Global glacio-hydrological model coupling for streamflow prediction

Global hydrological models (GHMs) have become an increasingly valuable tool in a range of global impact studies related to water resources. However, the parameterization of glaciers is often overly simplistic or non-existent in GHMs. The representation of glacier dynamics and evolution, including related products such as glacier runoff, can be improved by relying on dedicated global glacier models (GGMs). Coupling a GGM to a GHM could consequently lead to increased GHM predictive skills, decreased GHM uncertainty, and an increased understanding of the contribution of glaciers to catchment hydrology, particularly in light of climate change.To test this hypothesis, the GHM PCR-GLOBWB 2 (Sutanudjaja et al., 2018) is coupled with the GGM GloGEM (Huss and Hock, 2015) using eWaterCycle (Hut et al., 2018). For the years 2001-2012, the coupled model is evaluated against the uncoupled benchmark in 25 large (>50.000 km2) glacierized basins using GRDC streamflow observations. Across all basins, the coupled model produces higher runoff throughout the melt season, which can principally be attributed to the underrepresentation of glacial melt in PCR-GLOBWB 2. In highly glaciated basins this difference is pivotal, while in lowly glaciated basins it is negligible. In the evaluation against the GRDC observations, the performance increment of the coupled model at the peak of the melt season in highly glaciated basins stands out.This study underlines the importance of glacier representation in GHMs and demonstrates the potential of coupling a GHM with a GGM for better glacier representation and runoff predictions in glaciated basins.&#160;

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Using machine learning to downscale simulations of climate change induced changes to the shallow groundwater table

10.5194/egusphere-egu21-7170 ◽

2021 ◽

Author(s):

Raphael Schneider ◽

Hans Jørgen Henriksen ◽

Julian Koch ◽

Lars Troldborg ◽

Simon Stisen

Keyword(s):

Climate Change ◽

Climate Models ◽

Hydrological Model ◽

Shallow Groundwater ◽

Groundwater Table ◽

Training Data ◽

Computational Time ◽

Hydrological Models ◽

Shallow Groundwater Table ◽

Induced Changes

The DK-model (https://vandmodel.dk/in-english) is a national water resource model, covering all of Denmark. Its core is a distributed, integrated surface-subsurface hydrological model in 500m horizontal resolution. With recent efforts, a version at a higher resolution of 100m was created. The higher resolution was, amongst others, desired by end-users and to better represent surface and surface-near phenomena such as the location of the uppermost groundwater table. Being presently located close to the surface across substantial parts of the country and partly expected to rise, the groundwater table and its future development due to climate change is of great interest. A rising groundwater table is associated with potential risks for infrastructure, agriculture and ecosystems. However, the 25-fold jump in resolution of the hydrological model also increases the computational effort. Hence, it was deemed unfeasible to run the 100m resolution hydrological model nation-wide with an ensemble of climate models to evaluate climate change impact. The full ensemble run could only be performed with the 500m version of the model. To still produce the desired outputs at 100m resolution, a downscaling method was applied as described in the following.Five selected subcatchment models covering around 9% of Denmark were run with five selected climate models at 100m resolution (using less than 3% of the computational time for hydrological models compared to a national, full ensemble run at 100m). Using the simulated changes at 100m resolution from those models as training data, combined with a set of covariates including the simulated changes in 500m resolution, Random Forest (RF) algorithms were trained to downscale simulated changes from 500m to 100m.Generalizing the trained RF algorithms, Denmark-wide maps of expected climate change induced changes to the shallow groundwater table at 100m resolution were modelled. To verify the downscaling results, amongst others, the RF algorithms were successfully validated against results from a sixth hydrological subcatchment model at 100m resolution not used in training the algorithms.The experience gained also opens for various other applications of similar algorithms where computational limitations inhibit running distributed hydrological models at fine resolutions: The results suggest the potential to downscale other model outputs that are desired at fine resolutions.

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RAT (Robustness Assessment Test): a straightforward evaluation of hydrological model robustness to a changing climate

10.5194/egusphere-egu21-8403 ◽

2021 ◽

Author(s):

Vazken Andréassian ◽

Léonard Santos ◽

Torben Sonnenborg ◽

Alban de Lavenne ◽

Göran Lindström ◽

...

Keyword(s):

Climate Change ◽

Hydrological Model ◽

Hydrological Models ◽

Climate Variables ◽

Data Set ◽

Hydrological Characteristics ◽

Changing Climate ◽

Split Sample ◽

Sample Test ◽

Model Robustness

Hydrological models are increasingly used under evolving climatic conditions. They should thus be evaluated regarding their temporal transferability (application in different time periods) and extrapolation capacity (application beyond the range of known past conditions). In theory, parameters of hydrological models are independent of climate. In practice, however, many published studies based on the Split-Sample Test (Kleme&#353;, 1986), have shown that model performances decrease systematically when it is used out of its calibration period. The RAT test proposed here aims at evaluating model robustness to a changing climate by assessing potential undesirable dependencies of hydrological model performances to climate variables. The test compares, over a long data period, the annual value of several climate variables (temperature, precipitation and aridity index) and the bias of the model over each year. If a significant relation exists between the climatic variable and the bias, the model is not considered to be robust to climate change on the catchment. The test has been compared to the Generalized Split-Sample Test (Coron et al., 2012) and showed similar results.Here, we report on a large scale application of the test for three hydrological models with different level of complexity (GR6J, HYPE, MIKE-SHE) on a data set of 352 catchments in Denmark, France and Sweden. The results show that the test behaves differently given the evaluated variable (be temperature, precipitation or aridity) and the hydrological characteristics of each catchment. They also show that, although of different level of complexity, the robustness of the three models is similar on the overall data set. However, they are not robust on the same catchments and, then, are not sensitive to the same hydrological characteristics. This example highlights the applicability of the RAT test regardless of the model set-up and calibration procedure and its ability to provide a first evaluation of the model robustness to climate change.&#160;ReferencesCoron, L., V. Andr&#233;assian, C. Perrin, J. Lerat, J. Vaze, M. Bourqui, and F. Hendrickx, 2012. Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resour. Res., 48, W05552, doi:10.1029/2011WR011721Kleme&#353;, V., 1986. Operational testing of hydrological simulation models, Hydrol. Sci. J., 31, 13&#8211;24, doi:10.1080/02626668609491024&#160;

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Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Water ◽

10.3390/w11030594 ◽

2019 ◽

Vol 11 (3) ◽

pp. 594 ◽

Cited By ~ 9

Author(s):

Majid Fereidoon ◽

Manfred Koch ◽

Luca Brocca

Keyword(s):

Soil Moisture ◽

Hydrological Model ◽

Assessment Tool ◽

Swat Model ◽

Rainfall Data ◽

Coefficient Of Determination ◽

Hydrological Models ◽

Streamflow Prediction ◽

Soil Moisture Data ◽

Monthly Runoff

Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

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Implication of calibration period on modelling climate change impact on future runoff

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-371-3-2015 ◽

2015 ◽

Vol 371 ◽

pp. 3-6 ◽

Cited By ~ 1

Author(s):

F. H. S. Chiew ◽

H. Zheng ◽

J. Vaze

Keyword(s):

Climate Change ◽

Climate Change Impact ◽

Hydrological Model ◽

Historical Data ◽

Hydrological Models ◽

Calibration Period ◽

Calibration Dataset ◽

Change Impact ◽

Hydrologic Responses ◽

Data Period

Abstract. This paper explores the consideration and implication of calibration period on the modelled climate change impact on future runoff. The results show that modelled runoff and hydrologic responses can be influenced by the choice of historical data period used to calibrate and develop the hydrological model. Modelling approaches that do not take this into account may therefore underestimate the range and uncertainty in future runoff projections. Nevertheless, the uncertainty associated with the choice of hydrological models and consideration of calibration dataset for modelling climate change impact on runoff is likely to be small compared to the uncertainty in the future rainfall projections.

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-7191-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 7191-7229 ◽

Cited By ~ 3

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.

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Analysis of high streamflow extremes in climate change studies: How do we calibrate hydrological models?

10.5194/hess-2021-467 ◽

2021 ◽

Author(s):

Bruno Majone ◽

Diego Avesani ◽

Patrick Zulian ◽

Aldo Fiori ◽

Alberto Bellin

Keyword(s):

Climate Change ◽

Climate Models ◽

Hydrological Model ◽

High Flow ◽

Cumulative Distribution ◽

Hydrological Models ◽

Distributed Hydrological Model ◽

Empirical Cumulative Distribution Function ◽

Daily Streamflow ◽

Streamflow Extremes

Abstract. Climate change impact studies on hydrological extremes often rely on the use of hydrological models with parameters inferred by using observational data of daily streamflow. In this work we show that this is an error prone procedure when the interest is to develop reliable Empirical Cumulative Distribution Function curves of annual streamflow maximum. As an alternative approach we introduce a methodology, coined Hydrological Calibration of eXtremes (HyCoX), in which the calibration of the hydrological model is carried out by directly targeting the probability distribution of high flow extremes. In particular, hydrological simulations conducted during a reference period, as driven by climate models’ outputs, are constrained to maximize the probability that the modeled and observed high flow extremes belong to the same population. The application to the Adige river catchment (southeastern Alps, Italy) by means of HYPERstreamHS, a distributed hydrological model, showed that this procedure preserves statistical coherence and produce reliable quantiles of the annual maximum streamflow to be used in assessment studies.

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Using the Maximum Entropy Production approach to integrate energy budget modeling in a hydrological model

10.5194/hess-2018-636 ◽

2019 ◽

Author(s):

Audrey Maheu ◽

Islem Hajji ◽

François Anctil ◽

Daniel F. Nadeau ◽

René Therrien

Keyword(s):

Climate Change ◽

Entropy Production ◽

Maximum Entropy ◽

Energy Budget ◽

Hydrological Model ◽

Soil Column ◽

Hydrologic Model ◽

Hydrological Models ◽

Potential Evaporation ◽

Maximum Entropy Production

Abstract. Total terrestrial evaporation is a key process to understand the hydrological impacts of climate change given that warmer surface temperatures translate into an increase in the atmospheric evaporative demand. To simulate this flux, many hydrological models rely on the concept of potential evaporation (PET) although large differences have been observed in the response of PET models to climate change. The Maximum Entropy Production (MEP) model of land surface fluxes offers an alternative approach to simulate terrestrial evaporation in a simple and parsimonious way while fulfilling the physical constraint of energy budget closure and providing a distinct estimation of evaporation and transpiration. The objective of this work is to use the MEP model to integrate energy budget modeling within a hydrological model. We coupled the MEP model with HydroGeoSphere, an integrated surface and subsurface hydrologic model. As a proof-of-concept, we performed one-dimensional soil column simulations at three sites of the AmeriFlux network. The coupled HGS-MEP model produced realistic simulations of soil water content (RMSE between 0.03 and 0.05 m3 m−3, NSE between 0.30 and 0.92) and terrestrial evaporation (RMSE between 0.31 and 0.71 mm day−1, NSE between 0.65 and 0.88) under semiarid, Mediterranean and temperate climates. HGS-MEP outperformed the standalone HGS model where total terrestrial evaporation is derived from potential evaporation which we computed using the Penman-Monteith equation. This research demonstrated the potential of the MEP model to improve the simulation of total terrestrial evaporation in hydrological models, including for hydrological projections under climate change.

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An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources

Hydrology and Earth System Sciences ◽

10.5194/hess-17-565-2013 ◽

2013 ◽

Vol 17 (2) ◽

pp. 565-578 ◽

Cited By ~ 106

Author(s):

J. A. Velázquez ◽

J. Schmid ◽

S. Ricard ◽

M. J. Muerth ◽

B. Gauvin St-Denis ◽

...

Keyword(s):

Climate Change ◽

Water Resources ◽

Climate Models ◽

Hydrological Model ◽

Global Climate ◽

Regional Climate ◽

Structural Complexity ◽

Global Climate Models ◽

Hydrological Models ◽

The Impact

Abstract. Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e., lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by global climate models over a reference (1971–2000) and a future (2041–2070) period. The results show that, for our hydrological model ensemble, the choice of model strongly affects the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model.

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Usage of Differential Evolution Algorithm in the Calibration of Parametric Rainfall-Runoff Modeling

Advances in Computational Intelligence and Robotics - Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering ◽

10.4018/978-1-5225-4766-2.ch022 ◽

2018 ◽

pp. 481-499 ◽

Cited By ~ 1

Author(s):

Umut Okkan ◽

Nuray Gedik ◽

Halil Uysal

Keyword(s):

Climate Change ◽

Global Optimization ◽

Differential Evolution ◽

Hydrological Model ◽

Optimization Algorithms ◽

Differential Evolution Algorithm ◽

Hydrological Models ◽

Rainfall Runoff ◽

Lumped Model ◽

Evolution Algorithm

In recent years, global optimization algorithms are used in many engineering applications. Calibration of certain parameters at conceptualization of hydrological models is one example of these. An important issue in interpreting the effects of climate change on the basin depends on selecting an appropriate hydrological model. Not only climate change impact assessment studies, but also many water resources planning studies refer to such modeling applications. In order to obtain reliable results from these hydrological models, calibration phase of the models needs to be done well. Hence, global optimization methods are utilized in the calibration process. In this chapter, the differential evolution algorithm (DEA), which has rare application in the hydrological modeling literature, was explained. As an application, the use of the DEA algorithm in the hydrological model calibration phase was mentioned. DYNWBM, a lumped model with five parameters, was selected as the hydrological model. The calibration and then validation period performances of the DEA based DYNWBM model were tested and also compared with other global optimization algorithms. According to the results derived from the study, hydrological model appropriately reflects the rainfall-runoff relation of basin for both periods.

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Including the dynamic relationship between climatic variables and leaf area index in a hydrological model to improve streamflow prediction under a changing climate

Hydrology and Earth System Sciences ◽

10.5194/hess-19-2821-2015 ◽

2015 ◽

Vol 19 (6) ◽

pp. 2821-2836 ◽

Cited By ~ 13

Author(s):

Z. K. Tesemma ◽

Y. Wei ◽

M. C. Peel ◽

A. W. Western

Keyword(s):

Climate Change ◽

Hydrological Model ◽

Emission Scenario ◽

Annual Runoff ◽

Streamflow Prediction ◽

Area Index ◽

Changing Climate ◽

Millennium Drought ◽

Near Term ◽

Induced Changes

Abstract. Anthropogenic climate change is projected to enrich the atmosphere with carbon dioxide, change vegetation dynamics and influence the availability of water at the catchment scale. This study combines a nonlinear model for estimating changes in leaf area index (LAI) due to climatic fluctuations with the variable infiltration capacity (VIC) hydrological model to improve catchment streamflow prediction under a changing climate. The combined model was applied to 13 gauged sub-catchments with different land cover types (crop, pasture and tree) in the Goulburn–Broken catchment, Australia, for the "Millennium Drought" (1997–2009) relative to the period 1983–1995, and for two future periods (2021–2050 and 2071–2100) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5) which were compared with the baseline historical period of 1981–2010. This region was projected to be warmer and mostly drier in the future as predicted by 38 Coupled Model Intercomparison Project Phase 5 (CMIP5) runs from 15 global climate models (GCMs) and for two emission scenarios. The results showed that during the Millennium Drought there was about a 29.7–66.3 % reduction in mean annual runoff due to reduced precipitation and increased temperature. When drought-induced changes in LAI were included, smaller reductions in mean annual runoff of between 29.3 and 61.4 % were predicted. The proportional increase in runoff due to modeling LAI was 1.3–10.2 % relative to not including LAI. For projected climate change under the RCP4.5 emission scenario, ignoring the LAI response to changing climate could lead to a further reduction in mean annual runoff of between 2.3 and 27.7 % in the near-term (2021–2050) and 2.3 to 23.1 % later in the century (2071–2100) relative to modeling the dynamic response of LAI to precipitation and temperature changes. Similar results (near-term 2.5–25.9 % and end of century 2.6–24.2 %) were found for climate change under the RCP8.5 emission scenario. Incorporating climate-induced changes in LAI in the VIC model reduced the projected declines in streamflow and confirms the importance of including the effects of changes in LAI in future projections of streamflow.

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