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The regionalisation of flood frequencies is a precondition for the estimation of flood statistics for ungauged basins. We propose a flood type-specific regionalization, to take into account the different flood-generating processes. The different flood types are classified according to their meteorological causes. Their probability distributions are modelled by type-specific distribution functions which are combined into one statistical annual mixture model. For regionalisation, we specified the parameters of each type-specific probability distribution separately with hierarchical clustering and regressions from catchment attributes. By selection of the most relevant features, depending on the flood type, the specifics of flood-generating processes were considered. This approach offers a higher degree of freedom for regionalisation as it describes the relationships between catchment characteristics, meteorological causes of floods and response of watersheds. However, as for many regionalisation procedures, uncertainty is created by different observation periods of the gauged catchments used for regionalisation. Due to different observation periods, extreme events can be included in one sample, while for the neighboring catchment with shorter observation period this may not be the case. This stochastic uncertainty results in a heterogeneity of second and third moments which may bias the regionalization. We analyzed different scenarios to take into account this uncertainty and to include the information of long observation periods as well as the occurrence of extreme events for correcting the distribution parameters accordingly. This leads to more homogeneous regionalisation results.

How to cite: Fischer, S. and Schumann, A.: Consideration of sample size uncertainty for the regionalization of type-specific flood frequency analyses, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-58, https://doi.org/10.5194/iahs2022-58, 2022.

Large scale modelling is becoming increasingly important in hydrology, particularly to characterize and quantify changes in the hydrological regime, whose drivers are typically large-scale phenomena, up to the global scale (e.g., climate change). This can be done with distributed models by estimating spatially consistent model parameters, that are parameters having a functional relationship with catchment characteristics. In this study we adopt the newly developed PArameter Set Shuffling (PASS) approach, based on a machine learning decision tree algorithm, for the regional calibration of the SALTO (SAme Like The Others) model. The method exploits observed patterns of locally calibrated parameters and catchment (climatic and geomorphological) descriptors, to derive functional relationships between the variables. The results, for around 100 catchments located in North-Western Italy, demonstrate that the use of regionally calibrated parameter sets results in similarly good performances as for local calibration. This suggests that the predicted parameter sets can be efficiently used for streamflow prediction in ungauged basins or under changing conditions.

How to cite: Pesce, M., Viglione, A., Hardenberg, J., Tarasova, L., Basso, S., and Merz, R.: How much value is in climate and geomorphological descriptors for distributed hydrological modelling? A decision tree based approach in North-Western Italy, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-361, https://doi.org/10.5194/iahs2022-361, 2022.

Due to the limited length of locally available sequences of precipitation extremes, point rainfall depth associated with given duration and return period is usually estimated through regional frequency analysis. Several statistical regionalization methods proposed in the literature enable one to exploit sequences of precipitation extremes observed at homogeneous pooling groups of sites, that supposedly share the same frequency regime of rainfall extremes with the site of interest. Homogeneous sites can be identified by looking at specific climatic descriptors; for instance, some reliable authors successfully utilize Mean Annual Precipitation (MAP) as the sole proxy for locally characterizing the frequency regime of sub-daily rainfall extremes, and for grouping sequences of rainfall extremes records. We aim at advancing this traditional approach (1) by relaxing the hypothesis of the existence of a homogeneous pooling group of sites characterized by a unique regional parent distribution and (2) by incorporating additional morphological and climatic information in the regional model. We rely on more than 2350 Annual Maximum Series of rainfall depth for different time-aggregation intervals between 1 and 24 hours, observed since 1928 to 2011 in a vast study area in Northern Italy.  We refer to MAP as well as to additional morphologic descriptors (e.g.  minimum distance to Tyrrhenian (Adriatic) Sea, mean elevation and slope around the station, etc.).

We train a probabilistic neural network that models the frequency regime of observed annual maxima of rainfall depth resorting to a Generalized Extreme Value (GEV) distribution, whose parameters are data-driven functions of the local values of the selected descriptors and duration. Then, several cross-validation experiments are performed to assess the accuracy of the developed regional model relative to a simpler regional GEV model, whose parameters are functions of MAP and time-aggregation intervals.

Our analyses address several research problems: (a) identifying the most descriptive morphological proxies to combine with MAP for representing the frequency regime of sub-daily rainfall extremes in the study area, (b) highlighting limitations and potential of data-driven multivariate regional models of the frequency regime of rainfall extremes, (c) the advantages of a multivariate approach relative to a regionalization scheme based on MAP alone.

How to cite: Magnini, A., Lombardi, M., Valtancoli, E., and Castellarin, A.: Multivariate and data-driven regional frequency analysis for rainfall extremes, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-381, https://doi.org/10.5194/iahs2022-381, 2022.

This work presents trend analyses performed on time series. It follows the work done by Giuntoli et al. (2013) on trend analysis of low flows and their relationship with large-scale climate variability in France. A R package was implemented to allow the extraction of variables on time series and performing statistical trend analysis on the extracted variables. Detected trend can be summarised on maps. The methodology uses the Mann-Kendall statistical test to assess the significance of linear trend. Regional consistency can also be checked.

The methodology used was performed on 207 French daily streamflow over the period 1968-2020. Three period subsets are studied: 1968-2000, 1968-2010 and 1968-2020 to compare trend results and assess the variability over time.
Results confirm a North-South geographical split in temporal trends for droughts. They also show the increase of the severity of droughts over the last decades in Southern France.

Results also suggests a similar North-South geographical split for high and medium flows. They show that temporal trends are decreasing over the last decades in the Southern part of France for both high and medium flows. However, in the North part of France, results shows less significant trends in streamflow time series.

Giuntoli, I., Renard, B., Vidal J.-P., and Bard, A. (2013). Low flows in France and their relationship to large-scale climate indices, Journal of Hydrology, 482, 105-118, http://dx.doi.org/10.1016/j.jhydrol.2012.12.038.

How to cite: Mansanarez, V., Renard, B., and Lang, M.: A R package for quickly updating trend analysis: application to French streamflow time series. , IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-647, https://doi.org/10.5194/iahs2022-647, 2022.

A necessary first step in any hydrological study is the analysis of the available measurements and in particular their relevance to the future behaviour of the system. Periodic variations and linear trends are of special interest. However, an abrupt change in the system and the statistical processes that affect the measurements will, if uncorrected, interfere with the analysis. Detecting abrupt changes in time  series is therefore of considerable importance. The classical tests are primarily intended to test the null hypothesis “There is no change point”. If there is a change point (CP), then the usual approach reuses the classical test results to find a location. Additional work is then needed to determine the uncertainty in the position.

In 2018 two new approaches to change point analysis were presented in the statistical literature. One approach uses a deviance function and the other uses homogeneity tests. Both approaches construct confidence sets for the change point location at all confidence levels.  An interesting aspect of these approaches is that they can produce confidence sets, not just confidence intervals. For example,  in a series of annual maxima two points separated by several years may be marked as potential CP at a given confidence level without marking the intervening years as possible CPs at that level. The approach using homogeneity tests was applied to  both synthetic time series and measurement time series. The results for synthetic time series provide information on the statistical properties of the approach for small samples.  The results for measurement time series are compared with the CPs found for these series in the literature.

How to cite: van Nooijen, R., Kolechkina, A., and Zhou, C.: Building confidence curves with classical change point tests, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-714, https://doi.org/10.5194/iahs2022-714, 2022.

Global climate changes significantly contribute to increased frequency of hydrologic extremes. This significantly underestimates the hydrologic design parameters, bringing of hydro systems to increased failure risk. In order to address this concern, the current practice of development of hydrologic frequency tools need to be updated accounting for non-stationarity. This study first considered a diverse set of statistical tests to examine the trend, change points, non-stationarity and randomness of streamflow, rainfall and temperature time series of scales ranging from daily to annual. The annual maxima time series indicted non stationarity against the stationary behaviour of daily series of hydro-meteorological datasets of the basin. Subsequently, this study developed the Temperature Duration Frequency (TDF), rainfall Intensity Duration Frequency (IDF) and flood frequency (FF) curves of Greater Pamba river basin in Kerala India, the part of which was most severely affected by the near century return period flood event of 2018. The analysis was performed for a multitude of combinations of variations in distribution parameters with time and climatic drivers as physical covariates in the extreme value formulations. The study proposed a novel wavelet coherence (WC) based driver selection of most dominant combination of climatic precursors in developing FF and IDF relations of three locations of Kalloopara, Malakkara and Thumpamon and TDF curve of Kuttanad region in the basin, considering data of 1978-2015 period. The proposed WC framework considers bi-multi-and partial effects of climatic oscillations (COs) like ElNino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) in identifying potential drivers. The different WC formulations captured in-phase relationships of streamflows and rainfall with COs at intra-annual, annual and inter annual scales upto 4 years. The methods showed that addition of climatic precursors improved the NS estimates of flood and rainfall quantiles by more accurately capturing the magnitudes of extreme streamflows and rainfalls of 2018, 2021 than the time covariate formulations. However, the role of COs on extreme temperature is not found to be influential in developing TDF relationships, which needs further investigation.

How to cite: Nair, A., Adarsh, S., Meera, G. M., and Sreedevi, V.: Developing Non Stationary Frequency Relationships for Greater Pamba River basin, Kerala India incorporating dominant climatic precursors, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-743, https://doi.org/10.5194/iahs2022-743, 2022.

Time asymmetry, i.e., temporal irreversibility, has a very important role in many scientific fields and has been studied thoroughly. Its detection in time series indicates the need to preserve it in stochastic simulations. This also seems to be the case for the streamflow process in hydrological simulation. Relevant large-scale studies have shown that time asymmetry of the streamflow is absolutely evident at small scales (hours) and vanishes only at larger scales (several days). The latter highlights the need to reproduce it in flood simulations of fine-scale resolution. To this aim, an enhancement of a recently proposed simulation algorithm for irreversible processes was developed, based on an asymmetric moving average (AMA) scheme that allows for the explicit preservation of time asymmetry at two or more timescales simultaneously. The method is tested through some case studies from around the world to further explore the method’s strengths and limitations and to examine the stochastic characteristics of the simulated results.

How to cite: Vavoulogiannis, S., Iliopoulou, T., Dimitriadis, P., and Koutsoyiannis, D.: Time Asymmetry and Stochastic Modelling of Streamflow, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-270, https://doi.org/10.5194/iahs2022-270, 2022.

Hydrological projections in the context of a changing climate may display high levels of uncertainty, particularly when examining small temporal and spatial scales. To project the response of hydrological processes to the increasing global temperatures, scientists and practitioners rely on chains of numerical models, each contributing some degree of uncertainty to the overall outputs. Furthermore, the randomness intrinsic to climate phenomena, known as internal climate variability, contributes to the uncertainty of the hydrological projections in the form of an irreducible stochasticity. In this work, we quantify the impacts and partition the uncertainty of hydrological processes emerging from climate models and internal variability  for two mountainous catchments in the Swiss Alps and across a broad range of scales. To that end, we used high-resolution ensembles of climate and hydrological data produced using a two-dimensional stochastic weather generator (AWE-GEN-2d) and a distributed hydrological model (Topkapi-ETH). We quantified the uncertainty in hydrological projections towards the end of the century through the estimation of the values of signal-to-noise ratios (STNR). We found small STNR values (<-1) in the projection of annual streamflow for most sub-catchments in both study sites that are dominated by the large natural variability of precipitation (explains ~70% of total uncertainty). Furthermore, we investigated specific hydrological components that are critical in the model chain with detail. For example, snowmelt or liquid precipitation exhibits robust change signals, which translates into high STNR values for streamflow during warm seasons and at higher elevations, together with a larger contribution of climate model uncertainty, suggesting that an improvement of the involved models has the potential of significantly narrowing the uncertainty. In contrast, extreme flows show low STNR values due to large internal climate variability across all elevations, which limits the possibility of narrowing their estimation uncertainty due to a warming climate. This study demonstrates that high-resolution hydro-climatic ensembles enable the quantification of hydrological projections across spatial and temporal scales, which can be used to assess the potential for narrowing hydrological uncertainties.

How to cite: Moraga, J. S., Peleg, N., Molnar, P., Fatichi, S., and Burlando, P.: Using high-resolution stochastic climate ensembles to model the impacts and uncertainty of hydrology in mountainous catchments, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-273, https://doi.org/10.5194/iahs2022-273, 2022.

Hydropower regulations may significantly increase the variability of flow when compared with the natural hydrological regime to which river ecosystems have evolved over long time periods. This can be detrimental for river habitats and for many organisms. Attenuation of the variability in rivers improves ecological status at some distance downstream of the introduced variability. Being able to accurately estimate this distance is critical for the evaluation of ecological status. The attenuation of introduced flow variability has only been studied previously for specific rivers, and the dominant mechanisms have not been analyzed in detail. In this work, the attenuation rate and its important drivers is studied for regulated rivers in all of Sweden by comparing the results of hydrological and hydrodynamic models with observations. We performed Fourier transformation of flow time series from the Hydrological Predictions for the Environment (HYPE) model, an extracted model representing only river processes, the diffusion wave equation, and from observed flow at several hundred stations. The reduction of the amplitudes along rivers was then analysed.

In many regulated rivers in Sweden, flow variability of periodicity 7 days is dominant among periods varying from a couple of days up to one month. The analysis further shows that variability with periodicity days to months typically attenuate with an exponential rate that is largest for short periods. Attenuation of these periods is mainly driven by processes within rivers, as opposed to catchment features such as the distribution of rain or soil properties. Further, rivers in regulated systems often resemble cascades with long stretches of rivers with low gradients in elevation between the dams. The associated attenuation in these “lake-alike” rivers can be well described by hydrological simulations with HYPE using a simple linear attenuation box. In contrast, the sometimes-used diffusion wave equation is often unable to replicate the observed attenuation here. Our work supports the assessment of ecological status and management decisions by improving the estimates of distances required for attenuation, and provides important insights on attenuation processes. Further, the analysis of dominant modes can be used to parameterize short-term regulations in hydrological simulations to improve their forecast skills.

How to cite: Elenius, M. and Lindström, G.: Dominant discharge periodicities and their attenuation in rivers of Sweden, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-358, https://doi.org/10.5194/iahs2022-358, 2022.

When solving practical water resources management problems, situations when the amount of data is insufficient both terms of length and representativeness occur. Even the most extended observed series are short for reliable estimation and assessment long-term variability and extremes. A combination of stochastic weather generators, allowing the generation of arbitrarily long synthetic series of precipitation and air temperatures, and rainfall-runoff models, which transform them into an equally long synthetic series of river discharges, represent a feasible solution. This study aimed to develop a robust, practically oriented stochastic weather generator for rainfall-runoff modelling allowing the simulation of synthetic daily precipitation and air temperature series at multiple stations in catchments, considering seasonality, temporal dependency and spatial correlation. The design was based partly on the established methodological practices. In addition, it implements innovative components of temporal ad spatial multiscale disaggregation based process-oriented analysis of the processes involved and allows to consider the possibility of climate change. The ability of the weather generator to correctly reproduce characteristics of the observed rainfall and air temperature records was evaluated by both (temporal and spatial) statistical and hydrological process characteristics at each and across all stations.

Several case study results proved that the proposed concept of the generator is robust and practically applicable. It allows to reliably generate synthetic series of precipitation totals and air temperatures at individual stations in catchments and around simultaneously. Using a combined stochastic-deterministic rainfall-runoff modelling approach provides an infinite number of combinations of flow, including the extreme and the unobserved ones. It can be used to estimate extreme flood characteristics, determine hydrologic metrics of ecological flows, and detect changes in flow variability caused by land use and climate change.

Acknowledgements: This work was supported by the Slovak Research and Development Agency under Contract No. APVV-19-0340 and the VEGA Grant Agency No. 1/0632/19.

How to cite: Výleta, R., Valent, P., and Szolgay, J.: A process-based multisite multivariate stochastic weather generator for water resources management, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-362, https://doi.org/10.5194/iahs2022-362, 2022.

We present a new method for simulating and predicting hydrologic variables with uncertainty assessment and provide example applications to river flows. The method is identified with the acronym ``Bluecat'' and is based on the use of a deterministic model which is subsequently converted to a stochastic formulation. The latter provides an adjustment on statistical basis of the deterministic prediction along with its confidence limits. The distinguishing features of the proposed approach are the ability to infer the probability distribution of the prediction without requiring strong hypotheses on the statistical characterization of the prediction error (e.g. normality, homoscedasticity) and its transparent and intuitive use of the observations. Bluecat makes use of a rigorous theory to estimate the probability distribution of the predictand conditioned by the deterministic model output, by inferring the conditional statistics of observations. Therefore Bluecat bridges the gaps between deterministic (possibly physically-based, or deep learning-based) and stochastic models as well as between rigorous theory and transparent use of data with an innovative and user oriented approach. We present two examples of application to the case studies of the Arno river at Subbiano and Sieve river at Fornacina. The results confirm the distinguishing features of the method along with its technical soundness. We provide an open software working in the R environment, along with help facilities and detailed instructions to reproduce the case studies presented here.

How to cite: Koutsoyiannis, D. and Montanari, A.: Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-574, https://doi.org/10.5194/iahs2022-574, 2022.

According to both theoretical considerations and climatic projections, precipitation extremes are expected to increase under a warmer environment. Inferential analyses involving statistical testing procedures are frequently performed to validate this scenario. Recent research has found that the results of trend tests applied to hydrological data might be misinterpreted if (i) records exhibit autocorrelation and (ii) field significance is not taken into account when tests are performed multiple times. In this study, we investigate these two issues focusing on frequencies (or counts) of daily rainfall extremes. To this end, a sample of extreme precipitation frequency time series is derived from long-term (100-year) daily precipitation records retrieved by 1087 rain gauges belonging to the Global Historical Climate Network database. Several Monte Carlo simulations are performed involving random synthetic frequency time series generated through the Poisson first-order Integer-valued AutoRegressive model (Poisson-INAR(1)), reproducing the statistical properties of the observed counts and characterized by different sample size, autocorrelation level, and trend magnitude. The following are the key findings. (1) While empirical autocorrelations are likely due to the existence of trends, empirical trends cannot be explained solely by autocorrelation, suggesting that accounting for serial correlation may have a limited influence on trend analyses of extreme frequency time series. (2) Taking field significance into account enhances the interpretation of test results by reducing the type-I errors. (3) Parametric statistical trend tests based on linear and Poisson regression prove more powerful than non-parametric tests (e.g. Mann-Kendall test) in analyzing count series. (4) Finally, we use these insights to conduct trend assessments on observed counts, finding several clear spatial patterns of statistically significant increasing (decreasing) trends, mostly located in central and eastern United States and Northern Eurasia (southwestern United States, southern Europe, southern parts of Australia).

How to cite: Farris, S., Deidda, R., Viola, F., and Mascaro, G.: How much do serial correlation and field significance affect trend detection on extreme precipitation frequencies?, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-636, https://doi.org/10.5194/iahs2022-636, 2022.

Shallow geothermal systems represent a unique opportunity for heating and cooling of buildings with green energy and low operational costs. So far, the geothermal system studies have been built on simplifying assumption such as homogeneous porous medium or purely advective flow. However, solutions based on equivalent conductivity and effective macrodispersion coefficients may not be able to grasp the effects of aquifer heterogeneity on geothermal systems.

The aim of our research is to investigate how thermo-hydrological and engineering parameters impact the different heat transport dynamics and how they result in the GS efficiency. The study considers an open loop GS made by a well doublet placed into a confined heterogeneous aquifer of constant thickness; groundwater is abstracted upstream and, after the heat exchange, it is reinjected downstream with a constant altered temperature. The efficiency of GS is intrinsically related to the behavior of the breakthrough time distribution, i.e. the distribution of the travel time the water particles employ to move from the injection to the extraction well, and the recirculating ratio. By means of accurate numerical simulations that mimic the heat transport through a Lagrangian procedure we identify and explore the effect of hydraulic conductivity heterogeneity, pore scale dispersion, thermal diffusion, pumping rate and geometrical parameters.

The analysis hints that the hydraulic conductivity heterogeneity has a strong impact on the early operational time: due to channeling, the thermal plume travels faster in the highly conductive layers. As a result, the first breakthrough time, the key parameter adopted in the design of GS, decreases with heterogeneity, moreover, the uncertainty associated with early arrivals increases with heterogeneity.

The heterogeneity, as well as dispersion and convection, has a negligible effect on the long-term period.The recirculating ratio depends strongly on the pumping rate and other geometrical parameters.

Given that well screens usually cross a short depth we perform a detailed analysis on the uncertainty related to the ergodicity issue. Result of a single realization can significantly differ from its ergodic counterpart. As a practical consequence, a thermal feedback occurring in a heterogeneous medium could significantly differ from the expected theoretical one.

How to cite: Zarlenga, A., Di Dato, M., D'Angelo, C., and Casasso, A.: Efficiency of shallow geothermal systems in heterogeneous media, how to manage uncertainty, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-357, https://doi.org/10.5194/iahs2022-357, 2022.

Hydrological models are commonly applied to investigate impacts of climate change and variability on hydrology. Confidence in hydrological predictions is linked to the model’s performance in reproducing observed variable under consideration. Judgment of a model’s quality is challenged by the differences which exist among the various efficiency criteria. Some of the efficiency criteria commonly applied for assessment of hydrological models include Nash-Sutcliffe Efficiency, coefficient of determination and Kling and Gupta Efficiency. However, a plethora of recently introduced "goodness-of-fit" metrics exists including, for instance, the revised R-squared and Liu efficiency. Studies on comparative analysis of the various efficiency criteria applied to hydrological modeling considering both the old and recently introduced "goodness-of-fit" metrics are lacking. In this study, several efficiency criteria are compared with respect to the computation difficulty, and speed of computation while considering various sample sizes. The pros and cons of the various (both old and new) efficiency criteria are given. The results of this study show the need for modelers to gain insights into the impact of each "goodness-of-fit" metric on model performance assessment before making hydrological predictions for planning predictive adaptations to the impacts of climate change or variability on hydrology.

How to cite: Onyutha, C.: Pros and cons of various efficiency criteria for evaluating hydrological models, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-485, https://doi.org/10.5194/iahs2022-485, 2022.

To study long-term changes in the hydrology of snow-fed catchments, there is a need for long-term time series of snow cover data. Satellite imagery and climate reanalysis can both be used to quantify past snow cover, but they lack in baseline period length and spatial resolution respectively. In this study we apply statistical methods to generate synthetic high-resolution daily snow cover maps, and consequently use these maps as forcing in long-term hydrological modeling. The results are benchmarked against a case without synthetic snow cover forcing. Multiple hydrological models are used to reduce the uncertainty related to the model choice. The study is performed on the Thur catchment in Eastern Switzerland, a meso-scale catchment covering a wide elevation range and experiencing multiple periods of intermittent snow cover annually. We expect the synthetic snow cover maps to provide added value through the high-resolution spatial information on snow appearance and disappearance, leading to better estimates of snow melt runoff. However, we also expect them to show some physical inconsistencies, particularly after periods of high snow accumulation. Should it prove promising, this approach can be used to study both past and future hydrological changes in any snow-fed catchment using ERA5 and CMIP6 climate data, potentially spanning the entire period 1950-2100. Additionally, this framework of synthetic satellite data generation could be expanded to other hydrological variables or to environmental image time series in other fields than hydrology.

How to cite: Wiersma, P., Zakeri, F., and Mariéthoz, G.: The value of synthetic high-resolution daily snow cover maps for long-term hydrological modeling, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-535, https://doi.org/10.5194/iahs2022-535, 2022.

Conceptual rainfall-runoff models are commonly used in many hydrological applications in support of water resources management practices. They provide an advantage in data scarce areas due to their ability to use limited data and generate sufficiently reliable information. The main challenge with this type of hydrological models remains the ability to establish an optimal model parameter space due to a number of sources of uncertainties. This study is conducted in the Upper Benue River Basin (UBRB) in Cameroon with the aim to establish a rainfall-runoff model that is fit in the context of hydro-climate characteristics of the basin. The UBRB is the second-largest river in Cameroon and one of the most important water resources in northern Cameroon from both a water supply and hydropower generation perspective. A Monte Carlo procedure was implemented to calibrate the HYMOD conceptual rainfall-runoff model using five statistical performance measures: the Absolute percent bias (PBIAS), the ratio between root mean square error (RMSE) and standard deviation of observed data (STDEVobs) (RSR), the Pearson correlation coefficient (r), the Nash–Sutcliffe efficiency (NSE) and the Kling–Gupta efficiency (KGE). The results reveal that the model performance varies from good to very good with NSE greater than 0.78 and RSR less than 0.46 during the calibration and validation periods. Therefore, this model can be used to support various water resources management initiatives in the basin.

How to cite: Nonki, R. M., Amoussou, E., Tshimanga, R. M., Koubodana, H. D., Kemgang Ghomsi, F. E., and Lenouo, A.: Performance assessment of a daily time-step HYMOD conceptual rainfall-runoff model for the Upper Benue River, Cameroon, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-547, https://doi.org/10.5194/iahs2022-547, 2022.

The climate model projections obtained from the Regional Climate Models (RCMs) and their forcing through the hydrological models are known to include multi-source uncertainties. Using the subsequent modelling data for their intended purposes, such as watershed studies, flood mitigation, and climate change adaptation policies while containing unsupervised uncertainties of such nature, will prove detrimentally misjudged and potentially do more harm than good. The uncertainty propagation takes place at each stage along the modelling process and depends on the choice of climate model projections, Representative Concentration Pathways (RCPs), bias correction methods (BCs),  hydrological models, and hydrological parameters among other contributors.

The aim of this paper is to quantify the overall uncertainty in climate change impacts using Analysis of Variance (ANOVA) to decompose or disaggregate it into components and assess relative contribution of each of the components. The approach is demonstrated through a case study in Little River Experimental Watershed (LREW watershed) under two different emission scenarios (RCP 4.5 and RCP 8.5), five sets of RCM and driving GCM combinations, two bias correction methods by utilizing the Soil & Water Assessment Tool (SWAT) hydrological model. The results will indicate breakdown of the total uncertainty (T) into the respective uncertainties caused by the climate models (C), emission scenario (R), bias correction method (B) and unlike most of the uncertainty decomposition studies, further uncertainty breakdown due to the interactions between various components are also presented. The findings from this study will be useful to the modelers involved with flood mitigation or policy management by enhancing their understanding about the nature of streamflow projections and effectively aid in better decisions concerned with adapting to a changing climate subjected to uncertainties.

How to cite: Pogakula, T. and Bolisetti, T.: Uncertainty quantification in climate change impacts on hydrology using ANOVA, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-571, https://doi.org/10.5194/iahs2022-571, 2022.

We present a new runoff map for Norway for the reference period 1991-2020. A new framework combing precipitation runoff-modelling with geostatistical interpolation was used. The precipitation-runoff model WASMOD was used to simulate runoff on a 1x1 km grid covering all Norway and nearby catchments in Sweden and Finland. The parameters of WASMOD were conditioned on land-use and climate classes and calibrated globally using data from 215 streamflow stations. Figure 1a) shows the runoff estimates from WASMOD that are biased when compared to observations (Figure 2a) .

To correct the biases in the gridded simulations we used runoff observations from 732 locations of which 198 had data covering the whole period. A geostatistical approach was used to estimate the 30 year mean annual runoff from short records (1-29 years) for 482 catchments. For 45 catchments with substantial glacier coverage or regulation capacity, a manual extension of mean annual runoff was performed. Another 7 stations with between 25 and 30 years of data were included.

The biases from WASMOD were corrected by simple linear regression using the raw runoff map as a covariate and the runoff observations as the dependent variable. The regression coefficients were modelled as spatial fields using a geostatistical Bayesian approach where SPDE and INLA ensured fast Bayesian inference. The mean annual runoff after the correction is shown in Figure 1b), and in Figure 1c) the difference between the two previous maps is shown. Figure 2b shows a scatter plot of corrected and observed runoff. The scatter is now much smaller.

Figure 1. Mean annual runoff from a) the Wasmod model and b) after the correction. The difference in runoff between a) and b) is shown in c)

The new framework was evaluated by cross-validation. On average this new approach outperformed a WASMOD when predicting runoff for ungauged and partially gauged catchments and reduced the RMSE by 42% . Scatter plot of predicted runoff is shown in Figure 2c).

Figure 2. Scatterplot of mean annual runoff for a) WASMOD, b) corrected values, and c) predicted runoff , versus observations in 146 catchments in a split sample cross-validation test

How to cite: Engeland, K., Roksvåg, T., Beldring, S., Holmqvist, E., Pedersen, A. I., and Ruan, G.: New runoffmap for Norway by combining a precipitation runoff model with geostatistical interpolation, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-723, https://doi.org/10.5194/iahs2022-723, 2022.