scholarly journals Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region

2020 ◽  
Vol 24 (6) ◽  
pp. 3331-3359 ◽  
Author(s):  
Petra Hulsman ◽  
Hessel C. Winsemius ◽  
Claire I. Michailovsky ◽  
Hubert H. G. Savenije ◽  
Markus Hrachowitz
Keyword(s):  
Cross Section ◽  
Hydrological Model ◽  
Model Performance ◽  
Hydrological Modelling ◽  
Google Earth ◽  
Water Levels ◽  
Discharge Data ◽  
Model Parameter ◽  
Virtual Station ◽  
River Cross Section

Abstract. Limited availability of ground measurements in the vast majority of river basins world-wide increases the value of alternative data sources such as satellite observations in hydrological modelling. This study investigates the potential of using remotely sensed river water levels, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River basin in Zambia. A distributed process-based rainfall–runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. Next, three alternative ways of further restricting feasible model parameter sets using satellite altimetry time series from 18 different locations along the river were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash–Sutcliffe efficiency of ENS,Q=0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95=0.61–0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q=-1.4 (ENS,Q,5/95=-2.3–0.38) with respect to discharge was obtained. The direct use of altimetry-based river levels frequently led to overestimated flows and poorly identified feasible parameter sets (ENS,Q,5/95=-2.9–0.10). Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an overestimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95=-2.6–0.25). However, accounting for river geometry proved to be highly effective. This included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth and applying the Strickler–Manning equation to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well, with an optimum of ENS,Q=0.60 (ENS,Q,5/95=-0.31–0.50). It was further shown that more accurate river cross-section data improved the water-level simulations, modelled rating curve, and discharge simulations during intermediate and low flows at the basin outlet where detailed on-site cross-section information was available. Also, increasing the number of virtual stations used for parameter selection in the calibration period considerably improved the model performance in a spatial split-sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data-scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly gauged or ungauged basins.

scholarly journals Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in data scarce regions

2019 ◽  
Author(s):  
Petra Hulsman ◽  
Hessel C. Winsemius ◽  
Claire Michailovsky ◽  
Hubert H. G. Savenije ◽  
Markus Hrachowitz
Keyword(s):  
Water Level ◽  
River Water ◽  
Cross Section ◽  
River Discharge ◽  
Google Earth ◽  
Water Levels ◽  
Altimetry Data ◽  
Discharge Data ◽  
Model Parameter ◽  
River Cross Section

Abstract. To ensure reliable model understanding of water movement and distribution in terrestrial systems, sufficient and good quality hydro-meteorological data are required. Limited availability of ground measurements in the vast majority of river basins world-wide increase the value of alternative data sources such as satellite observations in modelling. In the absence of directly observed river discharge data, other variables such as remotely sensed river water level may provide valuable information for the calibration and evaluation of hydrological models. This study investigates the potential of the use of remotely sensed river water level, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River Basin in Zambia. A distributed process-based rainfall runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. Following a step-wise approach, various parameter identification strategies were tested to evaluate the potential of satellite altimetry data for model calibration. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. In a next step, three alternative ways of further restricting feasible model parameter sets based on satellite altimetry time-series from 18 different locations, i.e. virtual stations, along the Luangwa River and its tributaries were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash-Sutcliffe efficiency of ENS,Q = 0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95 = 0.61 – 0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q = −1.4 (ENS,Q,5/95 = −2.3 – 0.38) with respect to discharge was obtained. Depending on the parameter selection strategy, it could be shown that altimetry data can contain sufficient information to efficiently further constrain the feasible parameter space. The direct use of altimetry based river levels frequently over-estimated the flows and poorly identified feasible parameter sets due to the non-linear relationship between river water level and river discharge (ENS,Q,5/95 = −2.9 – 0.10); therefore, this strategy was of limited use to identify feasible model parameter sets. Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an over-estimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95 = −2.6 – 0.25). However, accounting for river geometry proved to be highly effective; this included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth, and applying the Strickler-Manning equation with effective roughness as free calibration parameter to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well with an optimum of ENS,Q = 0.60 (ENS,Q,5/95 = −0.31 – 0.50). It was further shown that more accurate river cross-section data improved the water level simulations, modelled rating curve and discharge simulations during intermediate and low flows at the basin outlet at which detailed on-site cross-section information was available. For this case, the Nash-Sutcliffe efficiency with respect to river water levels increased from ENS,SM,GE = −1.8 (ENS,SM,GE,5/95 = −6.8 – −3.1) using river geometry information extracted from Google Earth to ENS,SM,ADCP = 0.79 (ENS,SM,ADCP,5/95 = 0.6 – 0.74) using river geometry information obtained from a detailed survey in the field. It could also be shown that increasing the number of virtual stations used for parameter selection in the calibration period can considerably improve the model performance in spatial split sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly or ungauged basins.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

Hydrological modelling and anthropogenic water use

2021 ◽  
Author(s):  
Ponnambalam Rameshwaran ◽  
Ali Rudd ◽  
Vicky Bell ◽  
Matt Brown ◽  
Helen Davies ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Hydrological Model ◽  
Model Performance ◽  
River Flows ◽  
Discharge Data ◽  
New Approach ◽  
Water Demands ◽  
The Impact ◽  
Grid Based

<p>Despite Britain’s often-rainy maritime climate, anthropogenic water demands have a significant impact on river flows, particularly during dry summers. In future years, projected population growth and climate change are likely to increase the demand for water and lead to greater pressures on available freshwater resources.</p><p>Across England, abstraction (from groundwater, surface water or tidal sources) and discharge data along with ‘Hands off Flow’ conditions are available for thousands of individual locations; each with a licence for use, an amount, an indication of when abstraction can take place, and the actual amount of water abstracted (generally less than the licence amount). Here we demonstrate how these data can be used in combination to incorporate anthropogenic artificial influences into a grid-based hydrological model. Model simulations of both high and low river flows are generally improved when abstractions and discharges are included, though for some catchments model performance decreases. The new approach provides a methodological baseline for further work investigating the impact of anthropogenic water use and projected climate change on future river flows.</p>

scholarly journals Effect of length of the observed dataset on the calibration of a distributed hydrological model

2015 ◽  
Vol 368 ◽  
pp. 305-311 ◽  
Author(s):  
X. Cui ◽  
W. Sun ◽  
J. Teng ◽  
H. Song ◽  
X. Yao
Keyword(s):  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Model Performance ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Discharge Data ◽  
One Year ◽  
Parameter Values ◽  
Discharge Measurements

Abstract. Calibration of hydrological models in ungauged basins is now a hot research topic in the field of hydrology. In addition to the traditional method of parameter regionalization, using discontinuous flow observations to calibrate hydrological models has gradually become popular in recent years. In this study, the possibility of using a limited number of river discharge data to calibrate a distributed hydrological model, the Soil and Water Assessment Tool (SWAT), was explored. The influence of the quantity of discharge measurements on model calibration in the upper Heihe Basin was analysed. Calibration using only one year of daily discharge measurements was compared with calibration using three years of discharge data. The results showed that the parameter values derived from calibration using one year’s data could achieve similar model performance with calibration using three years’ data, indicating that there is a possibility of using limited numbers of discharge data to calibrate the SWAT model effectively in poorly gauged basins.

scholarly journals Three perceptions of the evapotranspiration landscape: comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances

2013 ◽  
Vol 17 (7) ◽  
pp. 2947-2966 ◽  
Author(s):  
T. Conradt ◽  
F. Wechsung ◽  
A. Bronstert
Keyword(s):  
Remote Sensing ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Hydrological Modelling ◽  
Weather Data ◽  
Discharge Data ◽  
Global Parameter ◽  
Surface Temperatures ◽  
Runoff Series ◽  
Distributed Hydrological Modelling

Abstract. A problem encountered by many distributed hydrological modelling studies is high simulation errors at interior gauges when the model is only globally calibrated at the outlet. We simulated river runoff in the Elbe River basin in central Europe (148 268 km2) with the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model). While global parameter optimisation led to Nash–Sutcliffe efficiencies of 0.9 at the main outlet gauge, comparisons with measured runoff series at interior points revealed large deviations. Therefore, we compared three different strategies for deriving sub-basin evapotranspiration: (1) modelled by SWIM without any spatial calibration, (2) derived from remotely sensed surface temperatures, and (3) calculated from long-term precipitation and discharge data. The results show certain consistencies between the modelled and the remote sensing based evapotranspiration rates, but there seems to be no correlation between remote sensing and water balance based estimations. Subsequent analyses for single sub-basins identify amongst others input weather data and systematic error amplification in inter-gauge discharge calculations as sources of uncertainty. The results encourage careful utilisation of different data sources for enhancements in distributed hydrological modelling.

scholarly journals Modelling the Mara River Basin with data uncertainty using water levels for calibration

2017 ◽  
Author(s):  
Petra Hulsman ◽  
Thom A. Bogaard ◽  
Hubert H. G. Savenije
Keyword(s):  
Time Series ◽  
Water Level ◽  
River Basin ◽  
Hydrological Model ◽  
Water Levels ◽  
Rating Curve ◽  
Discharge Time ◽  
Discharge Data ◽  
Single Station ◽  
Rating Curves

Abstract. Hydrological models play an important role in Water Resources Management. In hydrological modelling, discharge data is generally required for calibration. To obtain continuous time series, water levels are usually converted into discharge by using a rating curve. However with this methodology, uncertainties are introduced in the discharge data due to insufficient observations, inadequate rating curve fitting procedures, extrapolation or temporal changes in the river geometry. Unfortunately, this is often the case in many African river basins. In this study, a semi-distributed rainfall runoff model has been applied to the Mara River Basin for the assessment of the water availability. To reduce the effect of discharge uncertainties in this model, water levels instead of discharge time series were used for calibration. In this model, seven sub-catchments are distinguished and four hydrological response units: forest, shrubs, cropland and grassland. To calibrate the model on water level data, modelled discharges have been converted into water levels using cross-section observations and the Strickler formula. In addition, new geometric rating curves have been obtained based on modelled discharge, observed water level and the Strickler formula. This procedure resulted in good and consistent model results during calibration and validation. The hydrological model was able to reproduce the water depths for the entire basin as well as for the Nyangores sub-catchment in the north. The geometric and recorded (i.e. existing) rating curves were significantly different at Mines, the catchment outlet, probably due to uncertainties in the recorded discharge time series. At Nyangores however, the geometric and recorded discharge were almost identical. In addition, it has been found that the precipitation estimation methodology influenced the model results significantly. Application of a single station for each sub-catchment resulted in flashier responses whereas Thiessen averaged precipitation resulted in more dampened responses. In conclusion, by using water level time series for calibrating the hydrological model of the Mara River Basin promising model results were obtained. For this river basin, the main limitation for obtaining an accurate hydrograph representation was the inadequate knowledge on the spatial distribution of the precipitation.

scholarly journals Minimisation of Overestimation of River Flows in 1D- Hydrodynamic Modeling

2021 ◽  
Author(s):  
Komal Vashist ◽  
K. K. Singh
Keyword(s):  
Cross Section ◽  
Cross Sections ◽  
River Flow ◽  
Model Performance ◽  
Mean Square ◽  
Hydrodynamic Models ◽  
One Dimensional ◽  
Index Of Agreement ◽  
Error Index ◽  
River Cross Section

Abstract One-dimensional hydrodynamic models overestimate river cross-section derived from freely available SRTM DEMs. The present study aims to minimize the overestimation of river flow. DEM-extracted cross-sections obtained from 30 m and 90 m resolutions show higher elevation values than the actual river cross sections of Krishna and Bhima rivers, India. To minimize the overestimation of the river flow, DEM-extracted cross-sections are modified using known cross-section of the river. The corrections for cross sections extracted from DEM, are obtained by subtracting the DEM-derived cross-sections from a known cross-section of the river. Monsoons flows that occurred in years 2006 and 2009 in Krishna and Bhimariver have been used for modeling. The MIKE HYDRO River model performance with modified DEM-extracted cross-sections of river improves as the correlation coefficient, root mean square error, index of agreement, Nash Sutcliffe efficiency & Percentage deviation in peak (%) values are improved.

scholarly journals Is the groundwater reservoir linear? Learning from data in hydrological modelling

2006 ◽  
Vol 10 (1) ◽  
pp. 139-150 ◽  
Author(s):  
F. Fenicia ◽  
H. H. G. Savenije ◽  
P. Matgen ◽  
L. Pfister
Keyword(s):  
Iterative Process ◽  
A Priori ◽  
Model Performance ◽  
Calibration Procedure ◽  
Hydrological Modelling ◽  
Suitable Model ◽  
Discharge Data ◽  
Low Flows ◽  
Groundwater Reservoir ◽  
Linear Reservoir

Abstract. Although catchment behaviour during recession periods is better identifiable than in other periods, the representation of hydrograph recession is often weak in hydrological simulations. Among the various aspects that influence model performance during low flows, in this paper we concentrate on those more inherently related to the modelling, such as the development of a suitable model conceptualization, and the choice of an appropriate calibration strategy. In this context we develop a methodology where the calibration procedure is combined with an iterative process of model improvement, to obtain an optimal model configuration that performs well both during low flows and high flows. The methodology starts by calculating a synthetic master recession curve that represents the long-term recession of a given catchment. Subsequently, using a simple reservoir model, we determine the storage-discharge relation that simulates the slow hydrograph component. This relation is determined without making any a-priori assumption on its form and is inferred from discharge data available through an iterative process. Next, high flow related parameters are recalibrated separately, to avoid that the simulation of low discharges is neglected in favour of a higher performance in simulating peak discharges. This methodology is applied on several catchments in Luxembourg, and as a result we determined that in all catchments except one (where human interference is high) within the chosen model structure a linear reservoir describes best the observed groundwater behaviour. This result is used to trigger a discussion as to the general suitability of the use of a linear groundwater reservoir in hydrological modelling.

scholarly journals Computation of vertically averaged velocities in irregular sections of straight channels

2015 ◽  
Vol 19 (9) ◽  
pp. 3857-3873 ◽  
Author(s):  
E. Spada ◽  
T. Tucciarelli ◽  
M. Sinagra ◽  
V. Sammartano ◽  
G. Corato
Keyword(s):  
Friction Factor ◽  
Cross Section ◽  
Cross Sections ◽  
Longitudinal Velocity ◽  
Water Levels ◽  
New Methods ◽  
Ansys Cfx ◽  
Unsteady State Analysis ◽  
River Cross Section ◽  
Linear Shape Function

Abstract. Two new methods for vertically averaged velocity computation are presented, validated and compared with other available formulas. The first method derives from the well-known Huthoff algorithm, which is first shown to be dependent on the way the river cross section is discretized into several subsections. The second method assumes the vertically averaged longitudinal velocity to be a function only of the friction factor and of the so-called "local hydraulic radius", computed as the ratio between the integral of the elementary areas around a given vertical and the integral of the elementary solid boundaries around the same vertical. Both integrals are weighted with a linear shape function equal to zero at a distance from the integration variable which is proportional to the water depth according to an empirical coefficient β. Both formulas are validated against (1) laboratory experimental data, (2) discharge hydrographs measured in a real site, where the friction factor is estimated from an unsteady-state analysis of water levels recorded in two different river cross sections, and (3) the 3-D solution obtained using the commercial ANSYS CFX code, computing the steady-state uniform flow in a cross section of the Alzette River.

scholarly journals Three perceptions of the evapotranspiration landscape: comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances

2013 ◽  
Vol 10 (1) ◽  
pp. 1127-1183 ◽  
Author(s):  
T. Conradt ◽  
F. Wechsung ◽  
A. Bronstert
Keyword(s):  
Remote Sensing ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Hydrological Modelling ◽  
Weather Data ◽  
Discharge Data ◽  
Global Parameter ◽  
Surface Temperatures ◽  
Runoff Series ◽  
Distributed Hydrological Modelling

Abstract. A problem encountered by many distributed hydrological modelling studies is high simulation errors at interior gauges when the model is only globally calibrated at the outlet. We simulated river runoff in the Elbe River basin in Central Europe (148 268 km2) with the semi-distributed eco-hydrological model SWIM. While global parameter optimisation led to Nash–Sutcliffe efficiencies of 0.9 at the main outlet gauge, comparisons with measured runoff series at interior points revealed large deviations. Therefore, we compared three different stategies for deriving sub-basin evapotranspiration: (1) modelled by SWIM without any spatial calibration, (2) derived from remotely sensed surface temperatures, and (3) calculated from long-term precipitation and discharge data. The results show certain consistencies between the modelled and the remote sensing based evapotranspiration rates, but there seems to be no correlation between remote sensing and water balance based estimations. Subsequent analyses for single sub-basins identify input weather data and systematic error amplification in inter-gauge discharge calculations as sources of uncertainty. Further probable causes for epistemic uncertainties could be pinpointed. The results encourage careful utilisation of different data sources for calibration and validation procedures in distributed hydrological modelling.

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