Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change (original) (raw)
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Climatic Change, 2020
This study aimed to investigate the influence of hydrological model calibration/validation on discharge projections for three large river basins (the Rhine, Upper Mississippi and Upper Yellow). Three hydrological models (HMs), which have been firstly calibrated against the monthly discharge at the outlet of each basin (simple calibration), were re-calibrated against the daily discharge at the outlet and intermediate gauges under contrast climate conditions simultaneously (enhanced calibration). In addition, the models were validated in terms of hydrological indicators of interest (median, low and high flows) as well as actual evapotranspiration in the historical period. The models calibrated using both calibration methods were then driven by the same bias corrected climate projections from five global circulation models (GCMs) under four Representative Concentration Pathway scenarios (RCPs). The hydrological changes of the indicators were represented by the ensemble median, ensemble...
Uncertainties in projecting the changes in hydroclimatic variables (i.e., temperature and precipitation) under climate change partly arises from the inability of global circulation models (GCMs) in explaining the observed changes in hydrologic variables. Apart from the unexplained changes by GCMs, the process of customizing GCM projections to watershed scale through a model chain-spatial downscaling, temporal disaggregation, and hydrologic model-also introduces errors, thereby limiting the ability to explain the observed changes in hydrologic variability. Toward this, we first propose metrics for quantifying the errors arising from different steps in the model chain in explaining the observed changes in hydrologic variables (streamflow and groundwater). The proposed metrics are then evaluated using a detailed retrospective analyses in projecting the changes in streamflow and groundwater attributes in four target basins that span across a diverse hydroclimatic regimes over the U.S. Sunbelt. Our analyses focused on quantifying the dominant sources of errors in projecting the changes in eight hydrologic variables-mean and variability of seasonal streamflow, mean and variability of 3 day peak seasonal streamflow, mean and variability of 7 day low seasonal streamflow, and mean and standard deviation of groundwater depth-over four target basins using an Penn state Integrated Hydrologic Model (PIHM) between the period 1956-1980 and 1981-2005. Retrospective analyses show that small/humid (large/arid) basins show increased (reduced) uncertainty in projecting the changes in hydrologic attributes. Further, changes in error due to GCMs primarily account for the unexplained changes in mean and variability of seasonal streamflow. On the other hand, the changes in error due to temporal disaggregation and hydrologic model account for the inability to explain the observed changes in mean and variability of seasonal extremes. Thus, the proposed metrics provide insights on how the error in explaining the observed changes being propagated through the model under different hydroclimatic regimes.
Multiscale assessments of hydroclimatic modelling uncertainties under a changing climate
Journal of Water and Climate Change, 2022
Since the 1970s, climate change has led to decreasing water resources in the Sahel. To cope with climate change, reliable modelling of future hydroclimatic evolutions is required. This study uses multiclimate and hydrological modelling approaches to access past and future (1951–2100) hydroclimatic trends on nine headwater catchments of the Niger River Basin. Eight global climate models (GCMs) dynamically downscaled under the CORDEX CMIP5 project were used. The GCM data were bias-corrected with quantile–quantile mapping. Three rainfall–runoff models (IHACRES-CMD, IHACRES-CWI and Sacramento) were calibrated and validated with observed data and used to simulate runoff. The projected future runoff trend from 2061 to 2090 was compared across the three hydrological models to assess uncertainties from hydrological models. Results show that the bias correction positively enhanced the quality of eight GCMs across the nine catchments. An average Nash–Sutcliffe Efficiency (NSE) across the nine...
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Climate model uncertainty vs. conceptual geological uncertainty in hydrological modeling
Hydrology and Earth System Sciences Discussions, 2015
Projections of climate change impact are associated with a cascade of uncertainties including CO<sub>2</sub> emission scenario, climate model, downscaling and impact model. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM-RCM model combination (from the ENSEMBLES project) forced by the same CO<sub>2</sub> scenario (A1B). The changes from the reference period (1991–2010) to the future period (2081–2100) in projected hydrological variables are ev...
Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid
… of the Royal …, 2010
Uncertainties associated with the representation of various physical processes in global climate models (GCMs) mean that, when projections from GCMs are used in climate change impact studies, the uncertainty propagates through to the impact estimates. A complete treatment of this ‘climate model structural uncertainty’ is necessary so that decision-makers are presented with an uncertainty range around the impact estimates. This uncertainty is often underexplored owing to the human and computer processing time required to perform the numerous simulations. Here, we present a 189-member ensemble of global river runoff and water resource stress simulations that adequately address this uncertainty. Following several adaptations and modifications, the ensemble creation time has been reduced from 750 h on a typical single-processor personal computer to 9 h of high-throughput computing on the University of Reading Campus Grid. Here, we outline the changes that had to be made to the hydrological impacts model and to the Campus Grid, and present the main results. We show that, although there is considerable uncertainty in both the magnitude and the sign of regional runoff changes across different GCMs with climate change, there is much less uncertainty in runoff changes for regions that experience large runoff increases (e.g. the high northern latitudes and Central Asia) and large runoff decreases (e.g. the Mediterranean). Furthermore, there is consensus that the percentage of the global population at risk to water resource stress will increase with climate change.
Journal of Hydrology, 2019
Uncertainty in simulating hydrologic response to future climate is generally assumed to result from the combined uncertainties of the General Circulation Model (GCM), representative concentration pathway (RCP), downscaling method, and hydrologic model used. However, another source of uncertainty, the observed climate data source used to statistically downscale and bias-correct GCM projections, has largely been overlooked. This study assessed the shifts, variability, and uncertainty in streamflow simulation from three downscaling data sources (NCDC land-based weather stations, NEXRAD spatial grid, and PRISM spatial grid) relative to those introduced by six GCMs and three RCPs in west-central Kansas, U.S. Streamflow simulated by the Soil and Water Assessment Tool (SWAT) hydrologic model was found to be more sensitive to future precipitation than to maximum and minimum temperatures. The greatest uncertainty in simulated streamflow was associated with selection of the GCM. Uncertainty in simulated streamflow associated with the observed bias-correction data source (NCDC, PRISM, NEXRAD) was greater than with RCPs and was primarily related to uncertainty in precipitation. This study highlighted the importance of recognizing uncertainty from bias-correction data sources in representing future climate scenarios in hydrologic simulations.
Journal of Hydrology, 2004
Operational flood management and warning requires the delivery of timely and accurate forecasts. The use of distributed and physically based forecasting models can provide improved streamflow forecasts. However, for operational modelling there is a trade-off between the complexity of the model descriptions necessary to represent the catchment processes, the accuracy and representativeness of the input data available for forecasting and the accuracy required to achieve reliable, operational flood management and warning. Four sources of uncertainty occur in deterministic flow modelling; random or systematic errors in the model inputs or boundary condition data, random or systematic errors in the recorded output data, uncertainty due to sub-optimal parameter values and errors due to incomplete or biased model structure. While many studies have addressed the issues of sub-optimal parameter estimation, parameter uncertainty and model calibration very few have examined the impact of model structure error and complexity on model performance and modelling uncertainty. In this study a general hydrological framework is described that allows the selection of different model structures within the same modelling tool. Using this tool a systematic investigation is carried out to determine the performance of different model structures for the DMIP study Blue River catchment using a split sample evaluation procedure. This investigation addresses two questions. First, different model structures are expected to perform differently, but is there a trade-off between model complexity and predictive ability? Secondly, how does the magnitude of model structure uncertainty compare to the other sources of uncertainty? The relative performance of different acceptable model structures is evaluated as a representation of structural uncertainty and compared to estimates of the uncertainty arising from measurement uncertainty, parametric uncertainty and the rainfall input. The results show first that model performance is strongly dependent on model structure. Distributed routing and to a lesser extent distributed rainfall were found to be the dominant processes controlling simulation accuracy in the Blue River basin. Secondly that the sensitivity to variations in acceptable model structure are of the same magnitude as uncertainties arising from the other evaluated sources. This suggests that for practical hydrological predictions there are important benefits in exploring different model structures as part of the overall modelling approach. Furthermore the model structural uncertainty should be considered in assessing model uncertainties. Finally our results show that combinations of several model structures can be a means of improving hydrological simulations. q