scholarly journals A Union of Dynamic Hydrological Modeling and Satellite Remotely-Sensed Data for Spatiotemporal Assessment of Sediment Yields

2022 ◽  
Vol 14 (2) ◽  
pp. 400
Author(s):  
Pooja Preetha ◽  
Ashraf Al-Hamdan
Keyword(s):  
Hydrological Modeling ◽  
Dynamic Models ◽  
Assessment Tool ◽  
Remotely Sensed ◽  
Water Quality Modeling ◽  
Remotely Sensed Data ◽  
Sediment Yields ◽  
Dynamic Factors ◽  
Basin Scale ◽  
Factor C

(1) The existing frameworks for water quality modeling overlook the connection between multiple dynamic factors affecting spatiotemporal sediment yields (SY). This study aimed to implement satellite remotely sensed data and hydrological modeling to dynamically assess the multiple factors within basin-scale hydrologic models for a realistic spatiotemporal prediction of SY in watersheds. (2) A connective algorithm was developed to incorporate dynamic models of the crop and cover management factor (C-factor) and the soil erodibility factor (K-factor) into the Soil and Water Assessment Tool (SWAT) with the aid of the Python programming language and Geographic Information Systems (GIS). The algorithm predicted the annual SY in each hydrologic response unit (HRU) of similar land cover, soil, and slope characteristics in watersheds between 2002 and 2013. (3) The modeled SY closely matched the observed SY using the connective algorithm with the inclusion of the two dynamic factors of K and C (predicted R2 (PR2): 0.60–0.70, R2: 0.70–0.80, Nash Sutcliffe efficiency (NS): 0.65–0.75). The findings of the study highlight the necessity of excellent spatial and temporal data in real-time hydrological modeling of catchments.

scholarly journals A Review of SWAT Model Application in Africa

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1313
Author(s):  
George Akoko ◽  
Tu Hoang Le ◽  
Takashi Gomi ◽  
Tasuku Kato
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Water Resources ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Data Availability ◽  
Mitigation Measures ◽  
Model Parameterization ◽  
Basin Scale

The soil and water assessment tool (SWAT) is a well-known hydrological modeling tool that has been applied in various hydrologic and environmental simulations. A total of 206 studies over a 15-year period (2005–2019) were identified from various peer-reviewed scientific journals listed on the SWAT website database, which is supported by the Centre for Agricultural and Rural Development (CARD). These studies were categorized into five areas, namely applications considering: water resources and streamflow, erosion and sedimentation, land-use management and agricultural-related contexts, climate-change contexts, and model parameterization and dataset inputs. Water resources studies were applied to understand hydrological processes and responses in various river basins. Land-use and agriculture-related context studies mainly analyzed impacts and mitigation measures on the environment and provided insights into better environmental management. Erosion and sedimentation studies using the SWAT model were done to quantify sediment yield and evaluate soil conservation measures. Climate-change context studies mainly demonstrated streamflow sensitivity to weather changes. The model parameterization studies highlighted parameter selection in streamflow analysis, model improvements, and basin scale calibrations. Dataset inputs mainly compared simulations with rain-gauge and global rainfall data sources. The challenges and advantages of the SWAT model’s applications, which range from data availability and prediction uncertainties to the model’s capability in various applications, are highlighted. Discussions on considerations for future simulations such as data sharing, and potential for better future analysis are also highlighted. Increased efforts in local data availability and a multidimensional approach in future simulations are recommended.

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.

scholarly journals Do Land Use Changes Balance out Sediment Yields under Climate Change Predictions on the Sub-Basin Scale? The Carpathian Basin as an Example

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1499 ◽  
Author(s):  
Paulina Orlińska-Woźniak ◽  
Ewa Szalińska ◽  
Paweł Wilk
Keyword(s):  
Land Use ◽  
Sediment Yield ◽  
Assessment Tool ◽  
Land Use Changes ◽  
Carpathian Basin ◽  
Winter Period ◽  
Baseline Scenario ◽  
Sediment Yields ◽  
Basin Scale ◽  
Yield Responses

The issue of whether land use changes will balance out sediment yields induced by climate predictions was assessed for a Carpathian basin (Raba River, Poland). This discussion was based on the Macromodel DNS (Discharge–Nutrient–Sea)/SWAT (Soil and Water Assessment Tool) results for the RCP 4.5 and RCP 8.5 scenarios and LU predictions. To track sediment yield responses on the sub-basin level the studied area was divided into 36 units. The response of individual sub-basins to climate scenarios created a mosaic of negative and positive sediment yield changes in comparison to the baseline scenario. Then, overlapped forest and agricultural areas change indicated those sub-basins where sediment yields could be balanced out or not. The model revealed that sediment yields could be altered even by 49% in the selected upper sub-basins during the spring-summer months, while for the lower sub-basins the predicted changes will be less effective (3% on average). Moreover, the winter period, which needs to be re-defined due to an exceptional occurrence of frost and snow cover protecting soils against erosion, will significantly alter the soil particle transfer among the seasons. Finally, it has been shown that modeling of sediment transport, based on averaged meteorological values and LU changes, can lead to significant errors.

Water resources of the Texas Gulf Basin

1999 ◽  
Vol 39 (3) ◽  
pp. 121-133 ◽  
Author(s):  
J. G. Arnold ◽  
R. Srinivasan ◽  
T. S. Ramanarayanan ◽  
M. DiLuzio
Keyword(s):  
United States ◽  
Water Resources ◽  
Assessment Tool ◽  
Simulation Models ◽  
The United States ◽  
Hydrologic Model ◽  
Database Management System ◽  
United States Geological Survey ◽  
Sediment Yields ◽  
Basin Scale

A geographic information system (GIS) has been integrated with a distributed parameter, continuous time, nonpoint source pollution model SWAT (Soil and Water Assessment Tool) for the management of water resources. This integration has proven to be effective and efficient for data collection and to visualize and analyze the input and output of simulation models. The SWAT-GIS system is being used to model the hydrology of eighteen major river systems in the United States (HUMUS). This paper focuses on the integration of SWAT (basin scale hydrologic model) with the Geographical Resources Analysis Support System (GRASS-GIS) and a relational database management system. The system is then applied to the Texas Gulf River basin. Input data layers (soils, land use, and elevation) were collected at a scale of 1:250,000 from various sources. Average monthly simulated and observed stream flow records from 1970-1979 are presented for the hydrologic cataloging units (HCU) defined by the United States Geological Survey (USGS) in the Texas Gulf basin. Average annual sediment yields computed from sediment rating curves are compared against simulated sediment yields from seven river basins within the Texas Gulf showing reasonable agreement.

scholarly journals The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1615 ◽  
Author(s):  
Dejuan Jiang ◽  
Kun Wang
Keyword(s):  
Remote Sensing ◽  
Human Activities ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Machine Learning Techniques ◽  
Remotely Sensed Data ◽  
Coupled Models

A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in model calibration and simulation. Alternative approaches, such as machine learning techniques and coupled models, can be used for streamflow predictions. However, these models also suffer from their respective limitations, especially when data are unavailable. Satellite-based remote sensing may provide a valuable contribution for hydrological predictions due to its wide coverage and increasing tempo-spatial resolutions. In this review, we provide an overview of the role of satellite-based remote sensing in streamflow simulation. First, difficulties in hydrological modeling over highly regulated basins are further discussed. Next, the performance of satellite-based remote sensing (e.g., remotely sensed data for precipitation, evapotranspiration, soil moisture, snow properties, terrestrial water storage change, land surface temperature, river width, etc.) in improving simulated streamflow is summarized. Then, the application of data assimilation for merging satellite-based remote sensing with a hydrological model is explored. Finally, a framework, using remotely sensed observations to improve streamflow predictions in highly regulated basins, is proposed for future studies. This review can be helpful to understand the effect of applying satellite-based remote sensing on hydrological modeling.

scholarly journals Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

2021 ◽  
Vol 13 (5) ◽  
pp. 1025
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Mostafa Emadi ◽  
Ali Cherati ◽  
Brandon Heung ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Soil Texture ◽  
Management Practices ◽  
Hydrological Modeling ◽  
Ecological Modeling ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Mazandaran Province ◽  
Northern Iran ◽  
Ann Models ◽  
Artificial Neural

Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural network (ANN) models with bio-inspired metaheuristic optimization algorithms such as a genetic algorithm (GA-ANN), particle swarm optimization (PSO-ANN), bat (BAT-ANN), and monarch butterfly optimization (MBO-ANN) algorithms, were built for predicting PSFs for the Mazandaran Province of northern Iran. In total, 1595 composite surficial soil samples were collected, and 64 environmental covariates derived from terrain, climatic, remotely sensed, and categorical datasets were used as predictors. Models were tested using a repeated 10-fold nested cross-validation approach. The results indicate that the hybridized ANN methods were far superior to the reference approach using ANN with a backpropagation training algorithm (BP-ANN). Furthermore, the MBO-ANN approach was consistently determined to be the best approach and yielded the lowest error and uncertainty. The MBO-ANN model improved the predictions in terms of RMSE by 20% for clay, 10% for silt, and 24% for sand when compared to BP-ANN. The physiographical units, soil types, geology maps, rainfall, and temperature were the most important predictors of PSFs, followed by the terrain and remotely sensed data. This study demonstrates the effectiveness of bio-inspired algorithms for improving ANN models. The outputs of this study will support and inform sustainable soil management practices, agro-ecological modeling, and hydrological modeling for the Mazandaran Province of Iran.

scholarly journals Enhanced Watershed Modeling by Incorporating Remotely Sensed Evapotranspiration and Leaf Area Index

2021 ◽  
Author(s):  
Sangchul Lee ◽  
Gregory W. McCarty ◽  
Glenn E. Moglen ◽  
Haw Yen ◽  
Fangni Lei ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Assessment Tool ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Temporal Consistency ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Predictive Uncertainty ◽  
Time Space

Abstract. Remotely sensed evapotranspiration (RS-ET) products have been widely adopted as additional constraints on hydrologic modeling to enhance the model predictability while reducing predictive uncertainty. However, vegetation parameters, responsible for key time/space variation in evapotranspiration (ET), are often calibrated without the use of suitable constraints. Remotely sensed leaf area index (RS-LAI) products are increasingly available and provide an opportunity to assess vegetation dynamics and improve calibration of associated parameters. The goal of this study is to assess the Soil and Water Assessment Tool (SWAT) predictive uncertainty in estimates of ET using streamflow and two remotely sensed products (i.e., RS-ET and RS-LAI). We explore how the application of RS-ET and RS-LAI products contributes to 1) reducing the parameter uncertainty; 2) improving the model capacity to predict the spatial distribution of ET and LAI at the sub-watershed level; and 3) assessing the model predictions of ET and LAI at the basic modeling unit (i.e., the hydrologic response unit [HRU]) under the assumption that ET and LAI are related in croplands. Our results suggest that most of the parameter sets with acceptable performances for two constraints (i.e., streamflow and RS-ET; 12 parameter sets) are also acceptable for three constraints (i.e., streamflow, RS-ET, and RS-LAI; 11 parameter sets) at the watershed level. This finding is likely because both the ET simulation algorithm and the RS-ET products consider LAI as an input variable. Relative to the watershed-level assessment, the number of parameter sets that satisfactorily characterize spatial patterns of ET and LAI at the sub-watershed level are reduced from 11 to 6. Among the 11 parameter sets acceptable for three constraints (i.e., streamflow, RS-ET and RS-LAI) at the sub-watershed level, two parameter sets appear to provide high spatial and temporal consistency between ET and LAI at the HRU level. These results suggested that use of multiple remotely sensed products as model constraints enables model evaluations at finer scales – thereby constraining acceptable parameter sets and accurately representing the spatial characteristics of hydrologic variables. As such, this study highlights the potential of remotely sensed data to increase the predictability and utility of hydrologic models.

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