1. |
- Luo, Yifei, et al.
(author)
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Technology Roadmap for Flexible Sensors
- 2023
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In: ACS Nano. - : American Chemical Society. - 1936-0851 .- 1936-086X. ; 17:6, s. 5211-5295
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Research review (peer-reviewed)abstract
- Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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2. |
- Reynolds Puga, José Eduardo, 1982-
(author)
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Flood Prediction in data-scarce basins : Maximising the value of limited hydro-meteorological data
- 2019
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Doctoral thesis (other academic/artistic)abstract
- Floods pose a threat to society that can cause large socio-economic damages and loss of life in many parts of the world. Flood-forecasting models are required to provide simulations at temporal resolutions higher than a day in basins with concentration times smaller than 24 h. However, data at such resolutions are commonly limited or not available, especially in developing or low-income countries. This thesis covers issues related to the scarcity and lack of high temporal-resolution hydro-meteorological data and explores methods where the value of existing data is maximised to improve flood prediction.By varying the starting time of daily records (the day definition), it was shown that this definition had large implications on model calibration and runoff simulation and therefore, should be considered in regionalisation and flood-forecasting applications. A method was developed to treat empirically model-parameter dependencies on the temporal resolution of data. Model parameters seemed to become independent of the temporal resolution of data when the modelling time-step was sufficiently small. Thus, if sub-daily forcing data can be secured, flood forecasting in basins with sub-daily concentration times may be possible using model-parameter values calibrated from time series of daily data. A new calibration method using only a few event hydrographs could improve flood prediction compared to a scenario with no discharge data. Two event hydrographs may be sufficient for calibration, but accuracy and reduction in uncertainty may improve if data on more events can be acquired. Using flood events above a threshold with a high frequency of occurrence for calibration may be as useful for flood prediction as using only extreme events with a low frequency of occurrence. The accuracy of the rainfall forecasts strongly influenced the predictive performance of a flood model calibrated with limited discharge data. Between volume and duration errors of the rainfall forecast, the former had the larger impact on model performance.The methods previously described proved to be useful for predicting floods and are expected to support flood-risk assessment and decision making during the occurrence of floods in data-scarce regions. Further studies using more models and basins are required to test the generality of these results.
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3. |
- Westerberg, Ida, 1979-
(author)
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Observational Uncertainties in Water-Resources Modelling in Central America : Methods for Uncertainty Estimation and Model Evaluation
- 2011
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Doctoral thesis (other academic/artistic)abstract
- Knowledge about spatial and temporal variability of hydrological processes is central for sustainable water-resources management, and such knowledge is created from observational data. Hydrologic models are necessary for prediction for time periods and areas lacking data, but are affected by observational uncertainties. Methods for estimating and accounting for such uncertainties in water-resources modelling are of high importance, especially in regions such as Central America. Observational uncertainties were addressed in three ways in this thesis; quality control, quantitative estimation and development of model-evaluation techniques that addressed unquantifiable uncertainties. A first step in any modelling study should be the quality control and concurrent analysis of the representativeness of the observational data. In the characterisation of the precipitation regime in the Choluteca River basin in Honduras, four different quality problems were identified and 22% of the daily data had to be rejected. The monitoring network was found to be insufficient for a comprehensive characterisation of the high spatiotemporal variability of the precipitation regime. Quantitative estimations of data uncertainties can be made when sufficient information is available. Discharge-data uncertainties were estimated with a fuzzy regression for time-variable rating curves and from official rating curves for 35 stations in Honduras. The uncertainties were largest for low flows, as a result of measurement uncertainties and natural variability. A method for calibration with flow-duration curves was developed which enabled calibration to the whole flow range, accounting for discharge uncertainty and calibration with non-overlapping time periods for model input and evaluation data. The method compared favourably to traditional calibration in a test using two models applied in basins with different runoff-generation processes. A post-hoc analysis made it possible to identify potential model-structure errors and periods of disinformative data. Flow-duration curves were regionalised and used for calibration of a Central-American water-balance model. The initial model uncertainty for the ungauged basins was reduced by 70%. Non-representative precipitation data were found to be the main obstacle to comprehensive regional water-resources modelling in Central America. These methods bridged several problems related to observational uncertainties in water-balance modelling. Estimates of prediction uncertainty are an important basis for all types of decisions related to water-resources management.
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4. |
- Senkondo, William, 1982-
(author)
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Modelling water resources despite data limitations in Tanzania’s Kilombero Valley
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Water is a vital resource for survival on the Earth. Sustainable management of water resources is therefore required for the wellbeing of present and future generations. A cornerstone of water resources management is scientific guidance supported by relevant data (in terms of quantity and quality). Most developing regions, where such guidance is crucial due to the intimate connection between natural resources and livelihoods, unfortunately face data limitations. This thesis aims to develop systematic approaches for informing water resources management in data limited regions. Specifically, this work targets Tanzania’s Kilombero Valley (KV) basin as an exemplar of a data limited region undergoing social-economic development through expansion and intensification of agriculture and other water-related interventions. Through a synthesis of lessons learned from the ongoing evolution of hydrological modelling development for water resources management in the Eastern Africa, several promising approaches were identified that could potentially be robust despite data limitations across the region. Putting these approaches into practice, recession analysis based on non-continuous discharge data in conjunction with estimations of the actual evapotranspiration (ET) using remote sensing techniques provided a basis to improve process understanding and help characterize the hydrological systems in the KV basin. This understanding translated into more-informed parameter estimation and improved accuracy when integrated into the development of a hydrological modelling framework using the Soil and Water Assessment Tool (SWAT) model. The modeling framework established for KV has potential to be used as tool for estimating impacts of water resources management strategies relative to future anthropogenic pressures and climatic changes. What is even more promising, is the possibility to derive scientific guidance to assist water resources management in a data limited region through implementation of an integrated workflow which employs state-of-the-science approaches. The methodological framework for model development adopted in this thesis could be applied in any data limited region facing similar challenges as those of the KV basin.
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5. |
- Fuentes-Andino, Diana, 1984-
(author)
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Flood Hazard Assessment in Data-Scarce Basins : Use of alternative data and modelling techniques
- 2017
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Doctoral thesis (other academic/artistic)abstract
- Flooding is of great concern world-wide, causing damage to infrastructure, property and loss of life. Low-income countries, in particular, can be negatively affected by flood events due to their inherent vulnerabilities. Moreover, data to perform studies for flood risk management in low-income regions are often scarce or lacking sufficient quality.This thesis proposes new methodologies and explores the use of unconventional sources of information in flood hazard assessment in areas where the quantity or sufficient quality of traditional hydrometrical data are lacking. One method was developed to account for errors in spatially averaged rainfall, from a sparse rain-gauge network, used as input to a rainfall-runoff model. A spatially-averaged and event-dependent rainfall depth multiplier led to improvements of the hydrographs at calibration. And by using a distribution of the multiplier, identified from previous events in the catchment, improvement in predictions could also be obtained.A second method explored the possibility of reproducing an unmeasured extreme flood event using a combination of models, post-event data, precipitation and an uncertainty-analysis framework. This combination allowed the identification of likelihood-associated parameter sets from which the flood hazard map for the extreme event could be obtained.A third and fourth study made at the regional scale explored the value of catchment similarities, and the effects of climate on the hydrological response of catchments.Flood frequency curves were estimated for 36 basins, assumed ungauged, using regional information of short flow records, and local information about the frequency of the storm. In the second regional study, hydro-climatic information provided great value to constrain predictions of series of daily flow from a hydrological model.Previously described methods, used in combination with unconventional information within an uncertainty analysis, proven to be useful for flood hazard assessment at basins with data limitations. The explored data included: post-event measurements of an extreme flood event, hydro-climate regional information and local precipitation data. The methods presented in this thesis are expected to support development of hydrological studies underpinning flood-risk reduction in data-poor areas.
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6. |
- Kauffeldt, Anna, 1981-
(author)
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Disinformative and Uncertain Data in Global Hydrology : Challenges for Modelling and Regionalisation
- 2014
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Doctoral thesis (other academic/artistic)abstract
- Water is essential for human well-being and healthy ecosystems, but population growth and changes in climate and land-use are putting increased stress on water resources in many regions. To ensure water security, knowledge about the spatiotemporal distribution of these resources is of great importance. However, estimates of global water resources are constrained by limitations in availability and quality of data. This thesis explores the quality of both observational and modelled data, gives an overview of models used for large-scale hydrological modelling, and explores the possibilities to deal with the scarcity of data by prediction of flow-duration curves.The evaluation of the quality of observational data for large-scale hydrological modelling was based on both hydrographic data, and model forcing and evaluation data for basins worldwide. The results showed that a GIS polygon dataset outperformed all gridded hydrographic products analysed in terms of representation of basin areas. Through a screening methodology based on the long-term water-balance equation it was shown that as many as 8–43% of the basins analysed displayed inconsistencies between forcing (precipitation and potential evaporation) and evaluation (discharge) data depending on how datasets were combined. These data could prove disinformative in hydrological model inference and analysis.The quality of key hydrological variables from a numerical weather prediction model was assessed by benchmarking against observational datasets and by analysis of the internal land-surface water budgets of several different model setups. Long-term imbalances were found between precipitation and evaporation on the global scale and between precipitation, evaporation and runoff on both cell and basin scales. These imbalances were mainly attributed to the data assimilation system in which soil moisture is used as a nudge factor to improve weather forecasts.Regionalisation, i.e. transfer of information from data-rich areas to data-sparse areas, is a necessity in hydrology because of a lack of observed data in many areas. In this thesis, the possibility to predict flow-duration curves in ungauged basins was explored by testing several different methodologies including machine learning. The results were mixed, with some well predicted curves, but many predicted curves exhibited large biases and several methods resulted in unrealistic curves.
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7. |
- Wetterhall, Fredrik, 1971-
(author)
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Statistical Downscaling of Precipitation from Large-scale Atmospheric Circulation : Comparison of Methods and Climate Regions
- 2005
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Doctoral thesis (other academic/artistic)abstract
- A global climate change may have large impacts on water resources on regional and global scales. General circulation models (GCMs) are the most used tools to evaluate climate-change scenarios on a global scale. They are, however, insufficiently describing the effects at the local scale. This thesis evaluates different approaches of statistical downscaling of precipitation from large-scale circulation variables, both concerning the method performance and the optimum choice of predictor variables. The analogue downscaling method (AM) was found to work well as “benchmark” method in comparison to more complicated methods. AM was implemented using principal component analysis (PCA) and Teweles-Wobus Scores (TWS). Statistical properties of daily and monthly precipitation on a catchment in south-central Sweden, as well as daily precipitation in three catchments in China were acceptably downscaled.A regression method conditioning a weather generator (SDSM) as well as a fuzzy-rule based circulation-pattern classification method conditioning a stochastical precipitation model (MOFRBC) gave good results when applied on Swedish and Chinese catchments. Statistical downscaling with MOFRBC from GMC (HADAM3P) output improved the statistical properties as well as the intra-annual variation of precipitation.The studies show that temporal and areal settings of the predictor are important factors concerning the success of precipitation modelling. The MOFRCB and SDSM are generally performing better than the AM, and the best choice of method is depending on the purpose of the study. MOFRBC applied on output from a GCM future scenario indicates that the large-scale circulation will not be significantly affected. Adding humidity flux as predictor indicated an increased intensity both in extreme events and daily amounts in central and northern Sweden.
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