HS2.4.3

Global warming is associated with an increased rate of evaporation due to higher surface temperatures which also implies a higher hydrological cycle turn-around in a steady-state atmosphere with respect to the water budget. The latter is accompanied with increased atmospheric overturning and more convective activity. In addition, there have been indications of a decreasing area of 24-hr rainfall on a global scale over the last decades, suggesting that rainfall is becoming concentrated over smaller regions. There have also been indications of higher cloud tops. In sum, a consequence of an increased greenhouse effect and modified hydrological cycle is an increased probability for heavy rainfall on local scales and a greater risk of flooding. Changes in risks connected to meteorological and hydrological challenges make it necessary to adapt to new weather statistics. For instance, there is a need to estimate the frequency of heavy downpour and their return levels, both for 24-hr amounts and sub-daily timescales. It is common to account for extreme rainfall by designing infrastructure with the help of intensity-duration-frequency (IDF) curves. One problem is that the IDF curves are based on long records of hourly rainfall measurements that are not widely available. Traditional IDF curves have also been fitted assuming stationary statistics, while climate change implies non-stationary weather statistics. We propose a formula for downscaling sub-daily rainfall intensity based on 24-hr rainfall statistics that is not as limited by data availability nor assumes stationarity. This formula provides a crude and approximate and rule-of-thumb for sites with 24-hr rain gauge data and can be used in connection with downscaling of climate model results. It also represents a way of downscaling rainfall statistics in terms of the time dimension.

How to cite: Benestad, R.: A formula for downscaling extreme sub-daily rainfall intensities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-252, https://doi.org/10.5194/egusphere-egu21-252, 2021.

Extreme precipitation, a critical factor in flooding, has selectively increased with warmer temperatures in the Western U.S. Despite this, the streamflow measurements have captured no noticeable increase in large-scale flood frequency or intensity. As flood studies have mostly focused on specific flood events in particular areas, analyses of large-scale floods and their changes have been scarce. For floods during 1960-2013, we identify six flood generating mechanisms (FGMs) that are prominent across the Western U.S., including atmospheric rivers and non-atmospheric rivers, monsoons, convective storms, radiation-driven snowmelt, and rain-on-snow, in order to identify to what extent different types of floods are changing based on the dominant FGM. The inconsistency between extreme precipitation and lack of flood increase suggests that the impact of climate change on flood risk has been modulated by hydro-meteorological and physiographic processes such as sharp increases in temperature that drive increased evapotranspiration and decreased soil moisture. Our results emphasize the importance of FGMs in understanding the complex interactions of flooding and climatic changes and explain the broad spatiotemporal changes that have occurred across the vast Western U.S. for the past 50 years.

How to cite: Fayne, J., Huang, H., Fischella, M., Liu, Y., Ban, Z., Li, D., Cavanaugh, K., and Lettenmaier, D.: Changes in mechanisms and intensity of Western U.S. floods, 1960-2013, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3816, https://doi.org/10.5194/egusphere-egu21-3816, 2021.

Australia's large natural hydro-climatic variability has already seen many changes, such as declining rainfall in the southern part of the country. Understanding these shifts and associated impacts on water availability is an important issue for Australia, as water supply is dependent on the generation of surface water resources. Sustainable future urban and agriculture developments will depend on best available knowledge about the risks and vulnerabilities of future water availability.

To understand those risks and vulnerabilities and to mitigate the impact of a changing climate, Australia's water policy, management and infrastructure decision making needs detailed high-resolution climate and water information. This includes information on multi-decadal timescales from future projections in the context of past climate variabilities. In Australia, currently, hydrologic change information exists in various forms, ranging from multiple regional downscaling efforts, bias-correction methods and different interpretation methods for hydrologic impact assessment – all limiting a national, consistent impact assessment across multiple spatial and temporal scales. These regional downscaling and hydrological impact data collections are either not application-ready or are tailored for specific purposes only, which poses additional barriers to their use across the water and other sectors.

To overcome these barriers, the Bureau of Meteorology is soon to release a seamless national landscape water service known as the Australian Water Outlook (AWO), combining historical data on water availability with forecast products, as well as hydrological impact projections. This system's core is the Australian Landscape Water Balance model (AWRA-L) modelling hydrologic variables consistently across a large range of spatial and temporal scales. The AWRA-L model is underpinned by substantial scientific development including data assimilation approaches for model calibration as well as model evaluation approaches for past and present time scales. Additionally, consistent downscaling and bias-correction approaches are integrated for the hydrologic projections in the operational framework.

This presentation will share an overview of the soon to be released Australian Water Outlook seamless service with an emphasis on the Hydrologic Projections part: the methodology, the user centred-design, as well as the development of guidance material containing confidence statements and uncertainty assessments to help decision makers in understanding the service. The presentation will also provide an overview of the tactics we applied to ensure the applicability of the new service including demonstration cases developed in partnership with users.

How to cite: Bende-Michl, U., Sharples, W., Donnelly, C., Vogel, E., Peter, J., Hope, P., Srikanthan, S., Tuner, M., Oke, A., Matic, V., Lerat, J., Roussis, J., Duong, V. C., and Pipunic, R.: Hydrologic projections for Australia: understanding future changes to water availability and extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13973, https://doi.org/10.5194/egusphere-egu21-13973, 2021.

The study deals with probabilities of transitions from arid to humid environment and vice versa in
Europe. Aridity index, defined as a ratio of potential evapotranspiration and precipitation and
representing the ratio between energy availability and water availability, is used to characterize humid
(wet) and arid (dry) regions and allows us to study transitions between individual periods (wet-wet,
wet-dry, dry-dry, dry-wet). Three gridded datasets – CRU (UEA, 2020), E-OBS (ECAD, 2020) and ERA5
(ECMWF, 2020) – are used for this purpose. The aim of the study is to compare the three datasets as
to transitions between wet and dry conditions, which are determined according to the aridity index,
and evaluate the variability in Europe over 1950–2019. The changes in the aridity index since 1950 are
found to be most pronounced in Northern and Central Europe.

references:
ECAD, 2020: E-OBS gridded dataset, available from
<https://www.ecad.eu/download/ensembles/download.php>.
UEA, 2020: University of East Anglia – Climatic Research Unit, available from
<https://lr1.uea.ac.uk/cru/data>.
ECMWF, 2020: European Centre for Medium-Range Weather Forecasts – ERA5, available from
<https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>.

How to cite: Bestakova, Z., Maca, P., Kysely, J., Singh, U., Markonis, Y., and Hanel, M.: Transitions between dry and wet periods in Europe during 1950–2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9951, https://doi.org/10.5194/egusphere-egu21-9951, 2021.

Africa is affected by a high-level of temporal and spatial variability in climate, with large impacts on water resources, human lives and economies. Due to data scarcity, the impact of multi-year climate variations on hydrological variability and extremes, i.e. flood and drought, as well as how catchment properties could modulate those impacts, are generally poorly understood across the African continent. In this study, we first use machine learning algorithms to develop a new complete reconstructed daily streamflow dataset using more than 1500 stream gauges between 1950 and 2018, and covering most of Africa. We then examine historical trends and variability in hydrological extremes over the entire African continent, focusing on different hydrological characteristics, such as the timing, frequency and duration of high- and low-flow events, based on the peaks-over-threshold method. Following an assessment of the relative sensitivities of hydrological extreme indices to interannual (2-8-years) and decadal (>10-years) variability in the different regions of Africa, we analyze the respective contribution of different rainfall, temperature and soil moisture indices (e.g. frequency, duration and intensity of wet/dry or warmer/colder days) at both timescales, using relative importance analysis. We finally discuss how catchment properties (e.g. area, topography, land use/ land cover, drainage path lengths) modulate the relationship between hydrological extremes and climate.

How to cite: Ekolu, J., Dieppois, B., Sidibe, M., Eden, J., Tramblay, Y., Villarini, G., Mahé, G., Paturel, J.-E., and Van de Wiel, M.: Multi-year variability in hydrological extremes in Africa: what are the main drivers?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7981, https://doi.org/10.5194/egusphere-egu21-7981, 2021.

Climate modes can have a large influence on the interannual variability in rainfall and streamflow.  Moreover, changes in their spatial-temporal patterns are likely to shape changes in hydroclimate in the future as a result of a warming climate. Modeling the links between climate modes, rainfall and streamflow is therefore important for understanding the trajectories in water availability. We examined the effects of four climate variability modes, El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), and Interdecadal Pacific Oscillation (IPO) on variations in annual rainfall and streamflow in four hydroclimate regions in temperate Australian. Climate mode indices, rainfall, and streamflow data from 1975 to 2018 were analyzed for 92 predominately forested catchments in four study regions. The annual variation and long-term fluctuations of rainfall and streamflow in each region were explored using the coefficient of variation, trend analysis, and random forest models to examine relationships to ENSO, IOD, SAM, and IPO. Coefficient of variation analysis showed that the annual variation of streamflow in and among catchments in each region was higher than rainfall. Rainfall and streamflow in each region were strongly influenced by different climate modes, and a higher proportion of variation in rainfall was explained by climate modes. Extreme annual rainfall and streamflow in these regions are related to concurrent phases of regional climate phenomena. These results provide critical baseline information and context for a better understanding of how future spatial and temporal changes in rainfall and streamflow across temperate Australia may manifest.

How to cite: Khaledi, J., Nyman, P., Nitschke, C., Lane, P. N. J., and Penman, T.: The influence of atmosphere-ocean phenomenon on rainfall and streamflow variability across temperate Australia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10439, https://doi.org/10.5194/egusphere-egu21-10439, 2021.

Drought conditions of Southeast China are associated with the sea surface temperature warm pool in the tropical Western Pacific, which is related to low-frequency hydroclimatic patterns and their teleconnections. Empirically, the moisture influx to the region is linked to the interannual and decadal teleconnections, including the Pacific Decadal Oscillation (PDO), the Pacific-Japan Oscillation (PJO) and the Silk Road Pattern (SRP). However, it is still unclear how those teleconnection patterns affect drought conditions in Southeast China via changes in monsoons’ dynamics or wave activities. In this study, we use ERA5 reanalysis over the 1950-2019 period to explore the impacts of the PDO, PJO and SRP on Asian monsoons’ dynamics and regional drought conditions over Southeast China, based on a self-calibrating Palmer Drought Severity Index (scPDSI). We specially use station data from the Greater Bay Area (GBA) which is a national key region for development in Southeast China which is affected by seasonal droughts in winters. Results indicate that drought conditions in Southeast China are significantly related to monsoons: the East Asia Monsoon (EAM), the Western North Pacific Monsoon (WNPM) and the Webster-Yang Monsoon (WYM), between 1950-2019. The strength of monsoons is modulated by PDO, PJO and SRP. A negative phase of SRP corresponds to a southward shift of the Asian westerly jet, strengthening winter Asian monsoons and causing drier conditions in the GBA. Similarly, a cold phase of PDO contributes to drier conditions in the GBA, by weakening Asian monsoons. For the negative phase of PJO, the trade wind of the Walker cell is weakened by the meridional pressure dipole over the West Pacific adjacent to the Southeast China coast. This pressure dipole reduces moisture influx to the continent by the weakened trade wind and leads to less precipitation over East China. Such three climate factors are also interacted through the modulations of monsoons and wave-activities. An extension of the Eliassen-Palm (EP) flux shows that the SRP relates to convective and dynamic wave-activities, which could explain changes in monsoons’ dynamics and drought conditions in Southeast China. To investigate the future drought conditions over Southeast China, bias-corrected historical and RCP8.5 scenarios are used for six of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models (i.e. ACCESS1, BCC, CNRM, IPSL, MPI, and GFDL) between 1861-2100. Among six models, IPSL and GFDL models reproduce the teleconnections well between changes in the monsoons and drought conditions over the GBA, for both historical simulations and future projections. Our results provide insights into the mechanisms of teleconnection patterns affecting drought monitoring and risk management in Southeast China. 

How to cite: Chun, K. P., He, Q., Dieppois, B., Pohl, B., Yetemen, Ö., Chen, L., Yang, Q., Şen, Ö. L., Çağlar, F., Klaus, J., and Massei, N.: Drought variability driven by interannual and decadal teleconnection patterns in monsoon regions of Southeast China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9365, https://doi.org/10.5194/egusphere-egu21-9365, 2021.

Wetland ecosystems in river valleys are strongly related to hydrological and climatic conditions. Accurate exploration of these relationships is essential to achieving a proper projection of changes in these ecosystems under the climate change. The aim of the research is to identify the effects of climate change on the way the flood and inundation are formed in the natural river valleys of the temperate zone.  The research is conducted in the Biebrza catchment, which is located in north-eastern Poland and has an area of about 7000 km². Because of its natural character, this area is considered as a reference area for wetland research. For the study area an integrated hydrological model (HydroGeoSphere) was developed and used to simulate the contribution of various sources of water in inundation and floods in the period 1900-2015. The preliminary conclusions with respect to hydrology-climate linkage as well as the lessons learned from the model development and calibration are presented.

How to cite: Berezowski, T.: Climate-hydrology interactions explored using an integrated groundwater-surface water hydrological model for over a 100 year period in a natural temperate zone regional catchment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1314, https://doi.org/10.5194/egusphere-egu21-1314, 2021.

Often in geosciences, short time series are investigated to infer a linear trend (secular change), and the magnitude of trend is used to infer severity of change without considering the spatiotemporal variability of the observable. Therefore, it is not always known to what extent resultant trends are truly representative of severity of change, or whether the trends in short time series are driven by long wavelength signals that can only become apparent when additional years of data are available. Furthermore, same value of trend can have different interpretation over different regions.

GRACE, a novel satellite mission to monitor water mass redistribution, was launched in 2002 and a decade later several studies analyzed linear trends in GRACE time series to claim that some regions were experiencing unprecedented changes in regional water storage. Studies published more recently further suggest that some of those regions have recovered and some new regions have emerged as endangered. This update in our knowledge is driven by additional GRACE data that became available in the last five years, demonstrating that as the time series became longer, inferences from studying trends changed. In this presentation, we demonstrate that multi-decadal natural variability in water cycle influenced previous interpretations of linear trends from relatively short (<20 year) GRACE time series. We propose a new metric (trend to variability ratio or TVR) that incorporates standard deviation of historical natural variability to better interpret the severity of trends inferred from GRACE. Since, natural hydrological variability is different for different regions, same value of trend has different interpretation for different river catchments. Using this metric, we find that several regions that were thought to be losing water at a moderate rate are actually more endangered and vice-versa. We also provide a map that demarcates river catchments that have experienced severe water storage change between 2003 to 2015.

How to cite: Bamber, J. L., Vishwakarma, B. D., Bates, P., Sneeuw, N., and Westaway, R. M.: Evaluating linear trends with respect to past decadal variability can help us assess the severity of GRACE observed water storage change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8881, https://doi.org/10.5194/egusphere-egu21-8881, 2021.

Understanding the contributions of potential drivers on runoff is of great importance for the sustainable management of water resources. In this study, we develop a nonlinear hybrid model, which integrates extreme-point symmetric mode decomposition (ESMD), back propagation artificial neural networks (BPANN) and weights connection method, to represent the relationships between different drivers and runoff. ESMD allows to decompose the times series of drivers and runoff into different components. BPANN is then employed to simulate the relationship between the drivers and runoff at each time scale separately. The performance of this model is compared with multiple linear regression (MLR). We select the mountainous area of the Hotan River Basin as case study area. The results indicated that runoff exhibits oscillation periods of 2, 9 and 14 years. Climate variability strongly affects runoff and accounts for 81% of the runoff variation, while human activities play a minor role, accounting for 8%. In all performance measures, the proposed model substantially outperforms MLR. The proposed model can represent nonlinear relations and simulate the association between drivers and runoff at different time scales (even opposite associations), which is the improvement of this study.

How to cite: Qin, Y., Sun, X., Li, B., and Merz, B.: Assessing the impacts of climate variability and human activities on runoff with a nonlinear hybrid model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3296, https://doi.org/10.5194/egusphere-egu21-3296, 2021.

Due to climate changes, the stationary assumption in hydrology has become obsolete. Moreover, the uncertainty regarding the future evolution of the Earth's climate and its impact on flow regimes is still large. Over the last decade, new risk management approaches have been proposed to support water resources planning under deep uncertainty. Those approaches rely at some point on a hydrological model to derive time series of streamflows for various hydro-climatic scenarios. One of the key issue is to make sure that the hydrological model is robust, i.e. that it performs well over contrasted hydro-climatic conditions. The differential split-sample test principle proposed by Klemes in 1986 recommends partitioning the time series into numerous and independent subperiods with different stationary climate features. Then, the hydrological model calibration is achieved on a specific climate period, and the validation on other(s). Classical detection methods commonly used to partition the times series, such as Mann-Kendall test or Pettitt test, can only detect a single change point, and thus are unable to handle complex climate sequences with multiple change points. We propose a calibration/validation protocol of hydrological models which rely on both the differential split-sample test and on an Hidden Markov Model to identify a succession of subsequences in a time series based on the state of the underlying process. We applied the proposed protocol on the Senegal River (West Africa). The hydrological model used is the conceptual GR2M model. Results show that (i) when the river discharges time series does not display a clear climate trend, and have multiple change points, classical rupture tests are not suitable. Hidden Markov Models are a good alternative as long as the climate sub-sequences are long enough (typically around 30 years or more); (ii) including a Hidden Markov Models in such protocol open up the range of possibilities for calibrate/validate, which can lead to an enhancement of the criterion function (but not necessarily).

Klemes, V.: Operational testing of hydrological simulation models, Hydrological Sciences Journal, 31, 13-24, 415 https://doi.org/10.1080/02626668609491024, 1986.

How to cite: Guilpart, É., Espanmanesh, V., Tilmant, A., and Anctil, F.: Integration of Hidden Markov States in a hydrological model calibration/validation protocol, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8609, https://doi.org/10.5194/egusphere-egu21-8609, 2021.