, and water management crises are still occurring. This is exacerbated by the lack of accurate and adequate information on these systems. In Eswatini, for example, the pressure on the available water resources is mounting due to increasing water demand for irrigation while information about natural hydrological conditions and levels of water resources developments are uncertain. In addition, the practical application of hydrological models for water resources assessments that incorporate uncertainty in Eswatini has yet to be realised. The aim of the study, therefore, was to develop a water resource assessment system that is based on both observed and simulated information and that includes uncertainty. This study focusses on a regional water resource assessment using an uncertainty version of the Pitman monthly rainfall-runoff model whose outputs are constrained by six indices of natural hydrological response (i.e., mean monthly runoff, mean monthly groundwater recharge, Q10, Q50 and Q90 percentage points of the flow duration curve and % time of zero flows) for each of the 122 sub-basins of the transboundary catchments of Eswatini. A 2-step uncertainty modelling approach was tested, validated and then applied to all the sub-basins of Eswatini. The first step of the model run establishes behavioural, but uncertain model parameter ranges for natural incremental sub-basin hydrological responses and the model is typically run 100 000 times for each sub-basin. The parameter space that defines the uncertainty in parameter estimation is sampled based on simple Monte Carlo approach. The second step links all the sub-basin outputs and allows for water use parameters to be incorporated, where necessary, in order to generate cumulative sub-basin outflows. The results from the constraint index analysis have proved to be useful in constraining the model outputs. Generally, the behavioural model outputs produced realistic uncertainty estimates as well as acceptable simulations based on the assessment of the flow duration curves. The modelling results indicated that there is some degree of uncertainty that cannot be easily accounted for due to some identified data issues. The results also showed that there is still a possibility to improve the simulations provided such issues are resolved. The issues about the simulation of stream flow that were detected are mainly related to availability of data to estimate water use parameters. Another challenge in setting up the model was associated with establishing constraints that match the parameters for natural hydrological conditions for specific sub-basins and maintaining consistency in the adjustment of the model output constraints for other sub-basins. In an attempt to overcome this problem, the study recommends additional hydrological response constraints to be used with the Pitman model. Another main recommendation relates to the strong cooperation of relevant catchment management authorities and stakeholders including scientists in order to make information more available to users. The new hydrological insight is derived from the analysis of hydrological indices which highlighted the regional variations in hydrological processes and sub-basin response across the transboundary basins of Eswatini. The adopted modelling approach provides further insight into all the uncertainties associated with quantifying the available water resources of the country. The study has provided further understanding of the spatial variability of the hydrological response and existing development impacts than was previously available. It is envisaged that these new insights will provide an improved basis for future water management in Eswatini.

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Ground-Water Resource Assessment in the Rio Grande de Manati Alluvial Plain, Rio Arriba Saliente Area, Puerto Rico

10.3133/wri024132 ◽

2002 ◽

Keyword(s):

Puerto Rico ◽

Ground Water ◽

Water Resource ◽

Rio Grande ◽

Resource Assessment ◽

Alluvial Plain ◽

Water Resource Assessment ◽

Ground Water Resource

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Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

Arabian Journal of Geosciences ◽

10.1007/s12517-021-06699-y ◽

2021 ◽

Vol 14 (6) ◽

Author(s):

Jinming Yang ◽

Chengzhi Li

Keyword(s):

Remote Sensing ◽

Kalman Filter ◽

Water Resource ◽

Ensemble Kalman Filter ◽

Snow Depth ◽

Resource Assessment ◽

Measured Data ◽

Hydrological Process ◽

Depth Data ◽

Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

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