Displays

Mountainous and Nordic regions are experiencing more rapid temperature increases as compared to regions at lower altitudes and latitudes. This will impact the hydrology in these regions.  For Norway, there is increasing evidence for gradually increasing temperatures and recent changes in the amount, intensity, and frequency of precipitation as well as in the number of days with snow cover. The most pronounced differences regarding their hydro-meteorological regime can be found between Western and Eastern Norway (Vestlandet vs. Østlandet). Most catchments in these regions are characterized by mixed snowmelt/rainfall streamflow regimes with peak flows during spring (dominant in Østlandet) and autumn (dominant in Vestlandet). Changes in the hydro-meteorological drivers will have direct implications on the snow regime, and thus, also on streamflow via their direct effect on the relative importance of snowmelt vs. rainfall for streamflow generation.

In this study, we analyze daily-resolved streamflow trends for 112 catchments in Western vs. Eastern Norway for the period 1983-2012 and compare them with daily-resolved trends in the hydro-meteorological drivers. We also estimate the relative contribution of snowmelt and rainfall on daily streamflow for each catchment and identify trends therein. This process-orientated approach at high temporal resolution allows for a better identification of (in)consistencies with changes in the hydro-meteorological drivers than simple seasonal comparisons. Lastly, we aim to attribute observed changes in daily streamflow to the most dominant hydro-meteorological drivers by applying seasonal multiple-regressions. The major findings of this study are as follows:

• The high-resolution trend analysis allows for in-depth seasonal-specific insights into the hydrological response of catchments with different hydrological regimes to changes in the hydro-meteorological drivers.
• Increasing (decreasing) contributions of rainfall (snowmelt) to streamflow generally agree with prior expectations. The trends, however, show differences in magnitude and timing, depending on the geographical location (Vestlandet vs. Østlandet) and altitude.
• The seasonal multiple regression approach suggests that daily streamflow changes can be explained best by adding temperature as an additional predictor to snowmelt and rainfall, which may indicate the changing relevance of evapotranspiration particularly during summer.

How to cite: Vormoor, K., Skålevåg, A., and Bronstert, A.: Highly-resolved hydro-meteorological trends in Norway: impacts of observed climate change on snowmelt- and rainfall dominated streamflow in Western vs. Eastern Norway, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18855, https://doi.org/10.5194/egusphere-egu2020-18855, 2020.

Recent and projected changes in the climate are known to affect the hydrological cycle. Many studies have shown how these climate changes result in differences in for example, evaporation rates, melt of snow and ice, precipitation patterns and seasonality. Since these processes are influencing different parts of the hydrological cycle, the hydrological response as result of changes in climate can be rather complex (Buitink et al., 2019b). In this study, we investigate how the combined effects of changes in melt from frozen water and increased evaporation rates affect the hydrological response in the Rhine basin, using the new dS2 model (Buitink et al., 2019a). It is known that increased temperatures affect both the melt of frozen water and the energy available for evaporation. However, as temperatures will reach melting point earlier in the year, the contribution of meltwater to the total discharge will also peak earlier in the year. Contrary, evaporation will increase without strong changes in the seasonality. Since the Rhine depends for a significant fraction on meltwater from snow and ice during warm and dry summers, this change in timing can have significant impacts on the low flows. This study shows these effects both for the recent changes in climate, but also presents the sensitivity of the hydrological cycle to the changes in the climate.

Buitink, J., Melsen, L. A., Kirchner, J. W., and Teuling, A. J.: A distributed simple dynamical systems approach (dS2 v1.0) for computationally efficient hydrological modelling, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-150, in review, 2019a.

Buitink, J., Uijlenhoet, R., and Teuling, A. J.: Evaluating seasonal hydrological extremes in mesoscale (pre-)Alpine basins at coarse 0.5° and fine hyperresolution, Hydrol. Earth Syst. Sci., 23, 1593–1609, https://doi.org/10.5194/hess-23-1593-2019, 2019b.

How to cite: Buitink, J. and Teuling, A. J.: The hydrological cycle in a warmer world: combined effects of changes in snowmelt and evaporation on Rhine discharge evaluated with the new dS2 model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8970, https://doi.org/10.5194/egusphere-egu2020-8970, 2020.

Headwater systems are a major source of water, sediments, and nutrients (including nitrogen and carbon di-oxide) for downstream aquatic, riparian, and inland ecosystems. As precipitation changes are expected to exhibit considerable spatial variability in the future, we hypothesize that headwater contribution to major rivers will also change significantly. Quantifying these changes is essential for developing effective adaptation and mitigation strategies against climate change. However, the lack of hydrologic projections at high resolutions over large domains have hindered attempts to quantify climate change impacts on headwater systems.

Here, we overcome this challenge by developing an ensemble of hydrologic projections at an unprecedented resolution (1km) for Germany. These high resolution projections are developed within the framework of the Helmholtz Climate Initiative (https://www.helmholtz.de/en/current-topics/the-initiative/climate-research/). Our modeling chain consists of the following four components:

Climate Modeling: We statistically downscale and bias-adjust climate change scenarios from three representative concentration pathway (RCP) scenarios derived from the EURO-CORDEX ensemble - 2.6, 4.5, and 8.5 to a horizontal resolution of 1km over Germany (i.e, a total of 75 ensemble members). The EURO-CORDEX ensemble is generated by dynamically downscaling CMIP-5 general circulation models (GCM) using regional climate models (RCMs). Hydrologic Modeling: To account for model structure uncertainty, the climate model projections are used as forcings for three spatially distributed hydrologic models - a) the mesocale Hydrologic model (mHM), b) Noah-MP, and c) HTESSEL. The outputs that will be generated in the study are soil moisture, evapotranspiration, snow water equivalent, and runoff. Streamflow Routing: To minimize uncertainty from river routing schemes, we use the multiscale routing model (mRM v1.0) to route runoff from all the three models. River Temperature Modeling: A novel river temperature model is used to quantify the changes in river temperature due to anthropogenic warming.

The 225-member ensemble streamflow outputs (75 climate model members and 3 hydrologic models) are used to quantify the changes in the contribution of headwater watersheds to all the major rivers in Germany. Finally, we analyze changes in soil moisture, snow melt, and river temperature and their implications for headwater contributions. Previously, a high-resolution (5km) multi-model ensemble for climate change projections has been created within the EDgE project^1,2,3,4. The newly created projections in this project will be compared against those created in the EDgE project.  The ensemble used in this project will profit from the higher resolution of the regional climate models that provide a more detailed land orography.

References

[1] Marx, A. et al. (2018). Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 C. Hydrology and Earth System Sciences, 22(2), 1017-1032.

[2] Samaniego, L. et al. (2019). Hydrological forecasts and projections for improved decision-making in the water sector in Europe. Bulletin of the American Meteorological Society.

[3] Samaniego, L. and Thober, S., et al. (2018). Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change, 8(5), 421.

[4] Thober, S. et al. (2018). Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003.

How to cite: Koppa, A., Remke, T., Thober, S., Rakovec, O., Müller, S., Marx, A., and Samaniego, L.: Spatiotemporal Changes in Headwater Flow Contributions to Major Rivers of Germany under Changing Climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7639, https://doi.org/10.5194/egusphere-egu2020-7639, 2020.

Climate change has already affected many components of our natural environment which are well described in the literature. Water temperature has received less interest despite the fact that it is recognized as key variable for assessing water quality of freshwater ecosystems in streams and lakes. It influences the metabolic activity of aquatic organisms but also biochemical cycles. Water temperature is also a key variable for many industrial sectors, e.g. as cooling water for electricity production or in large buildings, and for the spreading of some diseases affecting fishes.

It is very likely that climate change has and will also have an important effect on the temperature of streams. This study (Michel et al., 2020) investigates first the past temperature evolution and corresponding discharge in Switzerland since 1979, showing an increase of +0.33 ± 0.03° per decade in water temperature. Some differences between catchment type (alpine vs. lowland) and some important seasonal features are identified.

In a second step, the response of selected catchments in Switzerland to the future forcing is numerically assessed using the CH2018 climate change scenarios for Switzerland. The approach uses a sequence of physical models including Snowpack, Alpine3D and StreamFlow. The CH2018 scenarios have been down-scaled to hourly resolution using a novel approach based on a delta method which preserves the seasonal aspect of the climate change scenario. The results show an increase in temperature for any of the RCP (2.6, 4.5, and 8.5) and a strong impact of climate change on alpine catchments caused by changes in snowfall/melt and glacier melt. As a consequence, river ecosystems including fish populations will be severely impacted and current legal limits for the usage of water for cooling in the energy production sector and in the industry will be reached more often in the future.

REFERENCES

Michel, A., Brauchli, T., Lehning, M., Schaefli, B., & Huwald, H.: Stream temperature and discharge evolution in Switzerland over the last 50 years: annual and seasonal behaviour , Hydrol. Hydrol. Earth Syst. Sci., 24, 115–142, https://doi.org/10.5194/hess-24-115-2020, 2020.

How to cite: Michel, A., Brauchli, T., Wever, N., Schaefli, B., Lehning, M., and Huwald, H.: Stream Temperature Evolution in Switzerland: Recent past and future, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16432, https://doi.org/10.5194/egusphere-egu2020-16432, 2020.

Robust hydrological models are critical for the assessment of climate change impacts on hydrological processes. This study analysis the future evolution of the spatiotemporal dynamics of multiple hydrological processes (i.e. streamflow, soil moisture, evaporation and terrestrial water storage) with the fully distributed mesoscale hydrologic Model (mHM), which is constrained with a novel multivariate calibration approach based on the spatial patterns of satellite remote sensing data (Dembélé et al., 2020). The experiment is done in the large and transboundary Volta River Basin (VRB) in West Africa, which is a hotspot of climate vulnerability. Climate change and land use changes lead to recurrent floods and drought that impact agriculture and affect the lives of the inhabitants.

Based on data availability on the Earth System Grid Federation (ESGF) platform, nine Global Circulation Models (i.e. CanESM2, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR and NorESM1-M) available from the CORDEX-Africa initiative and dynamically downscaled with the latest version of the Rossby Centre's regional atmospheric model (RCA4) are selected for this study. Daily datasets of meteorological variables (i.e. precipitation and air temperature) for the medium and high emission scenarios (RCP4.5 and RCP8.5) are bias-corrected and used to force the mHM model for the reference period 1991-2020, and the near- and long-term future periods 2021-2050 and 2051-2080.

The results show contrasting trends among the hydrological processes as well as among the GCMs. The findings reveal uncertainties in the spatial patterns of hydrological processes (e.g. soil moisture and evaporation), which ultimately have implications for flood and drought predictions. This study highlights the importance of plausible spatial patterns for the assessment of climate change impacts on hydrological processes, and thereby provide valuable information with the potential to reduce the climate vulnerability of the local population.

Reference

Dembélé, M., Hrachowitz, M., Savenije, H., Mariéthoz, G., & Schaefli, B. (2020). Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite datasets. Water Resources Research.

How to cite: Dembélé, M., Zwart, S., Ceperley, N., Mariéthoz, G., and Schaefli, B.: Multivariate and spatially calibrated hydrological model for assessing climate change impacts on hydrological processes in West Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9143, https://doi.org/10.5194/egusphere-egu2020-9143, 2020.

In a sick world with fever caused by global warming, the hydrological cycle will experience most certainly large changes in intensity and variability. One of the most intense phenomena that will probably be affected by the climate change is the flood hazard. For a long time the stakeholders have been dedicated resources to assess the risk linked to the floods magnitude and frequencies and shaping the public infrastructures based on the assumption of their immutability. Under the effect of the climate change this assumption can be broken and a different approach should be followed to avoid large disasters and threaten to the population health. In this study the biggest ever ensemble of hydroclimatic  simulations has been used to simulate the river floods over the European regions. A river routing model derived from a distributed hydrological model (CHyM) has been forced with 44 EURO-CORDEX, 5 CMIP5 and 7 CMIP6 simulations to assess the effects of the climate change on the floods magnitude under two different scenarios (RCP2.6 and RCP8.5 for EURO-CORDEX and CMIP5, SSP126 and SSP585 for CMIP6). The impact of the climate change has been evaluated using a 100 year return period discharge indicator (Q100) obtained fitting a Gumbel distribution on the yearly peak discharge values. Results show a decrease of magnitude of flood events over the Mediterranean, Scandinavia and the North Eastern European regions. Over these two last regions the signal appear particularly robust and in contrast to the projected mean flow signal that is shown to increase by the end of the century mainly driven by the related increase of mean precipitations. The reduction of snow accumulation during winter time linked to a large increase of late winter temperatures is the main reason behind the decrease of floods over the North Eastern regions. An opposite signal is projected  instead over Great Britain, Ireland, Northern Italy and Western Europe where a robust signal of floods magnitude increase is evident driven by e the increase of extreme precipitations. All these simulation are meant to feed the impact community and to shade the light on the use of climate information for impact assessment studies.

How to cite: Di Sante, F., Coppola, E., and Giorgi, F.: Future projections of river floods over the European region using EURO-CORDEX simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15847, https://doi.org/10.5194/egusphere-egu2020-15847, 2020.

Abstract

This article reports the research findings in a recent study (Kumar et al., 2020) that utilizes eight indices of climate change recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) for analyzing spatio-temporal trends in extreme precipitation and temperature at the daily scale across India. Observed gridded precipitation (1971-2017) and temperature (1971-2013) datasets from India Meteorological Department (IMD) are used along with reanalysis products from Climate Prediction Centre (CPC). The trends are estimated using non-parametric Mann-Kendall (MK) test and regression analysis. The trends in ‘wet days’ (daily precipitation greater than 95^th percentile) and ‘dry days’ (daily precipitation lower than 5^th percentile) are examined considering the entire year (annual) as well as monsoon months only (seasonal). At the annual scale, about 13% of the grid locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme ‘wet days’) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme ‘dry days’). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The spatio-temporal trends in ‘warm days’ (daily maximum temperature greater than 95^th percentile), ‘warm nights’ (daily minimum temperature greater than 95^th percentile), ‘cold days’ (daily maximum temperature lower than 5^th percentile) and ‘cold nights’ (daily minimum temperature lower than 5^th percentile) are also investigated for the aforementioned period. The number of ‘warm days’ per year increased significantly at 14% of the locations, while the number of ‘cold days’, ‘warm nights’ and ‘cold nights’ per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices are also investigated for the future using CanESM2 projected data for RCP8.5 after suitable bias correction. Most of the locations (49% to 84%) indicate significant increasing (decreasing) trend in ‘warm days’ (‘cold days’) in the three epochs, 2006-2040, 2041-2070 and 2071-2100. Moreover, most locations (60% to 81%) show an increasing trend in ‘warm nights’ and a decreasing trend in ‘cold nights’ in all the epochs. A similar investigation for the historical and future periods using CPC data as the reference indicates that the trends, on comparison with IMD observations, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.

Keywords: extreme precipitation and temperature, climate change indices, spatio-temporal variation, India

References:

Kumar S., Chanda, K., Srinivas P., (2020), Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India, Theoretical and Applied Climatology, Springer, In press, DOI: 10.1007/s00704-020-03088-5.

How to cite: Kumar, S., Chanda, K., and Pasupuleti, S.: Spatio-temporal variation of extreme indices derived from observed and reanalysis products for detection of climate change across India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5837, https://doi.org/10.5194/egusphere-egu2020-5837, 2020.

The status of natural water bodies in terms of water quality and quantity can be considered a criterion for the environmental status of their upstream catchment. The presence of natural water bodies with good condition can be a sign proper of water resource management activities in the upstream catchment for sustainable development. Iran has been undergoing a rapid development process in recent decades. Nowadays, in most water bodies in Iran, the water level has been decreased and even disappeared in some cases. Lake Urmia is a well-known example of drying lakes in Iran. This study aims at identifying the main effective drivers in drying up of the main lakes in Iran.

Iran is a country with an approximate area of 1,648,000 km2 that has an arid and semi-arid climate with an average precipitation of 311 mm/year. The most important water bodies in Iran are Lake Urmia and Maharloo, Hoor-al-Azim and Gavkhuni Wetlands, and Gorgan Bay. This study focuses on the mentioned waterbodies and upstream catchment information.

At first, climate conditions and changes such as drought and changes in their properties are studied to find the answer to this question. Then, non-climatic factors and their changes such as urban/rural population changes, industrial growth, agricultural changes such as land area, crop yield, and the type of irrigation were studied. To achieve this purpose, the time series of the surface level of these five waterbodies was measured using satellite images. Then the time of significant changes in the time series of the surface level of each waterbody was determined using the Pettit test. As a result, the time interval for each waterbody was divided into a two-time span, before and after the change point. This created a time interval for climatic and non-climatic comparisons to identify effective factors.

The climatic data from the synoptic stations located in and around each waterbody catchment have been used to study the climatic conditions, and the sum of precipitation and mean temperature have been evaluated as the main climate parameters along with the SPIE drought index and characteristic changes. In order to evaluate effective non-climatic factors, changes in urban/rural population factors, agricultural land level, the number of agricultural products, and industrial units were used based on official statistics.

The results of this study indicate that the year of significant changes in the time series of lakes was between 1996 and 2001. Crop yield change growth was the main factor in the upstream catchment of all lakes as a result of changes in the irrigation patterns.

How to cite: Moshir Panahi, D., Aminjafari, S., and Zahabiyon, B.: Why water bodies of Iran have been dried up?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17006, https://doi.org/10.5194/egusphere-egu2020-17006, 2020.

Arctic rivers’ flow regime has changed under climate change and its consequences on melting glaciers, thawing permafrost, and precipitation patterns. Reservoirs, hydro-power sites, and water diversions have also changed flow regimes in the Arctic. The flow regime alteration in the Arctic rivers has a strong influence on the conservation and sustainability of the native biodiversity of the riverine ecosystem. The main objective of this paper is to evaluate changes in the (1) magnitude of monthly stream flows, (2) magnitude and duration of annual maxima and minima flows, (3) timing of annual maxima and minima, (4) frequency and duration of high and low pulses, and (5) rate and frequency of daily flows in seven major Arctic Rivers. The analyses provide an important basis to characterize and understand the influence of climate change and anthropogenic activities on the flow regimes in the Arctic. Streamflow observations were obtained from the outlet of the Lena, Yenisei, Kolyma, Ob (Russia), Yukon (USA and Canada), Mackenzie (Canada), and Tana (Norway and Finland) rivers in this study. These rivers are main freshwater suppliers for Arctic Ocean. Of these, five have been regulated and two are considered pristine rivers. In addition, the impact of 16 reservoirs on flow regime in the headwaters and tributaries of Lena, Yenisei, Mackenzie, and Kolyma were evaluated. The annual flow showed an increasing trend in all rivers and with a statistically significant level in Yenisei, Lena, and Mackenzie. Our results also indicated that changes in the observed flow regimes at the outlet stations vary from low to incipient level. Out of 16 reservoirs that were analyzed for flow regimes changes, construction of Krasnoyarsk and Shushenskaya dams on the Yenisei River showed the highest impact on flow regime and flow regime alteration was classified as severe in this river.

How to cite: Fazel, N., Torabi Haghighi, A., Rasouli, K., and Kløve, B.: Flow regime variation in Arctic rivers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17701, https://doi.org/10.5194/egusphere-egu2020-17701, 2020.

The objective of the study is to verify a hypothesis that the hydrological model that successfully passed a comprehensive evaluation test is more suitable for impact study than the other model that failed the test. The hypothesis verification is carried out on an example of the physically-based hydrological models ECOMAG and SWAP, which are set up for the two great Arctic basins: the Lena and the Mackenzie rivers. Three versions of every model are compared: (1) the model with a priori assessed parameters (without any calibration); (2) the model calibrated against the streamflow observations at the basin outlets only, and (3) the model calibrated against the streamflow observations at several sites within the basins. The comprehensive evaluation procedure, which includes enhanced tests of model performance and robustness, is applied for all the versions of every model. The performance of the models is compared at multiple sites within the catchments and for multiple hydrological indicators of interest (high flow, low flow, multi-year trends). The robustness of the models is compared through statistical significance of the differences in the performance criteria of the model for climatically contrasting periods composed from the historical meteorological data. From the evaluation results, we identified the preferable (in terms of the assigned criteria) models and established the limits of the models applicability. Then all the compared models, being forced by the Global Climate Model ensemble data, were applied to simulate flow projections for the 21^st century and assess the projection uncertainty. The experiment demonstrates that the basin outlet flow projections simulated by the non-calibrated models differ from the projections of the calibrated models in terms of the mean ensemble trajectories and their uncertainty. Thus, under the study conditions (used models, studied basins), we answer "yes" to the question posed in the title of the presentation.       

How to cite: Gelfan, A., Kalugin, A., Krylenko, I., Nasonova, O., Gusev, Y., and Kovalev, E.: Does a comprehensive evaluation increase confidence in the hydrological model intended for climate impact assessment? , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4102, https://doi.org/10.5194/egusphere-egu2020-4102, 2020.

Many hydrological models that are used for long term projections require calibration of at least a few parameters. When calibrated on discharge only, a general rule of thumb is that 4 to 5 parameters can be calibrated. The general approach is to conduct a global sensitivity analysis, to determine the four to five most sensitive parameters, and to select these for calibration.

Parameter sensitivity differs over models, target variables, sensitivity analysis methods, and also over climates. This would also imply that parameter sensitivity could change in a changing climate, and that would interfere with the current standard calibration procedure for hydrological models. Therefore, the question is whether, within a plausible rate of change, climate change propagates into a change in parameter sensitivity.   

We investigated how parameter sensitivity changes as a consequence of climate change, and if and how this has consequences for the calibration strategy. We applied a hybrid local-global sensitivity analysis method to three frequently used hydrological models (SAC, VIC, and HBV) in 605 basins across the US, and link changes in sensitivity to changes in climate. Finally, we evaluated the impact on the top five most sensitive parameters.

The results show that in all three models especially snow parameters tend to become less sensitive in the future. However, the models differ in which parameters increase in sensitivity; for some models ET parameters increase, while for others deep layer parameters increase. Evaluating the top 5 most sensitive parameters per basin, we found that in 43% to 49% of the basins at least one parameter changes in the top 5 in the future, while a maximum of two parameter changes in the top 5 was observed over all basins (in 2 to 4% of the basins).

Overall, the results indicate that in about half of the investigated basins one parameter would have been chosen differently for calibration. If a particular model parameter is, within the current climate, not or hardly sensitive to discharge, it is not possible to calibrate this parameter – notwithstanding whether this parameter becomes sensitive in the future. Therefore, the consequence of these results is that for parameters that will become sensitive in the future, a range of feasible parameter values have to be sampled for future projections, thereby capturing predictive uncertainty as a consequence of changing sensitivities.

How to cite: Melsen, L. and Guse, B.: Climate change impacts parameter sensitivity - What does this mean for model calibration?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2609, https://doi.org/10.5194/egusphere-egu2020-2609, 2020.

The Arctic is warming at an unprecedented rate. This warming affects not just ecosystems, but also permafrost, landscape configuration, and water availability in watersheds. One relatively under researched process is how seasonally frozen soils and changes thereof affect the water cycle. As frozen soils thaw, flow pathways within a catchment open, allowing for enhanced hydrologic connectivity between groundwater and rivers. As the connectivity of flow paths increase, the storage-discharge relationship of a watershed changes, which can be perceived within a hydrograph. More specifically, previous studies hypothesized that storage-discharge relationships are relatively linear when soils are frozen and become increasingly non-linear as the landscape thaws.

The objective of our research is to expand on the assumption that soil thaw leads to increasingly non-linear storage-discharge relationships by quantifying trends and spatio-temporal differences of this relationship. We will present our analysis of sixteen watersheds within Northern Sweden throughout the years of 1951 and 2018. We focus on spring and summer storage-discharge relationships and show how they are affected by preceding winter conditions.

We found a clear increase in non-linearity of the storage-discharge relationship over time for all catchments with twelve out of sixteen watersheds (75%) having a statistically significant increase in non-linearity. For twelve watersheds, spring relationships were significantly more linear compared to summer, which supports the hypothesis that seasonally frozen soils have less hydrological connectivity leading to more linear storage-discharge relationships. Winter conditions that allow deep soil frost lead to more linear storage-discharge relationships for ten watersheds. Overall, we show that thawing soil leads to a more non-linear storage-discharge relationship which implies river runoff in the Arctic becomes more unpredictable.

How to cite: Hinzman, A., Sjöberg, Y., Lyon, S., Ploum, S., and van der Velde, Y.: Strong changes in the relationship between storage and discharge during a period of thawing soils and climate warming in Northern Sweden, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10113, https://doi.org/10.5194/egusphere-egu2020-10113, 2020.

Understanding the vegetation response to climate change, especially warming and elevated CO₂, is crucial for a better understanding of the present and future hydrological conditions and processes. Based on recent findings in estimating potential evapotranspiration (PET), the study presents an improved method of estimating PET, that was evaluated with actual evapotranspiration (ET) lysimeter data from a managed alpine grassland.

Research findings from field observations reported reduction in leaf-level stomatal conductance, as higher CO₂ drives partial stomatal closure, consequently reducing ET. Thus, a modified Penman-Monteith (PM) evapotranspiration method (Yang, 2018) was used, that introduces the vegetation response to elevated CO₂ into Penman-Monteiths (PM) formalism, directly targeting the surface resistance (r_s).

Comparing PET values computed with the original PM method with lysimeter data of actual evapotranspiration displayed underestimation of the mean PET. This was also found in a recent study (Schymanski, 2017) that revealed an omission in the Penman-Monteith equation, pointing out that the PM method neglects two-sided exchange of sensible heat by a planar leaf.

This study joined these findings and tested a new method for calculating PET in climate change studies. The proposed PM method accounts for both the plant physiological response to higher CO₂ and two-sided heat exchange of planar leafs. Additionally, other less data consumptive PET methods were evaluated to compare the model performance with the newly derived PET method.

The methods were evaluated and optimized based on lysimeter data of six high precision weighable lysimeters, where each of the grassland lysimeters was subjected to treatment, simulating elevated CO₂ concentrations and warming. The lysimeters are located at the AREC Raumberg-Gumpenstein (Styria, Austria) and are part of an experimental site, which incorporates a CO₂ enrichment technique (+ 300 ppm; miniFACE technique) and infrared heaters (+3° C; T-FACE-Technique). Using the corrected PM equation, that accounts for a two-sided heat exchange, the model performance of the PM equation was improved for both ambient and future conditions. Combining this equation, with the PM method accounting for the plant physiological response to higher CO₂, the corrected method produced much better fit to the lysimeter data compared to the original equation.

The results of this study present an improvement of the PM method that not only enhances the representation of transpiration and sensible heat to changes in atmospheric conditions, but also incorporates the response of elevated CO₂, which make it more suitable for climate change studies.

How to cite: Vremec, M., Forstner, V., Herndl, M., and Birk, S.: Implication of vegetation response to future climate conditions in current potential evapotranspiration methods – a grassland lysimeter study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15486, https://doi.org/10.5194/egusphere-egu2020-15486, 2020.

Abstract:

The hydroclimatic teleconnections between global Sea Surface Temperature (SST) fields and monthly rainfall for the summer (June to August) and winter (December to February) seasons over east and west Japan (divided along 138°E longitude) are investigated using the concept of Global Climate Pattern (GCP) (Chanda and Maity, 2015). It is established in a recent study that these teleconnections exhibit contrasting features and have different origins - rainfall anomalies over west Japan are associated with SST anomalies in the tropical Pacific and Indian Ocean, whereas those over east Japan are associated with high-latitude SST anomalies (Maity et al., 2020). Moreover, the teleconnections show inter-seasonal and intra-seasonal variations. For instance, the El Niño Modoki (La Niña Modoki) phenomena are found to influence the early summer (winter) rainfall over west Japan. In east Japan, early summer (June) and winter (December) rainfall is associated with positive SST anomaly differences in eastern sub-tropical Pacific and south Pacific respectively. Further, the study establishes that, beyond the traditional teleconnection patterns such as ENSO, El Niño Modoki, other climatic precursors are also instrumental in triggering below- and above- normal monthly rainfall in east and west Japan. The predictive potential of all such identified teleconnection patterns for monthly rainfall variation is assessed using a machine learning approach, Support Vector Regression (SVR) and a hybrid Graphical Modelling/C-Vine copula (GM-Copula) approach. The later technique helps to construct a conditional independence structure among the correlated variables to prune the redundant information in the predictor pool and develop a month-wise prediction model using the pruned predictor sets only. It is observed that the complex association between the predictors and the predictand is better captured by this GM-Copula approach with slightly better prediction performance in summer (R = 0.66 to 0.70) than in winter (R = 0.45 to 0.75) for both east and west Japan. Thus, it is concluded that, establishing the conditional dependence structure of the predictor pool is an important step to resolve the complexity and dimensionality of the model and the proposed model may be recommended for operational forecast of monthly rainfall over east and west Japan. Further details can be found in Maity et al., (2020).

Keywords: Rainfall prediction, Hydroclimatic teleconnection, Global climate pattern, Sea surface temperature, Machine learning, SVR, Graphical Model, Copula, Japan.

References:

Chanda K. and R. Maity, (2015). Uncovering Global Climate Fields Causing Local Precipitation Extremes. Hydrological Sciences Journal, Taylor and Francis. doi: 10.1080/02626667.2015.1006232.

Maity, R., K. Chanda, R. Dutta, J.V. Ratnam, M. Nonaka, S. Behera (2020), Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan, Meteorological Applications, Royal Meteorological Society (RMetS), In Press, doi: DOI: 10.1002/met.1881.

How to cite: Maity, R., Chanda, K., Dutta, R., Ratnam Jayanthi, V., Nonaka, M., and Behera, S.: How dissimilar are the large-scale hydroclimatic precursors and predictability of anomalous monthly rainfall in east and west Japan?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10496, https://doi.org/10.5194/egusphere-egu2020-10496, 2020.

Change detection and attribution of water cycle are increasingly crucial for promoting society‘s capacity to embed adaptation planning confronting both climate change and anthropogenic forces at catchment scale. Nevertheless, current researches either neglect the difference between internal climate variability and climate change (including internal climate variability and external radiative forcing) or don’t consider different anthropogenic activities (e.g. land use changes, reservoir operation and water consumption). In this study, a new stepwise multiply scenarios approach (SMSA), using model simulations of the Fifth Coupled Model Intercomparison Project (CMIP5) archive and the new generation Soil and Water Assessment Tool (SWAT), dubbed SWAT+ model to identify and quantify influence of total five different factors (internal climate variability, external radiative forcing, land use changes, reservoir operation and water consumption) on inter-annual and seasonal hydrological alteration. Application of this approach to a perennial basin in Southeast China highlights the role of reservoir operation. 

How to cite: Zha, X.: Attribution of inter-annual and seasonal hydrological alteration to climatic and anthropologic changes at a perennial basin in Southeast China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4152, https://doi.org/10.5194/egusphere-egu2020-4152, 2020.

The impact assessment of landuse / landcover change (LULCC) and climate change (CC) on the runoff in a highly elevated watershed has key importance in terms of sustainable water resources and ecological developments. In this research, statistical technique was deployed with the addition of Soil and Water Assessment Tool (SWAT) in the Water Towers of Yangtze River (WTYZ). The coefficient of determination (R²) and Nash-Sutcliffe Efficiency (NSE) were used as a decision criterion to ensure the performance of model simulations. The model performed satisfactory with monthly R² = 0.80 to 0.83 and NSE = 0.63 to 0.69 during calibration (1985 - 2000) and (2001 – 2016) periods. Major LULCC transformations were assessed from low grassland to medium grassland (2.017%) and wetlands (0.90%), bare land to medium grassland (0.23%) and glaciers to wetland (16.83%), high grassland to medium grassland (5.77%) during 1990s and 2005s. Impact of CC increased runoff by 97.97% and decreased evapotranspiration by -5.15% of total runoff and evapotranspiration respectively. It was also noteworthy that LULCC caused the increase in runoff and evapotranspiration by 2.02% and 105.15% relative to totals, respectively. Thus, the variations of runoff in the WTYZ are mainly impacted by landuse/landcover, while climate change have relatively least impacts.

How to cite: Ahmed, N., Wang, G., Xiangyang, S., Nabi, G., Hussain, F., Huang, K., Shakoor, A., and Munir, S.: Contribution of Climate Change and Landuse / Landcover Change on Variations of Hydrological Processes in The Water Towers of Yangtze River, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1511, https://doi.org/10.5194/egusphere-egu2020-1511, 2020.

The runoff in river systems has been significantly changed by climate change and land use/cover change (LUCC), while the magnitude and patterns vary because of the factors. Investigating the major factor impacting runoff variation is necessary for water resource management. In this work, five different water-energy balance models are used to analyze the cause of runoff variations; of these models, three are based on the Budyko framework and two are based on the ecohydrological conceptual framework. The approach is demonstrated using the upper-midstream of the Heihe Rivers. The results suggest LUCC is the dominant cause of runoff change in the range of 59.92% ~ 65.14%. The estimated impacts of climate change and LUCC are consistent among the five models. Cropping is the major human activity resulting in LUCC at the upper-midstream of the Heihe River. Meanwhile, the change in runoff is more sensitive to precipitation than to potential evapotranspiration. Our work summarizes five widely used water-energy balance models used to explain the impacts of climate change and LUCC on runoff, which may be of importance in explaining the mechanism of runoff change.

How to cite: Xiong, M.: Assessing the Impacts of Climate Change and Land Use/Cover Change on Runoff Based on Multiple Water-Energy Balance Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3276, https://doi.org/10.5194/egusphere-egu2020-3276, 2020.

The Dynamic Water Resources Asesment Tol (DWAT) acounts for water balance on dynamic (hourly or daily) as wel as static (monthly or yearly) bases. It can be aplied to a smal or a mid-sized basin for water resources planing and management with consideration of surface water as wel as groundwater. The DWAT clasifies a watershed into hydrologicaly homogeneous sub-basins so that runof characteristics resulting from geomorphological factors can be objectively represented, and infiltration, evaporation and groundwater flows can be simulated acording to soil layers. In aditon, as the physical input parameters can be easily extracted by the GIS preprocesing module within the system, it can be aplied to areas in various hydrological, geophysical and climatic conditons, such as tropical, rural, forest or newly developed urban areas. The DWAT has ben developed in Korea Instiute of Civil Enginering and Building Technology (KICT) since 2012 as a part of WMO (World Meteorological Organization) RA (Regional Asociation) II WGHS (Working Group on Hydrological Services) and CHy (Commision for Hydrology) AWG (Advisory Working Group) activites, and it has ben suported by the Han River Flod Control Ofice, Ministry of Environment, Republic of Korea. The first version 1.0 beta of the DWAT was developed in the end of 2017, which contains sub-algorithms such as evapotranspiration, infiltration, watershed runof, groundwater flow, chanel routing and user convenience systems. In the midle of 2018, the second version 1.0 was developed with the aditon of rice pady field, snowmelt and manual/automatic parameter optimization modules. In May 2019, the third version of 1.1 was developed in consideration of the recommendations made by the WMO panel of experts.

How to cite: Jang, C.: Development and Application of Dynamic Water resources Asesment Tol (DWAT), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3308, https://doi.org/10.5194/egusphere-egu2020-3308, 2020.

Consisting of evaporation from wet surfaces (E) and transpiration through plants (T), evapotranspiration (ET) is an integral part of earth’s ecological and climate systems. Since the different ET components have their certain water resource function and ecological significance, accurate estimation of regional ET components is essential to better understand water cycle and surface energy budget. Incorporating soil relative humidity (SRH) into remote sensing ET algorithm, this study presented an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit monthly SRH control on soil evaporation (E_s) and canopy transpiration (T). The updated algorithm (i.e., PT-SM) was evaluated using 17 eddy covariance towers across different biomes, and 24 hydrological catchments across different climatic regions of China, respectively. The PT-SM model shows increased R² and NSE, and reduced RMSE and Bias, with the greatest improvements occurring in water-limited regions. SRH incorporation into E_s can improve ET estimates by increasing R² and NSE by 3% and 17%, respectively, and RMSE and Bias were reduced by 13% and 26%, respectively, while SRH incorporation into T would improve ET estimates by raising R² and NSE by 6% and 27%, respectively, and RMSE and Bias were reduced by 32% and 63%, respectively. We apply the algorithm to the whole China using SRH data at depths [10-cm, 20-cm, and 50-cm] and a resolution of 0.5° × 0.5° assimilated by the farmland soil moisture observation, the NASA’s Gravity Recovery And Climate Experiment (GRACE) solutions and observed precipitation. The mean annual of total estimated ET increased from the northwest to the southeast, with E_s/ET and T/ET roughly presenting opposite spatial distribution characteristics.

How to cite: Xing, W., Wang, W., Deng, C., and Chen, Z.: Estimation of evapotranspiration components across China based on a modified Priestley–Taylor algorithm with assimilated soil moisture data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4292, https://doi.org/10.5194/egusphere-egu2020-4292, 2020.

The growing water extraction due to the economic development and population growth has caused over-utilization of water resources worldwide, especially in semiarid regions. In these regions, the sustainable water availability has often been sought and maintained by managing land, but it is highly uncertain in future climate conditions. Besides, prediction of water availability in such region is still challenging due to non-stationary rainfall-runoff relationship caused by intensive human interferences and poor ET simulation by hydrological models. Therefore, accurate estimation and maintaining of sustainable water availability under future climate conditions are important for the ecological conservation and social development of semiarid regions. In this study, impacts of land use and climate changes on vegetation dynamics (canopy LAI) and water cycle (ET and runoff) of the Xiong’an New Area (XNA) are investigated using an ecohydrological model (i.e., WAVES). The XNA, a typical semiarid region located in North China, is expected to need more water in order to increase the vegetation coverage from 10% to 40% by 2035. The WAVES model is chosen because it can simulate ET well by coupling water-carbon-heat. Here, water use (ET) and water yield (runoff) of three typical ecosystems (i.e., cropland, grassland and forestland) in different future periods (i.e., near-future: 2030s (2021-2040), mid-future: 2050s (2041-2060) and far-future: 2080s (2061-2100)) are assessed using projected future climate forcing from 18 GCMs under three RCPs (i.e., RCP2.6, RCP4.5 and RCP8.5). Projected precipitation (P) and air temperature (T_a) indicate the XNA will become warmer and wetter in the future. The WAVES model is capable to simulate the ecohydrological process well in the XNA with NSE ≥ 0.62, R² ≥ 0.65, RMSE ≤ 0.86 in LAI and NSE ≥ 0.61, R² ≥ 0.66, RMSE ≤ 0.71 mm·d^-1 in ET. During the baseline period of 1982-2012, modeling results show that the forested land evaporates more water (32 mm a⁻¹) than cropland while grassland use almost same water as cropland. Under future climate conditions, both cropland and the grassland will have more water use and water yield due to increased precipitation and suppressed vegetation growth due to warming. Forested land will use more than 20% water (76 mm a⁻¹) compared with that during the baseline period in the XNA, but it will generate more than 10% (12 mm a⁻¹) water yield in the 2050s and 2080s under RCP4.5 and RCP8.5 due to greater increases in precipitation. For the purpose of land management, it is recommended to plant crop or grass in the near-future and to plant forest in the mid-future and far-future to expand vegetation coverage in the XNA. This study highlights that both climate change and land management are of critical importance for sustaining water yield in semiarid regions with over-utilized water resources.

How to cite: Ye, L., Cheng, L., Liu, P., and Liu, D.: Land management for sustainable water yield under future climate conditions in semiarid regions with over-utilized water resources: A case study of Xiong’an New Area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10521, https://doi.org/10.5194/egusphere-egu2020-10521, 2020.

Climate and land use/cover changes are widely recognized as two main drivers of variations in ecosystem services including water yield. However, vegetation cover condition, which can also influence the hydrological cycle through evapotranspiration process, is seldom considered. In this study, we used the Seasonal Water Yield Model (SWYM) to assess the spatiotemporal water yield changes of Lhasa River Basin from 1990 to 2015, and analysed its influencing factors by focusing on precipitation change, land cover change, and vegetation cover change (indexed by Normalized Difference Vegetation Index, i.e. NDVI). We first examined the model through Morris Screening sensitivity analysis and validated it with observed flow data. Spatiotemporal variation of three indices of water yield, baseflow, quick flow and local recharge, were then assessed. To analyse the contribution of each factor to water yield change, three scenarios were built in which one factor was altered at a time. Results showed that, the precipitation and vegetation cover change were substantial during the study period, while land cover change was quite small. From 1990 to 2015, the baseflow, local recharge and quick flow decreased by 67.03%, 80.21% and 37.03% respectively, with the change mainly occurring during 2000-2010. The spatial pattern of water yield remained mostly unchanged. The upstream area had relatively high baseflow and local recharge, and was the main contributor of quick flow. The downstream area had relatively low or even zero baseflow, and most of its local recharge was negative due to high evapotranspiration. According to contribution analysis, precipitation and vegetation cover change were the main factors affecting water yield in the Lhasa River Basin. For baseflow, the influence of precipitation change was, on average, 7.98 times as big as vegetation cover change, and the influence of vegetation cover change was, on average, 115.45 times as big as land cover change. However, land cover change began to exert greater influence after 2010. We suggest that besides climate and land use/cover change, vegetation cover change should also be studied in greater depth to fully understand its effect on regional hydrological process and ecosystem service provision.

How to cite: Lu, H., Yan, Y., Zhu, J., Jin, T., Liu, G., Wu, G., Stringer, L. C., and Dallimer, M.: Spatiotemporal water yield variations and influencing factors in the Lhasa River Basin, Tibetan Plateau, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7403, https://doi.org/10.5194/egusphere-egu2020-7403, 2020.

Changes in the catchment scale water balance have important social implications for usable water now and in the future. Stream discharge is also directly related to radionuclides flux in the river water system. The aim of this study was to clarify the water balance in the Chernobyl Exclusion Zone (CEZ) under current and future climate conditions. A catchment scale hydrological model was used with long-term discharge data to project the future trend of radionuclides wash-off from the contaminated catchment at the CEZ in Ukraine. The Sakhan river catchment in the CEZ (51.41°N, 30.00°E) in Ukraine is one of the Pripyat river systems, and has a total surface area of 186.9 km². We found that under the current climate, 84% of annual input (sum of rainfall and snowmelt) was consumed as evapotranspiration, and discharge was estimated to be 16%. In future climates, annual precipitation is expected to increase. However, a projected increase in the vapor pressure deficit led the consumption of precipitation as evapotranspiration and no significant increase in discharge. The study found that warmer winter and spring temperatures will decrease the snowfall, and increase the rainfall, but it was not enough to increase evapotranspiration. As a result, the peak of discharge shifted from April to March. The increase of future average discharge during the winter and spring came from a combination of (1) increasing rainfall in the winter and spring, and (2) relatively small levels of evapotranspiration, which enhanced the catchment scale water recharge in soil moisture and gave rise to greater discharge during winter and spring. The reduction of extreme river discharge from the hydrological projections could reduce the probability of high radionuclides concentration in the river water system in the future, owing to the reduction of surface runoff water from the contaminated surface soil and/or top layer of floodplain soils in the CEZ.

How to cite: Igarashi, Y., Zheleznyak, M., Lisovyi, H., Wakiyama, Y., Onda, Y., Nanba, K., Konoplev, A., Laptev, G., Damiyanovich, V., Samoilov, D., and Serhii Kirieiev, S.: Catchment scale estimation of current and future water balance in the Chernobyl Exclusion Zone in Ukraine, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6300, https://doi.org/10.5194/egusphere-egu2020-6300, 2020.

Existing flood control plans have been implemented based on rainfall estimated from observation data. However, we have data from the past several decades. Thus, it is not enough to project future extreme events from existing observation data. Therefore, Japan has been created huge ensemble of high-resolution climate model simulation based on the laws of physics. The data consist of past and future climate situations (past climate: total 3,000 years, 4 K warmer climate: total 5,400 years). It has enabled to quantitatively evaluate the probability of heavy rainfall and flooding on the future 4K-warmed earth.

Moreover, we apply the statistical theory of extreme value to evaluate the probability of heavy rainfall and flooding in the future. The results from statistical method is equivalent to the results from the huge ensemble data from climate model. It supports Japanese governments in formulating and carrying out their adaptation plans.

How to cite: Yamada, T. and Hoshino, T.: Heavy rainfall and flood risk assessment method considering the future climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22266, https://doi.org/10.5194/egusphere-egu2020-22266, 2020.

According to the records, an average of 5.3 typhoons hit Taiwan each year over last decade. Typhoon Morakot in 2009 was considered the most severe typhoon, which caused huge damage in Taiwan, including 677 casualty and roughly NT$ 110 billion ($3.3 billion USD) in economic loss. More and more researches documented that typhoon intensity will increase with climate change in western North Pacific region. It will induce the more severe natural disasters, such as flooding, landslide, and water resources risks in Taiwan in the future. Most research focused on the disaster impact assessment in climate change and was assumed that the land use are unchanged in the future. On the other hand, land use changes is another key reason for increasing the hazard risks. Therefore, this study tries to build a land use change model to simulate the land use spatial distribution, and discuss whether the extreme precipitation or the land use change is the major factor to increase flooding risks in Taoyuan City, northern Taiwan in the future.

This study applied that Markov chain to project the land use demand in 2036 and used the binary logits regression to establish the land use change probability model to allocate the land use spatial distribution in the future. Then, there are two different precipitation intensities used and integrated the allocated land use to evaluate the risks of flooding in 2036.

We successfully established land use spatial allocation model, and linked the allocated results to disaster impact assessment. Assessment results showed that land use change slightly increases the flooding risks; but extreme precipitation induces more severe flooding risks than land use change. Our results point out that extreme precipitation will induce the more severe flooding risks than land use. In addition, the restricted land development policy could efficiently reduce the flooding risks. If government implement climate change adaptation activities with land use management policies at the same time would possibly reduce the climate change disaster impact in the future.

How to cite: Chao, Y.-C., Liu, P.-L., Chen, C.-C., Li, H.-C., Hsu, C.-T., and Chen, Y.-M.: Flooding risks change due to land use and precipitation change in Northern Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10144, https://doi.org/10.5194/egusphere-egu2020-10144, 2020.

The Helmand Transboundary River is the main drainage system in the southern part of Afghanistan and has a significant impact on the socioeconomic balance of the Sistan region in Iran. The persistent and intensifying hydrological drought causing the scarcity of water and depleted supplies for irrigation has been effective in reducing agricultural production and increasing migration in the Sistan region. For drought management, knowledge of factors affecting the development of hydrological droughts is essential. Due to the significant rise of drought in the Sistan region in recent decades, we quantified the drought characteristics throughout the basin to reveal the temporal and spatial pattern of drought in the Sistan Plain. The meteorological and hydrological droughts were reanalyzed based on the precipitation and streamflow records using the multi-month timescales during 1970–2006, a period of 37 years. To reproduce the river discharge to evaluate the hydrological drought, the distributed process-based hydrological model was first developed. The results indicate that the hydrological model performed quite well in both calibration and validation periods in the entire basin. The drought analysis represented that the trend of hydrological droughts in the Sistan Plain considerably changed in the last decade of the study period due to the increasing abstractions from the Helmand River associated with the rising evaporation, which have led to extending the severe drought of long duration in the Sistan area.

How to cite: Roodari, A., Hrachowitz, M., and hassanpour, F.: Downstream intensification of hydrological drought along a large Central Asian River over the past decades: the individual roles of climate variability and land use change , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3356, https://doi.org/10.5194/egusphere-egu2020-3356, 2020.

Climate change is set to increase the magnitude and frequency of fluvial flooding in many regions across the world, making it a growing risk to billions of people living near rivers. Changing drainage basin land cover and hydrological connectivity further complicates how these streamflow extremes may evolve. Engineered solutions to mitigate the risk of future high magnitude runoff events to populations may no longer be suitable to meet these needs due to these changes in climate and land cover.

By reducing the level of global CO₂ emissions, climate models predict that we can reduce the severity of climate change impacts upon communities. To achieve the goals set by the Paris Agreement to limit global warming, the UK has proposed a range of policies to reach net zero carbon emissions by 2050. One of these proposals includes widespread afforestation across the UK. Where to plant this woodland and the scale of impact it may have on the future hydrological cycle is currently unquantified. This project seeks to investigate three aspects of how future streamflow trends my change due to afforestation in respect to: woodland location, differing afforestation rates, and the hydrological responsiveness of drainage basins to land cover changes.

Physics-based models provide the possibility to explore the relative importance of climate and land cover on future streamflow trends, both together and separately. The Joint UK Land Environment Simulator (JULES) is used to explore catchment responses across the UK to potential extreme weather events with theoretical changes in land cover at a 1 km resolution. Theoretical land cover scenarios of afforestation were generated according to proximity to existing land cover, drainage basin structure and proposed afforestation sites. An extreme precipitation scenario (the winter of 2013/14) is explored to comprehend streamflow regime response to high magnitude precipitation events caused by changing climate and land cover using the Weather@home perturbed model ensembles and CHESS-met datasets. This approach provides the potential to explore how increasing afforestation could change the discharge dynamics of landscapes across the UK and thus its potential benefits and drawbacks to flood risk management. 

Results show how potential land cover changes will impact streamflow response to storms across the UK. These results help provide a clearer picture of how changing landscape systems impact river response to external climatic forcing and may provide evidence for management and policy strategies tailored to the requirements of individual drainage basins to reduce the risk of flooding upon downstream populations.

How to cite: Buechel, M., Dadson, S., and Slater, L.: Achieving Net Zero: Understanding the Potential Hydrological Impacts of Changing Climate and Land Cover in the UK, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6905, https://doi.org/10.5194/egusphere-egu2020-6905, 2020.

After the Paris Agreement of 2015 many studies on climate impact assessment, e.g. of floods, water resources and droughts, focused on understanding the projected changes at the time frame when a specific warming level is reached. The results of these studies assume that the pathway to reach a certain greenhouse concentration and corresponding warming level plays a minor role in the change of the physical variables that define the hazard. However, this hypothesis should be verified for each variable, as the links between the timing of the warming levels and the projected changes of the geophysical variables are not yet fully understood. To address this gap, in this contribution we compared the projected changes of annual mean, extreme high and extreme low river discharges in Europe at 1.5°C and 2°C under scenarios RCP8.5 and RCP4.5 from an ensemble of Regional Climate Model simulations. The statistical significance of the difference between the two scenarios for both warming levels has been then evaluated versus the other sources of uncertainty, through an Analysis of Variance (ANOVA). The results show that in the majority of Europe (>95% of the surface area for the annual mean discharge, >98% for high and low extremes), the differences in the changes projected in the two pathways are statistically small. These results suggest that in studies of changes at specific warming levels the projections of the two pathways can be merged into a single ensemble without major loss of information. With regard to the uncertainty of the merged ensemble, findings show that the projected changes of annual mean, extreme high and extreme low river discharges are statistically significant in large portions of Europe. Merging the 2 pathways comes with a two-fold advantage with respect to the separate treatment of the 2 scenarios. On the one hand, it improves the estimation of the statistical significance of the projected change, by increasing its size and by better taking into account the pathway-related uncertainty (the emission pathways are set ex-ante as a hypothesis for the CMIP experiment, and the related uncertainty is usually neglected). On the other hand, a multi-pathway ensemble can simplify the discussion of the projected changes by removing from the analysis the dependency from the emission pathway, and making the results clearer and more understandable by a non-scientific public.

How to cite: Mentaschi, L., Alfieri, L., Dottori, F., Cammalleri, C., Bisselink, B., De Roo, A., and Feyen, L.: On the independence from the emission pathway of the projected changes of river runoff, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7065, https://doi.org/10.5194/egusphere-egu2020-7065, 2020.

This study examines the role played by changes in the climate system and land use in the observed monthly baseflow records (1966-2015) for 458 U. S. Geological Survey sites across the U.S. Midwest. We developed parsimonious statistical models in which monthly baseflow is related to any combination of four predictors (precipitation, temperature, antecedent wetness, and agriculture). We found that precipitation and antecedent wetness were the strongest predictors for all months, pointing to the role of water availability and infiltration in driving baseflow. Temperature was an important factor in the winter and spring where snow-melt processes are the most relevant. Agriculture was selected in the Corn Belt region during the growing season (from April to August) indicating that corn and soybean production in the Midwest promote baseflow discharge to streams. Overall, the goodness-of-fit for our models and cross validation strongly support our modeling results for all months. Differences in model selection reported here can aid water managers in decision making for water availability, food security and economic growth.

How to cite: Ayers, J., Villarini, G., Schilling, K., and Jones, C.: On the influence of climate and land use change on monthly baseflow across the U.S. Midwest, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2765, https://doi.org/10.5194/egusphere-egu2020-2765, 2020.

The most urgent tasks facing hydrologists of Ukraine and the world include identifying patterns of rivers hydrological regime against the background of global warming, and assessing these changes. Changes in the annual runoff distribution under climate change impact require separate investigation of anthropogenically altered catchments, such as the Siverskyi Donets River Basin. Siverskyi Donets is the largest river in Eastern Ukraine and the main source of water supply for Kharkiv, Luhansk and Donetsk regions.

The annual runoff distribution of the Siverskyi Donets River Basin was evaluated by two periods: to the beginning of pronounced climatic changes and the current period. The research is proposed for three water year types: wet year, average year and dry year. The Siverskyi Donets Basin is a complicated water body with peculiar physico-geographical conditions, because of that annual runoff distribution is somewhat different for the left-bank tributaries, right-bank tributaries and, in fact, the Siverskyi Donets River itself.

It is found that the most runoff of the wet year for both periods is in the spring months. The current period is characterized by a much smaller runoff of spring flood (from the volume of annual runoff) than in the previous period. The annual runoff distribution is offset. Some differences can be observed between the left and right tributaries. For the left-bank tributaries, which has less anthropogenic load, climate change has led to a significant increase of winter and summer-autumn low flow periods. On the right tributaries of the Siverskyi Donets, which are flowing within the industrial part of the Donbass, the low flow period has not changed, or even decreased. Such situation is due to the decrease of mine water disposal because of the industrial production decrease in the region.

The largest part of the annual runoff in the average year falls on February and March. In the current period, the spring flood has decreased, but the summer and autumn low flow period has increased. The left-bank tributaries runoff during the winter low period is decrease. Instead, the runoff attributable to the autumn and winter low period has increased for the right-bank tributaries and the Siverskyi Donets itself.

Analyzing the runoff distribution of dry year, we can conclude that the most wet is February. At present, in dry years, spring flood practically are not allocated from the hydrograph; the baseflow months runoff significantly increased. The volume of winter runoff of the Siverskyi Donets River Basin is increased. Actually, for the Siverskyi Donets River the runoff of the summer period has increased and the runoff of the winter and autumn periods has decreased at the present stage.

The annual runoff distribution of the Siverskyi Donets River Basin in the current climate change has undergone significant changes: the spring flood has decreased and the summer-autumn low flow has increased.

How to cite: Bolbot, H. and Grebin, V.: Estimation of the annual runoff distribution of the Siverskyi Donets River Basin in the period of current climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7932, https://doi.org/10.5194/egusphere-egu2020-7932, 2020.

Multipurpose reservoirs and dams have been a necessity for water management worldwide. Despite their benefit on water distribution and hydropower generation, dammed reservoirs remain controversial in many river basins due to their potentially negative impacts on streamflow and, consequently, the environment and society. Southeast Asia (SEA) is the region with the highest investment for large dammed reservoirs. Since SEA is highly exposed to hydrological hazards, particularly under climate change, the effects of reservoir operations on streamflow remain an important issue and should be thoroughly examined in specific contexts of this tropical region. Although many studies have revealed the reservoir effects on long-term (monthly-seasonal) streamflow, they are insufficient for improving the real-time prediction and control of floods and droughts. Therefore, by focusing on an SEA basin, this study aims to (i) quantify the effects of reservoir operations on the water balance and daily flow regime and (ii) distinguish effects of reservoir management and extreme weather on extreme flows. We investigated the Chao Phraya River Basin in Thailand that represents the highly regulated and hazard-prone river basins in SEA. The distributed (1km) wflow_sbm model was used to simulate the rainfall-runoff processes and streamflow in both naturalized (no reservoir) and regulated conditions. To overcome the lack of in-situ data often occurring in SEA basins, we drove the model with global meteorological data. To avoid overparameterization and long computational time, we applied high-resolution, seamless distributed parameter maps obtained with pedo-transfer functions. The model results were analyzed in comparison to daily observations for the 1989-2014 period. Our study revealed the significant effects of the multipurpose reservoirs on the water balance and daily flow regime, including flow rate, magnitude, duration, timing, fluctuation and frequency, during the regular and extreme conditions. The study also showed that the reservoir operations had larger effects on streamflow than extreme weather events. In addition, the operation rules are, in reality, very flexible to satisfy the water demand, which was difficult to represent by the monthly operation rules used in the simulations. The disparity led to the difficulty in the simulation of daily reservoir discharge. To apply the proposed model for the real-time forecasting and decision-making system, a more complex reservoir function with (sub)daily parameters should be tested.

How to cite: Wannasin, C., Brauer, C., Weerts, A., van Verseveld, W., and Uijlenhoet, R.: Unraveling effects of reservoir operation on daily flow regime using distributed hydrologic model with global data: case study of the Chao Phraya Basin in Thailand, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9485, https://doi.org/10.5194/egusphere-egu2020-9485, 2020.

Faced to reduced future water availability, associated with climate change variability and population growth, it becomes important to study the hydrological response under various modifications of crop patterns currently present in an agricultural basin in Chile. The focus of this research is to improve the future water resources management knowing the behavior of the hydrological cycle under meteorological forcings during the historical period 1985 to 2015.

We selected the Rapel River basin, in Central Chile, with a relevant agricultural activity and high water consumption in the study area.

VIC (Variable Infiltration Capacity) hydrological model, was calibrated considering base land use and historical records determined with the product CR2Met (www.cr2.cl/datos-productos-grillados/) for a grid with cells of 5 km by 5 km. For the near future (2030-2060) we proposed agricultural land use scenarios, considering a set of 40 crops that are representative of the area. The variation of the future forcings was considered according to the climate change scenario RCP 8.5 for four Global Climate Models (CCSM4, CSIRO, IPSAL, and MIROC).

Results show the variation in evapotranspiration demand and runoff, according to crop class and geographical ubication. An important variation of both flows is revealed, which is mainly related to the class of crop.  For this reason, the selection of crops determines a specific hydrological response, so the study of the change in land use is crucial. Based on the hydrologic response of each class of crop over the basin,  crop arrays were obtained and patterns are recommended for future scenarios. The arrays consider the optimal location of the crop, which reduces evapotranspiration demand and increases runoff. Also, changes in the percentage of the cultivated area of each crop class are recommended.

How to cite: González Molina, M. J., Vargas Mesa, H. X., and Vásquez Placencia, N.: Hydrological response to land use scenarios under climate change. Adaptation measures for an agricultural basin: Rapel river basin in central Chile., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11462, https://doi.org/10.5194/egusphere-egu2020-11462, 2020.

This study reports a recently developed spatially varying Statistical Soil Moisture Profile (SSMP) model, which is able to impart spatial transferability and to couple memory and forcing to estimate the vertical Soil Moisture Content (SMC) profile (Pal et al., 2016; Pal and Maity, 2018). The availability of satellite estimated surface soil moisture maps (Pal et al., 2017) and the potential of the coupling approach to integrate it, form the motivation to develop the SSMP model to prepare a fine resolution, 3-dimensional soil moisture profile for large areas by incorporating spatial transferability. The SSMP model uses only surface soil moisture (0-5 cm) values and incorporates the Hydrologic Soil Group (HSG) information to ensure the spatial transferability by capturing the spatial variations of vertical SMC profile with change in soil properties. The extensive daily soil moisture data for the study is obtained from 171 stations from three networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information of all the selected stations are extracted from the Web Soil Survey (WSS) database. The justified incorporation of the HSGs can be observed during model development through the forcing coefficient values. The values of forcing coefficients are higher for HSG A having a high infiltration rate whereas, the same is lower for HSG D with lower rate of infiltration. Thus, the forcing coefficients are at least able to differentiate the infiltration trend through a comparative analysis within the HSGs. The efficacy of the proposed SSMP model in terms of spatial transferability (as claimed) is evaluated by applying it to the new locations of the corresponding HSG. The observed model performances during model development as well as spatial validation are promising for all four depth pairs (5-10, 10-20, 20-51 and 51-102 cm) of all four HSGs considering the complexity involved in the problem statement itself. The potential application of the proposed model shows the future scope to assimilate the satellite based surface SMC data into the proposed SSMP model to develop a vertical soil moisture profile map over a large area.

References:

Pal M., Maity, R. and Dey, S., (2016), Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing, Water Resources Management, Springer, 30(6), 1973-1986, DOI: 10.1007/s11269-016-1263-4.

Pal M., Rajib Maity, M. Suman, S.K. Das, P. Patel and H.S. Srivastava (2017), Satellite based   Probabilistic Assessment of Soil Moisture using C-band Quad-polarized RISAT 1 data, IEEE Transactions on Geoscience and Remote Sensing, 55(3), 1351-1362, DOI: 10.1109/TGRS.2016.2623378.

Pal, M., and Maity, R. (2018), Development of a Spatially-Varying Statistical Soil Moisture Profile Model by Coupling Memory and Forcing using Hydrologic Soil Groups, Journal of Hydrology, Elsevier, 570 (2019), 141-155, https://doi.org/10.1016/j.jhydrol.2018.12.042.

How to cite: Pal, M. and Maity, R.: Spatially-Varying Statistical Soil Moisture Profile Model by Coupling Memory and Forcing using Hydrologic Soil Groups to Estimate Vertical Soil Moisture Profile, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6856, https://doi.org/10.5194/egusphere-egu2020-6856, 2020.

Abstract

This study aims to evaluate the future evolution of agricultural drought propensity across the Indian subcontinent through Drought Management Index (DMI), a probabilistic measure based on the concept of Reliability-Resilience-Vulnerability (RRV) of soil moisture series at a location/region (Chanda et al., 2014; Chanda and Maity, 2017). In this study, monthly gridded soil moisture products from the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework are used after suitable bias correction, if needed. In the realm of RRV analysis, the fall of soil moisture below a threshold (e.g., Permanent Wilting Point, PWP) is considered as the ‘failure state’. The joint distribution of resilience (the ability of the soil moisture system to recover from a failure state) and vulnerability (severity of the deficit in soil moisture during a failure state) of soil moisture series is modelled through copulas (Nelsen, 2006; Maity, 2018) to develop the DMI.  The results of this study help to assess the evolution of agricultural drought propensity, in terms of DMI, in the near (2011-2040), intermediate (2041-2070) and far future (2071-2099). The findings from multiple emission pathways, designated as Representative Concentration Pathways (RCPs), are compared against each other during the future period and also against the historical period. As an outcome of the study, specific regions across the Indian mainland are identified that need immediate attention for managing sustainable agricultural and allied activities in future.

Keywords: Drought Management Index (DMI), soil moisture, future drought propensity, Reliability-Resilience-Vulnerability (RRV), CORDEX

References

Chanda, K., Maity, R., Sharma, A., and Mehrotra, R. (2014). Spatiotemporal variation of long-term drought propensity through reliability-resilience-vulnerability based Drought Management Index, Water Resources Research, 50(10), 7662-7676.

Chanda, K., and Maity, R. (2017). Assessment of Trend in Global Drought Propensity in the Twenty-First Century Using Drought Management Index, Water Resources Management, 31(4), 1209-1225.

Maity, R. (2018). Statistical Methods in Hydrology and Hydroclimatology. Springer.

Nelsen, R. B. (2007). An introduction to copulas. Springer Science & Business Media.

How to cite: Das, P., Chanda, K., and Maity, R.: How useful are CORDEX products for the assessment of future agricultural drought propensity across the Indian subcontinent?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15885, https://doi.org/10.5194/egusphere-egu2020-15885, 2020.

Modeling the interaction between meteorological and hydrological variables (hydro-meteorological association) is challenging owing to its high spatiotemporal variability. However, the reliable modeling of these hydro-meteorological association can help in ensuring future water security under changing climate, and it might reduce the cost of managing water resources. These associations are expected to evolve with time. Hence, analysis of the meteorological and hydrological variable at their constituent wavelet level might help in modeling of underlying association between them. This article reports the research findings of a recent study (Suman and Maity, 2019), in which a method based on Multi-Resolution Stationary Wavelet Transformation (MRSWT) is used for transforming the variables (target variable: hydrological variables; forcing variables: meteorological variables) to their wavelet components. The memory of the components of the target variable is modeled by a kernel-based auto-regressive (AR) model and the prediction residuals are modeled using auto-regressive model with exogenous inputs (ARX). The MRSWT components of the meteorological variables are considered as the exogenous inputs. The predicted components of the target variables are inverse-transformed to obtain its predicted value. This hybrid Wavelet-ARX approach is applied for predicting total monthly precipitation over Upper Mahanadi Basin using 16 predictor meteorological variables. The efficacy of the model (compared to other modeling frameworks, such as ARX, Vector ARX) in modeling hydro-meteorological association is observed given the poorly associated hydro meteorological variables. Additionally, a relative importance analysis (RIM) framework in the context of the model is formulated using dominance analysis (DARIM) and Birnbaum Importance Measure (BIM). These RIM frameworks help in separating a set of predictor variables, which have stronger hydro-meteorological association with total monthly precipitation compared to other meteorological variables. Under these frameworks, five most important meteorological variables with the strongest hydro-meteorological association are selected, and the model is again trained using these five inputs. The effectiveness of RIM frameworks in selecting predictors with stronger hydro-meteorological association is observed as the similar model performance is obtained with five selected predictors. Hence, hybrid wavelet-ARX model can effectively model hydro-meteorological association, and RIM frameworks can help in figuring out the predictors with the stronger hydro-meteorological association, leading less complexity and computation requirement in modeling. The developed model is suitable for extracting meteorological forcings and is desirable in a changing climate.

Keywords: Hydro-meteorological association; Rainfall prediction/simulation; Climate change; Hybrid Wavelet-ARX model; Relative Importance analysis.

Reference: Suman, M. and Maity, R., 2019. Hybrid Wavelet-ARX approach for modeling association between rainfall and meteorological forcings at river basin scale. Journal of Hydrology, 577, p.123918.

How to cite: Suman, M. and Maity, R.: Modeling of Basin Scale Hydro-meteorological association by Hybrid Wavelet-ARX approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12766, https://doi.org/10.5194/egusphere-egu2020-12766, 2020.

Commonly, the analysis of climate change impacts on hydrology involves a series of steps that begin with a General Circulation Model followed by the application of a downscaling or bias correction method and then coupling the climate outputs to a hydrological model. Nevertheless, frequently the hydrological models employed in these analyses are not tested to assess their skill to simulate the hydrology of a catchment under changing climate regimes. We evaluate such skill by applying a Differential Split Sampling Test (DSST) using the available observations. The models are calibrated during the three most extreme dry (or wet) years and evaluated on the three most wet (or dry) years. The DSST is applied on three catchments located across Europe: Denmark, France and Spain. This spatial distribution allows us to evaluate the method on diverse climatic and hydrological regimes. Furthermore, the DSST is applied to three different models in each of the catchments and case-specific metrics are evaluated to determine the practical usefulness of the models. Based on the DSST results, we assign a weight to the hydrological models and drive them with six Euro-CORDEX Regional Climate Models to assess climate change scenarios for the case-specific metrics. This methodology allows us to increase the confidence of our projections considering the hydrological model uncertainty for transient climatic conditions.

How to cite: Pasten-Zapata, E., Royer-Gaspard, P., Pimentel, R., Sonnenborg, T. O., Lemoine, A., Pérez-Palazón, M. J., Schneider, R., and Photiadou, C.: Testing the simulation skill of hydrological models under transient climate conditions for European case studies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13228, https://doi.org/10.5194/egusphere-egu2020-13228, 2020.

Climate services provide data dealing with future climate scenarios and projections. Climatic models ensemble mean is commonly used as the recommended value to assess climate change effects in impact studies. This ensemble is composed of different combinations of Global Circulation Models (GCM) and Regional Climate Models (RCM), not being a fixed number the quantity of RCM-GCM combinations needed to calculate this ensemble.  Recommendations found in literature indicate a range usually between 5 to 10 endmembers, but the suitability of some of the models is not always included in the assessment of the applications. How to choose correctly these number of models or to reduce its number is an issue currently under debate. In heterogeneous and/or small areas where the spatial significant scales cannot be adequately captured by coarse grids, climatic models often have problem to correctly represent hydrometeorological variables due to the GCM-RCM parameterizations. Moreover, some of these combinations give completely uneven simulated climate regime during the reference period and, consequently, hydrological variables like river flow are poorly simulated from these generated drivers.

This work proposes an alternative methodology to project hydrological variables without using model ensemble mean, selecting only the model that best represent climate regime, defining transfer functions to overpass the spatial scale issues, and assessing uncertainty by using stochastics techniques. The methodology is applied in the Guadalfeo River Basin, a mountainous semiarid watershed in Sierra Nevada (southern Spain), where alpine and Mediterranean climate coexist, and the highest summits of the Iberian Peninsula are located; hence,  snow plays a key role in the water availability and management, and future impacts are key to assess adaptation plans . The projected variables are used to assess changes in climatic impact indicators in future scenarios projections for water allocation for three different end-user sectors: small hydropower generation, water allocation in a reservoir system, coastal municipality dealing with water allocation conditioned by agriculture and tourism. 

This work was funded by the project AQUACLEW, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Commission [Grant 690462].

How to cite: Perez-Palazon, M. J., Herrera-Grimaldi, P., Pimentel, R., and Polo, M. J.: Uncertainty assessment of climate impact indicators in future scenarios projections for water allocation in small catchments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13894, https://doi.org/10.5194/egusphere-egu2020-13894, 2020.

In the second half of the 20^th century, hydrological regimes in central Western Europe were largely characterised by large-scale winter floods. This type of event was predominantly triggered by westerly atmospheric fluxes, bringing moist and mild air masses from the Atlantic Ocean to the European continent. Since the late 1990’s, major flooding events seem to have shifted in time and magnitude. Flash flood events, while being a well-known phenomenon in Mediterranean catchments, are increasingly also reported at higher latitudes. Unlike the large-scale winter flood events, flash floods are of very narrow spatial extension and triggered by rather short, but highly intense rainfall events.

Here, we focus on the specific case of rivers in Luxembourg that have experienced several flash flood events in recent years, while only small to moderate winter flood events have been reported since the late 1990’s. National hydro-meteorological monitoring and flood forecasting systems have been designed for large-scale floods and are not suited for simulating local flash flood events. Therefore, there is a need to increase our understanding of the hydro-meteorological processes underlying flash flood occurrences in our area of interest.

While increasing air temperature is known to allow a higher air moisture content that can lead to more intense rainfall events and possible flooding, we moreover hypothesize that the recent increase in flash flood occurrences in Luxembourg is reinforced by a change in atmospheric circulation patterns. To test this hypothesis, we analyse the prevailing atmospheric patterns on rainy days during summer and winter months over the period 1954 - 2019, with a particular focus on rainfall events that lead to moderate and extreme floods. In a next step, we intend to extend our findings for Luxembourg in a larger European context. This analysis should allow to better assess the current situation of hydrological extreme events in central Western Europe in order to take precaution measures and prepare for a diversifying hazard.

How to cite: Meyer, J., Douinot, A., Zehe, E., Tamez-Meléndez, C., Francis, O., and Pfister, L.: Impact of Atmospheric Circulation on Flooding Occurrence and Type in Luxembourg (Central Western Europe), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13953, https://doi.org/10.5194/egusphere-egu2020-13953, 2020.

Climate change raises many questions about the future availability of water resources in West Africa. Indeed, water in this region is a fundamental element for many socio-economic activities. This study proposes an assessment of the impact of climate change on the hydrology of the Faleme basin, located in the Sahel (West Africa). The applied methodology consists in calibrating and validating the hydrological model GR4J before simulating the future evolution of flows in this catchment under of 1.5 and 2°C global warming.  Observed rainfall, potential evapotranspiration (PET), and river flows were used for calibration and validation of the GR4J model. Furthermore, output of three regional climate models (DMI-HIRHAM, SHIM-RCA, and BCCR-WRF) were bias corrected with the cumulative distribution function-transform (CDF-t) before used as input to the GR4J hydrological model to simulate future flows at the watershed scale. During the historical period the results shows a good correspondence between the simulated flows and those observed during calibration and validation, with Nash–Sutcliffe efficiencies (NSE) greater than 70%. Projections show a general increase in mean annual temperature and PET; a decrease in mean annual rainfall is projected by the DMI-HIRHAM, BCCR-WRF models and the overall mean; while a slight increase is noted with the SMHI-RCA model. As for future flows, a downward trend in annual and monthly average flows is expected in the two sub-basins of the Faleme (Kidira and Gourbassi) with input from the DMI-HIRHAM, BCCR-WRF models and the overall mean; however,  the GR4J forced by the SMHI-RCA model output, project increased flows. Furthermore, the decrease is more pronounced at Gourbassi sub-basin than at Kidira sub-basin. Thus, recommendations were made to mitigate the likely impacts of climate change on socio-economic activities that use water resources.

How to cite: Mbaye, M. L., Sy, K., Faty, B., and Sall, S. M.: Modeling the impact of 1.5 and 2.0◦C global warming on the hydrology of the Faleme river basin (West Africa), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1938, https://doi.org/10.5194/egusphere-egu2020-1938, 2020.

The 0.5° resolution of many global observational datasets is not sufficient for the requirements of current state-of-the-art regional climate model (RCM) simulations over Europe. Here, the ERA5 reanalysis of the ECMWF (C3S 2017) and E-OBS data (Cornes et al. 2018) are frequently used as reference datasets when RCM results are evaluated on resolutions higher than 0.5°. In addition, ERA5 data are also commonly used to force regional ocean models. As ERA data do not comprise river discharges, the lateral forcing of freshwater inflow from land is taken from other data sources, such as station data, runoff climatologies, etc. However, these data are not necessarily consistent with the ERA5 forcing over the ocean surface. If such data are derived from station data, they are only available for specific rivers and not spatially homogeneously distributed for all coastal areas. In addition, they might not be representative for the river mouth if the respective station location is too far away from the river mouth, which is often the case.

In order to allow a consistent forcing of river discharges and evaluation of simulated hydrological fluxes, we extended ERA5 and E-OBS v20.0e with high resolution river discharge. This also allows a consistent assessment of hydrological changes from these two datasets. The discharge was simulated with the recently developed 5 Min. version of the Hydrological discharge (HD) model (Hagemann et al., submitted). Note that for the development of this HD model version, no river specific parameter adjustments were conducted so that the HD model is generally applicable for climate change studies and over ungauged catchments.

The HD model requires gridded fields of surface and subsurface runoff as input with a daily temporal resolution or higher. As no large-scale observations of these variables exist, they need to be calculated by a land surface scheme or hydrology model using observed or re-analyzed meteorological data. Here, we used the HydroPy global hydrological model, which is the successor of the MPI-HM model (Stacke and Hagemann 2012). The latter has contributed to the WATCH Water Model Intercomparison Project (WaterMIP; Haddeland et al. 2011) and the inter-sectoral impact model intercomparison project (ISIMIP; Warszawski et al. 2014). Note that ERA5 also comprises archived fields of surface and subsurface runoff, but it turned out that its separation of total runoff is not suitable to generate adequate river discharges with the HD model. In our presentation, we evaluate the simulated discharge using various metrics and consider significant discharge trends over Europe.

References

C3S (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS)

Cornes, R., et al. (2018) J. Geophys. Res. Atmos. 123, doi:10.1029/2017JD028200

Haddeland, I., et al. (2011). J. Hydrometeorol. 12, doi: 10.1175/2011jhm1324.1

Hagemann, S., T. Stacke and H. Ho-Hagemann, High resolution discharge simulations over Europe and the Baltic Sea catchment. Frontiers in Earth Sci., submitted.

Stacke, T. and Hagemann, S. (2012). Hydrol. Earth Syst. Sci. 16, doi: 10.5194/hess-16-2915-2012

Warszawski, L., et al. (2014) Proc. Natl. Acad. Sci. USA 111, doi: 10.1073/pnas.1312330110

How to cite: Hagemann, S. and Stacke, T.: Complementing ERA5 and E-OBS20 with high resolution river discharge, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7135, https://doi.org/10.5194/egusphere-egu2020-7135, 2020.

Quantifying how land-use change affects hydrological components is a challenge in hydrological science. It is not yet clear how changes in land use relate to runoff extremes and why some catchments are more sensitive to land-use change than others. Identifying which areas are hydrologically more sensitive to land-use change can lead to better land-use planning, reduction of the impacts of extreme rainfall events and extended dry periods. In this study we aim to quantify how land-use change and climate change are affecting the hydrological response of  Vietnam’s basins. Over the past decades the country’s land use has shifted from forest to agriculture, with very high production of rice, coffee, tea, pepper and sugar cane.

We combine the historical, the Intergovernmental Panel on Climate Change’s (IPCC) Representative Concentration Pathway (RCP) RCP4.5 and RCP8.5 climate change scenarios developed for Vietnam, with two different land cover maps (from the years 1992 and 2017). The combined and separate effect of land use and climate change are assessed and the most sensitive to change areas are identified. The Variable infiltration Capacity (VIC) surface water and energy balance model applied here is a grid-based model that calculates evapotranspiration, runoff, base flow, soil moisture and other hydrological fluxes. Surface heterogeneity within VIC is represented by a tiled approach, whereby the surface of each grid-box comprises fractions of the different surface types. For each surface type of the grid-box, the energy and water balances are solved, and a weighted average is calculated from the individual surface fluxes for each grid-box. Hydrological fluxes were compared for each grid cell and basin to analyse the degree of difference between the scenarios.

Significant changes in future hydrologic fluxes arise under both climate change scenarios pointing towards a severe increase in hydrological extremes. The changes in all the examined hydrological components are greater in the combined land-use and climate change experiments.

How to cite: Moschini, F., Ferrario, I. F., and Hofmann, B.: Modelling the impacts of climate change and land-use change on the hydrology of Vietnam’s river basins, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19026, https://doi.org/10.5194/egusphere-egu2020-19026, 2020.

The uncertainty of projections from climate models can be significant, especially with respect to precipitation. This represents a challenge for decision makers as the spread of the climate model ensemble can be large and, even there can be no consensus on the direction of the climate change signal. This problem is carried through to impact models such as hydrological models. Here, we evaluate different approaches to reduce the uncertainty using 16 Euro-CORDEX Regional Climate Models (RCMs) that drive three different setups of the integrated and distributed MIKE-SHE hydrological model for a catchment in Denmark. Each model is calibrated against an extensive database of hydrological observations (stream discharge, hydraulic head, actual evapotranspiration, soil moisture). We evaluate the skills of the raw and bias-corrected RCMs to simulate precipitation in a historical period using sets of nine, six, five, and three metrics for nine steps. After each step, the lowest-performing model is removed from the ensemble and the standard deviation of the new ensemble is estimated. Subsequently, the uncertainty on the projected groundwater head and stream discharge are evaluated. Based on the evaluation of raw RCM simulations, the largest decrease in the uncertainty of projected discharge (5^th, 50^th and 95^th percentiles) is obtained using the set of five metrics. When evaluating the bias-corrected RCMs, the largest uncertainty reduction in stream discharge is obtained when the set of all nine metrics is considered. Similar results are obtained for groundwater head . The reduction of initial uncertainty is almost a factor of two higher when the evaluation of models is based on bias-corrected compared to raw climate models results. This analysis gives an insight of how different approaches could decrease the uncertainty of future projections for hydrological analyses of the impact of climate change.

How to cite: Sonnenborg, T. O., Pasten-Zapata, E., Eberhart, T., and Jensen, K. H.: Uncertainty reduction of climate model projections for hydrologic scenarios , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13188, https://doi.org/10.5194/egusphere-egu2020-13188, 2020.

Difficult climatic conditions characterized The North Caucasus and high flood hazard in this region. Over the past decades, significant climate changes have occurred, which has influenced the flow of mountain rivers, including the maximum flow. Since the end of the last century, there has been an increase in the number of dangerous floods in the basins of North Caucasus rivers, which led to significant material damage and deaths. In the flood zone were several tens of thousands of houses. In addition, infrastructure facilities were destroyed and hydraulic structures damaged. In this regard, there is an urgent task of analyzing the spatio-temporal changes in the characteristics of the maximum runoff and the factors that determine them.

In the course of the study, methods of statistical analysis, geoinformation methods, graphoanalytic, and the method of geographical generalization were used. Calculation of statistical parameters and visualization were carried out using the programming languages R and FORTRAN.

The spatial variability of the maximum runoff of the rivers of the North Caucasus over the past 70-80 years was analyzed. The results indicate a predominantly negative trend of maximum water discharge in the highlands of the North Caucasus and a positive in the middle reaches of the Kuban. This is consistent with data on the absence of a positive trend in average annual temperatures in the highlands due to lower temperatures in the winter, as well as with an increase in the number of days with heavy rainfall. The latter factor determined the almost universal increase in interannual variability of maximum expenditures, which indicates an increase in flood hazard throughout the region.

An analysis of the characteristics of the flood flow showed that the dependence of floods on precipitation in the mountains manifests itself at extreme values, while for all the main factor is air temperature. The maximum discharge of rain floods tends to increase in foothill areas, while no changes have been detected in the mountains.

The results can give a clearer view of the processes of changing the maximum flow, and become the basis for the development of measures to minimize the damage from such natural disasters.

This work was financial supported by RFBR (Project 20-35-70024)

How to cite: Durmanov, I., Rets, E., and Kireeva, M.: Maximum river runoff regime in The North Caucasus under the influence of recent climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18161, https://doi.org/10.5194/egusphere-egu2020-18161, 2020.

Assessment of climate change impacts on hydrological processes is often based on simulations driven by precipitation and temperature series derived from bias-adjusted output from Regional Climate Models (RCMs) using boundary conditions from Global Climate Models (GCMs).  This procedure gives, in principle, locally ‘correct’ results, but is also very demanding of time and resources. In some cases, the dynamical downscaling (i.e. RCM) followed by bias adjustment procedures fails to preserve the climate change signal found in the underlying GCM simulations, thus undermining the reliability of the resulting hydrological simulations. As an alternative, we have used the stochastic weather generator D2Gen (Mezghani and Hingray, 2009, J. Hydrol., 377(3–4): 245–60) to create multiple realisations of catchment-scale precipitation and temperature data series directly from two GCMs (MPI-ESM-LR and NorESM-M1) for the period 1951-2100. D2Gen builds on a suite of Generalised Linear Models (GLMs) to generate precipitation and temperature (i.e. predictands) as a function of explanatory climate variables (or predictors) derived from the GCM such as surface temperature, sea level pressure, westerly and zonal wind components, relative humidity and total precipitation. In this study, we have applied D2Gen on area-averaged precipitation and temperature data for 18 hydrological catchments distributed across Norway. Weather generation is then undertaken based on the expected mean modelled by the GLM plus a noise component to account for local features and random effects introduced by local physical processes that are otherwise not accounted for.  The weather generator was trained for each catchment based on observed precipitation and temperature series for the period 1985-2014, and stochastic weather generation was then performed to construct catchment-scale precipitation and temperature series for the period 1951-2100 that were further used in hydrological simulations based on the HBV hydrological model for the 18 catchments. 

Validation of the D2Gen results was based on comparisons with observed annual, seasonal and maximum temperature and precipitation, as well as with observed average annual and maximum annual discharge using 30-year time slices.  Comparisons were also made with projected changes generated from hydrological simulations based on a) EURO-CORDEX RCM simulations (MPI-ESM-LR_SMHI-RCA4 and MPI_CCLM-CM5) for the MPI GCM; and b) high resolution (4 km) simulations with the WRF model driven by a bias-corrected NorESM GCM.  Results suggest that in most catchments the D2gen approach performs equally well or sometimes even better than the traditional ‘bias-corrected RCM approach’ in reproducing the 30-year average annual flood during the historical period. We also found that for the projection period, the simulations based directly on the GCM output (via d2gen) tend to give slightly larger projected increases in the average annual flood in rainfall-dominated catchments than does the use of bias-corrected RCM simulations. Overall, the results indicate that the D2Gen weather generator offers a feasible alternative approach for projecting catchment-scale impacts on changes in flood regimes under a changing climate.  It also offers the significant advantage that it can be used directly with the CMIP-6 ensemble of GCMs without the time delay associated with the production of the next round of EURO-CORDEX based simulations.

How to cite: Lawrence, D., Mezghani, A., Pontopiddan, M., Benestad, R., Parding, K., and Helene Birkeland-Erlandsen, H.: Catchment-scale assessment of future changes in flood regimes in Norway using stochastic weather generation based on GCM output, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13350, https://doi.org/10.5194/egusphere-egu2020-13350, 2020.

The impact of future climate change under IPCC scenarios RCP4.5 and RCP8.5 on hydrological regimes in plain catchments up to 650 m high and in mountainous areas of Bulgaria is discussed. A hydrological simulation models (TUWmodel) were calibrated on recorded data and ‘forced’ in the selected scenarios with precipitation and air temperature data from ALADIN 5.2, a local version of the French global atmospheric model ARPEGE, downscaled to a grid of 12 km. Simulations for the future periods 2013-2042, 2021-2050 and 2071-2100 are compared to the flows in the reference period 1976-2005.

Results indicate increased seasonality of flows, with noticeably drier summers and increase of river discharge in winter. In most of the cases the analysis of extreme events suggests significant increases in the frequency of both high‐ and low‐flow events. The change in the extreme runoff with a large repetition period required for the design of flood protection structures and systems has been investigated in regions with different mechanisms for flood generation. With the push of RCP4.5 or RCP8.5 scenarios the significant increase in flood peaks is observed in most of the river basins. There is a general trend of decreasing runoff with a 95% probability of exceedance.

How to cite: Mavrova-Guirguinova, M.: Climate Change effects on the River Discharge in Bulgaria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19523, https://doi.org/10.5194/egusphere-egu2020-19523, 2020.