scholarly journals Analyzing the Variability of Remote Sensing and Hydrologic Model Evapotranspiration Products in a Watershed in Michigan

2020 ◽  
Vol 56 (4) ◽  
pp. 738-755
Author(s):  
Matthew R. Herman ◽  
A. Pouyan Nejadhashemi ◽  
Juan Sebastian Hernandez‐Suarez ◽  
Ali M. Sadeghi
Keyword(s):  
Remote Sensing ◽  
Hydrologic Model

scholarly journals Monitoring temporary ponds dynamics in arid areas with remote sensing and spatial modelling

2010 ◽  
Vol 7 (1) ◽  
pp. 103-133
Author(s):  
V. Soti ◽  
C. Puech ◽  
D. Lo Seen ◽  
A. Bertran ◽  
C. Vignolles ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Pond Water ◽  
Temporal Variations ◽  
Hydrologic Model ◽  
Meteorological Station ◽  
Rainfall Data ◽  
Temporary Ponds

Abstract. A hydrologic pond model was developed that simulates daily spatial and temporal variations (area, volume and height) of temporary ponds around Barkedji, a village located in the Ferlo Region in Senegal. The model was tested with rainfall input data from a meteorological station and from Tropical Rainfall Measuring Mission (TRMM) satellites. During calibration phase, we used climatic, hydrologic and topographic field data of Barkedji pond collected daily during the 2002 rainy season. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) and a QuickBird satellite image acquired in August 2005 (2.5 m pixel size) were used to apply the hydrologic model to all ponds (98 ponds) of the study area. With input rainfall data from the meteorological station, simulated water heights values for years 2001 and 2002 were significantly correlated with observed water heights for Furdu, Mous 2 and Mous 3 ponds, respectively with 0.81, 0.67 and 0.88 Nash coefficients. With rainfall data from TRMM satellite as model input, correlations were lower, particularly for year 2001. For year 2002, the results were acceptable with 0.61, 0.65 and 0.57 Nash coefficients for Barkedji, Furdu and Mous 3 ponds, respectively. To assess the accuracy of our model for simulating water areas, we used a pond map derived from Quickbird imagery (August 2007). The validation showed that modelled water areas were significantly correlated with observed pond surfaces (r2=0.90). Overall, our results demonstrate the possibility of using a simple hydrologic model with remote sensing data (Quickbird, ASTER DEM, TRMM) to assess pond water heights and water areas of a homogeneous arid area.

scholarly journals Uncertainty reduction and parameters estimation of a~distributed hydrological model with ground and remote sensing data

2014 ◽  
Vol 11 (6) ◽  
pp. 6215-6271
Author(s):  
F. Silvestro ◽  
S. Gabellani ◽  
R. Rudari ◽  
F. Delogu ◽  
P. Laiolo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
Parameters Estimation ◽  
State Variables ◽  
Test Bed ◽  
Sensing Data

Abstract. During the last decade the opportunity and usefulness of using remote sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite based products often provide the advantage of observing hydrologic variables in a distributed way while offering a different view that can help to understand and model the hydrological cycle. Moreover, remote sensing data are fundamental in scarce data environments. The use of satellite derived DTM, which are globally available (e.g. from SRTM as used in this work), have become standard practice in hydrologic model implementation, but other types of satellite derived data are still underutilized. In this work, Meteosat Second Generation Land Surface Temperature (LST) estimates and Surface Soil Moisture (SSM) available from EUMETSAT H-SAF are used to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. This work aims at proving that satellite observations dramatically reduce uncertainties in parameters calibration by reducing their equifinality. Two parameter estimation strategies are implemented and tested: a multi-objective approach that includes ground observations and one solely based on remotely sensed data. Two Italian catchments are used as the test bed to verify the model capability in reproducing long-term (multi-year) simulations.

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 Observed and simulated hydroclimatology using distributed hydrologic model from in-situ and multi-satellite remote sensing datasets in Lake Victoria region in East Africa

2010 ◽  
Vol 7 (4) ◽  
pp. 4785-4816 ◽  
Author(s):  
S. I. Khan ◽  
P. Adhikari ◽  
Y. Hong ◽  
H. Vergara ◽  
T. Grout ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Water Cycle ◽  
Lake Victoria ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
East African ◽  
Catchment Scale ◽  
Distributed Hydrologic Model

Abstract. Floods and droughts are common, recurring natural hazards in East African nations. Studies of hydro-climatology at daily, seasonal, and annual time scale is an important in understanding and ultimately minimizing the impacts of such hazards. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10-year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia Basin. The spatiotemporal variability of the water cycle components were quantified using a physically-based, distributed hydrologic model, with in-situ and multi-satellite remote sensing datasets. Moreover, the hydrologic capability of remote sensing data such as TRMM-3B42V6 was tested in terms of reconstruction of the water cycle components. The spatial distribution and time series of modeling results for precipitation (P), evapotranspiration (ET), and change in storage (dS/dt) showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The hydrologic model captured the spatial variability of the soil moisture storage. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. Relatively high flows were experienced near the basin outlet from previous rainfall, with a new flood peak responding to the rainfall in the upper part of the basin. The monthly peak runoff was observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. The model was found to be useful in poorly gauged catchments using satellite forcing data and showed the potential to be used not only for the investigation of the catchment scale water balance but also for addressing issues pertaining to sustainability of the resources within the catchment.

scholarly journals Calibration and sequential updating of a coupled hydrologic-hydraulic model using remote sensing-derived water stages

2009 ◽  
Vol 13 (3) ◽  
pp. 367-380 ◽  
Author(s):  
M. Montanari ◽  
R. Hostache ◽  
P. Matgen ◽  
G. Schumann ◽  
L. Pfister ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Hydrologic Model ◽  
Hydraulic Model ◽  
Water Levels ◽  
Storm Event ◽  
Observation Data ◽  
Flood Inundation ◽  
Rainfall Runoff

Abstract. Two of the most relevant components of any flood forecasting system, namely the rainfall-runoff and flood inundation models, increasingly benefit from the availability of spatially distributed Earth Observation data. With the advent of microwave remote sensing instruments and their all weather capabilities, new opportunities have emerged over the past decade for improved hydrologic and hydraulic model calibration and validation. However, the usefulness of remote sensing observations in coupled hydrologic and hydraulic models still requires further investigations. Radar remote sensing observations are readily available to provide information on flood extent. Moreover, the fusion of radar imagery and high precision digital elevation models allows estimating distributed water levels. With a view to further explore the potential offered by SAR images, this paper investigates the usefulness of remote sensing-derived water stages in a modelling sequence where the outputs of hydrologic models (rainfall-runoff models) serve as boundary condition of flood inundation models. The methodology consists in coupling a simplistic 3-parameter conceptual rainfall-runoff model with a 1-D flood inundation model. Remote sensing observations of flooded areas help to identify and subsequently correct apparent volume errors in the modelling chain. The updating of the soil moisture module of the hydrologic model is based on the comparison of water levels computed by the coupled hydrologic-hydraulic model with those estimated using remotely sensed flood extent. The potential of the proposed methodology is illustrated with data collected during a storm event on the Alzette River (Grand-Duchy of Luxembourg). The study contributes to assess the value of remote sensing data for evaluating the saturation status of a river basin.

scholarly journals Integration of remote sensing evapotranspiration (ET) model and hydrologic model for mapping daily ET time series at river basin scale

2016 ◽  
Vol 48 (2) ◽  
pp. 311-325 ◽  
Author(s):  
Jian Yin ◽  
Chesheng Zhan ◽  
Huixiao Wang ◽  
Feiyu Wang
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Hydrological Model ◽  
Hydrologic Model ◽  
Percentage Error ◽  
Integrated Method ◽  
Daily Streamflow ◽  
Basin Scale ◽  
Continuous Modeling ◽  
Hydrology Modeling

Hydrological models and remote sensing evapotranspiration (ET) models usually are used to estimate regional ET. This study aims to integrate the advantages of both the models to simulate the daily ET processes. A compromise between these two methodologies is represented by improving the optimization of the hydrological model on the basis of a new probability optimal ET series, which is produced by a data assimilation scheme combining sparse remote estimates and continuous modeling of regional ETs. The distributed time-variant gain hydrological model (DTVGM) and a two-layer remote sensing ET model are chosen. First, the DTVGM is optimized by maximizing the Nash–Sutcliffe efficiency of daily streamflow in the Shahe River basin, and simulates the daily hydrological processes of 1999–2007. For improving the accuracy of continuous ET simulation, the DTVGM is further optimized by dual objective functions composed of the assimilated ETs and observed outlet discharge. The results show that the accuracy of the DTVGM-based daily ETs is improved after the dual optimization, and the mean absolute percentage error between the DTVGM-based ETs and the measured ETs in the study area is reduced by 5.84%. The integrated method is proved better, and improves the hydrology modeling accuracy.

scholarly journals Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data

2015 ◽  
Vol 19 (4) ◽  
pp. 1727-1751 ◽  
Author(s):  
F. Silvestro ◽  
S. Gabellani ◽  
R. Rudari ◽  
F. Delogu ◽  
P. Laiolo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Test Bed ◽  
Sensing Data ◽  
Scarce Data

Abstract. During the last decade the opportunity and usefulness of using remote-sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite-based products often allow for the advantage of observing hydrologic variables in a distributed way, offering a different view with respect to traditional observations that can help with understanding and modeling the hydrological cycle. Moreover, remote-sensing data are fundamental in scarce data environments. The use of satellite-derived digital elevation models (DEMs), which are now globally available at 30 m resolution (e.g., from Shuttle Radar Topographic Mission, SRTM), have become standard practice in hydrologic model implementation, but other types of satellite-derived data are still underutilized. As a consequence there is the need for developing and testing techniques that allow the opportunities given by remote-sensing data to be exploited, parameterizing hydrological models and improving their calibration. In this work, Meteosat Second Generation land-surface temperature (LST) estimates and surface soil moisture (SSM), available from European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) H-SAF, are used together with streamflow observations (S. N.) to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. The first part of the work aims at proving that satellite observations can be exploited to reduce uncertainties in parameter calibration by reducing the parameter equifinality that can become an issue in forecast mode. In the second part, four parameter estimation strategies are implemented and tested in a comparative mode: (i) a multi-objective approach that includes both satellite and ground observations which is an attempt to use different sources of data to add constraints to the parameters; (ii and iii) two approaches solely based on remotely sensed data that reproduce the case of a scarce data environment where streamflow observation are not available; (iv) a standard calibration based on streamflow observations used as a benchmark for the others. Two Italian catchments are used as a test bed to verify the model capability in reproducing long-term (multi-year) simulations. The results of the analysis evidence that, as a result of the model structure and the nature itself of the catchment hydrologic processes, some model parameters are only weakly dependent on discharge observations, and prove the usefulness of using data from both ground stations and satellites to additionally constrain the parameters in the calibration process and reduce the number of equifinal solutions.

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