Key Points
ANOVA method applied to climate‐impact modeling
Detailed assessment of changes in water balance quantities due to climate change
Interactions of uncertainty sources
The quantification of ...uncertainties in projections of climate impacts on river streamflow is highly important for climate adaptation purposes. In this study, we present a methodology to separate uncertainties arising from the climate model (CM), the statistical postprocessing (PP) scheme, and the hydrological model (HM). We analyzed ensemble projections of hydrological changes in the Alpine Rhine (Eastern Switzerland) for the near‐term and far‐term scenario periods 2024–2050 and 2073–2099 with respect to 1964–1990. For the latter scenario period, the model ensemble projects a decrease of daily mean runoff in summer (−32.2%, range −45.5% to −8.1%) and an increase in winter (+41.8%, range +4.8% to +81.7%). We applied an analysis of variance model combined with a subsampling procedure to assess the importance of different uncertainty sources. The CMs generally are the dominant source in summer and autumn, whereas, in winter and spring, the uncertainties due to the HMs and the statistical PP gain importance and even partly dominate. In addition, results show that the individual uncertainties from the three components are not additive. Rather, the associated interactions among the CM, the statistical PP scheme, and the HM account for about 5%–40% of the total ensemble uncertainty. The results indicate, in distinction to some previous studies, that none of the investigated uncertainty sources are negligible, and some of the uncertainty is not attributable to individual modeling chain components but rather depends upon interactions.
Recent technological advances in representation of processes in numerical climate models have led to skillful predictions, which can consequently increase the confidence of hydrological predictions ...and usability of hydroclimatic services. Given that many water‐related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability. Here we analyze the seasonal forecasts of streamflow volumes across about 35,400 basins in Europe, which lie along a strong gradient in terms of climatology, scale, and hydrological regime. We then link the seasonal volumetric errors to various physiographic‐hydroclimatic descriptors and meteorological biases in order to identify the key drivers controlling predictability. Streamflow volumes over Europe are well predicted, yet with some geographic and seasonal variability; however, the predictability deteriorates with increasing lead time particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of descriptors, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin's hydrological regime, with limited predictability in relatively flashy basins. On the contrary, snow and/or baseflow dominated regions with long recessions show high streamflow predictability. Finally, climatology and precipitation forecast biases are also related to streamflow predictability, highlighting the importance of developing robust bias adjustment methods. Overall, this investigation shows that the seasonal streamflow predictability can be clustered, and hence regionalized, based on a priori knowledge of local hydroclimatic conditions.
Plain Language Summary
Hydrological information for the months ahead is of great value to existing decision‐making practices, particularly to those affected by the vagaries of the climate and who would benefit from better understanding and managing climate‐related risks. Currently, there is limited knowledge of the factors controlling the quality of the seasonal streamflow forecasts. We analyze such forecasts over Europe and link their predictability to basin descriptors and meteorological biases. This allows the identification of the key drivers along a strong hydroclimatic gradient. The seasonal streamflow predictability varies geographically and seasonally with acceptable values for the first lead months. Predictability deteriorates with increasing lead time particularly in the winter months. The hydrological regime is strongly linked to the forecast quality, with quickly reacting basins showing low values. Basin climatology and precipitation forecast biases are also related to the predictability of streamflow.
Key Points
Forecast quality of seasonal streamflow volume varies geographically and seasonally, while streamflow predictability can be regionalized
Streamflow predictability is strongly dependent on the basin's hydrological regime, climatology, and precipitation forecast biases
Predictability is higher in river systems of long streamflow memory than in systems immediately responding to the precipitation signal
VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, ...process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques.
Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on).
Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value‐cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value‐cost.eu/validationportal.
The largest and most comprehensive to date intercomparison of statistical downscaling methods is presented, with a total of over 50 downscaling methods representative of the most common approaches and techniques. Overall, most of the downscaling methods greatly improve raw model biases and no approach is superior in general, due to the large method‐to‐method variability. The main factors influencing the results are the seasonal calibration of the methods and their stochastic nature, for biases in the mean and variance.
Uncertainties in hydro‐climatic projections are (in part) related to various components of the production chain. An ensemble of numerous projections is usually considered to characterize the overall ...uncertainty; however in practice a small set of scenario combinations are constructed to provide users with a subset that is manageable for decision‐making. Since projections are unavoidably uncertain, and multiple projections are typically informationally redundant to a considerable extent, it would be helpful to identify an informationally representative subset in a large model ensemble. Here a framework rooted in the information theoretic Maximum Information Minimum Redundancy concept is proposed for identifying a representative subset from an available ensemble of hydro‐climatic projections. We analyze an ensemble of 16 precipitation and temperature projections for Sweden, and use these as inputs to the HBV hydrological model to project river discharge until the mid of this century. Representative subsets are judged in terms of different statistical properties of three essential climate variables (precipitation, temperature and discharge), whilst we further assess the sensitivity of the optimized subset for different seasons and future periods. Our results indicate that a quarter to a third of the available set of projections can represent more than 80% of the total information of hydro‐climatic changes. We find that the representative subsets are sensitive to the regional hydro‐climatic characteristics and the choice of variables, seasons and periods of interest. Therefore we recommend that any selection process should not be solely driven by climatic variables but, rather, should also consider variables of the impact model.
Plain Language Summary
The need for better understanding of climate change and its impact has led to an increasing number of climate models and consequently hydro‐climatic projections. Using a large set of projections, we present the information content and the redundant information when an ensemble is considered, and hence demonstrate how representative projections can be identified in order to overcome artifacts/biases introduced by a common hand‐picking approach. An identified subset reduced by almost 70% from the large ensemble is capable of representing more than 80% of the total information for precipitation, temperature and discharge; however the selected projections are sensitive to choice of variables, seasons, and period of interest. The size of the representative subset is also related to the regional hydro‐climatic characteristics.
Key Points
Redundant information is present in large model ensembles; subsets should target towards maximizing independence and minimizing redundancy
A subset of 20–35% of the total available projections can represent a large fraction of the ensemble range for hydro‐climatic changes
The identified subsets are sensitive to the choice of variables, seasons and future periods
A transient climate scenario experiment of the regional climate model COSMO-CLM is analyzed to assess the elevation dependency of 21st century European climate change. A focus is put on near-surface ...conditions. Model evaluation reveals that COSMO-CLM is able to approximately reproduce the observed altitudinal variation of 2 m temperature and precipitation in most regions and most seasons. The analysis of climate change signals suggests that 21st century climate change might considerably depend on elevation. Over most parts of Europe and in most seasons, near-surface warming significantly increases with elevation. This is consistent with the simulated changes of the free-tropospheric air temperature, but can only be fully explained by taking into account regional-scale processes involving the land surface. In winter and spring, the anomalous high-elevation warming is typically connected to a decrease in the number of snow days and the snow-albedo feedback. Further factors are changes in cloud cover and soil moisture and the proximity of low-elevation regions to the sea. The amplified warming at high elevations becomes apparent during the first half of the 21st century and results in a general decrease of near-surface lapse rates. It does not imply an early detection potential of large-scale temperature changes. For precipitation, only few consistent signals arise. In many regions precipitation changes show a pronounced elevation dependency but the details strongly depend on the season and the region under consideration. There is a tendency towards a larger relative decrease of summer precipitation at low elevations, but there are exceptions to this as well.
In the framework of the IAHS initiative on Predictions in Ungauged Basins, the predictive uncertainty in hydrological simulations constitutes a key issue. The Three Gorges Area located in central ...China is a poorly gauged macro-scale catchment with an area of about 57,000
km
2 which is frequently hit by floods. The semi-distributed hydrological model PREVAH was implemented in this catchment as a part of the Changjiang Flood Assistance Project. The precipitation correction, being the most sensitive tuneable parameter in the basin, was chosen to be regionally allocated in order to cope with regionally varying precipitation measurement errors due to differences in the measurement network setup. The model was calibrated on one single discharge time series at the basin’s outlet by means of the Adaptive Metropolis algorithm. The estimated posteriori parameter probability distribution revealed that the regional allocation of the precipitation correction induced a strong parameter interdependence. The calculated predictive uncertainty is large but nevertheless suggests that additional uncertainty sources should be included to get a sound probabilistic simulation. Despite the large uncertainty and parameter interdependence, the model performance in the flood season of the year 2007 shows that the Adaptive Metropolis algorithm successfully inferred a well behaving best-bin parameter set.
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