Development and application of a soil classification-based conceptual catchment-scale hydrological model

This study uses a high-frequency discharge and nitrate concentration dataset from the Weida catchment in Germany for the catchment scale hydrologic response analysis. Nitrate transport in the catchment is mostly conservative as indicated by the nitrate stable isotope (δ15N and δ18O) analysis. Discharge–nitrate concentration data from the catchment show distinctive patterns, suggesting flushing and dilution response. A self-organizing feature map-based methodology was employed to identify such patterns or cluster in the datasets. Based on knowledge of the catchment conditions and prevailing understanding of discharge–nitrate concentration relationship, the clusters were characterized into five qualitative flow responses: (1) baseflow; (2) subsurface flow increase; (3) surface runoff increase; (4) surface runoff recession; and (5) subsurface flow decrease. Such qualitative flowpaths were used as soft data for a multi-objective calibration of a hydrological model (WaSiM-ETH). The calibration led to a reasonable simulation of overall discharge (Nash–Sutcliffe coefficient: 0.84) and qualitative flowpaths (76% agreement). A prerequisite for using such methodology is limited biogeochemical transformation of nitrate (such as denitrification).

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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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Assessment of surface water resources availability using catchment modeling and the results of tracer studies in the meso-scale Migina Catchment, Rwanda

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-15375-2013 ◽

2013 ◽

Vol 10 (12) ◽

pp. 15375-15408 ◽

Cited By ~ 1

Author(s):

O. Munyaneza ◽

A. Mukubwa ◽

S. Maskey ◽

J. Wenninger ◽

S. Uhlenbrook

Keyword(s):

Decision Making ◽

Water Resources ◽

Hydrological Model ◽

Base Flow ◽

Hydrological Processes ◽

Model Parameters ◽

Water Resources Planning ◽

Catchment Scale ◽

Streamflow Data ◽

Resources Planning

Abstract. In the last couple of years, different hydrological research projects were undertaken in the Migina catchment (243.2 km2), a tributary of the Kagera river in Southern Rwanda. These projects were aimed to understand hydrological processes of the catchment using analytical and experimental approaches and to build a pilot case whose experience can be extended to other catchments in Rwanda. In the present study, we developed a hydrological model of the catchment, which can be used to inform water resources planning and decision making. The semi-distributed hydrological model HEC-HMS (version 3.5) was used with its soil moisture accounting, unit hydrograph, liner reservoir (for base flow) and Muskingum-Cunge (river routing) methods. We used rainfall data from 12 stations and streamflow data from 5 stations, which were collected as part of this study over a period of two years (May 2009 and June 2011). The catchment was divided into five sub-catchments each represented by one of the five observed streamflow gauges. The model parameters were calibrated separately for each sub-catchment using the observed streamflow data. Calibration results obtained were found acceptable at four stations with a Nash–Sutcliffe Model Efficiency of 0.65 on daily runoff at the catchment outlet. Due to the lack of sufficient and reliable data for longer periods, a model validation (split sample test) was not undertaken. However, we used results from tracer based hydrograph separation from a previous study to compare our model results in terms of the runoff components. It was shown that the model performed well in simulating the total flow volume, peak flow and timing as well as the portion of direct runoff and base flow. We observed considerable disparities in the parameters (e.g. groundwater storage) and runoff components across the five sub-catchments, that provided insights into the different hydrological processes at sub-catchment scale. We conclude that such disparities justify the need to consider catchment subdivisions, if such parameters and components of the water cycle are to form the base for decision making in water resources planning in the Migina catchment.

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Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment

Hydrology and Earth System Sciences ◽

10.5194/hess-25-4887-2021 ◽

2021 ◽

Vol 25 (9) ◽

pp. 4887-4915

Author(s):

Markus Hrachowitz ◽

Michael Stockinger ◽

Miriam Coenders-Gerrits ◽

Ruud van der Ent ◽

Heye Bogena ◽

...

Keyword(s):

Travel Time ◽

Hydrological Model ◽

Storage Capacity ◽

Water Storage ◽

Model Parameters ◽

Hydrological Response ◽

Water Fluxes ◽

Catchment Scale ◽

Water Storage Capacity ◽

Water Fractions

Abstract. Deforestation can considerably affect transpiration dynamics and magnitudes at the catchment scale and thereby alter the partitioning between drainage and evaporative water fluxes released from terrestrial hydrological systems. However, it has so far remained problematic to directly link reductions in transpiration to changes in the physical properties of the system and to quantify these changes in system properties at the catchment scale. As a consequence, it is difficult to quantify the effect of deforestation on parameters of catchment-scale hydrological models. This in turn leads to substantial uncertainties in predictions of the hydrological response after deforestation but also to a poor understanding of how deforestation affects principal descriptors of catchment-scale transport, such as travel time distributions and young water fractions. The objectives of this study in the Wüstebach experimental catchment are therefore to provide a mechanistic explanation of why changes in the partitioning of water fluxes can be observed after deforestation and how this further affects the storage and release dynamics of water. More specifically, we test the hypotheses that (1) post-deforestation changes in water storage dynamics and partitioning of water fluxes are largely a direct consequence of a reduction of the catchment-scale effective vegetation-accessible water storage capacity in the unsaturated root zone (SU, max) after deforestation and that (2) the deforestation-induced reduction of SU, max affects the shape of travel time distributions and results in shifts towards higher fractions of young water in the stream. Simultaneously modelling streamflow and stable water isotope dynamics using meaningfully adjusted model parameters both for the pre- and post-deforestation periods, respectively, a hydrological model with an integrated tracer routine based on the concept of storage-age selection functions is used to track fluxes through the system and to estimate the effects of deforestation on catchment travel time distributions and young water fractions Fyw. It was found that deforestation led to a significant increase in streamflow accompanied by corresponding reductions of evaporative fluxes. This is reflected by an increase in the runoff ratio from CR=0.55 to 0.68 in the post-deforestation period despite similar climatic conditions. This reduction of evaporative fluxes could be linked to a reduction of the catchment-scale water storage volume in the unsaturated soil (SU, max) that is within the reach of active roots and thus accessible for vegetation transpiration from ∼258 mm in the pre-deforestation period to ∼101 mm in the post-deforestation period. The hydrological model, reflecting the changes in the parameter SU, max, indicated that in the post-deforestation period stream water was characterized by slightly yet statistically not significantly higher mean fractions of young water (Fyw∼0.13) than in the pre-deforestation period (Fyw∼0.12). In spite of these limited effects on the overall Fyw, changes were found for wet periods, during which post-deforestation fractions of young water increased to values Fyw∼0.37 for individual storms. Deforestation also caused a significantly increased sensitivity of young water fractions to discharge under wet conditions from dFyw/dQ=0.25 to 0.36. Overall, this study provides quantitative evidence that deforestation resulted in changes in vegetation-accessible storage volumes SU, max and that these changes are not only responsible for changes in the partitioning between drainage and evaporation and thus the fundamental hydrological response characteristics of the Wüstebach catchment, but also for changes in catchment-scale tracer circulation dynamics. In particular for wet conditions, deforestation caused higher proportions of younger water to reach the stream, implying faster routing of stable isotopes and plausibly also solutes through the sub-surface.

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A comparison of soil survey methods in relation to catchment hydrology

Soil Research ◽

10.1071/s95029 ◽

1997 ◽

Vol 35 (6) ◽

pp. 1379 ◽

Cited By ~ 4

Author(s):

S. E. Cook ◽

N. A. Coles

Keyword(s):

Survey Methods ◽

Soil Classification ◽

Geostatistical Analysis ◽

Soil Survey ◽

Survey Method ◽

Catchment Scale ◽

Hydrologic Modelling ◽

Property Variation ◽

Variogram Models ◽

Western Australian

Eight primary catchments within the Western Australian wheatbelt were surveyed in detail to examine the abilities of conventional soil classification and geostatistical analysis to provide detailed information of soil spatial variation for catchment-scale hydrologic modelling. Nine soil physical properties were measured. The results illustrate potential diculties with both methods. Classification by using the Factual Key was unable to describe the major component of soil property variation. The relative variance accounted for by soil classes was usually <10%. Only the yellow duplex soils appeared distinct from other soil classes. Potential diculties with geostatistical analysis also arose because of fluctuations in the variogram models. Contrasts occurred between variograms for the same property over different catchments and for different properties over the same catchment. Within the areas studied, nugget and linear (unbounded) variogram models were more common than spherical or exponential models. It is proposed that the surveyor would have to select a survey method on the basis of prior knowledge about which model of variation is more likely to be successful for the scale and location of survey.

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Simulating catchment scale river discharges and flood events from large scale atmospheric information: Example of the Upper Rhône River (European Alps)

10.5194/egusphere-egu21-7843 ◽

2021 ◽

Author(s):

Caroline Legrand ◽

Benoît Hingray ◽

Bruno Wilhelm

Keyword(s):

Time Series ◽

Extreme Precipitation ◽

Large Scale ◽

Climate Models ◽

Hydrological Model ◽

European Alps ◽

Earth System ◽

Flood Events ◽

Catchment Scale ◽

Mountainous Catchments

Floods are highly destructive natural hazards causing widespread impacts on socio-ecosystems. This hazard could be further amplified with the ongoing climate change, which will likely alter magnitude and frequency of floods. Estimating how flood regimes could change in the future is however not straightforward. The classical approach is to estimate future hydrological regimes from hydrological simulations forced by time series scenarii of weather variables for different future climate scenarii. The development of relevant weather scenarii for this is often critical. To be adapted to the critical space and time scales of the considered basins, weather scenarii are thus typically produced from climate models with downscaling models (either dynamic or statistical).In this study, we aim to evaluate the capacity of such a simulation chain to reproduce floods observed in the upper Rh&#244;ne River (10900 km&#178;, European Alps) over the last century. The modeling chain is made up of (i) the atmospheric reanalysis ERA-20C (1900-2010), (ii) the statistical downscaling model Analog, and (iii) the glacio-hydrological model GSM-SOCONT (Glacier and Snowmelt SOil CONTribution model; Schaefli et al., 2005). To assess the performance of this modeling chain, the simulated scenarii of mean areal precipitation and temperature are compared to the observed time series over the common period (1961-2010), whereas the discharge scenarii are compared to the reference time series (1920-2010).In this presentation, we will discuss (i) the results obtained by the basic Analog method, namely a flood events underestimation due to an underestimation of extreme precipitation values, in particular 3-day and 5-day extreme precipitation, and (ii) the enhanced results obtained by the improved version of Analog SCAMP (Sequential Constructive Atmospheric Analogues for Multivariate weather Predictions; Raynaud et al., 2020) combined to the Schaake Shuffle method.References:Schaefli, B., Hingray, B., M. Niggli, M., Musy, A. (2005). A conceptual glacio-hydrological model for high mountainous catchments. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 9, 95-109.Raynaud, D., Hingray, B., Evin, G., Favre, A.-C., Chardon, J. (2020). Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 24(9), 4339-4352.

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Surface water–groundwater interactions in catchment scale water resources assessments—understanding and hypothesis testing with a hydrological model

Hydrological Sciences Journal ◽

10.1080/02626667.2015.1052453 ◽

2015 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

J.L. Tanner ◽

D.A. Hughes

Keyword(s):

Surface Water ◽

Water Resources ◽

Hypothesis Testing ◽

Hydrological Model ◽

Catchment Scale

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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 ◽

10.5194/hess-15-279-2011 ◽

2011 ◽

Vol 15 (1) ◽

pp. 279-294 ◽

Cited By ~ 162

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.

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