scholarly journals The relevance of glacier melt in the water cycle of the Alps: an example from Austria

2010 ◽  
Vol 7 (3) ◽  
pp. 2897-2913 ◽  
Author(s):  
G. R. Koboltschnig ◽  
W. Schöner
Keyword(s):  
Hydrological Model ◽  
Water Cycle ◽  
Point Of View ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Ice Melt ◽  
Glacier Melt ◽  
The Alps ◽  
The Mean

Abstract. This paper gives an overview on available methods how the contribution of glacier melt to runoff can be calculated with and without glaico-hydrological models. Further we applied an approach, which shows the potential of glacier melt contribution during the extreme hot and dry summer of 2003 by calculating the quotient qA03 of the mean monthly August runoff in 2003 and the long-term mean August runoff. The extreme summer 2003 was worth to be analysed as from the meteorological and glaciological point of view an extraordinary situation was observed. During June and July nearly the entire snow-cover melted and during the hot and dry August mainly ice melt of glaciers contributed to runoff. The mean runoff in August 2003 was calculated from observed mean daily runoff data of a selected period in August 2003 (3 to 27 August). This was done for 27 Austrian gauging stations in the glacierized basins of the rivers Inn, Salzach and Drau with a degree of glaciation between 2 and 76%. The quotient qA03 was calculated between 0.63 and 1.82, which means for the lower value that only 63% of the long-term mean August runoff and for the higher value 82% more than the long-term mean August runoff was observed in 2003. Additionally two stations at river Danube (0.4 and 1% glacierized) and further six gauging stations in catchments with no glacier cover were investigated to define qA03 quotients for non-glacierized basins. These qA03 quotients were calculated between 0.31 and 0.54. Hence, it was possible to qualitatively visualize the decreasing impact of glacier melt for a decreasing degree of glaciation. Nevertheless, for the accurate calculation of the glacier melt contribution for a certain catchment scale and time a glaio-hydrological model is needed.

scholarly journals The relevance of glacier melt in the water cycle of the Alps: the example of Austria

2011 ◽  
Vol 15 (6) ◽  
pp. 2039-2048 ◽  
Author(s):  
G. R. Koboltschnig ◽  
W. Schöner
Keyword(s):  
River Runoff ◽  
Water Cycle ◽  
Point Of View ◽  
Statistical Interpretation ◽  
Study Region ◽  
Ice Melt ◽  
Glacier Melt ◽  
Precipitation Increase ◽  
The Mean

Abstract. This paper quantifies the contribution of glacier melt to river runoff from compilation and statistical interpretation of data from available studies based on observations or glacio- hydrological modelling for the region of Austria (Austrian Salzach and Inn river basin). A logarithmic fit between the glacier melt contribution and the relative glacierized area was found not only for the long-term mean glacier contributions but also for the glacier melt contribution during the extreme hot an dry summer of 2003. Interestingly, the mean contributions of glacier melt to river runoff do not exceed 15 % for both river catchments and are uncorrelated to glacierization for glacierization values >10 %. This finding, however, has to be seen in the light of the general precipitation increase with altitude for the study region which levels out the increase of absolute melt with glacierization thus resulting in the rather constant value of glacier melt contribution. In order to qualitatively proof this finding another approach has been applied by calculating the quotient qA03 of the mean monthly August runoff in 2003 and the long-term mean August runoff for 38 gauging stations in Austria. The extreme summer 2003 was worth to be analysed as from the meteorological and glaciological point of view an extraordinary situation was observed. During June and July nearly the entire snow-cover melted and during August mainly bare ice melt of glaciers contributed to runoff. The qA03 quotients were calculated between 0.32 for a non-glacierized and 2.0 for a highly glacierized catchment. Using the results of this study the mean and maximum possible glacier melt contribution of catchments can be estimated based on the relative glacierized area. It can also be shown that the found correlation of glacierized area and glacier melt contribution is applicable for the Drau basin where yet no results of modelled glacier melt contributions are available.

scholarly journals Climate change impacts model parameter sensitivity – implications for calibration strategy and model diagnostic evaluation

2021 ◽  
Vol 25 (3) ◽  
pp. 1307-1332
Author(s):  
Lieke Anna Melsen ◽  
Björn Guse
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Climate Change Impacts ◽  
The United States ◽  
Parameter Sensitivity ◽  
Hydrological Models ◽  
The Future ◽  
The Mean ◽  
The Impact

Abstract. Hydrological models are useful tools for exploring the impact of climate change. To prioritize parameters for calibration and to evaluate hydrological model functioning, sensitivity analysis can be conducted. Parameter sensitivity, however, varies over climate, and therefore climate change could influence parameter sensitivity. In this study we explore the change in parameter sensitivity for the mean discharge and the timing of the discharge, within a plausible climate change rate. We investigate whether changes in sensitivity propagate into the calibration strategy and diagnostically compare three hydrological models based on the sensitivity results. We employed three frequently used hydrological models (SAC, VIC, and HBV) and explored parameter sensitivity changes across 605 catchments in the United States by comparing GCM(RCP8.5)-forced historical and future periods. Consistent among all hydrological models and both for the mean discharge and the timing of the discharge is that the sensitivity of snow parameters decreases in the future. Which other parameters increase in sensitivity is less consistent among the hydrological models. In 45 % to 55 % of the catchments, dependent on the hydrological model, at least one parameter changes in the future in the top-5 most sensitive parameters for mean discharge. For the timing, this varies between 40 % and 88 %. This requires an adapted calibration strategy for long-term projections, for which we provide several suggestions. The disagreement among the models on the processes that become more relevant in future projections also calls for a strict evaluation of the adequacy of the model structure for long-term simulations.

scholarly journals Developing and Demonstrating Climate Indicators for Monitoring the Changing Water Cycle

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Xia Feng ◽  
Paul Houser
Keyword(s):  
Water Cycle ◽  
Spatial Scales ◽  
Atmospheric Moisture ◽  
Spatial Correlations ◽  
Climate Indicators ◽  
Terrestrial Water ◽  
The Mean ◽  
Atmospheric Moisture Content ◽  
Daily Total

In this study, we developed a suite of spatially and temporally scalable Water Cycle Indicators (WCI) to examine the long-term changes in water cycle variability and demonstrated their use over the contiguous US (CONUS) during 1979–2013 using the MERRA reanalysis product. The WCI indicators consist of six water balance variables monitoring the mean conditions and extreme aspects of the changing water cycle. The variables include precipitation (P), evaporation (E), runoff (R), terrestrial water storage (dS/dt), moisture convergence flux (C), and atmospheric moisture content (dW/dt). Means are determined as the daily total value, while extremes include wet and dry extremes, defined as the upper and lower 10th percentile of daily distribution. Trends are assessed for annual and seasonal indicators at several different spatial scales. Our results indicate that significant changes have occurred in most of the indicators, and these changes are geographically and seasonally dependent. There are more upward trends than downward trends in all eighteen annual indicators averaged over the CONUS. The spatial correlations between the annual trends in means and extremes are statistically significant across the country and are stronger forP,E,R, andCcompared todS/dtanddW/dt.

scholarly journals Ensemble long-term soil moisture forecast using hydrological modeling

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Bibiana Rodrigues Colossi ◽  
Carlos Eduardo Morelli Tucci
Keyword(s):  
Soil Moisture ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Computing Time ◽  
Ensemble Member ◽  
Lead Times ◽  
Hydrologic Simulation ◽  
The Mean ◽  
Group B

ABSTRACT Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time.

scholarly journals A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models

2010 ◽  
Vol 7 (5) ◽  
pp. 7191-7229 ◽  
Author(s):  
S. N. Gosling ◽  
R. G. Taylor ◽  
N. W. Arnell ◽  
M. C. Todd
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Climate Model ◽  
Annual Runoff ◽  
Structural Uncertainty ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Monthly Runoff ◽  
Global Mean ◽  
Impacts Of Climate Change

Abstract. We present a comparative analysis of projected impacts of climate change on river runoff from two types of distributed hydrological model, a global hydrological model (GHM) and catchment-scale hydrological models (CHM). Analyses are conducted for six catchments that are global in coverage and feature strong contrasts in spatial scale as well as climatic and developmental conditions. These include the Liard (Canada), Mekong (SE Asia), Okavango (SW Africa), Rio Grande (Brazil), Xiangxi (China) and Harper's Brook (UK). A single GHM (Mac-PDM.09) is applied to all catchments whilst different CHMs are applied for each catchment. The CHMs include SLURP v. 12.2 (Liard), SLURP v. 12.7 (Mekong), Pitman (Okavango), MGB-IPH (Rio Grande), AV-SWAT-X 2005 (Xiangxi) and Cat-PDM (Harper's Brook). Simulations of mean annual runoff, mean monthly runoff and high (Q5) and low (Q95) monthly runoff under baseline (1961–1990) and climate change scenarios are presented. We compare the simulated runoff response of each hydrological model to (1) prescribed increases in global-mean air temperature of 1.0, 2.0, 3.0, 4.0, 5.0 and 6.0 °C relative to baseline from the UKMO HadCM3 Global Climate Model (GCM) to explore response to different amounts of climate forcing, and (2) a prescribed increase in global-mean air temperature of 2.0 °C relative to baseline for seven GCMs to explore response to climate model structural uncertainty. We find that the differences in projected changes of mean annual runoff between the two types of hydrological model can be substantial for a given GCM, and they are generally larger for indicators of high and low monthly runoff. However, they are relatively small in comparison to the range of projections across the seven GCMs. Hence, for the six catchments and seven GCMs we considered, climate model structural uncertainty is greater than the uncertainty associated with the type of hydrological model applied. Moreover, shifts in the seasonal cycle of runoff with climate change are represented similarly by both hydrological models, although for some catchments the monthly timing of high and low flows differs. This implies that for studies that seek to quantify and assess the role of climate model uncertainty on catchment-scale runoff, it may be equally as feasible to apply a GHM as it is to apply a CHM, especially when climate modelling uncertainty across the range of available GCMs is as large as it currently is. Whilst the GHM is able to represent the broad climate change signal that is represented by the CHMs, we find however, that for some catchments there are differences between GHMs and CHMs in mean annual runoff due to differences in potential evapotranspiration estimation methods, in the representation of the seasonality of runoff, and in the magnitude of changes in extreme (Q5, Q95) monthly runoff, all of which have implications for future water management issues.

scholarly journals Czasowa zmienność i przestrzenne zróżnicowanie ewapotranspiracji w zlewni nizinnej rzeki Łasicy

2021 ◽  
Vol 66 (3) ◽  
pp. 35-46
Author(s):  
Urszula Somorowska
Keyword(s):  
Surface Energy Balance ◽  
Water Cycle ◽  
Spatial Differentiation ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Catchment Scale ◽  
Water Demands ◽  
Agricultural Areas ◽  
Accurate Quantification

Accurate quantification of evapotranspiration is necessary for understanding the water cycle at a local scale. At catchment scale, evapotranspiration might be approximated using remote sensing data useful in spatialtemporal analyses. In this study, the long-term and seasonal variability of evapotranspiration in the Łasica River catchment in the years 2003–2020 was assessed on the basis of data acquired from the SSEBop project (Operational Simplified Surface Energy Balance). Additionally, using the index of precipitation utilization (WWO), the degree of precipitation consumption for the water demands of plants was determined. The highest evapotranspiration occurs in forest areas, slightly lower in marshy belts covered with meadow vegetation, and the lowest in agricultural areas and anthropogenically transformed areas. The spatial differentiation of evapotranspiration is particularly marked during the growing season, from April to October. Mean annual evapotranspiration sum is 403 mm, of which 96% falls on the growing season. Extremely low annual ET sums occurred in 2015 (329 mm), 2019 (342 mm) and 2003 (384 mm), while particularly high – in 2010 (455 mm) and 2013 (447 mm). In dry years, WWO is even 71–77%, while in particularly wet years, WWO is much lower and amounts to 54–58%.

scholarly journals The Role of Hydrological Signatures in Calibration of Conceptual Hydrological Model

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3401
Author(s):  
Eva Melišová ◽  
Adam Vizina ◽  
Linda R. Staponites ◽  
Martin Hanel
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Goodness Of Fit ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Balance Assessment ◽  
The Czech Republic ◽  
Hydrological Signatures

Determining an optimal calibration strategy for hydrological models is essential for a robust and accurate water balance assessment, in particular, for catchments with limited observed data. In the present study, the hydrological model Bilan was used to simulate hydrological balance for 20 catchments throughout the Czech Republic during the period 1981–2016. Calibration strategies utilizing observed runoff and estimated soil moisture time series were compared with those using only long-term statistics (signatures) of runoff and soil moisture as well as a combination of signatures and time series. Calibration strategies were evaluated considering the goodness-of-fit, the bias in flow duration curve and runoff signatures and uncertainty of the Bilan model. Results indicate that the expert calibration and calibration with observed runoff time series are, in general, preferred. On the other hand, we show that, in many cases, the extension of the calibration criteria to also include runoff or soil moisture signatures is beneficial, particularly for decreasing the uncertainty in parameters of the hydrological model. Moreover, in many cases, fitting the model with hydrological signatures only provides a comparable fit to that of the calibration strategies employing runoff time series.

Soil Moisture Prediction for Huaihe River Basin Using Hydrological Model XXT and TOPMODEL

2013 ◽  
Vol 433-435 ◽  
pp. 1817-1820 ◽  
Author(s):  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Peng Wang ◽  
Shuang Liu
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Temporal Change ◽  
Agricultural Drought ◽  
Hydrological Models ◽  
Huaihe River ◽  
Change Pattern ◽  
The Mean ◽  
The Huaihe River ◽  
Future Work

Soil moisture plays an important role in agricultural drought predicting. Hydrological models can be employed to forecast soil moisture. In order to get better predicted soil moisture information, we use two basin hydrological models, i.e. XXT and TOPMODEL, to forecast the soil moisture for Huaihe River watershed. The performance of both the two models was tested in the Linyi watershed with a drainage area of 10040 km2, a tributary of the Huaihe river, China. The results show that the soil moisture simulated by the XXT is more agree with the observed ones than that simulated by TOPMODE compared to the filed observed soil moisture at 10 cm or the mean ones of 10 cm, 20 com, and 40 cm from surface, and that the predicted soil moisture by both the models has the similar trend and temporal change pattern with the observed one. However, both the models need to be improved in soil moisture forecasting in the future work.

scholarly journals A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models

2011 ◽  
Vol 15 (1) ◽  
pp. 279-294 ◽  
Author(s):  
S. N. Gosling ◽  
R. G. Taylor ◽  
N. W. Arnell ◽  
M. C. Todd
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Climate Model ◽  
Annual Runoff ◽  
Structural Uncertainty ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Monthly Runoff ◽  
Global Mean ◽  
Impacts Of Climate Change

Abstract. We present a comparative analysis of projected impacts of climate change on river runoff from two types of distributed hydrological model, a global hydrological model (GHM) and catchment-scale hydrological models (CHM). Analyses are conducted for six catchments that are global in coverage and feature strong contrasts in spatial scale as well as climatic and developmental conditions. These include the Liard (Canada), Mekong (SE Asia), Okavango (SW Africa), Rio Grande (Brazil), Xiangxi (China) and Harper's Brook (UK). A single GHM (Mac-PDM.09) is applied to all catchments whilst different CHMs are applied for each catchment. The CHMs include SLURP v. 12.2 (Liard), SLURP v. 12.7 (Mekong), Pitman (Okavango), MGB-IPH (Rio Grande), AV-SWAT-X 2005 (Xiangxi) and Cat-PDM (Harper's Brook). The CHMs typically simulate water resource impacts based on a more explicit representation of catchment water resources than that available from the GHM and the CHMs include river routing, whereas the GHM does not. Simulations of mean annual runoff, mean monthly runoff and high (Q5) and low (Q95) monthly runoff under baseline (1961–1990) and climate change scenarios are presented. We compare the simulated runoff response of each hydrological model to (1) prescribed increases in global-mean air temperature of 1.0, 2.0, 3.0, 4.0, 5.0 and 6.0 °C relative to baseline from the UKMO HadCM3 Global Climate Model (GCM) to explore response to different amounts of climate forcing, and (2) a prescribed increase in global-mean air temperature of 2.0 °C relative to baseline for seven GCMs to explore response to climate model structural uncertainty. We find that the differences in projected changes of mean annual runoff between the two types of hydrological model can be substantial for a given GCM (e.g. an absolute GHM-CHM difference in mean annual runoff percentage change for UKMO HadCM3 2 °C warming of up to 25%), and they are generally larger for indicators of high and low monthly runoff. However, they are relatively small in comparison to the range of projections across the seven GCMs. Hence, for the six catchments and seven GCMs we considered, climate model structural uncertainty is greater than the uncertainty associated with the type of hydrological model applied. Moreover, shifts in the seasonal cycle of runoff with climate change are represented similarly by both hydrological models, although for some catchments the monthly timing of high and low flows differs. This implies that for studies that seek to quantify and assess the role of climate model uncertainty on catchment-scale runoff, it may be equally as feasible to apply a GHM (Mac-PDM.09 here) as it is to apply a CHM, especially when climate modelling uncertainty across the range of available GCMs is as large as it currently is. Whilst the GHM is able to represent the broad climate change signal that is represented by the CHMs, we find however, that for some catchments there are differences between GHMs and CHMs in mean annual runoff due to differences in potential evapotranspiration estimation methods, in the representation of the seasonality of runoff, and in the magnitude of changes in extreme (Q5, Q95) monthly runoff, all of which have implications for future water management issues.

Can an ensemble of downscaled global hydrological model outputs improve the performance and spatially disaggregate the output of a catchment scale model in data scarce contexts?

2020 ◽  
Author(s):  
Marko Kallio ◽  
Joseph H.A. Guillaume ◽  
Alexander J. Horton ◽  
Timo A. Räsänen
Keyword(s):  
Hydrological Model ◽  
Interpolation Method ◽  
Global Climate ◽  
Model Averaging ◽  
Scale Model ◽  
Distributed Model ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Stream Network ◽  
Set Up

<p>Global climate and hydrological modelling have shown that human influence on the hydrosphere has been growing and is projected to continue increasing. Global models can inform us of the regional trends and events occurring in the stream network, however, operational water management and research often require tailored and detailed modelling to support decision making. Decisions on which kind of hydrological model (lumped, distributed) and at what scale can, however, impact on the usability of the model outputs for use cases which were not anticipated during the model set-up.</p><p>Here we conduct two experiments with an objective to determine whether an ensemble of a downscaled Global Hydrological Models (GHM) can be used 1) to improve the performance, and 2) to spatially disaggregate the output of a catchment scale model to its sub-basins. We use two existing distributed models set up for research purposes in the Sekong, Sesan, and Srepok Rivers (a major tributary of the Mekong), and in the Grijalva-Usumacinta catchments in Mexico. In the first experiment, we downscale off-the-shelf runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) using a recently developed areal interpolation method, route the downscaled runoff, and apply model averaging on an ensemble consisting of the downscaled GHM timeseries and the output of the distributed model at the observation stations. In the second experiment, we downscale and route runoff from the GHMs down the river network, as in the first experiment. During the routing step we record the sub-basin of origin and the timestep of runoff as it reaches an observation station. This record is then used to reconstruct a distributed estimate of discharge (back-traced from the existing model output) in all river reaches. We validate the reconstructed distributed estimates by comparing their spatial distribution to the outputs of the original distributed hydrological models, and against streamflow records.</p><p>Our initial experiments show that the downscaled estimates from GHMs have potential to increase the performance of the model outputs. We also show that the reconstruction of hydrographs in sub-basins of the modelled area is possible, however, the uncertainties related to the method are large and the estimates are sensitive to the routing solution used in the back-tracing, and to the performance of the ensemble of GHMs.</p><p>The methodology has potential for improving the usability of GHMs in local contexts. Owing to the promptly available GHM outputs, the method allows for swift exploration of hydrological questions before a proper modelling experiment is set up. Using GHMs as supplementary ensemble members can also aid in locations where calibration of the models is difficult due to scarce or ill-fitting data, or when the original choice of model fails to capture some aspects of the hydrograph.</p>

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