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- Guerrero, Jose-Luis, et al.
(författare)
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Temporal variability in stage-discharge relationships
- 2012
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 446, s. 90-102
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Tidskriftsartikel (refereegranskat)abstract
- Although discharge estimations are central for water management and hydropower, there are few studies on the variability and uncertainty of their basis; deriving discharge from stage heights through the use of a rating curve that depends on riverbed geometry. A large fraction of the world's river-discharge stations are presumably located in alluvial channels where riverbed characteristics may change over time because of erosion and sedimentation. This study was conducted to analyse and quantify the dynamic relationship between stage and discharge and to determine to what degree currently used methods are able to account for such variability. The study was carried out for six hydrometric stations in the upper Choluteca River basin, Honduras, where a set of unusually frequent stage-discharge data are available. The temporal variability and the uncertainty of the rating curve and its parameters were analysed through a Monte Carlo (MC) analysis on a moving window of data using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. Acceptable ranges for the values of the rating-curve parameters were determined from riverbed surveys at the six stations, and the sampling space was constrained according to those ranges, using three-dimensional alpha shapes. Temporal variability was analysed in three ways: (i) with annually updated rating curves (simulating Honduran practices), (ii) a rating curve for each time window, and (iii) a smoothed, continuous dynamic rating curve derived from the MC analysis. The temporal variability of the rating parameters translated into a high rating-curve variability. The variability could turn out as increasing or decreasing trends and/or cyclic behaviour. There was a tendency at all stations to a seasonal variability. The discharge at a given stage could vary by a factor of two or more. The quotient in discharge volumes estimated from dynamic and static rating curves varied between 0.5 and 1.5. The difference between discharge volumes derived from static and dynamic curves was largest for sub-daily ratings but stayed large also for monthly and yearly totals. The relative uncertainty was largest for low flows but it was considerable also for intermediate and large flows. The standard procedure of adjusting rating curves when calculated and observed discharge differ by more than 5% would have required continuously updated rating curves at the studied locations. We believe that these findings can be applicable to many other discharge stations around the globe.
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- Westerberg, Ida, 1979-
(författare)
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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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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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- Westerberg, Ida, et al.
(författare)
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Precipitation data in a mountainous catchment in Honduras: quality assessment and spatiotemporal characteristics
- 2010
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Ingår i: Theoretical and Applied Climatology. - : Springer Nature. - 0177-798X .- 1434-4483. ; 101:3-4, s. 381-396
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Tidskriftsartikel (refereegranskat)abstract
- An accurate description of temporal and spatial precipitation variability in Central America is important for local farming, water supply and flood management. Data quality problems and lack of consistent precipitation data impede hydrometeorological analysis in the 7,500 km2 Choluteca River basin in central Honduras, encompassing the capital Tegucigalpa. We used precipitation data from 60 daily and 13 monthly stations in 1913–2006 from five local authorities and NOAA's Global Historical Climatology Network. Quality control routines were developed to tackle the specific data quality problems. The quality-controlled data were characterised spatially and temporally, and compared with regional and larger-scale studies. Two gapfilling methods for daily data and three interpolation methods for monthly and mean annual precipitation were compared. The coefficient-of-correlation-weighting method provided the best results for gap-filling and the universal kriging method for spatial interpolation. In-homogeneity in the time series was the main quality problem, and 22% of the daily precipitation data were too poor to be used. Spatial autocorrelation for monthly precipitation was low during the dry season, and correlation increased markedly when data were temporally aggregated from a daily time scale to 4–5 days. The analysis manifested the high spatial and temporal variability caused by the diverse precipitationgenerating mechanisms and the need for an improved monitoring network.
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- Wetterhall, Fredrik, 1971-
(författare)
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Statistical Downscaling of Precipitation from Large-scale Atmospheric Circulation : Comparison of Methods and Climate Regions
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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- Widén-Nilsson, Elin, 1975-
(författare)
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Global-Scale Modelling of the Land-Surface Water Balance : Development and Analysis of WASMOD-M
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Water is essential for all life on earth. Global population increase and climate change are projected to increase the water stress, which already today is very high in many areas of the world. The differences between the largest and smallest global runoff estimates exceed the highest continental runoff estimates. These differences, which are caused by different modelling and measurement techniques together with large natural variabilities need to be further addressed. This thesis focuses on global water balance models that calculate global runoff, evaporation and water storage from precipitation and other climate data.A new global water balance model, WASMOD-M was developed. Already when tuned against the volume error it reasonable produced within-year runoff patterns, but the volume error was not enough to confine the model parameter space. The parameter space and the simulated hydrograph could be better confined with, e.g., the Nash criterion. Calibration against snow-cover data confined the snow parameters better, although some equifinality still persisted. Thus, even the simple WASMOD-M showed signs of being overparameterised. A simple regionalisation procedure that only utilised proximity contributed to calculate a global runoff estimate in line with earlier estimations. The need for better specifications of global runoff estimates was highlighted. Global modellers depend on global data-sets that can have low quality in many areas. Major sources of uncertainty are precipitation and river regulation. A new routing method that utilises high-resolution flow network information in low-resolution calculations was developed and shown to perform well over all spatial scales, while the standard linear reservoir routing decreased in performance with decreasing resolution. This algorithm, called aggregated time-delay-histogram routing, is intended for inclusion in WASMOD-M.
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