Modelling hydrological factors from DEM using GIS

Chowdhury, Md. Sharafat

doi:10.1016/j.mex.2023.102062

Cited by 14 publications

(6 citation statements)

References 29 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This index explains how the elevation of each DEM pixel compares to the average height of the area around reference pixel. TPI has a positive value in hilly regions and negative values in valley areas 48 . We also use compound Topographic Index (CTI) to define potentially saturated areas based on contributing catchment areas and the local slope gradient.…”

Section: Figure 2 Research Methodsmentioning

confidence: 99%

Gully erosion mapping based on remote sensing data

Khasanah,

Danoedoro,

Anggri Setiawan

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

View full text Add to dashboard Cite

Gully erosion is the most destructive type of soil erosion, induced by soil structure detachment. As a result, modest to massive incisions are made in the field. The process can degrade the quantity and quality of soil and potentially cause structural damage. Field studies are used to map the position of gullies, but they are inefficient in terms of time and cost, especially on a regional scale. Therefore, another approach is applied to visualize the probability of gully erosion development using geoenvironmental factors. Remote sensing data can be used to examine the condition of the land, leading to an accurate representation of the earth's surface. This research's primary goal is to predict the location of gully erosion using remote sensing data in the upper section of the Sapi Watershed, Banjarnegara, Indonesia. This location primarily consists of mountainous areas used for massive cultivation. Parameters comprising land use and vegetation area (ie. anomaly NDVI) derived from SENTINEL 2A, and topographic data from DEMNAS. The mapping process considers the actual location of the gully and other geographical characteristics using Random Forest. A total of 85 gully location records were collected and verified using Google Earth and field surveys. Nongully data were obtained using median DoD filters to distinguish between river and mountain top. 70% of the data are used for modelling and the rest for validation of model results. RF-generated prediction maps could provide an important instrument for planning and land conservation in the early phases of gully formation.

show abstract

Section: Figure 2 Research Methodsmentioning

confidence: 99%

Gully erosion mapping based on remote sensing data

Khasanah,

Danoedoro,

Anggri Setiawan

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

View full text Add to dashboard Cite

show abstract

“…Currently, models applied to flood simulation both domestically and internationally include SWMM, HEC-RAS, PCSWMM, MIKE, InfoWorks ICM, etc. After establishing the model network, high-precision DEM raster data of the study area, as well as vector data of building layers, road layers, river layers, waterway layers, and greenbelt layers, are imported into the model in sequence [31,32]. Given that the surface runoff time in Beijing's historic and cultural districts is generally less than 1 h, in this study, 2 h short-duration design rainfall data with return periods of 100 years, 200 years, 500 years, and 1000 years were selected as the input parameters for simulating rainstorm events.…”

Section: Construction Methods Of a Refined Early-warning Model For Ra...mentioning

confidence: 99%

Methods for Constructing a Refined Early-Warning Model for Rainstorm-Induced Waterlogging in Historic and Cultural Districts

Wu,

Li,

Wang

et al. 2024

Water

View full text Add to dashboard Cite

Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts’ waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing’s historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of “1 h rainfall intensity water logging index” and “the waterlogging risk index in historical and cultural districts” were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts.

show abstract

“…These are slope, aspect, Topographical Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Topographic Position Index (TPI) at each resolution (Figures 2a–d). For detailed information on the definition and method for calculating these topographical indices, see Chowdhury (2023). ArcGIS topography/surface analysis tools were employed to calculate topographic indices from DEM data.…”

Section: Methodsmentioning

confidence: 99%

Effects of spatial data resolution on the modelling and mapping of soil organic carbon content in hill country grassland landscapes

Tran,

Dominati,

Lowry

et al. 2023

Soil Use and Management

View full text Add to dashboard Cite

Limited use has been made of spatially explicit modelling of soil organic carbon (SOC) in highly complex farmed landscapes to advance current mapping efforts. This study aimed to address this gap in knowledge by evaluating the spatial prediction of SOC content in the 0‐75 mm soil depth in hill country landscapes in New Zealand (NZ) using point‐based training data, along with topographic covariates and Sentinel 2 spectral band ratios using an automated set of machine learning (AutoML) tools in ArcGIS. Subsequently, it also focused on quantifying the effects of spatial data resolution (i.e., 1m, 8m, 15m, and 25m) in terms of predicted map accuracy. Farmlets with contrasting phosphorus fertiliser and sheep grazing histories located at the Ballantrae Hill Country Research Station, NZ were selected to conduct the research. Six candidate algorithms incorporated in the AutoML tools (i.e., XGBoost, LightGBM, linear regression, decision trees, extra trees, random forest) and ensemble model were utilised to model the spatial pattern of SOC content. The results show that the ensemble model that combine predictions of various algorithms applied for 1m data resolution enables the highest performance and accuracy (i.e., R2 = 0.76, RMSE = 0.66%). Among the predictive variables used in the model, slope, wetness, and topographic position indices were found to be the most important topographical features that explain SOC patterns in the study area. Inclusion of spectral indices derived from remote sensing, including surface soil moisture and clay minerals ratio, made further improvement to the SOC content prediction. The study reveals that a decrease in the resolution of the geospatial data does not substantively affect the mean SOC content estimation of a farm‐scale modelling. However, using coarser resolution data reduces the ability of the model to predict changes in the spatial pattern of SOC content across a hill country grassland landscape.

show abstract

Modelling hydrological factors from DEM using GIS

Cited by 14 publications

References 29 publications

Gully erosion mapping based on remote sensing data

Gully erosion mapping based on remote sensing data

Methods for Constructing a Refined Early-Warning Model for Rainstorm-Induced Waterlogging in Historic and Cultural Districts

Effects of spatial data resolution on the modelling and mapping of soil organic carbon content in hill country grassland landscapes

Contact Info

Product

Resources

About