scholarly journals Stepwise prediction of runoff using proxy data in a small agricultural catchment

2021 ◽  
Vol 69 (1) ◽  
pp. 65-75
Author(s):  
Borbála Széles ◽  
Juraj Parajka ◽  
Patrick Hogan ◽  
Rasmiaditya Silasari ◽  
Lovrenc Pavlin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Model Calibration ◽  
Agricultural Land ◽  
Pearson Correlation ◽  
Hydrologic Model ◽  
State Variables ◽  
Runoff Generation ◽  
Runoff Simulation ◽  
Proxy Data ◽  
Simulation Performance

AbstractIn this study, the value of proxy data was explored for calibrating a conceptual hydrologic model for small ungauged basins, i.e. ungauged in terms of runoff. The study site was a 66 ha Austrian experimental catchment dominated by agricultural land use, the Hydrological Open Air Laboratory (HOAL). The three modules of a conceptual, lumped hydrologic model (snow, soil moisture accounting and runoff generation) were calibrated step-by-step using only proxy data, and no runoff observations. Using this stepwise approach, the relative runoff volume errors in the calibration and first and second validation periods were –0.04, 0.19 and 0.17, and the monthly Pearson correlation coefficients were 0.88, 0.71 and 0.64, respectively. By using proxy data, the simulation of state variables improved compared to model calibration in one step using only runoff data. Using snow and soil moisture information for model calibration, the runoff model performance was comparable to the scenario when the model was calibrated using only runoff data. While the runoff simulation performance using only proxy data did not considerably improve compared to a scenario when the model was calibrated on runoff data, the more accurately simulated state variables imply that the process consistency improved.

The additional value of using proxy data besides runoff for calibrating a conceptual hydrological model in a small agricultural catchment

2020 ◽  
Author(s):  
Borbála Széles ◽  
Juraj Parajka ◽  
Patrick Hogan ◽  
Rasmiaditya Silasari ◽  
Lovrenc Pavlin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Overland Flow ◽  
Hydrological Model ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Runoff Generation ◽  
Proxy Data ◽  
One Step ◽  
Additional Value ◽  
Conceptual Hydrological Model

<p>The aim of this study was to explore the additional value of using proxy data besides runoff for calibrating a conceptual hydrological model. The study area was the Hydrological Open Air Laboratory (HOAL), a 66 ha large experimental catchment in Austria. A conceptual, HBV type, spatially lumped hydrological model was calibrated following two approaches. First, the model was calibrated in one step using only runoff data. Second, we proposed a step-by-step approach, where the modules of the model (snow, soil moisture and runoff generation) were calibrated using proxy data besides runoff, such as snow, actual evapotranspiration, soil moisture, overland flow and groundwater level. The two approaches were evaluated on annual, seasonal and daily time scales. Using the proposed step-by-step approach, the runoff volume errors in the calibration and validation periods were 0% and -1%, the monthly Pearson correlation coefficients were 0.92 and 0.82, and the daily logarithmic Nash Sutcliffe efficiencies were 0.59 and 0.18, respectively. The additional benefit of using proxy data besides runoff was the improved overall process consistency compared to the approach when only runoff was used for model calibration. Soil moisture and evapotranspiration observations had the largest influence on simulated runoff, while the calibration of the snow and runoff generation modules had a smaller influence.</p>

scholarly journals Using Remotely Sensed Information to Improve Vegetation Parameterization in a Semi-Distributed Hydrological Model (SMART) for Upland Catchments in Australia

2020 ◽  
Vol 12 (18) ◽  
pp. 3051
Author(s):  
Seokhyeon Kim ◽  
Hoori Ajami ◽  
Ashish Sharma
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Vegetation Dynamics ◽  
Pearson Correlation ◽  
Hydrologic Model ◽  
Catchment Scale ◽  
Simple Method ◽  
Area Index ◽  
The Impact ◽  
Upland Catchments

Appropriate representation of the vegetation dynamics is crucial in hydrological modelling. To improve an existing limited vegetation parameterization in a semi-distributed hydrologic model, called the Soil Moisture and Runoff simulation Toolkit (SMART), this study proposed a simple method to incorporate daily leaf area index (LAI) dynamics into the model using mean monthly LAI climatology and mean rainfall. The LAI-rainfall sensitivity is governed by a parameter that is optimized by maximizing the Pearson correlation coefficient (R) between the estimated and satellite-derived LAI time series. As a result, the LAI-rainfall sensitivity is smallest for forest, shrub, and woodland regions across Australia, and increases for grasslands and croplands. The impact of the proposed method on catchment-scale simulations of soil moisture (SM), evapotranspiration (ET) and discharge (Q) in SMART was examined across six eco-hydrologically contrasted upland catchments in Australia. Results showed that the proposed method produces almost identical results compared to simulations by the satellite-derived LAI time series. In addition, the simulation results were considerably improved in nutrient/light limited catchments compared to the cases with the default vegetation parameterization. The results showed promise, with possibilities of extension to other hydrologic models that need similar specifications for inbuilt vegetation dynamics.

Multi-site multi-variable hydrologic model development for spatially heterogeneous river basins to achieve realistic basin modelling

2020 ◽  
Author(s):  
Saumya Srivastava ◽  
Nagesh Kumar Dasika
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Uncertainty Analysis ◽  
River Basin ◽  
Performance Measures ◽  
Model Calibration ◽  
Sediment Load ◽  
Hydrologic Model ◽  
Performance Measure ◽  
Hydrologic Models

<p>Hydrologic modelling is an indispensable tool for simulation of river basin processes in water resources planning and management. Hydrologic models are used to understand dynamic interactions between climate and river basin hydrology. Model calibration, validation, parameter sensitivity and uncertainty analysis are essential prior to the application of hydrologic models. A large catchment with high spatial variability and heterogeneity can be modeled realistically when calibration is done at multiple locations, for multiple hydrologic variables like streamflow, soil moisture, sediment flow, evapotranspiration, etc. This ensures maximum utilization of field measurements of the hydrological variables, reduces the uncertainty in parameter identification and highlights the areas that need greater calibration effort. In the present study, hydrologic model simulations are run for the Mahanadi river basin in India using SWAT (Soil and Water Assessment Tool) and model calibration, uncertainty analysis, sensitivity analysis and validation are performed using SUFI-2 optimization algorithm in SWAT-CUP (SWAT Calibration and Uncertainty Programs). Entire Mahanadi basin is calibrated for several variables like streamflow, soil moisture, sediment load and evapotranspiration at various locations. The spatial heterogeneity of the catchment is taken into account in model calibration by choosing appropriate ranges of different parameters for each sub basin based on the soil types, slope classes and land use land cover present in the sub basins. When multi-site multi-variable calibration is carried out, serial calibration for individual variables and locations gives different result when compared with the simultaneous calibration for all variables and locations. In this study, a comparison of serial calibration for individual hydrologic variables and calibration sites versus simultaneous calibration for all hydrologic variables and calibration sites is made. Various performance measures like Nash-Sutcliffe efficiency (NSE), percent bias, coefficient of determination, modified NSE, etc. are used to quantify the model fit between the observed and the simulated values of various variables. The choice of performance measure affects the calibration solution, and depends on the calibration variables for which observed data is available. The performances of the fitted parameters are conditional with respect to the calibration variables and the choice of the performance measure. The present study talks about the suitability of the performance measure to different hydrologic variables like streamflow, sediment load, soil moisture, etc. The model simulation results for the Mahanadi river basin are compared with the observed values of hydrologic variables using different performance measures for calibration and validation of the model. The results show that model performance is enhanced when it is calibrated at multiple locations, for multiple variables, by taking the spatial variability of parameters across various sub-basins into account. This study explores the suitability of different performance measures for different hydrologic variables and compares the serial and simultaneous calibration for multiple hydrologic variables at multiple locations.</p>

scholarly journals Implications of Parameter Uncertainty on Soil Moisture Drought Analysis in Germany

2013 ◽  
Vol 14 (1) ◽  
pp. 47-68 ◽  
Author(s):  
Luis Samaniego ◽  
Rohini Kumar ◽  
Matthias Zink
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Model Simulation ◽  
Parametric Uncertainty ◽  
Hydrologic Model ◽  
Spatiotemporal Variability ◽  
Classification Error ◽  
Severe Drought ◽  
Drought Event ◽  
State Variables

Abstract Simulated soil moisture is increasingly used to characterize agricultural droughts but its parametric uncertainty, which essentially affects all hydrological fluxes and state variables, is rarely considered for identifying major drought events. In this study, a high-resolution, 200-member ensemble of land surface hydrology simulations obtained with the mesoscale Hydrologic Model is used to investigate the effects of the parametric uncertainty on drought statistics such as duration, extension, and severity. Simulated daily soil moisture fields over Germany at the spatial resolution of 4 × 4 km2 from 1950 to 2010 are used to derive a hydrologically consistent soil moisture index (SMI) representing the monthly soil water quantile at every grid cell. This index allows a quantification of major drought events in Germany. Results of this study indicated that the large parametric uncertainty inherent to the model did not allow discriminating major drought events without a significant classification error. The parametric uncertainty of simulated soil moisture exhibited a strong spatiotemporal variability, which significantly affects all derived drought statistics. Drought statistics of events occurring in summer with at most 6 months duration were found to be more uncertain than those occurring in winter. Based on the ensemble drought statistics, the event from 1971 to 1974 appeared to have a 67% probability of being the longest and most severe drought event since 1950. Results of this study emphasize the importance of accounting for the parametric uncertainty for identifying benchmark drought events as well as the fact that using a single model simulation would very likely lead to inconclusive results.

Effects of Averaging and Separating Soil Moisture and Temperature in the Presence of Snow Cover in a SVAT and Hydrological Model for a Southern Ontario, Canada, Watershed

2006 ◽  
Vol 7 (2) ◽  
pp. 298-304 ◽  
Author(s):  
S. R. Fassnacht ◽  
Z-L. Yang ◽  
K. R. Snelgrove ◽  
E. D. Soulis ◽  
N. Kouwen
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Land Surface ◽  
Climate Models ◽  
Hydrologic Model ◽  
State Variables ◽  
Time Step ◽  
Southern Ontario ◽  
Surface Scheme ◽  
Soil Temperature And Moisture

Abstract The energy and water balances at the earth's surface are dramatically influenced by the presence of snow cover. Therefore, soil temperature and moisture for snow-covered and snow-free areas can be very different. In computing these soil state variables, many land surface schemes in climate models do not explicitly distinguish between snow-covered and snow-free areas. Even if they do, some schemes average these state variables to calculate grid-mean energy fluxes and these averaged state variables are then used at the beginning of the next time step. This latter approach introduces a numerical error in that heat is redistributed from snow-free areas to snow-covered areas, resulting in a more rapid snowmelt. This study focuses on the latter approach and examines the sensitivity of soil moisture and streamflow to the treatment of the soil state variables in the presence of snow cover by using WATCLASS, a land surface scheme linked with a hydrologic model. The model was tested for the 1993 snowmelt period on the Upper Grand River in Southern Ontario, Canada. The results show that a more realistic simulation of streamflow can be obtained by keeping track of the soil states in snow-covered and snow-free areas.

scholarly journals Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

2019 ◽  
Author(s):  
Mehmet Cüneyd Demirel ◽  
Alparslan Özen ◽  
Selen Orta ◽  
Emir Toker ◽  
Hatice Kübra Demir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Model Calibration ◽  
Hydrologic Model ◽  
Substantial Contribution ◽  
Lumped Model ◽  
Groundwater Levels ◽  
Distributed Models ◽  
Physically Based ◽  
Spatial Calibration

Although the complexity of physically based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e. HBV. This has been rarely done for conceptual models as satellite data are often used in spatial calibration of the distributed models. Three different soil moisture products from ESA CCI SM v04.4, AMSR-E and SMAP, and total water storage anomalies from GRACE are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture are used to analyse the contribution of each individual source of information. Firstly, the most important parameters are selected using sensitivity analysis and then, these parameters are included in a subsequent model calibration. The results of our multi-objective calibration reveal substantial contribution of remote sensing products to the lumped model calibration even if their spatially distributed information is lost during the spatial aggregation. Inclusion of new observations such as groundwater levels from wells and remotely sensed soil moisture to the calibration improves the model’s physical behaviour while it keeps a reasonable water balance that is the key objective of every hydrologic model.

scholarly journals Finding behavioral parameterization for a 1-D water balance model by multi-criteria evaluation

2019 ◽  
Vol 67 (3) ◽  
pp. 213-224 ◽  
Author(s):  
Markus C. Casper ◽  
Hadis Mohajerani ◽  
Sibylle Hassler ◽  
Tobias Herdel ◽  
Theresa Blume
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Expert Knowledge ◽  
Water Balance Model ◽  
Balance Model ◽  
Water Retention Curve ◽  
State Variables ◽  
Sources Of Information ◽  
Runoff Generation ◽  
Multi Criteria Evaluation

Abstract Evapotranspiration is often estimated by numerical simulation. However, to produce accurate simulations, these models usually require on-site measurements for parameterization or calibration. We have to make sure that the model realistically reproduces both, the temporal patterns of soil moisture and evapotranspiration. In this study, we combine three sources of information: (i) measurements of sap velocities; (ii) soil moisture; and (iii) expert knowledge on local runoff generation and water balance to define constraints for a “behavioral” forest stand water balance model. Aiming for a behavioral model, we adjusted soil moisture at saturation, bulk resistance parameters and the parameters of the water retention curve (WRC). We found that the shape of the WRC influences substantially the behavior of the simulation model. Here, only one model realization could be referred to as “behavioral”. All other realizations failed for a least one of our evaluation criteria: Not only transpiration and soil moisture are simulated consistently with our observations, but also total water balance and runoff generation processes. The introduction of a multi-criteria evaluation scheme for the detection of unrealistic outputs made it possible to identify a well performing parameter set. Our findings indicate that measurement of different fluxes and state variables instead of just one and expert knowledge concerning runoff generation facilitate the parameterization of a hydrological model.

scholarly journals Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria

2009 ◽  
Vol 13 (2) ◽  
pp. 259-271 ◽  
Author(s):  
J. Parajka ◽  
V. Naeimi ◽  
G. Blöschl ◽  
J. Komma
Keyword(s):  
Soil Moisture ◽  
Model Calibration ◽  
Winter Season ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Sources Of Information ◽  
Average Correlation ◽  
Robust Model ◽  
Spatial Coverage ◽  
Top Soil

Abstract. This study compares ERS scatterometer top soil moisture observations with simulations of a dual layer conceptual hydrologic model. The comparison is performed for 148 Austrian catchments in the period 1991–2000. On average, about 5 to 7 scatterometer images per month with a mean spatial coverage of about 37% are available. The results indicate that the agreement between the two top soil moisture estimates changes with the season and the weight given to the scatterometer in hydrologic model calibration. The hydrologic model generally simulates larger top soil moisture values than are observed by the scatterometer. The differences tend to be smaller for lower altitudes and the winter season. The average correlation between the two estimates is more than 0.5 in the period from July to October, and about 0.2 in the winter months, depending on the period and calibration setting. Using both ERS scatterometer based soil moisture and runoff for model calibration provides more robust model parameters than using either of these two sources of information.

scholarly journals Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria

2008 ◽  
Vol 5 (6) ◽  
pp. 3313-3353 ◽  
Author(s):  
J. Parajka ◽  
V. Naeimi ◽  
G. Blöschl ◽  
J. Komma
Keyword(s):  
Soil Moisture ◽  
Model Calibration ◽  
Winter Season ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Sources Of Information ◽  
Average Correlation ◽  
Robust Model ◽  
Spatial Coverage ◽  
Top Soil

Abstract. This study compares ERS scatterometer top soil moisture observations with simulations of a dual layer conceptual hydrologic model. The comparison is performed for 148 Austrian catchments in the period 1991–2000. On average, about 5 to 7 scatterometer images per month with a mean spatial coverage of about 37% are available. The results indicate that the agreement between the two top soil moisture estimates changes with the season and the weight given to the scatterometer in hydrologic model calibration. The hydrologic model generally simulates larger top soil moisture values than are observed by the scatterometer. The differences tend to be smaller for lower altitudes and the winter season. The average correlation between the two estimates is more than 0.5 in the period from July to October, and about 0.2 in the winter months, depending on the period and calibration setting. Using both ERS scatterometer based soil moisture and runoff for model calibration provides more robust model parameters than using either of these two sources of information.

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