| 1. |
- Blösch, Günter, et al.
(författare)
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Twenty-three unsolved problems in hydrology (UPH) - a community perspective
- 2019
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Ingår i: Hydrological Sciences Journal. - : Informa UK Limited. - 0262-6667 .- 2150-3435. ; 64:10, s. 1141-1158
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Tidskriftsartikel (refereegranskat)abstract
- This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.
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| 2. |
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| 3. |
- 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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| 4. |
- Kauffeldt, Anna, et al.
(författare)
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Disinformative data in large-scale hydrological modelling
- 2013
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 17:7, s. 2845-2857
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale hydrological modelling has become an important tool for the study of global and regional water resources, climate impacts, and water-resources management. However, modelling efforts over large spatial domains are fraught with problems of data scarcity, uncertainties and inconsistencies between model forcing and evaluation data. Model-independent methods to screen and analyse data for such problems are needed. This study aimed at identifying data inconsistencies in global datasets using a pre-modelling analysis, inconsistencies that can be disinformative for subsequent modelling. The consistency between (i) basin areas for different hydrographic datasets, and (ii) between climate data (precipitation and potential evaporation) and discharge data, was examined in terms of how well basin areas were represented in the flow networks and the possibility of water-balance closure. It was found that (i) most basins could be well represented in both gridded basin delineations and polygon-based ones, but some basins exhibited large area discrepancies between flow-network datasets and archived basin areas, (ii) basins exhibiting too-high runoff coefficients were abundant in areas where precipitation data were likely affected by snow undercatch, and (iii) the occurrence of basins exhibiting losses exceeding the potential-evaporation limit was strongly dependent on the potential-evaporation data, both in terms of numbers and geographical distribution. Some inconsistencies may be resolved by considering subgrid variability in climate data, surface-dependent potential-evaporation estimates, etc., but further studies are needed to determine the reasons for the inconsistencies found. Our results emphasise the need for pre-modelling data analysis to identify dataset inconsistencies as an important first step in any large-scale study. Applying data-screening methods before modelling should also increase our chances to draw robust conclusions from subsequent model simulations.
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| 5. |
- Kizza, Michael, et al.
(författare)
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Estimating areal rainfall over Lake Victoria and its basin using ground-based and satellite data
- 2012
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Ingår i: Journal of Hydrology. - : Elsevier. - 0022-1694 .- 1879-2707. ; 464, s. 401-411
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Tidskriftsartikel (refereegranskat)abstract
- A gridded monthly rainfall dataset having a spatial resolution of 2 km and covering the period 1960–2004 was derived for the Lake Victoria basin. The lake and its basin support more than 30 million people and also contribute substantially to the River Nile flow. The major challenge in the estimation of the Lake Victoria water balance is the estimation of the rainfall over the lake, which is further complicated by the varying quality and spatial coverage of rain-gauge data in the basin. In this study, these problems were addressed by using rain-gauge data for 315 stations around the basin and satellite-derived precipitation data from two products to derive a monthly precipitation dataset for the entire basin, including the lake. First, the rain-gauge data were quality controlled. Thereafter short gaps were filled in the daily data series which resulted in 9,429 additional months of data. Two spatial interpolation methods were used for generating the gridded rainfall dataset and the universal kriging method performed slightly better than the inverse distance weighting method. The enhancement of rainfall over the lake surface was addressed by estimating a relationship between rain-gauge and satellite data. Two satellite rainfall products, TRMM 3B43 and PERSIANN were compared to the interpolated monthly rain-gauge data for the land part of the basin. The bias in the TRMM 3B43 rainfall estimates was higher than the bias for PERSIANN but its correlation was higher with a better representation of the intra-annual variability. The TRMM 3B43 product showed an enhancement of lake rainfall over basin rainfall of 33% while the PERSIANN product gave a much higher enhancement of up to 85%.
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| 6. |
- Quesada-Montano, Beatriz, 1984-, et al.
(författare)
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Automation of hydrological drought typology to study drought propagation in a tropical catchment
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Understanding different types of drought and how they propagate through the hydrological cycle from precipitation to streamflow and groundwater deficits is important for improving water and risk management policies. Drought in the tropics is a recurrent phenomenon, but limited knowledge exists about drought severity and duration as well as the processes that cause different types of drought. At the catchment scale, analysing drought propagation is usually done manually. This can be time consuming (e.g. when dealing with long time series or many catchments) and may introduce subjective elements into the analysis that affect the comparability between catchments and studies. In this study, we developed a methodology to provide an automated objective procedure for drought typology to study hydrological drought propagation in the tropics. We selected the Savegre catchment in Costa Rica as a proof-of-concept pilot study. The first step was to analyse if the types of drought affecting this catchment could be explained in terms of the process-based typology available in the literature: classical rainfall deficit drought, wet-to-dry season drought, and composite drought. Then, based on the manual typology, we defined different criteria for the hydrological drought types to make the typology automated and objective. Finally, we analysed drought propagation using a set of duration, timing and deficit indicators. We found that the process-based hydrological typology available in the literature is suitable to describe the different drought processes occurring in Savegre. The classification obtained with the automated typology was highly similar to the manual typology, with the exception of one event. We found that most of the detected droughts (71% and 73% from all river discharge and groundwater droughts, respectively) were classical rainfall deficits droughts, which suggests that droughts in this catchment are highly climate dominated. However, the importance of storage control was reflected during the dry season, when some of the longest and most severe events took place. The most severe events were composite and wet-to-dry season droughts, but we also found highly severe classical rainfall deficits droughts. Our results can potentially be applied to the wider tropics facilitating automatic drought classification using process-based selection criteria. Our study contributes to the overall knowledge of drought propagation in tropical catchments and is useful for supporting drought monitoring and forecasting, which is a much needed tool for water and drought-related disaster management in the tropics.
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| 7. |
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| 8. |
- Quesada-Montano, Beatriz, 1984-
(författare)
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Hydro-Climatic Variability and Change in Central America : Supporting Risk Reduction Through Improved Analyses and Data
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Floods and droughts are frequent in Central America and cause large social, economic and environmental impacts. A crucial step in disaster risk reduction is to have a good understanding of the causing mechanisms of extreme events and their spatio-temporal characteristics. For this, a key aspect is access to a dense network of long and good-quality hydro-meteorological data. Unfortunately, such ideal data are sparse or non-existent in Central America. In addition, the existing methods for hydro-climatic studies need to be revised and/or improved to find the most suitable for the region’s climate, geography and hydro-climatic data situation. This work has the ultimate goal to support the reduction of risks associated with hydro-climatic-induced disasters in Central America. This was sought by developing ways to reduce data-related uncertainties and by improving the available methods to study and understand hydro-climatic variability processes. In terms of data-uncertainty reduction, this thesis includes the development of a high resolution air temperature dataset and a methodology to reduce uncertainties in a hydrological model at ungauged basins. The dataset was able to capture the spatial patterns with a detail not available with existing datasets. The methodology significantly reduced uncertainties in an assumed-to-be ungauged catchment. In terms of methodological improvements, this thesis includes an assessment of the most suitable combination of (available) meteorological datasets and drought indices to characterise droughts in Central America. In addition, a methodology was developed to analyse drought propagation in a tropical catchment, in an automated, objective way. Results from the assessment and the drought propagation analysis contributed with improving the understanding of drought patterns and generating processes in the region. Finally, a methodology was proposed for assessing changes in both hydrological extremes in a consistent way. This contrasts with most commonly used frameworks that study each extreme individually. The method provides important characteristics (frequency, duration and magnitude), information that can be useful for decisions within risk reduction and water management. The results presented in this thesis are a contribution, in terms of hydro-climatic data and assessment methods, for supporting risk reduction of disasters related with hydro-climatic extremes in Central America.
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| 9. |
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| 10. |
- Westerberg, Ida, et al.
(författare)
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Hydrological data uncertainty and its implications.
- 2018
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Ingår i: WIREs Water. - : Wiley. - 2049-1948. ; e1319:5
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Tidskriftsartikel (refereegranskat)abstract
- Hydrologic data are at the core of our understanding of physical hydrologic processes, our simulation models and forecasts of water resources and hazards, and our monitoring of water quantity and quality. However, hydrologic data are subject to multiple sources of uncertainty that can introduce bias and error into our analyses and decision‐making if not properly accounted for. In this article, we summarize five categories of data uncertainty: measurement uncertainty, derived data uncertainty, interpolation uncertainty, scaling uncertainty, and data management uncertainty. Hydrologic data uncertainty magnitudes are typically in the range 10–40%. To quantify data uncertainty, hydrologists should first construct a perceptual model of uncertainty that itemizes uncertainty sources. The magnitude of each source can then be estimated using replicates (repeated, nested or subsampled measurements), or information from the literature (in‐depth uncertainty results from experimental catchments, colocated gauges or method comparisons). Multiple uncertainty sources can be combined using Monte Carlo methods to determine total uncertainty. Data uncertainty analysis improves hydrologic process understanding by enabling robust hypothesis testing and identification of spatial and temporal patterns that relate to true process differences rather than data uncertainty. By quantifying uncertainty in data used for input or evaluation of hydrologic models, we can prevent parameter bias, exclude disinformative data, and enhance model performance evaluation. In water management applications, quantifying data uncertainty can lead to robust risk analysis, reduced costs, and transparent results that improve the trust of the public and water managers.
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