scholarly journals Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin

2020 ◽  
Vol 12 (22) ◽  
pp. 3768
Author(s):  
T. A. Jeewanthi G. Sirisena ◽  
Shreedhar Maskey ◽  
Roshanka Ranasinghe
Keyword(s):  
Remote Sensing ◽  
Model Calibration ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Study Region ◽  
Calibration Methods ◽  
Streamflow Data ◽  
Best Parameter ◽  
Parameter Values ◽  
Water Assessment

Conventional calibration methods adopted in hydrological modelling are based on streamflow data measured at certain river sections. However, streamflow measurements are usually sparse and, in such instances, remote-sensing-based products may be used as an additional dataset(s) in hydrological model calibration. This study compares two main calibration approaches: (a) single variable calibration with streamflow and evapotranspiration separately, and (b) multi-variable calibration with both variables together. Here, we used remote sensing-based evapotranspiration data from Global Land Evaporation: the Amsterdam Model (GLEAM ET), and measured streamflow at four stations to calibrate a Soil and Water Assessment Tool (SWAT) and evaluate the performances for Chindwin Basin, Myanmar. Our results showed that when one variable (either streamflow or evapotranspiration) is used for calibration, it led to good performance with respect to the calibration variable but resulted in reduced performance in the other variable. In the multi-variable calibration using both streamflow and evapotranspiration, reasonable results were obtained for both variables. For example, at the basin outlet, the best NSEs (Nash-Sutcliffe Efficiencies) of streamflow and evapotranspiration on monthly time series are, respectively, 0.98 and 0.59 in the calibration with streamflow alone, and 0.69 and 0.73 in the calibration with evapotranspiration alone. Whereas, in the multi-variable calibration, the NSEs at the basin outlet are 0.97 and 0.64 for streamflow and evapotranspiration, respectively. The results suggest that the GLEAM ET data, together with streamflow data, can be used for model calibration in the study region as the simulation results show reasonable performance for streamflow with an NSE > 0.85. Results also show that many different sets of parameter values (‘good parameter sets’) can produce results comparable to the best parameter set.

scholarly journals Integrating remote sensing-derived evapotranspiration and ground-monitored discharge data for the improvement of hydrological modelling: A case study of the Sekong River Basin

2021 ◽  
Author(s):  
Raksmey Ang ◽  
S. Shrestha ◽  
Salvatore Virdis ◽  
Saurav KC
Keyword(s):  
Remote Sensing ◽  
Model Calibration ◽  
Hydrological Model ◽  
Hydrologic Model ◽  
Physically Based Model ◽  
Physically Based ◽  
Streamflow Data ◽  
The Impact ◽  
Streamflow Simulation ◽  
Calibration Approach

This study analyses the efficiency of integrating remotely sensed evapotranspiration into the process of hydrological model calibration. A joint calibration approach, employing both remote sensing-derived evapotranspiration and ground-monitored streamflow data was compared with a conventional ground-monitored streamflow calibration approach through physically-based hydrological, Soil and Water Assessment Tool (SWAT) model setups. The efficacy of the two calibration schemes was investigated in two modelling setups: 1) a physically-based model with only the outlet gauge available for calibration, and 2) a physically-based model with multiple gauges available for calibration. Joint calibration was found to enhance the skill of hydrological models in streamflow simulation compared to ground-monitored streamflow-only calibration at the unsaturated zone in the upstream area, where essential information on evapotranspiration is also required. Additionally, the use of remote sensing-derived evapotranspiration can significantly improve high flow compared to low flow simulation. A more consistent model performance improvement, obtained from using remote sensing-derived evapotranspiration data was found at gauged sites not used in the calibration, due to additional information on spatial evapotranspiration in internal locations being enhanced into a process-based model. Eventually, satellite-based evapotranspiration with fine resolution was found to be competent for calibrating and validating the hydrological model for streamflow simulation in the absence of measured streamflow data for model calibration. Furthermore, the impact of using evapotranspiration for hydrologic model calibration tended to be stronger at the upstream and tributary sub-basins than at downstream sub-basins.

scholarly journals Exploring the application of flood scaling property in hydrological model calibration

2021 ◽  
Author(s):  
Yanchen Zheng ◽  
Jianzhu Li ◽  
Ting Zhang ◽  
Youtong Rong ◽  
Ping Feng
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Flood Frequency ◽  
Efficiency Coefficient ◽  
Distributed Hydrological Model ◽  
Flood Peak ◽  
Scaling Property ◽  
Streamflow Data

Abstract Model calibration has always been one major challenge in hydrological community. Flood scaling property (FS) is often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling property (MLR) as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in Northern China, eight calibration scenarios are designed with adopting different combination of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with less requirements of observed streamflow data, exhibiting better simulation on flood peak than Nash-Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieve better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among sub-basins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments.

scholarly journals SWAT Model Adaptability to a Small Mountainous Forested Watershed in Central Romania

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 860
Author(s):  
Nicu Constantin Tudose ◽  
Mirabela Marin ◽  
Sorin Cheval ◽  
Cezar Ungurean ◽  
Serban Octavian Davidescu ◽  
...  
Keyword(s):  
Metropolitan Area ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Decision Makers ◽  
Forested Watershed ◽  
Sensitive Parameters ◽  
Lower Uncertainty ◽  
Validation Stage ◽  
Water Assessment

This study aims to build and test the adaptability and reliability of the Soil and Water Assessment Tool hydrological model in a small mountain forested watershed. This ungauged watershed covers 184 km2 and supplies 90% of blue water for the Brașov metropolitan area, the second largest metropolitan area of Romania. After building a custom database at the forest management compartment level, the SWAT model was run. Further, using the SWAT-CUP software under the SUFI2 algorithm, we identified the most sensitive parameters required in the calibration and validation stage. Moreover, the sensitivity analysis revealed that the surface runoff is mainly influenced by soil, groundwater and vegetation condition parameters. The calibration was carried out for 2001‒2010, while the 1996‒1999 period was used for model validation. Both procedures have indicated satisfactory performance and a lower uncertainty of model results in replicating river discharge compared with observed discharge. This research demonstrates that the SWAT model can be applied in small ungauged watersheds after an appropriate parameterisation of its databases. Furthermore, this tool is appropriate to support decision-makers in conceiving sustainable watershed management. It also guides prioritising the most suitable measures to increase the river basin resilience and ensure the water demand under climate change.

scholarly journals How good are hydrological models for gap-filling streamflow data?

2018 ◽  
Vol 22 (8) ◽  
pp. 4593-4604 ◽  
Author(s):  
Yongqiang Zhang ◽  
David Post
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Hydrological Response ◽  
Gap Filling ◽  
Annual Streamflow ◽  
Response Variable ◽  
Benchmark Data ◽  
Streamflow Data ◽  
Streamflow Trend

Abstract. Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood, and drought analysis and hydrological response variable estimates and predictions. However, there is a lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing data rate is less than 10 %, the gap-filled streamflow data obtained using calibrated hydrological models perform almost the same as the benchmark data (less than 1 % missing) when estimating annual trends for 217 unregulated catchments widely spread across Australia. Furthermore, the relative streamflow trend bias caused by the gap filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends.

scholarly journals Enhancing Soil and Water Assessment Tool Snow Prediction Reliability with Remote-Sensing-Based Snow Water Equivalent Reconstruction Product for Upland Watersheds in a Multi-Objective Calibration Process

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3190
Author(s):  
Zhu Liu ◽  
Jina Yin ◽  
Helen E. Dahlke
Keyword(s):  
Model Calibration ◽  
Prediction Accuracy ◽  
Snow Water Equivalent ◽  
Reference Data ◽  
Assessment Tool ◽  
Swat Model ◽  
Lapse Rate ◽  
Snow Water ◽  
Prediction Reliability ◽  
Water Assessment

Precipitation occurs in two basic forms defined as liquid state and solid state. Different from rain-fed watershed, modeling snow processes is of vital importance in snow-dominated watersheds. The seasonal snowpack is a natural water reservoir, which stores snow water in winter and releases it in spring and summer. The warmer climate in recent decades has led to earlier snowmelt, a decline in snowpack, and change in the seasonality of river flows. The Soil and Water Assessment Tool (SWAT) could be applied in the snow-influenced watershed because of its ability to simultaneously predict the streamflow generated from rainfall and from the melting of snow. The choice of parameters, reference data, and calibration strategy could significantly affect the SWAT model calibration outcome and further affect the prediction accuracy. In this study, SWAT models are implemented in four upland watersheds in the Tulare Lake Basin (TLB) located across the Southern Sierra Nevada Mountains. Three calibration scenarios considering different calibration parameters and reference datasets are applied to investigate the impact of the Parallel Energy Balance Model (ParBal) snow reconstruction data and snow parameters on the streamflow and snow water-equivalent (SWE) prediction accuracy. In addition, the watershed parameters and lapse rate parameters-led equifinality is also evaluated. The results indicate that calibration of the SWAT model with respect to both streamflow and SWE reference data could improve the model SWE prediction reliability in general. Comparatively, the streamflow predictions are not significantly affected by differently lumped calibration schemes. The default snow parameter values capture the extreme high flows better than the other two calibration scenarios, whereas there is no remarkable difference among the three calibration schemes for capturing the extreme low flows. The watershed and lapse rate parameters-induced equifinality affects the flow prediction more (Nash-Sutcliffe Efficiency (NSE) varies between 0.2–0.3) than the SWE prediction (NSE varies less than 0.1). This study points out the remote-sensing-based SWE reconstruction product as a promising alternative choice for model calibration in ungauged snow-influenced watersheds. The streamflow-reconstructed SWE bi-objective calibrated model could improve the prediction reliability of surface water supply change for the downstream agricultural region under the changing climate.

scholarly journals Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria

2019 ◽  
Vol 23 (2) ◽  
pp. 1113-1144 ◽  
Author(s):  
Abolanle E. Odusanya ◽  
Bano Mehdi ◽  
Christoph Schürz ◽  
Adebayo O. Oke ◽  
Olufiropo S. Awokola ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Model Calibration ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Potential Evapotranspiration ◽  
Swat Model ◽  
Water Balance Components ◽  
Southwestern Nigeria ◽  
Calibration And Validation

Abstract. The main objective of this study was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data from the Global Land Evaporation Amsterdam Model (GLEAM_v3.0a) and from the Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) for the Ogun River Basin (20 292 km2) located in southwestern Nigeria. Three potential evapotranspiration (PET) equations (Hargreaves, Priestley–Taylor and Penman–Monteith) were used for the SWAT simulation of AET. The reference simulations were the three AET variables simulated with SWAT before model calibration took place. The sequential uncertainty fitting technique (SUFI-2) was used for the SWAT model sensitivity analysis, calibration, validation and uncertainty analysis. The GLEAM_v3.0a and MOD16 products were subsequently used to calibrate the three SWAT-simulated AET variables, thereby obtaining six calibrations–validations at a monthly timescale. The model performance for the three SWAT model runs was evaluated for each of the 53 subbasins against the GLEAM_v3.0a and MOD16 products, which enabled the best model run with the highest-performing satellite-based AET product to be chosen. A verification of the simulated AET variable was carried out by (i) comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, which is a product that has different forcing data than the version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95 % uncertainty of the SWAT-simulated variable bracketed most of the satellite-based AET data in each subbasin. A validation of the simulated soil moisture dynamics for GS1 was carried out using satellite-retrieved soil moisture data, which revealed good agreement. The SWAT model (GS1) also captured the seasonal variability of the water balance components at the outlet of the watershed. This study demonstrated the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged large river basin with reasonable accuracy. The novelty of the study is the use of these freely available satellite-derived AET datasets to effectively calibrate and validate an eco-hydrological model for a data-scarce catchment.

scholarly journals Analysis of the spatial variation in the parameters of the SWAT model with application in Flanders, Northern Belgium

2004 ◽  
Vol 8 (5) ◽  
pp. 931-939 ◽  
Author(s):  
G. Heuvelmans ◽  
B. Muys ◽  
J. Feyen
Keyword(s):  
Stream Flow ◽  
Hydrological Model ◽  
Swat Model ◽  
Single Parameter ◽  
Model Parameters ◽  
Study Region ◽  
Entire Study ◽  
Time Periods ◽  
Parameter Values ◽  
Parameter Approach

Abstract. Operational applications of a hydrological model often require the prediction of stream flow in (future) time periods without stream flow observations or in ungauged catchments. Data for a case-specific optimisation of model parameters are not available for such applications, so parameters have to be derived from other catchments or time periods. It has been demonstrated that for applications of the SWAT in Northern Belgium, temporal transfers of the parameters have less influence than spatial transfers on the performance of the model. This study examines the spatial variation in parameter optima in more detail. The aim was to delineate zones wherein model parameters can be transferred without a significant loss of model performance. SWAT was calibrated for 25 catchments that are part of eight larger sub-basins of the Scheldt river basin. Two approaches are discussed for grouping these units in zones with a uniform set of parameters: a single parameter approach considering each parameter separately and a parameter set approach evaluating the parameterisation as a whole. For every catchment, the SWAT model was run with the local parameter optima, with the average parameter values for the entire study region (Flanders), with the zones delineated with the single parameter approach and with the zones obtained by the parameter set approach. Comparison of the model performances of these four parameterisation strategies indicates that both the single parameter and the parameter set zones lead to stream flow predictions that are more accurate than if the entire study region were treated as one single zone. On the other hand, the use of zonal average parameter values results in a considerably worse model fit compared to local parameter optima. Clustering of parameter sets gives a more accurate result than the single parameter approach and is, therefore, the preferred technique for use in the parameterisation of ungauged sub-catchments as part of the simulation of a large river basin. Keywords: hydrological model, regionalisation, parameterisation, spatial variability

Multi-objective calibration of a distributed eco-hydrological model using several remotely sensed information

2020 ◽  
Author(s):  
Félix Francés ◽  
Carlos Echeverría ◽  
Maria Gonzalez-Sanchis ◽  
Fernando Rivas
Keyword(s):  
Remote Sensing ◽  
Model Calibration ◽  
Hydrological Model ◽  
Model Performance ◽  
Temporal Information ◽  
Temporal Data ◽  
Hydrological Models ◽  
Validation Period ◽  
Spatio Temporal ◽  
Calibration Approach

<p>Calibration of eco-hydrological models is difficult to carry on, even more if observed data sets are scarce. It is known that calibration using traditional trial-and-error approach depends strongly of the knowledge and the subjectivity of the hydrologist, and automatic calibration has a strong dependency of the objective-function and the initial values established to initialize the process.</p><p>The traditional calibration approach mainly focuses on the temporal variation of the discharge at the catchment outlet point, representing an integrated catchment response and provides thus only limited insight on the lumped behaviour of the catchment. It has been long demonstrated the limited capabilities of such an approach when models are validated at interior points of a river basin. The development of distributed eco-hydrological models and the burst of spatio-temporal data provided by remote sensing appear as key alternative to overcome those limitations. Indeed, remote sensing imagery provides not only temporal information but also valuable information on spatial patterns, which can facilitate a spatial-pattern-oriented model calibration.</p><p>However, there is still a lack of how to effectively handle spatio-temporal data when included in model calibration and how to evaluate the accuracy of the simulated spatial patterns. Moreover, it is still unclear whether including spatio-temporal data improves model performance in face to an unavoidable more complex and time-demanding calibration procedure. To elucidate in this sense, we performed three different multiobjective calibration configurations: (1) including only temporal information of discharges at the catchment outlet (2) including both temporal and spatio-temporal information and (3) only including spatio-temporal information. In the three approaches, we calibrated the same distributed eco-hydrological model (TETIS) in the same study area: Carraixet Basin, and used the same multi-objective algorithm: MOSCEM-UA. The spatio-temporal information obtained from satellite has been the surface soil moisture (from SMOS-BEC) and the leaf area index (from MODIS).</p><p>Even though the performance of the first calibration approach (only temporal information included) was slightly better than the others, all calibration approaches provided satisfactory and similar results within the calibration period. To put these results into test, we also validated the model performance by using historical data that was not used to calibrate the model (validation period). Within the validation period, the second calibration approach obtained better performance than the others, pointing out the higher reliability of the obtained parameter values when including spatio-temporal data (in this case, in combination with temporal data) in the model calibration. It is also reliable to mention that the approaches considering only spatio-temporal information provided interesting results in terms of discharges, considering that this variable was not used at all for calibration purposes.</p>

The Calibration of Gravity, Entropy, and Related Models of Spatial Interaction

1972 ◽  
Vol 4 (2) ◽  
pp. 205-233 ◽  
Author(s):  
M Batty ◽  
S Mackie
Keyword(s):  
Maximum Likelihood ◽  
Spatial Interaction ◽  
Interaction Models ◽  
Spatial Interaction Models ◽  
Calibration Methods ◽  
The Family ◽  
Newton Raphson ◽  
Best Parameter ◽  
Parameter Values ◽  
Raphson Method

This paper presents a methodology for deriving best statistics for the calibration of spatial interaction models, and several procedures for finding best parameter values are described. The family of spatial interaction models due to Wilson is first outlined, and then some existing calibration methods are briefly reviewed. A procedure for deriving best statistics based on the principle of maximum-likelihood is then developed from the work of Hyman and Evans, and the methodology is illustrated using the example of a retail gravity model. Five methods for solving the maximum-likelihood equations are outlined: procedures based on a simple first-order iterative process, the Newton—Raphson method for several variables, multivariate Fibonacci search, search using the Simplex method, and search based on quadratic convergence, are all tested and compared. It appears that the Newton—Raphson method is the most efficient, and this is further tested in the calibration of disaggregated residential location models.

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