scholarly journals Comparison of MODIS and SWAT Evapotranspiration over a Complex Terrain at Different Spatial Scales

2017 ◽  
Author(s):  
Olanrewaju O. Abiodun ◽  
Huade Guan ◽  
Vincent E. A. Post ◽  
Okke Batelaan
Keyword(s):  
Complex Terrain ◽  
Spatial Scales ◽  
Swat Model ◽  
South Australia ◽  
Remotely Sensed ◽  
Distributed Model ◽  
Climate Data ◽  
Remotely Sensed Data ◽  
Catchment Scale ◽  
Distributed Hydrological Models

Abstract. In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely-sensed data based ET algorithms and distributed hydrological models has provided improved spatially-upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005) and 7-year validation period (2007–2013). Differences in ET estimation between the two methods of up to 48 %, 21 % and 16 % were observed at respectively 1 km2, 5 km2 and 10 km2 spatial resolutions. Land cover differences, mismatches between the two methods and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.

scholarly journals Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales

2018 ◽  
Vol 22 (5) ◽  
pp. 2775-2794 ◽  
Author(s):  
Olanrewaju O. Abiodun ◽  
Huade Guan ◽  
Vincent E. A. Post ◽  
Okke Batelaan
Keyword(s):  
Complex Terrain ◽  
Spatial Scales ◽  
Swat Model ◽  
South Australia ◽  
Remotely Sensed ◽  
Distributed Model ◽  
Climate Data ◽  
Remotely Sensed Data ◽  
Catchment Scale ◽  
Distributed Hydrological Models

Abstract. In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely sensed data based ET algorithms and distributed hydrological models has provided improved spatially upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model on graduated spatial scales for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. ET from both models was further compared with the coarser-resolution AWRA-L model at catchment scale. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005) and 7-year validation period (2007–2013). Differences in ET estimation between the SWAT and MOD16 methods of up to 31, 19, 15, 11 and 9 % were observed at respectively 1, 4, 9, 16 and 25 km2 spatial resolutions. Based on the results of the study, a spatial scale of confidence of 4 km2 for catchment-scale evapotranspiration is suggested in complex terrain. Land cover differences, HRU parameterisation in AWRA-L and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.

Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data

CATENA ◽  
1999 ◽  
Vol 37 (3-4) ◽  
pp. 291-308 ◽  
Author(s):  
S.M. de Jong ◽  
M.L. Paracchini ◽  
F. Bertolo ◽  
S. Folving ◽  
J. Megier ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Remotely Sensed ◽  
Distributed Model ◽  
Regional Assessment ◽  
Remotely Sensed Data

Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data

2011 ◽  
Vol 42 (5) ◽  
pp. 338-355 ◽  
Author(s):  
Luis Samaniego ◽  
Rohini Kumar ◽  
Conrad Jackisch
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Shuttle Radar Topography Mission ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Distributed Model ◽  
Remotely Sensed Data ◽  
Model Driven ◽  
Moderate Resolution ◽  
Daily Discharge ◽  
Moderate Resolution Imaging Spectroradiometer

The goal of this study was to assess the feasibility of using Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) products to drive a mesoscale hydrologic model (mHM) in a poorly gauged basin. Other remotely sensed products such as LandSat and Shuttle Radar Topography Mission (SRTM) were also used to complement the local geoinformation. For this purpose, three data blending techniques that combine satellite with in situ observations were implemented and evaluated in the Mod basin (512 km2) in India. The climate of the basin is semi-arid and monsoon-dominated. The rainfall gauging network comprised six stations with daily records spanning 9 years. Daily discharge time series was only 4 years long and incomplete. Lumped and distributed versions of mHM were evaluated. Parameters of the lumped version were obtained through calibration. A multiscale regionalization technique was used to parameterize the distributed version using global parameters from other gauged basins. Both mHM versions were evaluated during six monsoon seasons. Results of numerical experiments indicated that driving mHM with satellite-based products is possible and promising. The distributed model with regionalized parameters was at least 20% more efficient than that of its lumped version. Initialization conditions must be carefully considered when the model is only driven by remotely sensed inputs.

scholarly journals Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

2021 ◽  
Vol 13 (4) ◽  
pp. 2375
Author(s):  
Sangchul Lee ◽  
Junyu Qi ◽  
Hyunglok Kim ◽  
Gregory W. McCarty ◽  
Glenn E. Moglen ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Assessment Tool ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Hydrologic Models ◽  
Daily Streamflow ◽  
Model Predictions ◽  
Improved Model ◽  
Water Assessment

There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling.

Remotely-Sensed Oil and Shoreline Interaction Modeling

2017 ◽  
Vol 2017 (1) ◽  
pp. 2600-2619
Author(s):  
Zachary Nixon ◽  
Jacqueline Michel ◽  
Scott Zengel
Keyword(s):  
Oil Spill ◽  
Spatial Scales ◽  
Information Service ◽  
Deepwater Horizon ◽  
Remotely Sensed ◽  
Water Levels ◽  
Remotely Sensed Data ◽  
Oil Distribution ◽  
Wide Range ◽  
Interaction Modeling

ABSTRACT No. 2017-233 The broad adoption of remotely sensed data and derivative products from satellite and aerial platforms available to describe the distribution of spilled oil on the water surface was an important factor during Deepwater Horizon (DWH) oil spill both for tactical response and damage assessment. The availability and utility of these data in describing on-water oil distribution provide strong temptation to make estimates about on-shoreline oil distribution. The mechanisms by which floating oil interact with the shoreline, however, are extremely complex, heterogeneous at fine spatial scales, and generally not well described or quantified beyond broad conceptual or spill-specific empirical models. In short, oil on water does not necessarily lead to oil on adjacent shorelines. We combine data derived from NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) using a variety of satellite platforms of opportunity describing the remotely-sensed, daily composite anomaly polygons representing oil on water over multiple months with ground observations made in the field, collocated in time and space extracted from a newly compiled database of ground survey data (SCAT, NRDA and others) from the northwestern Gulf of Mexico. Because this new compiled dataset is very large (100,000s of observations) and spans a wide range of habitats, geography, and time, it is particularly suitable for inference and predictive modeling. We use these combined datasets to make inference about the relative influence on shoreline oiling probability and loading of distance from on-water oil observation via multiple distance metrics, shoreline morphology, water levels and ranges, wind direction and speed, wave energy, shoreline aspect and geometry. We also construct predictive models using machine-learning modeling methods to make predictions about shoreline oiling probability given observed distributions of on-water oil. The importance of this work is three part: firstly, the relationships between these parameters can assist hind-cast modeling of shoreline oiling probability for the Deepwater Horizon oil spill. Secondly, these data and models can permit similar modeling for future spills. Lastly, we propose that this dataset serve as a nucleus that can be expanded using data from subsequent or future spills to allow iteratively improvements in shoreline oil probability modeling using remotely sensed data, as well as an improved understanding of oil-shoreline interactions more generally.

scholarly journals Statistical properties of spatial snowcover in mountainous catchments in Norway

2004 ◽  
Vol 35 (2) ◽  
pp. 101-117 ◽  
Author(s):  
Wolf-Dietrich Marchand ◽  
Ånund Killingtveit
Keyword(s):  
Empirical Distribution ◽  
Snow Accumulation ◽  
Distribution Functions ◽  
Statistical Properties ◽  
Distributed Model ◽  
Catchment Scale ◽  
Mountainous Catchments ◽  
Snow Distribution ◽  
Distributed Hydrological Models ◽  
Log Normal

The spatial distribution of snowcover in a catchment is determined by complex interactions between meteorological and physiographical factors, integrated over time. The snowcover shows variability over scales ranging from centimeters up to hundreds of kilometers. An important and necessary decision for modelers is to determine spatial resolution in a distributed model. Since the spatial variability in snowcover may be quite large, even within a few meters, it is difficult to use modeling units small enough so that the snow can be assumed evenly distributed within the unit. A possible method to compensate for this is to use larger units, and describe the snow distribution within each unit by a statistical model (e.g. normal, log-normal, gamma, etc). This technique requires information about spatial statistical properties of snowcover within a unit. As many of the distributed hydrological models operate on a grid basis, it would be desirable to find a statistical distribution on a sub-grid scale. However, as an initial approach, the study presented here was done on a catchment scale. The catchment scale presented the possibility of incorporating data from several historical snow surveys. These surveys were taken at the time of maximum snow accumulation in various mountainous catchments in Norway. Comparing empirical distribution functions with different theoretical distribution functions, it was shown that a mixed distribution combining two separate log-normal distributions clearly gave the best fit in most of the catchments. This seems to indicate that a mixture of at least two different populations of SWE values exists.

scholarly journals Modelling and Mapping Above Ground Biomass Using Sentinel 2 and Planet Scope Remotely Sensed Data in West Usambara Tropical Rainforests, Tanzania

2021 ◽  
Author(s):  
Ernest William Mauya ◽  
Sami Madundo
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Tropical Rainforests ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Remotely Sensed Data ◽  
Tropical Regions ◽  
Sentinel 2

Abstract Forest biomass estimation using field -based inventories at a large scale is challenging and generally entails large uncertainty in tropical regions. With their wall-to-wall coverage ability, optical remote sensing signals had gained a wide acceptance for larger scale estimation of AGB at different spatial scales, ranging from local to global. However, their applicability in tropical forests is still limited. In this study, we investigated the performance of Sentinel 2 and Planet Scope remotely sensed data for AGB modelling, predicting and mapping in the tropical rainforest of Tanzania. A total of 296 field inventory plots were measured across the west Usambara mountain forests. AGB values were computed for each of the field plot in Mg/ha, and related with remotely sensed predictor variables using parametric and non- parametric statistical methods. Band values, vegetation indices and texture based variables were derived from each of the remotely sensed data. The AGB models were developed and validated using k-fold cross validation and their relative root mean square error (cvRMSEr%) were used to judge their accuracies. Relative efficiency (RE) of each dataset as compared to pure field inventory was also computed. The results showed that, Sentinel 2 based model fitted using generalized linear models (RMSEr = 67.00 % and pseudo-R2= 20%) had better performance as compared to Planet Scope based models (cvRMSEr = 72.1 % and pseudo-R2= 5.2%). Overall GLMs resulted into a models with less prediction error as compared to random forest when using Sentinel 2 data. However, for the Planet Scope, there was marginal improvement of using random forest (cvRMSEr = 72.0%) as compared to GLMs. Models, that in cooperated texture variables resulted into better prediction accuracy as compared to those with band values and indices only. The R.E values for Sentinel2 and Planet Scope were 1.2 and 1.1 respectively. Our study had demonstrated that Sentinel 2 and Planet Scope remotely sensed data can be used to develop cost-effective method for AGB estimation within the context of tropical rainforests of Tanzania. Further studies are however encouraged to look more on the best way of optimizing the efficiency of the two data sources in AGB estimations.

Sign in / Sign up

Export Citation Format

Share Document