topographic wetness index
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Abstract. Since the industrial revolution, the world is experiencing a huge change in its climate, which causes many imbalances such as flash floods (FF). The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco. For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421 non-flood points) and 9 flash-flood predictors (Elevation , Slope , Aspect , LU/LC , Stream Power Index , Plan curvature , Profile Curvature , Topographic Position Index and Topographic Wetness Index ) that were extracted from the DEM (10m resolution) and satellite imagery (Sentinel 2B) of the study area . Models were trained on 70% and tested on 30% of this dataset also they were evaluated using several metrics such as the Receiver Operating Characteristic (ROC) Curve, precision, recall, score and kappa index. The result demonstrated that RF (AUC = 0.99, Accuracy = 96%, Kappa statistics = 0.92) has the highest performance, followed by ANN (AUC = 0.98, Accuracy = 95%, Kappa statistics = 0.89) and SVM (AUC = 0.96, Accuracy = 92%, Kappa statistics = 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster.

Abstract Purpose of Review Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.

In mountain areas, both the ecosystem and the local population highly depend on water availability. However, water storage dynamics in mountains is challenging to assess because it is highly variable both in time and space. This calls for innovative observation methods that can tackle such measurement challenge. Among them, gravimetry is particularly well-suited as it is directly sensitive–in the sense it does not require any petrophysical relationship–to temporal changes in water content occurring at surface or underground at an intermediate spatial scale (i.e., in a radius of 100 m). To provide constrains on water storage changes in a small headwater catchment (Strengbach catchment, France), we implemented a hybrid gravity approach combining in-situ precise continuous gravity monitoring using a superconducting gravimeter, with relative time-lapse gravity made with a portable Scintrex CG5 gravimeter over a network of 16 stations. This paper presents the resulting spatio-temporal changes in gravity and discusses them in terms of spatial heterogeneities of water storage. We interpret the spatio-temporal changes in gravity by means of: (i) a topography model which assumes spatially homogeneous water storage changes within the catchment, (ii) the topographic wetness index, and (iii) for the first time to our knowledge in a mountain context, by means of a physically based distributed hydrological model. This study therefore demonstrates the ability of hybrid gravimetry to assess the water storage dynamics in a mountain hydrosystem and shows that it provides observations not presumed by the applied physically based distributed hydrological model.

Flood is one of the most frequent hydrometeorological disasters which leads in economic losses. The first step in flood disaster mitigation efforts is mapping vulnerable areas. Kencong District frequently affected by the annual flooding event. This study aims to assess flood hazard mapping by integrating the AHP method and Geographic Information System. This study used a descriptive quantitative approach through the correlation matrix of the AHP model for each physical environmental factor. These factors include slope, altitude, distance from the river, soil type, Topographic Wetness Index (TWI), and Curvature. Furthermore, with the Geographic Information System (GIS), the weighted overlay stage was carried out to obtain the results of flood-prone areas. Based on the AHP analysis, the most significant factors in determining flood-prone areas were the distance from rivers, slopes, and TWI. The results of flood-prone areas mapping were divided into five classes: from deficient 0.02%, low 4.26%, medium 37.11%, high 51.89%, and very high 6.72%. Validation of GIS mapping results with data in the field has an AUC value of 84%, which indicates that the prediction of the AHP-GIS model is perfect in flood-prone areas mapping in the Kencong District. The integration of AHP method and Geographic Information System in flood hazard assessment were able to produce a model to evaluate the spatial distribution of flood-prone areas. Keywords : Flood Hazard Mapping; Multi-criteria decision analysis; AHP Model; GIS; Jember   Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Several regions across the world are presently experiencing a continuous increase in water scarcity due to the rise in water consumption resulting from population development, agricultural and industrial expansion, climate change, and pollution. Droughts are increasing in recurrence, severity, duration, and spatial extent as a result of climate change. Drought will be one of the most serious threats posed by climate change, often in conjunction with other effects such as rising temperatures and shifting ecosystems. Therefore, this study analyzes the spatial distribution of the Drought Hazard Index (DHI) by integrating AHP-GIS-Remote Sensing in Gorontalo Regency. AHP was used to determine the significance of each map as an input parameter for the DHI, while GIS-Remote Sensing was utilized to supply and analyze all input maps and the study outcome. The DHI assessment consists of four criteria, namely with Normalized Difference Vegetation Index accounting for the highest proportion at 42.9%, followed by Land Surface Temperature (33.6%), Normalized Difference Moisture Index (16.8%), and Topographic Wetness Index (6.7%), with the consistency of the underlying expert opinion measured by the consistency ratio of 0.048. The results indicated that the general hazard of drought in the Gorontalo Regency area was low (43.53%), with 17.87% of the whole area experiencing high hazard. The high class of drought was discovered to be centered in the central region of Gorontalo Regency, which was mostly used for agricultural and economic purposes, thereby enabling policymakers to have evidence to develop management policies suitable for local conditions. Therefore, despite the limits of climatology data, this study established the value of satellite-derived data needed to support policymakers in guiding operational actions to drought hazards reduction.

Plecoptera, an environmentally sensitive order of aquatic insects commonly used in water quality monitoring is experiencing decline across the globe. This study addresses the landscape factors that impact the species richness of stoneflies using the US Geological Survey Hierarchical Unit Code 8 drainage scale (HUC8) in the state of Indiana. Over 6300 specimen records from regional museums, literature, and recent efforts were assigned to HUC8 drainages. A total of 93 species were recorded from the state. The three richest of 38 HUC8s were the Lower East Fork White (66 species), the Blue-Sinking (58), and the Lower White (51) drainages, all concentrated in the southern unglaciated part of the state. Richness was predicted using nine variables, reduced from 116 and subjected to AICc importance and hierarchical partitioning. AICc importance revealed four variables associated with Plecoptera species richness, topographic wetness index, HUC8 area, % soil hydrolgroup C/D, and % historic wetland ecosystem. Hierarchical partitioning indicated topographic wetness index, HUC8 area, and % cherty red clay surface geology as significantly important to predicting species richness. This analysis highlights the importance of hydrology and glacial history in species richness of Plecoptera. The accumulated data are primed to be used for monograph production, niche modeling, and conservation status assessment for an entire assemblage in a large geographic area.

Abstract Mapping of groundwater potential over space, built by synergizing environmental variables and machine learning models, was of great significance for regional water resources management. Taking the Chihe River basin in Anhui province as an example, thirteen influence factors were used to predict the spatial distribution of groundwater, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), drainage density, distance to rivers, distance to faults, lithology, soil type, land use, and normalized difference vegetation index (NDVI). The potential of groundwater resource in this region was predicted using GIS-based machine learning models, including logistic regression (LR), deep neural networks (DNN), and random forest (RF) model. Then, the accuracy of prediction results was evaluated by calculating the RMSE, MAE and R evaluation index. The results show that there is no collinearity among the 13 environmental impact factors, which can provide corresponding environmental variables for the evaluation of regional groundwater potential. Machine learning models show that groundwater potential is concentrated in moderate to high potential areas. Among them, the moderate to the high potential of this area accounted for 81.14% in the LR model, 90.36% and 87.55% in the DNN model and the RF model, respectively. According to the result of these evaluation indexes, the three models all have high prediction accuracy, among which the LR model performs more prominently. The good prediction capabilities of these machine learning technologies can provide a reliable scientific basis for spatial prediction of groundwater potential and management of water resources.

AbstractThe main objective of this study is to understand the overall impact of earthquake in upper Indrawati Watershed, located in the high mountainous region of Nepal. Hence, we have assessed the relationship between the co-seismic landslide and underlying causative factors as well as performed landslide susceptibility mapping (LSM) to identify the landslide susceptible zone in the study area. We assessed the landslides distribution in terms of density, number, and area within 85 classes of 13 causal factors including slope, aspect, elevation, formation, land cover, distance to road and river, soil type, total curvature, seismic intensity, topographic wetness index, distance to fault, and flow accumulation. The earthquake-induced landslide is clustered in Northern region of the study area, which is dominated by steep rocky slope, forested land, and low human density. Among the causal factors, 'slope' showed positive correlation for landslide occurrence. Increase in slope in the study area also escalates the landslide distribution, with highest density at 43%, landslide number at 4.34/km2, and landslide area abundance at 2.97% in a slope class (> 50°). We used logistic regression (LR) for LSM integrating with geographic information system. LR analysis depicts that land cover is the best predictor followed by slope and distance to fault with higher positive coefficient values. LSM was validated by assessing the correctly classified landslides under susceptibility categories using area under curve (AUC) and seed cell area index (SCAI). The LSM approach showed good accuracy with respective AUC values for success rate and prediction rate of 0.843 and 0.832. Similarly, the decreasing SCAI value from very low to very high susceptibility categories advise satisfactory accuracy of the LSM approach.

Abstract Flood is one of the disaster threats downstream of Welang river, Pasuruan. A flood susceptibility map is needed to anticipate floods disasters. This research aimed to map flood Susceptibility in the Welang watershed using a Geographical Information System. In determining flood hazard, the Frequency Ratio (FR) approach was used. Flood locations were identified from the interpretation of field survey data as training data and model validation. The data were represented in a Digital Elevation Model (DEM) map, geological data, land use, river data, and Landsat Satellite Imagery and processed into a spatial database on the GIS platform. The factors that caused flooding consisted of Flood inventory, slope, Elevation, Topographic Wetness Index (TWI), Standardized Precipitation Index (SPI), Flow Accumulation, Distance to the river, River Density, Rainfall, Vegetation Index (NDVI), and Landuse. The map results with acceptable accuracy showed that the FR model gained an Area Under Curve (AUC) value of 90%, and the incidence for the Area Under Curve ( AUC ) was 93%. It is known that 1% of the flood-prone area is very high. The local Government can use the research to minimize the risk of flooding in the Welang watershed.

Abstract. Export of dissolved organic carbon (DOC) from riparian zones (RZs) is an important component of temperate catchment carbon budgets, but export mechanisms are still poorly understood. Here we show that DOC export is predominantly controlled by the microtopography of the RZ (lateral variability) and by riparian groundwater level dynamics (temporal variability). From February 2017 until July 2019 we studied topography, DOC quality and water fluxes and pathways in the RZ of a small forested catchment and the receiving stream in central Germany. The chemical classification of the riparian groundwater and surface water samples (n=66) by Fourier transform ion cyclotron resonance mass spectrometry revealed a cluster of plant-derived, aromatic and oxygen-rich DOC with high concentrations (DOCI) and a cluster of microbially processed, saturated and heteroatom-enriched DOC with lower concentrations (DOCII). The two DOC clusters were connected to locations with distinctly different values of the high-resolution topographic wetness index (TWIHR; at 1 m resolution) within the study area. Numerical water flow modeling using the integrated surface–subsurface model HydroGeoSphere revealed that surface runoff from high-TWIHR zones associated with the DOCI cluster (DOCI source zones) dominated overall discharge generation and therefore DOC export. Although corresponding to only 15 % of the area in the studied RZ, the DOCI source zones contributed 1.5 times the DOC export of the remaining 85 % of the area associated with DOCII source zones. Accordingly, DOC quality in stream water sampled under five event flow conditions (n=73) was closely reflecting the DOCI quality. Our results suggest that DOC export by surface runoff along dynamically evolving surface flow networks can play a dominant role for DOC exports from RZs with overall low topographic relief and should consequently be considered in catchment-scale DOC export models. We propose that proxies of spatial heterogeneity such as the TWIHR can help to delineate the most active source zones and provide a mechanistic basis for improved model conceptualization of DOC exports.