scholarly journals The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites

2021 ◽  
Author(s):  
Rui Tong ◽  
Juraj Parajka ◽  
Borbála Széles ◽  
Isabella Pfeil ◽  
Mariette Vreugdenhil ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Multiple Objective ◽  
Model Parameters ◽  
Local Similarity ◽  
Ungauged Catchments ◽  
Ungauged Sites ◽  
Runoff Prediction ◽  
Alpine Catchments ◽  
Global And Local

Abstract. The recent advances in remote sensing provide opportunities for more reliably estimating the parameters of conceptual hydrologic models. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (ASCAT), and snow cover (MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of model transfer methods based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple objective calibrations.

scholarly journals Incorporating Advanced Scatterometer Surface and Root Zone Soil Moisture Products into the Calibration of a Conceptual Semi-Distributed Hydrological Model

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3366
Author(s):  
Martin Kubáň ◽  
Juraj Parajka ◽  
Rui Tong ◽  
Isabella Pfeil ◽  
Mariette Vreugdenhil ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Forest Cover ◽  
Hydrological Model ◽  
Root Zone ◽  
Retrieval Algorithm ◽  
Multiple Objective ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Alpine Catchments

The role of soil moisture is widely accepted as a significant factor in the mass and energy balance of catchments as a controller in surface and subsurface runoff generation. The paper examines the potential of a new dataset based on advanced scatterometer satellite remote sensing of soil moisture (ASCAT) for multiple objective calibrations of a dual-layer, conceptual, semi-distributed hydrological model. The surface and root zone soil moisture indexes based on ASCAT data were implemented into calibration of the hydrological model. Improvements not only in the instrument specifications, i.e., better temporal and spatial sampling, but also in the higher radiometric accuracy and retrieval algorithm, were applied. The analysis was performed in 209 catchments situated in different physiographic and climate zones of Austria for the period 2007–2018. We validated the model for two validation periods. The results show that multiple objective calibrations have a substantial positive effect on constraining the model parameters. The combined use of soil moisture and discharges in the calibration improved the soil moisture simulation in more than 73% of the catchments, except for the catchments with higher forest cover percentages. Improvements also occurred in the runoff model efficiency, in more than 27% of the catchments, mostly in the watersheds with a lower mean elevation and a higher proportion of farming land use, as well as in the Alpine catchments where the runoff is not significantly influenced by snowmelt and glacier runoff.

scholarly journals Improving runoff prediction through the assimilation of the ASCAT soil moisture product

2010 ◽  
Vol 7 (4) ◽  
pp. 4113-4144 ◽  
Author(s):  
L. Brocca ◽  
F. Melone ◽  
T. Moramarco ◽  
W. Wagner ◽  
V. Naeimi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Central Italy ◽  
Model Performance ◽  
Remotely Sensed ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Catchment Scale ◽  
Runoff Prediction ◽  
Initial Soil ◽  
Soil Wetness

Abstract. The role and the importance of soil moisture for meteorological, agricultural and hydrological applications is widely known. Remote sensing offers the unique capability to monitor soil moisture over large areas (catchment scale) with, nowadays, a temporal resolution suitable for hydrological purposes. However, the accuracy of the remotely sensed soil moisture estimates has to be carefully checked. The validation of these estimates with in-situ measurements is not straightforward due the well-known problems related to the spatial mismatch and the measurement accuracy. The analysis of the effects deriving from assimilating remotely sensed soil moisture data into hydrological or meteorological models could represent a more valuable method to test their reliability. In particular, the assimilation of satellite-derived soil moisture estimates into rainfall-runoff models at different scales and over different regions represents an important scientific and operational issue. In this study, the soil wetness index (SWI) product derived from the Advanced SCATterometer (ASCAT) sensor onboard of the Metop satellite was tested. The SWI was firstly compared with the soil moisture temporal pattern derived from a continuous rainfall-runoff model (MISDc) to assess its relationship with modeled data. Then, by using a simple data assimilation technique, the linearly rescaled SWI that matches the range of variability of modelled data (denoted as SWI*) was assimilated into MISDc and the model performance on flood estimation was analyzed. Moreover, three synthetic experiments considering errors on rainfall, model parameters and initial soil wetness conditions were carried out. These experiments allowed to further investigate the SWI potential when uncertain conditions take place. The most significant flood events, which occurred in the period 2000–2009 on five subcatchments of the Upper Tiber River in Central Italy, ranging in extension between 100 and 650 km2, were used as case studies. Results reveal that the SWI derived from the ASCAT sensor can be conveniently adopted to improve runoff prediction in the study area, mainly if the initial soil wetness conditions are unknown.

scholarly journals A comparison of regionalisation methods for catchment model parameters

2005 ◽  
Vol 2 (2) ◽  
pp. 509-542 ◽  
Author(s):  
J. Parajka ◽  
R. Merz ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Predictive Accuracy ◽  
Model Parameters ◽  
Stream Network ◽  
Ungauged Catchments ◽  
Verification Period ◽  
Model Efficiency ◽  
Complete Set ◽  
Rainfall Runoff Model ◽  
Catchment Model

Abstract. In this study we examine the relative performance of a range of methods for transposing catchment model parameters to ungauged catchments. We calibrate 11 parameters of a semi-distributed conceptual rainfall-runoff model to daily runoff and snow cover data of 320 Austrian catchments in the period 1987-1997 and verify the model for the period 1976-1986. We evaluate the predictive accuracy of the regionalisation methods by jack-knife cross-validation against daily runoff and snow cover data. The results indicate that two methods perform best. The first is a kriging approach where the model parameters are regionalised independently from each other based on their spatial correlation. The second is a similarity approach where the complete set of model parameters is transposed from a donor catchment that is most similar in terms of its physiographic attributes (mean catchment elevation, stream network density, lake index, areal proportion of porous aquifers, land use, soils and geology). For the calibration period, the median Nash-Sutcliffe model efficiency ME of daily runoff is 0.67 for both methods as compared to ME=0.72 for the at-site simulations. For the verification period, the corresponding efficiencies are 0.62 and 0.66. All regionalisation methods perform similar in terms of simulating snow cover.

scholarly journals Improving runoff prediction through the assimilation of the ASCAT soil moisture product

2010 ◽  
Vol 14 (10) ◽  
pp. 1881-1893 ◽  
Author(s):  
L. Brocca ◽  
F. Melone ◽  
T. Moramarco ◽  
W. Wagner ◽  
V. Naeimi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Central Italy ◽  
Model Performance ◽  
Remotely Sensed ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Catchment Scale ◽  
Runoff Prediction ◽  
Initial Soil ◽  
Soil Wetness

Abstract. The role and the importance of soil moisture for meteorological, agricultural and hydrological applications is widely known. Remote sensing offers the unique capability to monitor soil moisture over large areas (catchment scale) with, nowadays, a temporal resolution suitable for hydrological purposes. However, the accuracy of the remotely sensed soil moisture estimates has to be carefully checked. The validation of these estimates with in-situ measurements is not straightforward due the well-known problems related to the spatial mismatch and the measurement accuracy. The analysis of the effects deriving from assimilating remotely sensed soil moisture data into hydrological or meteorological models could represent a more valuable method to test their reliability. In particular, the assimilation of satellite-derived soil moisture estimates into rainfall-runoff models at different scales and over different regions represents an important scientific and operational issue. In this study, the soil wetness index (SWI) product derived from the Advanced SCATterometer (ASCAT) sensor onboard of the Metop satellite was tested. The SWI was firstly compared with the soil moisture temporal pattern derived from a continuous rainfall-runoff model (MISDc) to assess its relationship with modeled data. Then, by using a simple data assimilation technique, the linearly rescaled SWI that matches the range of variability of modelled data (denoted as SWI*) was assimilated into MISDc and the model performance on flood estimation was analyzed. Moreover, three synthetic experiments considering errors on rainfall, model parameters and initial soil wetness conditions were carried out. These experiments allowed to further investigate the SWI potential when uncertain conditions take place. The most significant flood events, which occurred in the period 2000–2009 on five subcatchments of the Upper Tiber River in central Italy, ranging in extension between 100 and 650 km2, were used as case studies. Results reveal that the SWI derived from the ASCAT sensor can be conveniently adopted to improve runoff prediction in the study area, mainly if the initial soil wetness conditions are unknown.

scholarly journals A comparison of regionalisation methods for catchment model parameters

2005 ◽  
Vol 9 (3) ◽  
pp. 157-171 ◽  
Author(s):  
J. Parajka ◽  
R. Merz ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Predictive Accuracy ◽  
Model Parameters ◽  
Stream Network ◽  
Ungauged Catchments ◽  
Verification Period ◽  
Model Efficiency ◽  
Complete Set ◽  
Rainfall Runoff Model ◽  
Catchment Model

Abstract. In this study we examine the relative performance of a range of methods for transposing catchment model parameters to ungauged catchments. We calibrate 11 parameters of a semi-distributed conceptual rainfall-runoff model to daily runoff and snow cover data of 320 Austrian catchments in the period 1987-1997 and verify the model for the period 1976-1986. We evaluate the predictive accuracy of the regionalisation methods by jack-knife cross-validation against daily runoff and snow cover data. The results indicate that two methods perform best. The first is a kriging approach where the model parameters are regionalised independently from each other based on their spatial correlation. The second is a similarity approach where the complete set of model parameters is transposed from a donor catchment that is most similar in terms of its physiographic attributes (mean catchment elevation, stream network density, lake index, areal proportion of porous aquifers, land use, soils and geology). For the calibration period, the median Nash-Sutcliffe model efficiency ME of daily runoff is 0.67 for both methods as compared to ME=0.72 for the at-site simulations. For the verification period, the corresponding efficiencies are 0.62 and 0.66. All regionalisation methods perform similar in terms of simulating snow cover.

scholarly journals Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1540 ◽  
Author(s):  
Mohamed Saadi ◽  
Ludovic Oudin ◽  
Pierre Ribstein
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hydrological Model ◽  
Model Parameters ◽  
Learning Tools ◽  
Local Calibration ◽  
Ungauged Catchments ◽  
Ungauged Sites ◽  
Nonlinear Features ◽  
Urban Catchments

This study investigated the potential of random forest (RF) algorithms for regionalizing the parameters of an hourly hydrological model. The relationships between model parameters and climate/landscape catchment descriptors were multidimensional and exhibited nonlinear features. In this case, machine-learning tools offered the option of efficiently handling such relationships using a large sample of data. The performance of the regionalized model using RF was assessed in comparison with local calibration and two benchmark regionalization approaches. Two catchment sets were considered: (1) A target pseudo-ungauged catchment set was composed of 120 urban ungauged catchments and (2) 2105 gauged American and French catchments were used for constructing the RF. By using pseudo-ungauged urban catchments, we aimed at assessing the potential of the RF to detect the specificities of the urban catchments. Results showed that RF-regionalized models allowed for slightly better streamflow simulations on ungauged sites compared with benchmark regionalization approaches. Yet, constructed RFs were weakly sensitive to the urbanization features of the catchments, which prevents their use in straightforward scenarios of the hydrological impacts of urbanization.

scholarly journals How does climate change affect mesoscale catchments in Switzerland? – a framework for a comprehensive assessment

2010 ◽  
Vol 27 ◽  
pp. 111-119 ◽  
Author(s):  
N. Köplin ◽  
D. Viviroli ◽  
B. Schädler ◽  
R. Weingartner
Keyword(s):  
Water Balance ◽  
Climate Models ◽  
Peak Flow ◽  
Regional Climate ◽  
Quantitative Information ◽  
Model Parameters ◽  
Flow Conditions ◽  
Ungauged Catchments ◽  
Entire Area ◽  
Alpine Catchments

Abstract. Within the framework of this study we identify mesoscale catchments in Switzerland that exhibit sensitivity towards a change in climate with a focus on alterations of the water balance and peak flow conditions. For this study, the hydrological modelling system PREVAH is used, which is a semi-distributed and conceptual yet process-oriented model forced with hourly meteorological input on basis of a spatial resolution of 500×500 m2. We calibrate the model where measured discharge records are available and transfer the calibrated model parameters to ungauged catchments through regionalisation, to arrive at a comprehensive set of model parameters for the entire area of Switzerland. To assess future changes, we apply an extensive set of 16 Regional Climate Models (RCMs) to the catchments. The RCM data are downscaled to a dense network of meteorological stations for the period from 2021 to 2050 using the Delta Change Approach. This downscaling method incorporates a bias correction of the RCM output and provides change rates and values for precipitation and temperature. In the present paper we describe the application of a calibration and regionalisation procedure developed previously for Northern Alpine catchments to Southern catchments. The necessity to differentiate between a Northern and a Southern Alpine region, with their distinct climatologic and physiogeographic features, has proved true as the calibrated parameter sets show systematic differences between those regions, e.g. for the runoff forming parameters percolation rate (PERC) or storage time for quick runoff (KOH). For the Southern Alpine area, we calibrated two thirds of the available catchments, i.e. 23 out of 36, successfully for standard and flood conditions according to a combined model score of a linear and logarithmic Nash-Sutcliffe-Efficiency (NSE, NSEln) and a mean annual volumetric deviation (VDa). The rate of successfully calibrated catchments is rather small in comparison with the results for the Northern Alpine catchments, where 140 out of 159 calibrations have been successful, and the distribution of the Southern catchments is more irregular. However, as the median NSE and NSEln as well as the range of VDa show an overall good model fit, a successful regionalisation may be expected. Next steps are the regionalisation of the Southern Alpine model parameters and the application of climate scenarios to the complete set of catchments, i.e. about 200 Swiss mesoscale catchments with an average area of 150 km2. Thus we can identify process-based relationships between climate sensitivity and catchment characteristics and provide quantitative information on future water balance and peak flow conditions of Swiss mesoscale catchments.

scholarly journals The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model

2020 ◽  
Author(s):  
Rui Tong ◽  
Juraj Parajka ◽  
Andreas Salentinig ◽  
Isabella Pfeil ◽  
Jürgen Komma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Snow Cover ◽  
Soil Water ◽  
Hydrologic Model ◽  
Multiple Objective ◽  
Water Index ◽  
Agricultural Catchments ◽  
Soil Moisture Data ◽  
Soil Moisture Model

Abstract. Recent advances in soil moisture remote sensing have produced satellite datasets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the ASCAT Soil Water Index dataset for multiple objective calibration of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV type hydrologic model is calibrated to runoff data, ASCAT soil moisture data, and MODIS snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations; the inclusion of snow data mainly improves the snow simulations; and including both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient in improving snow simulations than the soil moisture data are in improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data, and to a lesser extent by soil moisture data, while the soil-related parameters are only affected by the inclusion of soil moisture data.

scholarly journals A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 102
Author(s):  
Frauke Kachholz ◽  
Jens Tränckner
Keyword(s):  
Land Use ◽  
River Flow ◽  
Land Use Changes ◽  
Small Rivers ◽  
Small Scale ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Model Based ◽  
Current State ◽  
The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

scholarly journals Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

2014 ◽  
Vol 18 (6) ◽  
pp. 2393-2413 ◽  
Author(s):  
H. Sellami ◽  
I. La Jeunesse ◽  
S. Benabdallah ◽  
N. Baghdadi ◽  
M. Vanclooster
Keyword(s):  
Model Prediction ◽  
Hydrological Model ◽  
Swat Model ◽  
Behavioral Model ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Southern France ◽  
Model Parameter ◽  
Ungauged Catchment ◽  
Prediction Uncertainty

Abstract. In this study a method for propagating the hydrological model uncertainty in discharge predictions of ungauged Mediterranean catchments using a model parameter regionalization approach is presented. The method is developed and tested for the Thau catchment located in Southern France using the SWAT hydrological model. Regionalization of model parameters, based on physical similarity measured between gauged and ungauged catchment attributes, is a popular methodology for discharge prediction in ungauged basins, but it is often confronted with an arbitrary criterion for selecting the "behavioral" model parameter sets (Mps) at the gauged catchment. A more objective method is provided in this paper where the transferrable Mps are selected based on the similarity between the donor and the receptor catchments. In addition, the method allows propagating the modeling uncertainty while transferring the Mps to the ungauged catchments. Results indicate that physically similar catchments located within the same geographic and climatic region may exhibit similar hydrological behavior and can also be affected by similar model prediction uncertainty. Furthermore, the results suggest that model prediction uncertainty at the ungauged catchment increases as the dissimilarity between the donor and the receptor catchments increases. The methodology presented in this paper can be replicated and used in regionalization of any hydrological model parameters for estimating streamflow at ungauged catchment.

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