HS7.4

With the growing importance of climate change risk assessment, the use of climate models as a tool to model the impact of a warmer climate on water resources has now become quite common. When working with climate model outputs, bias correction is considered an important and necessary step to ensure that impact models provide realistic simulations in the current and future climates. The past decades have seen continuous improvements in the spatial and temporal resolution of global and regional climate models. Climate model outputs are now available at the sub-daily temporal resolution and very few studies have looked at the need for correcting biases present in the representation of the diurnal cycles of model variables. This study has looked at the impact of correcting such biases on simulated streamflow over 133 North American catchments. The temperature and precipitation hourly outputs from a 50-member large-ensemble regional climate model (ClimEx) were used to model the impact of sub-daily bias correction on simulated streamflow using a hydrological model. To better understand the importance of diurnal bias correction as a function of the spatial scale, the impact of bias-correcting the diurnal cycle was evaluated on three classes of catchment area:  small (<500 km²), medium (500< area <1000 km²) and large (>1000 km²). Bias correcting the diurnal cycle resulted in small but systematic improvements in the representation of simulated streamflow, with an average bias reduction of 5%, likely due to a better representation of the daily evapotranspiration cycle.  The improvements were especially noticeable on the small catchments.

How to cite: Faghih, M. and Brissette, F.: Should we correct biases in the diurnal cycle of climate model for hydrological studies?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1393, https://doi.org/10.5194/egusphere-egu22-1393, 2022.

Convection‐permitting climate models (CMPs) give a much more realistic representation of sub-daily precipitation statistics compared to coarser resolution climate models, thanks to the explicit representation of convection. Their higher spatial and temporal resolution allows to used them directly to study future changes in the frequency, intensity, and spatiotemporal patterns of heavy rainfall over complex terrain. However, the high computational requirements of CPM runs restricts the existing simulations to relatively short time periods (10–20 years), too short for deriving precipitation frequency analyses with conventional approaches. Alternative methods, based on the so called Metastatistical Extreme Value Distribution, were recently proposed (e.g. Marani and Ignaccolo, 2015) for deriving frequency analyses from shorter data records, promising improved applications based on CPMs. These approaches rely on the concept of ordinary events, which are all the independent events that share the statistical properties of extremes: once the upper tail of the distribution of ordinary events is known, it is possible to derive an extreme value distribution by explicitly considering their yearly occurrence frequency.

Here, we investigate the CPM ability to represent the upper tail of sub-daily precipitation in a complex-orography region in the Eastern Italian Alps. In this area, different orographic impacts on sub-daily precipitation upper tail were reported at different durations (Formetta et al., 2021), and significant temporal trends in their intensity were reported during the last few decades (Libertino et al., 2019), making it a challenging and interesting test case for CPM simulations. As CPM we used the COSMO model run at 2.2 km resolution over Europe, driven with ERA Interim for the period 2000-2009. We use 180 rain gauges to benchmark the CPM simulation. CPM time series are extracted for the grid points corresponding to the rain gauges, and hourly time series are created from both stations and CPMs. In each time series, independent storms are separated by 24-hour dry hiatuses, and ordinary events for 9 durations between 1 and 24 hours are defined as the corresponding peak intensity of each storm. Ordinary events upper tails are modeled using a Weibull distribution (two-parameter stretched exponential), which was previously reported to well reproduce the statistics of extremes in the area. The ability of CPMs to reproduce the model parameters and extreme quantiles up to 100-year return period, and their dependence on elevation are evaluated, together with the dependence of the biases with elevation. A general overestimation is found for annual maxima (10-40%), and the estimated quantiles (10-60%), especially for short durations. The bias significantly depends on elevation, with increasing overestimation of the 1-hour quantiles with elevation. It seems that CPMs cannot represented well the “reversed orographic effect” reported by previous studies.

How to cite: Dallan, E., Marra, F., Giuseppe, F., Fosser, G., Marani, M., Schaer, C., and Borga, M.: How well do convection-permitting climate models represent sub-daily precipitation upper tail in complex orography?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-541, https://doi.org/10.5194/egusphere-egu22-541, 2022.

A warming climate is associated with increasing hydroclimatic extremes, which are often interconnected through complex processes, prompting their concurrence and/or succession, and causing compound extreme events. It is critical to analyze the risks of compound events, given their disproportionately high adverse impacts. To account for the variability in two or more hydroclimatic variables (e.g., temperature and precipitation) and their dependence, a rising number of publications focuses on multivariate analysis, among which the notion of copula-based probability distribution has attracted tremendous interest. Copula is a mathematical function that expresses the joint cumulative probability distribution of multiple variables. Our focus is to re-emphasize the fundamental requirements and limitations of applying copulas. Confusion about these requirements may lead to misconceptions and pitfalls, which can potentially compromise the robustness of risk analyses for environmental processes and natural hazards. We conducted a systematic literature review of copulas, as a prominent tool in the arsenal of multivariate methods used for compound event analysis, and underpinned them with a hydroclimatic case study in Sweden to illustrate a practical approach to copula-based modeling. Here, we (1) provide end-users with a didactic overview of necessary requirements, statistical assumptions and consequential limitations of copulas, (2) synthesize common perceptions and practices, and (3) offer a user-friendly decision support framework to employ copulas, thereby support researchers and practitioners in addressing hydroclimatic hazards, hence demystify what can be an area of confusion. 

How to cite: Tootoonchi, F., Sadegh, M., Haerter, J. O., Räty, O., Grabs, T., and Teutschbein, C.: Copulas for hydroclimatic analysis: A practice-oriented overview, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10418, https://doi.org/10.5194/egusphere-egu22-10418, 2022.

Hydrological models require calibration to provide accurate simulation, and the calibration usually often requires long-term historical hydrometeorological data. The calibrated parameters obtained from historical data are assumed to be stationary. However, the stationary assumption for the para terms in the hydrological modeling may not be appropriate for the future climate, especially in a changing climate. This study aims to explore different approaches to minimize this issue by comparing calibration frameworks and offer alternative strategies to improve model robustness for climate change impact studies. The optimization strategies consider nonstationarity in the model parameters associated with different climate regimes and provide a functional form with dynamic climate predictors to better represent abnormal climates informed by a set of climate change scenarios over South Korea.

How to cite: Lee, Y.-R., Kwon, Y.-J., Kim, H., and Kwon, H.-H.: Hydrological Model Calibration Strategy for Climate Change Impacts Study , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12320, https://doi.org/10.5194/egusphere-egu22-12320, 2022.

Since ancient times water has been a substantial factor for driving economic growth, as abundance in water resources can be linked to the development of prosperous communities. This study examines the effect of water resources availability on different sectors of the economy, by identifying components of Gross Domestic Product which are most affected by key water cycle processes and water infrastructures. In this analysis, we investigate the correlation among the above processes, on both temporal and spatial scale with the implementation of stochastic methods, in order to assess the sensitivity of the economy to hydroclimatic variability. We also take into consideration the effect of hydroclimatic extremes such as droughts and the limitations they may impose on growth. Differences between climate zones are taken into consideration by the Köppen climate index.

How to cite: Arvanitidis, I., Diamanta, M., Sargentis, G.-F., Iliopoulou, T., Dimitriadis, P., and Koutsoyiannis, D.: Identifying links between hydroclimatic variability and economical components using stochastic methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5944, https://doi.org/10.5194/egusphere-egu22-5944, 2022.

West Mani, an attractive place in western Peloponnese, Greece, faces water shortage. The problem lies not only in the quantity but also in the quality of the available water. Investigating the options for the sustainable management of water resources, utilizing surface water seems to be the optimal solution. However, the complex geomorphology and geology of the study area, and its particular its karstic structure, when combined with the scarcity of hydrological data, makes the estimation of surface water availability challenging. As a result, it is considered necessary to take hydrological uncertainty into account using stochastic analysis. To this aim, we generate synthetic rainfall and streamflow timeseries based on available meteorological data from basins near the area of interest. We then appropriately adjust them so that they represent the magnitude and the variability of the rainfall and streamflow of the study area. For the timeseries generation algorithm, we employ a stochastic model following the Hurst-Kolmogorov dynamics by reproducing marginal distribution, seasonality and persistence.

How to cite: Siganou, A., Nikolinakou, M., Markantonis, D., Moraiti, K., Sargentis, G.-F., Iliopoulou, T., Dimitriadis, P., Chiotinis, M., Mamassis, N., and Koutsoyiannis, D.: Stochastic simulation of hydrological timeseries for data scarce regions - Case study at the Municipality of Western Mani, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3086, https://doi.org/10.5194/egusphere-egu22-3086, 2022.

Water availability is a critical issue for growing local communities. For example, in the Municipality of Western Mani (western Peloponnese, Greece) tourist development has caused scarcity of water intensifying during the summer period. In this context, multiple solutions are being studied in order to assist the local communities of Western Mani to deal with this situation.

This study focuses on traditional water harvesting structures and more specifically cisterns. In the past, a cistern was present nearby or almost at every house, collecting rain water so as to cover the various needs of the inhabitants, including human consumption and irrigation. However, although cisterns today have fallen into disuse due to the developments of modern water supply systems, they remain an important part of cultural heritage and an architectural element of great interest.

In this work, we evaluate the potential of traditional water infrastructures to cover domestic needs employing the method of stochastic simulation based on hydrological data and by also taking into account traditional architecture.

How to cite: Nikolinakou, M., Moraiti, K., Siganou, A., Markantonis, D., Sargentis, G.-F., Iliopoulou, T., Dimitriadis, P., Meletopoulos, I. T., Mamassis, N., and Koutsoyiannis, D.: Investigating the water supply potential of traditional rainwater harvesting techniques used – A case study for the Municipality of Western Mani, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3063, https://doi.org/10.5194/egusphere-egu22-3063, 2022.

Water infrastructure is an indicator of human civilization and its evolution. The sustainable water management and distribution to local communities remains a critical engineering priority so that the most efficient usage is achieved. In this analysis the design of water-infrastructure establishments is studied for the community of the Municipality of Western Mani (western Peloponnese, Greece).

One of the main issues that arise is the presence of karstic-limestone geological structure at the study area with no permanent watercourses. Furthermore, the lack of data about the current quantity of surface water makes it difficult to formulate trustworthy conclusions on the availability of water resources. Additionally, the notable growth of the tourist sector during the summer months in the past few years exacerbates this issue. Due to the above reasons, the available water is not enough to cover the needs of the Municipality, especially during the summer.

After examining all the possible options that have been proposed to increase the water availability (e.g., through dams, wells, desalination, water ponds etc.), we investigate an optimal solution that aims to achieve a more efficient water management and distribution to the communities of Western Mani. To this aim, we apply a multi-criteria decision-making approach by also considering local traditional water harvesting systems to increase water resilience.

How to cite: Moraiti, K., Markantonis, D., Nikolinakou, M., Siganou, A., Sargentis, G.-F., Iliopoulou, T., Dimitriadis, P., Meletopoulos, I. T., Mamassis, N., and Koutsoyiannis, D.: Optimizing water infrastructure solutions for small-scale distributed settlements – Case study at the Municipality of Western Mani., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3055, https://doi.org/10.5194/egusphere-egu22-3055, 2022.

Infrastructures for the supply of water are one of the most necessary facilities in modern life. The optimal design of such infrastructures (for example, dams or even small-size tanks) is often a great challenge in civil engineering, given the large number of factors required for their design (e.g., feasibility, reliability, cost effectiveness, resilience). One of the most critical decisions that may have a great impact on the optimization procedure is the determination of the scale of the proposed system.

During a study of such a design of a water supply infrastructure in the Municipality of Western Mani, it became clear that several solutions of different scales coexisted. Ultimately, the cost-benefit factors were the most heavily considered ones, provided that the required reliability was met. Stochastic methods have been proven to be appropriate tools for studying such highly complex and uncertain puzzles. The current study intends to approach this problem by considering solutions of different scales, and to establish the long-term cost effectiveness as the main criterion to evaluate the different solutions.

How to cite: Markantonis, D., Siganou, A., Moraiti, K., Nikolinakou, M., Sargentis, G.-F., Dimitriadis, P., Chiotinis, M., Iliopoulou, T., Mamassis, N., and Koutsoyiannis, D.: Determining optimal scale of water infrastructure considering economical aspects with stochastic evaluation – Case study at the Municipality of Western Mani, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3039, https://doi.org/10.5194/egusphere-egu22-3039, 2022.

Simulation of daily rainfall for the region of Bologna produced by 13 climate models for the period 1850 - 2100 are considered. Data are compared with the historical series of daily rainfall observed in Bologna for the period 1850 - 2014. In particular, we focus on annual rainfall data, seasonality and extremes to derive information on the future development of water resources availability and flood risk. The results prove that rainfall seasonality is fairly well simulated by models, while the historical sequence of annual rainfall is not satisfactorily reproduced. Future projections for different emission scenarios allow to assess the impact of climate change on cumulative rainfall and extremes, therefore outlining important technical indications.

How to cite: Guo, R. and Montanari, A.: Future changes of average and extreme rainfall for the Bologna region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4704, https://doi.org/10.5194/egusphere-egu22-4704, 2022.

This study attempts to estimate the extreme rainfall quantile using the climate model data of the Shared Socioeconomic Pathways (SSP) scenarios presented in the sixth Assessment Report (AR6), published by the Intergovernmental Panel on Climate Change (IPCC). Generally, the applied research related to climate change is conducted using numerical simulation data from various climate models. Generally, an ensemble scenario based on various regional climate models is used as a way to reduce the uncertainty from one climate model. In this study, the ensemble rainfall data (based on HadGEM3-RA, WRF, CCLM, GRIMs, and RegCM4) were obtained from the climate information portal (CIP, http://www.climate.go.kr/). The observed rainfall data was extracted and the regional quantile delta mapping (RQDM) method was applied for bias correction. Regional frequency analysis (RFA) was performed to estimate the rainfall quantile. In addition, the generalized extreme value (GEV) distribution was applied as an appropriate probability distribution and the L-moments method was used for parameter estimation. As a result, the rainfall quantiles were estimated, and the effects of climate change were analyzed quantitatively in the study area.

How to cite: Kim, S., Kim, H., Kim, G., and Heo, J.-H.: Extreme rainfall quantile estimation based on SSP scenarios: Focusing on the Hangang river basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10822, https://doi.org/10.5194/egusphere-egu22-10822, 2022.

An ensemble of ten regional climate model (RCM) simulations, forced by two global climate models (GCM) such as HadGEM2-AO, MPI-ESM-LR, and GFDL2M, at 25km spatial resolution from the CORDEX-EA Phase-2 is explored to assess the changes in rainfall intensity-duration-frequency (IDF), commonly employed in the hydrologic study, in a changing climate. This study first constructs a probability density function (PDF) for the observed precipitation. The log-likelihood for the modeled precipitation is then estimated from the PDF to rank the RCMs. Ensemble construction is further performed based on these rankings. The temporal downscaling approach employed in this study is based on a conditional copula function method developed by So et al. (2018), which incorporates a quantile mapping approach for bias correction. The proposed ensemble modeling framework for constructing future IDF relationships could provide a better representation of the uncertainty associated with climate models. A detailed discussion of the potential application of the ensemble approach in extreme analysis is further provided.

How to cite: Yu, J.-U., Moon, J., Kim, Y., and Kwon, H.-H.: Changes in Intensity-Duration-Frequency Curves with an Ensemble of EA-CORDEX over South Korea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11845, https://doi.org/10.5194/egusphere-egu22-11845, 2022.