Hydrological Uncertainty Research Articles

Influence of soil textural properties on hydrologic fluxes in the Mississippi river basin

AbstractHydrological simulation of states and fluxes represents a viable approach for predicting the occurrence of extreme events and diagnosing their underlying causes. In recent decades, databases of soil textural properties have been published that provide a basis for estimating hydrologic model parameters. The aim of this study is to quantify the impact of soil textural assumptions on hydrologic states and fluxes: an often overlooked source of hydrologic modelling uncertainty. Mesoscale hydrologic model simulations driven by the digital general soil map of the USA (STATSGO2) soil database (1 : 250 000) were compared with those using the Food and Agriculture Organization (FAO)‐based harmonized world soil database (1 : 5 000 000) over the Mississippi river basin. Annual and seasonal differences in modelled water fluxes and states were explored, in addition to major historical flood and drought events during 1988, 1993 and 2012. Model simulations using STATSGO2 consistently led to greater separation between fast and slow runoff responses (by approximately 24–39%) and, generally, more extreme responses to historic flood and drought events. Simulated soil moisture and runoff from the coarser harmonized world soil database produced greater persistence by roughly 0.10 autocorrelation points at 1‐, 3‐ and 6‐month lags, due to larger active water storage capacity. Overall, the choice of soil database altered the partitioning of precipitation between evapotranspiration and runoff, and affected the correlation structure between forcing and modelled fluxes by up to 0.2 points. As compared with other decisions needed to make hydrologic predictions, this analysis demonstrates that choice of soil textural properties for a single model can be an appreciable source of uncertainty and therefore warrants careful consideration. Copyright © 2015 John Wiley & Sons, Ltd.

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.

Generation Capacity Expansion Planning Under Hydro Uncertainty Using Stochastic Mixed Integer Programming and Scenario Reduction

Generation capacity expansion planning (GCEP) is the process of deciding on a set of optimal new investments in generation capacity to adequately supply future loads, while satisfying technical and reliability constraints. This paper shows the application of stochastic mixed-integer programming (SMIP) to account for hydrological uncertainty in GCEP for the Chilean Central Interconnected System, using a two-stage SMIP multi-period model with investments and optimal power flow (OPF). The substantial computational challenges posed by GCEP imply compromising between the detail of the stochastic hydrological variables and the detail of the OPF. We selected a subset of hydrological scenarios to represent the historical hydro variability using moment-based scenario reduction techniques. The tradeoff between modeling accuracy and computational complexity was explored both regarding the simplification of the MIP problem and the differences in the variables of interest. Using a simplified OPF model, we found the difference of using a subset of hydro scenarios to be small when compared with using a full representation of the stochastic variable. Overall, SMIP with scenario reduction provided optimal capacity expansion plans whose investment plus expected operational costs were between 1.3% and 1.9% cheaper than using a deterministic approach and proved to be more robust to hydro variability.

Comments on Uncertainty in Groundwater Governance in the Volcanic Canary Islands, Spain

The uncertainty associated with natural magnitudes and processes is conspicuous in water resources and groundwater evaluation. This uncertainty has an essential component and a part that can be reduced to some extent by increasing knowledge, improving monitoring coverage, continuous elaboration of data and accuracy and addressing the related economic and social aspects involved. Reducing uncertainty has a cost that may not be justified by the improvement that is obtainable, but that has to be known to make the right decisions. With this idea, this paper contributes general comments on the evaluation of groundwater resources in the semiarid Canary Islands and on some of the main sources of uncertainty, but a full treatment is not attempted, nor how to reduce it. Although the point of view is local, these comments may help to address similar situations on other islands where similar problems appear. A consequence of physical and hydrological uncertainty is that different hydrogeological and water resource studies and evaluations may yield different results. Understanding and coarsely evaluating uncertainty helps in reducing administrative instability, poor decisions that may harm groundwater property rights, the rise of complaints and the sub-optimal use of the scarce water resources available in semiarid areas. Transparency and honesty are needed, but especially a clear understanding of what numbers mean and the uncertainty around them, to act soundly and avoid conflicting and damaging rigid attitudes. However, the different situations could condition that what may be good in a place, may not always be the case in other places.

Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability

Abstract. An approach is proposed to assess hydrological simulation uncertainty originating from internal atmospheric variability. The latter is one of three major factors contributing to uncertainty of simulated climate change projections (along with so-called "forcing" and "climate model" uncertainties). Importantly, the role of internal atmospheric variability is most visible over spatio-temporal scales of water management in large river basins. Internal atmospheric variability is represented by large ensemble simulations (45 members) with the ECHAM5 atmospheric general circulation model. Ensemble simulations are performed using identical prescribed lower boundary conditions (observed sea surface temperature, SST, and sea ice concentration, SIC, for 1979–2012) and constant external forcing parameters but different initial conditions of the atmosphere. The ensemble of bias-corrected ECHAM5 outputs and ensemble averaged ECHAM5 output are used as a distributed input for the ECOMAG and SWAP hydrological models. The corresponding ensembles of runoff hydrographs are calculated for two large rivers of the Arctic basin: the Lena and Northern Dvina rivers. A number of runoff statistics including the mean and the standard deviation of annual, monthly and daily runoff, as well as annual runoff trend, are assessed. Uncertainties of runoff statistics caused by internal atmospheric variability are estimated. It is found that uncertainty of the mean and the standard deviation of runoff has a significant seasonal dependence on the maximum during the periods of spring–summer snowmelt and summer–autumn rainfall floods. Noticeable nonlinearity of the hydrological models' results in the ensemble ECHAM5 output is found most strongly expressed for the Northern Dvina River basin. It is shown that the averaging over ensemble members effectively filters the stochastic term related to internal atmospheric variability. Simulated discharge trends are close to normally distributed around the ensemble mean value, which fits well to empirical estimates and, for the Lena River, indicates that a considerable portion of the observed trend can be externally driven.

Climate noise effect on uncertainty of hydrological extremes: numerical experiments with hydrological and climate models

Abstract. An approach has been proposed to analyze the simulated hydrological extreme uncertainty related to the internal variability of the atmosphere ("climate noise"), which is inherent to the climate system and considered as the lowest level of uncertainty achievable in climate impact studies. To assess the climate noise effect, numerical experiments were made with climate model ECHAM5 and hydrological model ECOMAG. The case study was carried out to Northern Dvina River basin (catchment area is 360 000 km2), whose hydrological regime is characterised by extreme freshets during spring-summer snowmelt period. The climate noise was represented by ensemble ECHAM5 simulations (45 ensemble members) with identical historical boundary forcing and varying initial conditions. An ensemble of the ECHAM5-outputs for the period of 1979–2012 was used (after bias correction post-processing) as the hydrological model inputs, and the corresponding ensemble of 45 multi-year hydrographs was simulated. From this ensemble, we derived flood statistic uncertainty caused by the internal variability of the atmosphere.

Risk analysis for seasonal flood-limited water level under uncertainties

For floodwater utilization, seasonal flood-limited water level (FLWL) plays a more and more role in compromising between flood control and beneficial use in reservoir operation during flood season. The prerequisite of determining a seasonal FLWL is that flood control risks should not be increased in reservoir operation as compared with the original operating rule using a fixed FLWL. In this paper, a risk analysis model for deriving seasonal FLWL that considers uncertainties of hydrology, hydraulic condition and reservoir volume is proposed and developed. The risk analysis model consists of three modules: the first is a hydrological uncertainty analysis module, the second is a hydraulic uncertainty analysis module, as well as the third is a reservoir volume uncertainty analysis module. The acceptable risk constraints are given, and the upper limitation of seasonal FLWL is estimated by using Monte Carlo simulation. The China’ Wanjiazhai reservoir (WR) is selected as a case study. The application results show that (1) the hydrological uncertainty and the reservoir volume uncertainty are major contribution factors to seasonal FLWL while the discharge capacity uncertainty is inapparent influence of seasonal FLWL, (2) the most reasonable upper limitations of seasonal FLWL in WR during main-flood and post-flood seasons are 972.3 and 974.1 m, respectively, which considers hydrological uncertainty, minimum hydraulic capability and minimum reservoir volume. The relative magnitudes of seasonal FLWL and the flood water utilization rates during main-flood and post-flood seasons are 0.65% and 61.05%, as well as 0.84% and 81.60%, respectively. Seasonal FLWL can effectively enhance flood water utilization rate without lowering the annual flood control standard compared with annual FLWL.

A Dynamical Climate Model–Driven Hydrologic Prediction System for the Fraser River, Canada

Abstract Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.

Hydrological model uncertainty due to spatial evapotranspiration estimation methods

Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03′N, 12°40′E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

Assessing hydrological model predictive uncertainty using stochastically generated geological models

In distributed and coupled surface water–groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. Copyright © 2015 John Wiley & Sons, Ltd.

Estimation of catchment yield and associated uncertainties due to climate change in a mountainous catchment in Australia

This paper examines the impacts of climate change on future water yield with associated uncertainties in a mountainous catchment in Australia using a multi-model approach based on four global climate models (GCMs), 200 realisations (50 realisations from each GCM) of downscaled rainfalls, 2 hydrological models and 6 sets of model parameters. The ensemble projections by the GCMs showed that the mean annual rainfall is likely to reduce in the future decades by 2–5% in comparison with the current climate (1987–2012). The results of ensemble runoff projections indicated that the mean annual runoff would reduce in future decades by 35%. However, considerable uncertainty in the runoff estimates was found as the ensemble results project changes of the 5th (dry scenario) and 95th (wet scenario) percentiles by −73% to +27%, −73% to +12%, −77% to +21% and −80% to +24% in the decades of 2021–2030, 2031–2040, 2061–2070 and 2071–2080, respectively. Results of uncertainty estimation demonstrated that the choice of GCMs dominates overall uncertainty. Realisation uncertainty (arising from repetitive simulations for a given time step during downscaling of the GCM data to catchment scale) of the downscaled rainfall data was also found to be remarkably high. Uncertainty linked to the choice of hydrological models was found to be quite small in comparison with the GCM and realisation uncertainty. The hydrological model parameter uncertainty was found to be lowest among the sources of uncertainties considered in this study. Copyright © 2015 John Wiley & Sons, Ltd.

Expanded Decision-Scaling Framework to Select Robust Long-Term Water-System Plans under Hydroclimatic Uncertainties

This paper presents a decision-scaling based framework to determine whether one or more preselected planning alternatives for a multiobjective water-resources system are robust to a variety of nonstationary hydroclimatic conditions and modeling uncertainties. The decision-scaling methodology is advanced beyond previous applications with an efficient procedure to select realizations of climate variability and Bayesian methods to assess the effects of hydrologic uncertainty. Monte Carlo simulations are used to identify long-term planning alternatives that are robust despite the hydroclimatic uncertainties. A new metric is proposed to define robustness in this context. The framework is coupled with a host of long-term projections to understand the likelihood of potential future changes and provide useful guidance for planning. The effects of climate model downscaling and credibility on the decision process are discussed. The approach is demonstrated in a case study for a dual-purpose surface water reservoir in Texas. The results suggest that both internal climate variability and hydrologic uncertainty can substantially alter the assessment of system robust for long-term planning purposes.

Establishment and evaluation of operation function model for cascade hydropower station

Toward solving the actual operation problems of cascade hydropower stations under hydrologic uncertainty, this paper presents the process of extraction of statistical characteristics from long-term optimal cascade operation, and proposes a monthly operation function algorithm for the actual operation of cascade hydropower stations through the identification, processing, and screening of available information during long-term optimal operation. Applying the operation function to the cascade hydropower stations on the Jinshajiang-Yangtze River system, the modeled long-term electric generation is shown to have high precision and provide benefits. Through comparison with optimal operation, the simulation results show that the operation function proposed retains the characteristics of optimal operation. Also, the inadequacies and attribution of the algorithm are discussed based on case study, providing decision support and reference information for research on large-scale cascade operation work.

Impact of complexity of radar rainfall uncertainty model on flow simulation

A large number of radar rainfall uncertainty (RRU) models have been proposed due to many error sources in weather radar measurements. It is recognized that these models should be integrated into overall uncertainty analysis schemes with other kinds of model uncertainties such as model parameter uncertainty when the radar rainfall is applied in hydrological modeling. We expect that the RRU model can be expressed in a mathematically extensible and simple format. However, the complexity of the RRU has been growing as more and more factors are considered such as spatio-temporal dependence and non-Gaussian distribution. This study analyzes how the RRU propagates through a hydrological model (the Xinanjiang model) and investigates which features of the RRU model have significant impacts on flow simulation. A RRU model named Multivariate Distributed Ensemble Generator (MDEG) is implemented in the Brue catchment in England under different model complexities. The generated ensemble rainfall values by MDEG are then input into the Xinanjiang model to produce uncertainty bands of ensemble flows. Comparison of five important indicators that describe the characteristics of uncertainty bands shows that the ensemble flows generated by MDEG with non-Gaussian marginal and joint distributions are close to the ones with Gaussian distributions. In addition, the dispersion of the uncertainty bands increases dramatically with the growth of the MDEG model complexity. It is concluded that the Gaussian marginal distribution and spatio-temporal dependence using Gaussian copula is considered to be the preferred configuration of the MDEG model for hydrological model uncertainty analysis. Further studies should be carried out in a variety of catchments under different climate conditions and geographical locations to check if the conclusion is valid beyond the Brue catchment under the British climate.

Challenging the standard dike freeboard: Methods to quantify statistical uncertainties in river flood protection

In most developed and developing nations, a freeboard is being applied to flood defense structures as a margin of uncertainty in the estimated flood stage. In some jurisdictions, practice is shifting towards the use of confidence intervals based on the fitted flood probability distribution, albeit often relying on only one statistical distribution and on only annual maximum flows. In this paper, we argue that, independent of geotechnical, geomorphological or hydrological uncertainties pertaining to the estimation of flood stage, the application of standard freeboards ignores stochastic uncertainty, the inclusion of which would provide a more scientifically defensible measure for allowable freeboard. The river stage estimate is subject to multiple sources of uncertainty, including but not limited to model and parameter uncertainty. Consequently, freeboards should be determined via a frequency analysis that explicitly takes into consideration, as well as minimizes, the uncertainty of the estimate due to known factors. Confidence intervals are a common way to represent uncertainty of a statistical estimate such as for the river stage. The choice of the confidence level will be critical, and in many cases will be associated with significant cost implications for the construction or upgrade of flood defense structures. Quantitative flood risk assessments, which are emerging as a standard in developed nations, are well suited to address this issue by allowing loss and mitigation cost comparisons for different flood scenarios. Our paper provides guidance for confidence interval calculations of river stage using an extension of the classical peaks-over-threshold method for daily river levels.

Mitigating hydrologic financial risk in hydropower generation using index-based financial instruments

Variability in streamflows can lead to reduced generation from hydropower producers and result in reductions in revenues that can be financially disruptive. This link between hydrologic and financial uncertainties, and the possibility of increased hydrologic variability in the future, suggests that hydropower producers need to begin to consider new strategies and tools for managing these financial risks. This study uses an integrated hydro-economic model of the Roanoke River Basin to characterize the financial risk faced by hydropower generators as a result of hydrologic variability, and develops several index-based financial hedging contracts intended to mitigate this risk. Several different indices are evaluated in terms of their ability to serve as the basis for effective financial contracts. Contract structures are then developed and evaluated using a 100-year simulation that describes hydropower operations in the Roanoke basin. Basis risk, contract pricing, and risk mitigation are investigated for three styles of contracts: insurance, binary, and collar. In all three cases, the contracts are shown to be capable of substantially reducing the risks of very low revenue years for costs that are a small fraction of total annual revenues (1–3%).

Projecting hydropower production under future climates: a guide for decision‐makers and modelers to interpret and design climate change impact assessments

Hydropower is a key energy source in almost all world regions. It fuels social and economic development, ensures electricity security, and is a pillar for renewable electricity production. But hydropower and its environmental impacts are vulnerable to climate change. This discussion of model‐based climate change impact assessments and underlying modeling assumptions will help decision‐makers and scientists analyzing existing studies and identifying the most urgent open questions. Rooted in hydrological uncertainty analysis, this discussion focuses on the importance of local factors and on modeling uncertainties for a critical view on our ability to project future hydropower production in different world regions. WIREs Water 2015, 2:271–289. doi: 10.1002/wat2.1083This article is categorized under: Engineering Water > Planning Water Science of Water > Water and Environmental Change

The water footprint of California's energy system, 1990-2012.

California's energy and water systems are interconnected and have evolved in recent decades in response to changing conditions and policy goals. For this analysis, we use a water footprint methodology to examine water requirements of energy products consumed in California between 1990 and 2012. We combine energy production, trade, and consumption data with estimates of the blue and green water footprints of energy products. We find that while California's total annual energy consumption increased by just 2.6% during the analysis period, the amount of water required to produce that energy grew by 260%. Nearly all of the increase in California's energy-related water footprint was associated with water use in locations outside of California, where energy products that the state consumes were, and continue to be, produced. We discuss these trends and the implications for California's future energy system as it relates to climate change and expected water management challenges inside and outside the state. Our analysis shows that while California's energy policies have supported climate mitigation efforts, they have increased vulnerability to climate impacts, especially greater hydrologic uncertainty. More integrated analysis and planning are needed to ensure that climate adaptation and mitigation strategies do not work at cross purposes.

A comparison of hydrologic models for ecological flows and water availability

AbstractRobust hydrologic models are needed to help manage water resources for healthy aquatic ecosystems and reliable water supplies for people, but there is a lack of comprehensive model comparison studies that quantify differences in streamflow predictions among model applications developed to answer management questions. We assessed differences in daily streamflow predictions by four fine‐scale models and two regional‐scale monthly time step models by comparing model fit statistics and bias in ecologically relevant flow statistics (ERFSs) at five sites in the Southeastern USA. Models were calibrated to different extents, including uncalibrated (level A), calibrated to a downstream site (level B), calibrated specifically for the site (level C) and calibrated for the site with adjusted precipitation and temperature inputs (level D). All models generally captured the magnitude and variability of observed streamflows at the five study sites, and increasing level of model calibration generally improved performance. All models had at least 1 of 14 ERFSs falling outside a +/−30% range of hydrologic uncertainty at every site, and ERFSs related to low flows were frequently over‐predicted. Our results do not indicate that any specific hydrologic model is superior to the others evaluated at all sites and for all measures of model performance. Instead, we provide evidence that (1) model performance is as likely to be related to calibration strategy as it is to model structure and (2) simple, regional‐scale models have comparable performance to the more complex, fine‐scale models at a monthly time step. Copyright © 2015 John Wiley & Sons, Ltd.

Climate change impacts on the seasonality and generation processes of floods – projections and uncertainties for catchments with mixed snowmelt/rainfall regimes

Abstract. Climate change is likely to impact the seasonality and generation processes of floods in the Nordic countries, which has direct implications for flood risk assessment, design flood estimation, and hydropower production management. Using a multi-model/multi-parameter approach to simulate daily discharge for a reference (1961–1990) and a future (2071–2099) period, we analysed the projected changes in flood seasonality and generation processes in six catchments with mixed snowmelt/rainfall regimes under the current climate in Norway. The multi-model/multi-parameter ensemble consists of (i) eight combinations of global and regional climate models, (ii) two methods for adjusting the climate model output to the catchment scale, and (iii) one conceptual hydrological model with 25 calibrated parameter sets. Results indicate that autumn/winter events become more frequent in all catchments considered, which leads to an intensification of the current autumn/winter flood regime for the coastal catchments, a reduction of the dominance of spring/summer flood regimes in a high-mountain catchment, and a possible systematic shift in the current flood regimes from spring/summer to autumn/winter in the two catchments located in northern and south-eastern Norway. The changes in flood regimes result from increasing event magnitudes or frequencies, or a combination of both during autumn and winter. Changes towards more dominant autumn/winter events correspond to an increasing relevance of rainfall as a flood generating process (FGP) which is most pronounced in those catchments with the largest shifts in flood seasonality. Here, rainfall replaces snowmelt as the dominant FGP primarily due to increasing temperature. We further analysed the ensemble components in contributing to overall uncertainty in the projected changes and found that the climate projections and the methods for downscaling or bias correction tend to be the largest contributors. The relative role of hydrological parameter uncertainty, however, is highest for those catchments showing the largest changes in flood seasonality, which confirms the lack of robustness in hydrological model parameterization for simulations under transient hydrometeorological conditions.