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

2015 ◽  
Vol 19 (6) ◽  
pp. 2821-2836 ◽  
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.

scholarly journals Reconciling the dynamic relationship between climate variables and vegetation productivity into a hydrological model to improve streamflow prediction under climate change

2014 ◽  
Vol 11 (9) ◽  
pp. 10593-10633
Author(s):  
Z. K. Tesemma ◽  
Y. Wei ◽  
M. C. Peel ◽  
A. W. Western
Keyword(s):  
Climate Change ◽  
Annual Runoff ◽  
Surface Model ◽  
Emission Scenarios ◽  
Streamflow Prediction ◽  
Vegetation Productivity ◽  
Area Index ◽  
Changing Climate ◽  
Millennium Drought ◽  
The Future

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. This study combines a simple model for estimating changes in leaf area index (LAI) due to climate fluctuations with the variable infiltration capacity (VIC) land surface model to improve catchment streamflow prediction under a changing climate. The combined model was applied to thirteen gauged catchments with different land cover types (crop, pasture and tree) in the Goulburn–Broken catchment, Australia during the "Millennium Drought" (2000–2009), and two future periods (2021–2050 and 2071–2100) for two emission scenarios (RCP4.5 and RCP8.5). The future climatic and modelled streamflow results were compared with the baseline historical period of 1981–2010. This region is projected to be warmer and mostly drier in the future as predicted by 38 Coupled Model Inter-comparison Project Phase 5 (CMIP5) simulations from 15 Global Climate Models (GCMs) and for two emission scenarios. The results showed that during the Millennium Drought there was about a 30–65% reduction in mean annual runoff due to reduced rainfall and increased temperature. This climate based reduction in mean annual runoff was partially offset by a drought related decline in LAI that reduced the climate related reduction of mean annual runoff, effectively increased runoff, by 2–9%. Projected climate change may reduce mean annual runoff by between 6 and 31% in the study catchments. However, when LAI is allowed to respond to changes in climate the projected declines in runoff were reduced to between 2 and 22% in comparison to when the historical LAI was considered. Incorporating changes in LAI in VIC to respond to changing climate reduced the projected declines in streamflow and confirms the importance of including the effects of changes in vegetation productivity in future projections of streamflow.

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

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

<p>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.</p><p>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.</p><p>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.</p><p>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.</p>

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

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

<p>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 (Klemeš, 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.</p><p>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.</p><p> </p><p><strong>References</strong></p><p>Coron, L., V. Andréassian, 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:10.1029/2011WR011721</p><p>Klemeš, V., 1986. Operational testing of hydrological simulation models, Hydrol. Sci. J., 31, 13–24, doi:10.1080/02626668609491024</p><p> </p>

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.

Global glacio-hydrological model coupling for streamflow prediction

2021 ◽  
Author(s):  
Pau Wiersma ◽  
Rolf Hut ◽  
Jerom Aerts ◽  
Niels Drost ◽  
Harry Zekollari ◽  
...  
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Coupled Model ◽  
Model Coupling ◽  
Hydrological Models ◽  
Catchment Hydrology ◽  
Streamflow Prediction ◽  
Global Impact ◽  
Glacier Runoff ◽  
Glacier Dynamics

<p>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.</p><p>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.</p><p>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.</p><p> </p>

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.

scholarly journals Effects of IPCC SRES* emissions scenarios on river runoff: a global perspective

2003 ◽  
Vol 7 (5) ◽  
pp. 619-641 ◽  
Author(s):  
N. W. Arnell
Keyword(s):  
Climate Change ◽  
North America ◽  
Southern Africa ◽  
Return Period ◽  
River Runoff ◽  
Climate Models ◽  
Hydrological Model ◽  
Annual Runoff ◽  
Macro Scale ◽  
Emissions Scenarios

Abstract. This paper describes an assessment of the implications of future climate change for river runoff across the entire world, using six climate models which have been driven by the SRES emissions scenarios. Streamflow is simulated at a spatial resolution of 0.5°x0.5&#176 using a macro-scale hydrological model, and summed to produce total runoff for almost 1200 catchments. The effects of climate change have been compared with the effects of natural multi-decadal climatic variability, as determined from a long unforced climate simulation using HadCM3. By the 2020s, change in runoff due to climate change in approximately a third of the catchments is less than that due to natural variability but, by the 2080s, this falls to between 10 and 30%. The climate models produce broadly similar changes in runoff, with increases in high latitudes, east Africa and south and east Asia, and decreases in southern and eastern Europe, western Russia, north Africa and the Middle East, central and southern Africa, much of North America, most of South America, and south and east Asia. The pattern of change in runoff is largely determined by simulated change in precipitation, offset by a general increase in evaporation. There is little difference in the pattern of change between different emissions scenarios (for a given model), and only by the 2080s is there evidence that the magnitudes of change in runoff vary, with emissions scenario A1FI producing the greatest change and B1 the smallest. The inter-annual variability in runoff increases in most catchments due to climate change — even though the inter-annual variability in precipitation is not changed — and the frequency of flow below the current 10-year return period minimum annual runoff increases by a factor of three in Europe and southern Africa and of two across North America. Across most of the world climate change does not alter the timing of flows through the year but, in the marginal zone between cool and mild climates, higher temperatures mean that peak streamflow moves from spring to winter as less winter precipitation falls as snow. The spatial pattern of changes in the 10-year return period maximum monthly runoff follows changes in annual runoff. Keywords: SRES emissions scenarios, climate change impacts on runoff, multi-decadal variability, macro-scale hydrological model, drought frequency, flood frequency

scholarly journals Integrated assessment of climate and human contributions to variations in streamflow in the Ten Great Gullies Basin of the Upper Yellow River, China

2020 ◽  
Vol 68 (3) ◽  
pp. 249-259
Author(s):  
Tong Liu ◽  
He Qing Huang ◽  
Ming an Shao ◽  
Jiong Cheng ◽  
Xiang Dong Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Hydrological Model ◽  
Yellow River ◽  
Annual Runoff ◽  
Integrated Method ◽  
Climate Elasticity ◽  
Relative Contribution ◽  
Pettitt Test ◽  
Monthly Runoff

AbstractClimate change and human activity are two linked factors that alter the spatiotemporal distribution of the available water. Assessing the relative contribution of the two factors on runoff changes can help the planners and managers to better formulate strategies and policies regarding regional water resources. In this work, using two typical sub-basins of the Yellow River as the study area, we first detected the trend and the breakpoint in the annual streamflow data with the Pettitt test during the period 1964–2011. Next, a Budyko-based climate elasticity model and a monthly hydrological model were employed as an integrated method to distinguish the relative contributions of climate change and human activities to the long-term changes in runoff. The results showed that a significant decline in the annual runoff occurred in the two sub-basins during the study period, and the abrupt change point in the annual runoff at the two sub-basins both occurred in 1997. The conceptual hydrological model performed well in reproducing monthly runoff time series at the two sub-basins. The Nash-Sutcliffe efficiency (NSE) between observed and simulated runoff during the validation period exceeds 0.83 for the two sub-basins. Climate elasticity method and hydrological model give consistent attribution results: human activities are the major drivers responsible for the decreased annual runoff in the Ten Great Gullies Basin. The relative contributions of climate change and human activities to the changes in the annual runoff were 22–32% and 68–78%, respectively.

scholarly journals Modelling of the Discharge Response to Climate Change under RCP8.5 Scenario in the Alata River Basin (Mersin, SE Turkey)

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 483
Author(s):  
Ümit Yıldırım ◽  
Cüneyt Güler ◽  
Barış Önol ◽  
Michael Rode ◽  
Seifeddine Jomaa
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Hydrological Model ◽  
Baseline Period ◽  
Change Scenario ◽  
Climate Projections ◽  
Worst Case ◽  
Distributed Process ◽  
In The Beginning ◽  
Se Turkey

This study investigates the impacts of climate change on the hydrological response of a Mediterranean mesoscale catchment using a hydrological model. The effect of climate change on the discharge of the Alata River Basin in Mersin province (Turkey) was assessed under the worst-case climate change scenario (i.e., RCP8.5), using the semi-distributed, process-based hydrological model Hydrological Predictions for the Environment (HYPE). First, the model was evaluated temporally and spatially and has been shown to reproduce the measured discharge consistently. Second, the discharge was predicted under climate projections in three distinct future periods (i.e., 2021–2040, 2046–2065 and 2081–2100, reflecting the beginning, middle and end of the century, respectively). Climate change projections showed that the annual mean temperature in the Alata River Basin rises for the beginning, middle and end of the century, with about 1.35, 2.13 and 4.11 °C, respectively. Besides, the highest discharge timing seems to occur one month earlier (February instead of March) compared to the baseline period (2000–2011) in the beginning and middle of the century. The results show a decrease in precipitation and an increase in temperature in all future projections, resulting in more snowmelt and higher discharge generation in the beginning and middle of the century scenarios. However, at the end of the century, the discharge significantly decreased due to increased evapotranspiration and reduced snow depth in the upstream area. The findings of this study can help develop efficient climate change adaptation options in the Levant’s coastal areas.

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