Geospatial Tools Research Articles

From viewsheds to viewscapes: Trends in landscape visibility and visual quality research

The study of visibility and visual quality (VVQ) spans scientific disciplines, methods, frameworks and eras. Recent advances in line-of-sight computation and geographic information systems (GIS) have propelled VVQ research into the realm of high performance computing via a cache of geospatial tools accessible to a broad range of research disciplines. However, in the disciplines that use VVQ analysis most (archaeology, architecture, geosciences and planning), methods and terminology can vary markedly, which may encumber interdisciplinary progress. A multidisciplinary systematic review of past VVQ research is timely to assess past efforts and effectively advance the field. In this study, we summarize the state of VVQ research in a systematic review of peer-reviewed publications spanning the past two decades. Our search yielded 528 total studies, 176 of which we reviewed in depth. VVQ analysis in peer-reviewed research increased 21-fold in the last 20 years, applied primarily in archaeology and natural resources research. We found that methods, tools and study designs varied across disciplines and scales. Research disproportionately represented the Global North and primarily employed medium resolution bare-earth elevation models, despite their known limitations. We propose a framework for standardized reporting of methods that emphasizes cross-disciplinary collaboration to propel visibility research into the future.

Cancer Data Visualization: Developing Tools to Serve the Needs of Diverse Stakeholders

AbstractPurpose of reviewTo describe how cancer data visualization tools can catalyze novel research and inform local cancer control planning.Recent findingsIn recent years, cancer centers across the nation have increasingly embraced geospatial tools to respond to the unique distributions of disease and multilevel risk factors within their catchment areas. Sylvester Comprehensive Cancer Center in Miami, Florida, recently developed a publicly available, interactive, web-based cancer data visualization tool called SCAN360. Using SCAN360 as an example, we discuss key considerations and challenges for the development of cancer data visualization tools that serve the needs of stakeholders in community, academic and government settings.SummaryCancer data visualization tools are useful for stakeholders from diverse sectors and may facilitate a range of cancer control efforts, from identifying regions for targeted outreach and engagement, to informing the allocation of resources for government and community initiatives, and motivating collaborative, interdisciplinary cancer research.

Multi-scale threat assessment of riverine ecosystems in the Colorado River Basin

Freshwater ecosystems are facing a deepening biodiversity crisis. Developing robust indicators to assess ecological integrity across large spatial scales and identifying the specific threats and pathways of impairment are thus critically needed if we are to inform freshwater conservation strategies. Here we present the first comprehensive threat assessment across the Colorado River Basin – one of the largest and most endangered river basins in North America – using a spatial framework accounting for the wide range of human activities (land uses, transportation infrastructure, exploitative activities, water withdrawals), pathways (local footprint, overland runoff, upstream cumulative effects), and spatial extent of influence (valley bottom, catchment and river network) known to affect the ecological integrity of riverine ecosystems. We quantified and mapped 69 individual threat indices with geospatial tools for each permanent, ephemeral, and intermittent stream segment within the Basin, encompassing a total of >1,067,700 river kilometers. We further aggregated these indices into components of water quality (diffuse and point-source pollution), hydrology (flow regulation/uses and climate change), and physical system (connectivity and geomorphology). To demonstrate the potential of our framework to inform spatial planning decision processes, we examined the typical combinations of threats experienced by different hydrologic areas and stream segment types, identified candidate watersheds for habitat restoration and enhancement where hotspots of biodiversity and threat overlapped, and assessed the associations between threat indices and in situ measurements of ecological integrity describing a suite of biological (benthic macroinvertebrate, fish), chemical (total nitrogen load, water conductivity), hydrological (flow alteration) and physical indicators (streambed stability, instream habitat complexity). Our assessment highlights clear disparities in term of overall degree of threat that result from different combinations and contributions of individual stressors, with different priorities emerging for perennial versus intermittent or ephemeral stream segments, and between the upper and lower parts of the Basin. Importantly, we showed that our threat indices were generally correlated with biological, chemical, hydrological and physical indicators of ecological integrity they were intended to capture. In addition to its implications for the conservation and management of the highly imperiled Colorado River Basin, our case study illustrates how multi-faceted threat mapping can be used to assess the ecological integrity of riverine ecosystems in the absence of spatially extensive in situ measurements.

A SCS-CN TECHNIQUE FOR GEOSPATIAL ESTIMATION OF RUNOFF PEAK DISCHARGE IN THE KUBANNI DRAINAGE BASIN, ZARIA, NIGERIA

The problem of soil loss is becoming widespread due to increasing unwholesome land use practices and population pressure on limited landscape. This study employed the integration of satellite imageries, rainfall and soil data and modern GIS technology to estimate runoff peak discharge in the Kubanni drainage basin. Some of the contributions of this study include the determination of the Hydrologic Soil Group (HSG) and Soil Conservation Service Curve Number (SCS CN) for the Kubanni drainage basin with a view to investigating runoff peak discharge using geospatial technology. Satellite images of Landsat OLI for February, July and November 2019, rainfall data from 2014 to 2018, soil data and SRTM DEM of 30-meter resolution were utilized for the study. A maximum likelihood supervised classification method was adopted in processing the satellite images to determine the Land Use and Land Cover (LULC) classes for the Kubanni drainage basin landscape. The LULC classes for the study area include built up area, water, vegetation, farmland and bare land. The SCS CN values for Goruba, Maigamo, Tukurwa and Malmo sub basins were discovered to be 79.72, 76.51, 71.47 and 66.00 respectively. The runoff peak discharges for the Kubanni drainage basin was found to be , , and for the years 2014, 2015, 2016, 2017 and 2018 respectively. The study has demonstrated the viability of adopting the SCS CN technique, satellite images, rainfall data and geospatial tools for the estimation of runoff peak discharge in the Kubanni drainage basin.

Dynamics of Carbon Stock at Selected Primary Linkages of Central Forest Spine (CFS) Corridors in Peninsular Malaysia

The Improving Connectivity of the Central Forest Spine Project (IC-CFS) under the United Nations of Development Programme (UNDP) funded by the Global Environment Facility (GEF) and the Malaysian Government (GOM) is aimed at increasing capacity at the Federal and State level to execute the CFS Master Plan. Currently there are 37 CFS corridors that have been identified and prescribed in the CFS Master Plan. Out of this, 17 corridors are classified as Primary Linkages (PL) and 20 as Secondary Linkages (SL), with amounted a total extent of 507,976 ha. The study areas involved three PL corridors, which summing a total area of 35,905 ha, which are PL-1 in Pahang (4,355 ha), PL-2 in Perak (24,759 ha) and PL-3 in Johor (6,791 ha). Accurate assessment and efficient management of these corridors landscape require a systematic geospatial tool. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tool, developed by the Natural Capital Project were chosen to assess the carbon dynamics at the study areas particularly using Carbon model to determine ecosystem services and trade-offs between past (year 2010) and current (year 2020) landuse pattern in the study areas. The study found that there are changes of landuse within the corridors and thus resulted in the changes of carbon stock (C) within the ten years period. PL-1 and PL-2 showed positive increases of 23,690.34 and 830,818.10 Mg C, respectively. However, the other corridor, i.e., PL-3 indicated depletion of C at -523,484.89 Mg C between 2010 and 2020. These fluctuations were due to the conversion of forests to other land use types. Areas that indicated increased in C were undergoing protective actions and conserved the remaining forests within the corridors. The results were presented in spatially distributed maps together with the magnitude of changes. The study also discussed the potential of the InVEST tools in supporting decision making and conservation efforts in CFS and other landscapes in Peninsular Malaysia.

Assessment of Anthropogenic Stress on Surinsar Wetland using Geospatial Tools

Surinsar Wetland, a Ramsar Site is situated in the lower Shiwalik region about 35 kms from Jammu city of India. Various anthropogenic causes have put immense pressure on this wetland. Unplanned growth and environmental degradation has led to the decrease of the spread of clear water. The present study highlights the various threats being faced by this wetland due to human activities. The study aimed to examine the land-use land-covers (LULC) changes and degradation of wetland in order to form a baseline for future conservation actions using geospatial tools. The Landsat data has been used for LULC classification around the buffer of the wetland and then the change detection technique was used to quantify the LULC change in GIS environment. During the present course of investigation, it was observed that Surinsar wetland is under stress of diverse type of anthropogenic pressure. The result shows a drastic change in LULC during 10 years (from 2008 to 2018). Degradation in wetland is primarily due to encroachment, agricultural, forestry and other land use activities in the catchment contributing nutrients to the wetland and leading to cultural eutrophication of the wetland.

Morphometric Analysis in Varuna River Basin: A Geoinformatics Based Analysis

Abstract: Varuna River is an important tributary of river Ganga. For the present study River Varuna has been chosen for the morphometric analysis and the demarcation of the basinal area of the river has been automatically done through Geospatial tool known as Arc Hydro. The study regarding the morphometry of the river lets us infer about the basin geometry, network system of the drainage and more of the river basin characteristics. Calculations of the morphometry will give us the quantitative description of the drainage system, which is an important aspect of the basins. This will describe the shape, slope, elongation, drainage density and many more important values to further incorporate water management processes in the varuna river basin. GIS tools will help us achieving morphometric analysis. With the help of morphometric analysis we are able to gain knowledge regarding the basin geomorphology and its responses to various hydrological processes. Keywords: Morphometric Analysis, Varuna River Basin, Arc Hydro, DEM, GIS

Geo-Information enabled crime predictive analysis model

Crime is a serious threat to society and handicaps it in its normal functioning with full potential. It also hinders the development of a society along with creating hurts of various kinds and degrees. It is, therefore, required to tackle the crime properly for its control and management. Associated with human society with its very existence, it has been evolving with the time in differing magnitudes. Especially, during the contemporary time of rapid technological and societal change, crime has got new types, forms spatial scale with the advent of globalization since 1990. However, the spatial studies on crimes are recent ones despite place plays an important role in understanding and tackling crimes. The sophisticated geospatial tools like Remote Sensing, Geographical Information System and GPS have provided vast scope to map and study crime in relation to space and other aspects of crime. This study focuses on the crime mapping for the years 2001, 2006, and 2012 covering aspects of crimes like changing patterns and trends, seasonality-diurnality, and zonation maps with identification of hotspots using the latest tools of mapping. It also attempts to work on a spatially enabled Predictive Analysis Model using various characteristics and associations. It is expected to provide a base for highlighting the importance of geographical factors in crime analysis, prediction and management for an intelligence system for the past, present and future. This study has utilized both primary as well as secondary data for the geographical analysis in assessing crime pattern in the area of study.

Global Navigation Satellite System (GNSS) and other geospatial tools for various applications

Nowadays the Global Satellite Navigation Systems (GNSS) play a fundamental role in many areas as civil aviation, maritime and land navigation and geomatics, owing to the ability to provide worldwide, tridimensional, all-weather position, and velocity and time synchronization. The final product of the Global Navigation Satellite System exercise is the Three - Dimensional coordinates (3D) of the receiving station. These coordinates have been found reliable for most geospatial applications. However, some applications in data management required other information apart from geodetic coordinates. Hence; GNSS has been integrated with other methods of data acquisition to improve the quality of data for various applications. These have helped in solving a lot of problems where individual methods failed. This paper examines some satellite-based systems and reports the integration of GNSS with other data acquisition tools such as geodetic levelling, Remote Sensing, Geographic Information System (GIS), Inertial Navigation System (INS) and many others. In some cases, the synergy had resulted in other satellite or payload programs such as Gravity Recovery and Climate Experiment (GRACE), while it has improved the GNSS applications in so many areas. GNSS integration is therefore recommended for any application that requires the use of the location.

Building a spatial decision support system for tourism and infrastructure planning: technical solution and data integration challenges

Mobility planning in rural areas with a high number of tourists is important for creating sustainable destinations. By identifying mobility gaps in the transportation system, measures to improve the situation can be implemented. In order to identify such mobility gaps, decision-makers need a spatial decision support system (SDSS). The aim of this paper is to identify vital aspects of creating such an SDSS and to build a prototype. Two important aspects were identified, data and system design. The result of the analysis of available data shows a lack of data portals with disaggregated socio-economic and intra-destination travel data. Further, it shows that data on points of interest (POI) and public transit data are primarily found in company databases. The system design analysis showed that most SDSS today are relying on public data and are not designed to integrate disparate data sources. They are primarily developed to be used by experts. Based on these findings an SDSS that automatically integrates both public and private data was developed. It comprises of a self-hosted web mapping system and several geospatial tools. Our main conclusion is that both data and system design are important aspects to consider when building an SDSS for mobility planning. By using the architecture proposed in this article, new data can easily be incorporated in an SDSS. Furthermore, the system design also facilitates the involvement of stakeholders in the planning process.

Assessing the Hydropower Potential Using Hydrological Models and Geospatial Tools in the White Bandama Watershed (Côte d'Ivoire, West Africa)

An assessment of hydropower potential at the watershed scale was conducted at the White Bandama Watershed (WBW) in Côte d'Ivoire (West Africa). The method used involves the application of a hydrological model [Soil and Water Assessment Tool (SWAT)] coupled with a Quantum Geographic Information System (QGIS), denoted as QSWAT, to assess the water resource availability and hydropower potential of streams and determine potential hydropower sites for future hydropower development in the watershed. Geospatial data about the topography, soil types, land use/land cover, weather, and discharge were considered in hydrological and hydro-geomorphological characterization of the watershed. Streamflow and climate data-enabled model operation and simulation of the hydrology of the watershed. The model performance and robustness were confirmed with the p-factor, r-factor, coefficient of determination R2, and Nash–Sutcliffe (NS) efficiency coefficient. The hydropower potential of streams was evaluated by considering the simulated streamflow and water head. A total of 22 future hydropower sites was identified, geolocated, and classified with an estimated total production capacity of 538.56 MW.

Assessment of Malaria Risk Using GIS and Multi Criteria: The Case Study of East Gojjam Zone, Ethiopia

Malaria is one of the most severe public health problems worldwide with 300 to 500 million cases and about one million deaths reported to date, 90% of which were reported from Sub Saharan African countries like Ethiopia. The main objective of the study was Assessment of malaria risk areas by using the GIS-based MCA approaches in East Gojjam zone. Weighted overlay technique of multi-criteria analysis was used to develop the malaria risk map. The malaria risk map was produced depending upon the overlay analysis of the malaria hazard map and some factors like land use land cover, population density, health stations. The malaria risk map was classified into four suitability index such as very highly suitable, high suitable, moderately suitable, and low suitable. The result shows that around 21.02% areas is very highly suitable for malaria risk, 24.49% is high suitable, 24.66% is moderately suitable and 29.83 % is low suitable for malaria risk areas. It is suggested that effective identification and mapping of malaria risk areas can be made using geospatial tools, to contribute for the prevention system easily manageable and controlling the disease.

Performance of the SSM-iCrop model for predicting growth and nitrogen dynamics in winter wheat

Process-based crop models are essential tools for representing the fundamental interactions between the cropping environment (weather, soil, and management) and plant development, growth, resource use, and yield formation. Due to these capabilities, crop models are considered as an integral component of smart farming tools for evaluating and improving crop management at field, farm, and regional scales. However, prior to application of a crop model in geospatial decision support tools, its robustness should be established by comparing model predictions with observations from the target cropping environment. The objective of this study was to assess the performance of the Simple Simulation Model (SSM-iCrop) for predicting growth and nitrogen (N) dynamics of winter wheat (Triticum aestivum) cultivars in a temperate environment. Detailed plant and soil data were collected from three field experiments conducted with four widely-grown cultivars under four N application rates in Austria. Variation in N fertilisation and differences in soil properties and weather conditions in the three field experiments generated a wide range of observed crop total dry mass (585–2034 g m−2), N uptake (5–32 g N m−2), and grain yield (211–898 g m−2). The SSM-iCrop model required parameterisation of a relatively small number of plant input parameters. As these parameters could be directly calculated from the experimental data, except for two phenology-related coefficients, there was no need for calibrating the model. In initial simulations, SSM-iCrop was not able to predict the response of leaf area index (LAI) to decreasing N supply. Introducing an additional parameter defining the minimum stem N concentration from emergence to begin grain growth improved the model performance substantially. The simulated time-course of crop attributes through the growing season showed good overall correspondence with observed data. Across the three field experiments, the model performed well in simulating above-ground dry mass (CV=5.9, RMSE=115.6 g N m−2), grain yield (CV=1.9, RMSE=60.5 g N m−2), total crop N uptake (CV=4.5, RMSE=1.9 g N m−2), and grain N content (CV=1.1; RMSE=2.2 g N m−2). Overall, The results of this study confirmed the robustness of SSM-iCrop for predicting wheat development, growth, N dynamics, and yield in the target cropping environments. The relatively simple structure and high degree of transparency make the SSM-iCrop suitable for integration in smart farming tools for improving tactical decision making in crop production. This study also highlights the essential role of high-quality detailed experimental data for adequate parameterisation and evaluation of crop models..

Key drivers of LULCC in co-managed and governmentmanaged forest reserves in Malawi (1999–2018)

Interventions through co-management (CM) and government-management (GM) for forest reserves can mitigate degradation and deforestation. Few studies have investigated the driving forces of Land Use/Cover Change (LULCC) using Remote sensing and socioeconomic data to assess the impact of management strategies on woodlands. This study investigated factors influencing LULCC in two co-managed and two governmentmanaged forest reserves (FR) between 1999 and 2018 in Malawi. Images from the Environment for Analysing Images and data from respondents representing 30% of the communities surrounding four FR were analysed in SPSS. Woodland loss to grassland, agriculture was observed in Liwonde CM, Kaning'ina and Thambani GM FR. Communities' perceptions confirmed woodland conversion. Population increases and poverty exacerbated agriculture and wood energy use. Up-scaling CM requires improved empowerment processes and capacity building. There is a need to curb corruption, monitor licensing, and develop forest plans and law enforcement in GM FR. Promoting geospatial and socioeconomic analysis tools will enhance forest monitoring.

Quantification of Soil Erosion by Integrating Geospatial and Revised Universal Soil Loss Equation in District Dir Lower, Pakistan

This study is aimed to estimate soil erosion risk by integrating Revised Universal Soil Loss Equation (RUSLE) and geospatial tool in District Lower Dir, Eastern Hindu Kush. Soil erosion is among the biggest threats to agricultural production. Mountainous areas of Pakistan are exposed to erosion hazards due to immature geology, fragile slope, and deforestation. RUSLE factors were derived from data acquired from various sources. The Rainfall erosivity (R) factor was derived from monthly data obtained from Pakistan Meteorological Department, Peshawar. The soil erodibility (K) factor was prepared from the map of soil, Survey of Pakistan. The topography (LS) factor was calculated from 12.5 m DEM downloaded from the Alaska Satellite Facility. The cover management (C) factor was calculated from the Red and Near-Infrared band of Landsat 8 satellite image downloaded from USGS earth explorer. The Digital Elevation Model (DEM) and Landsat image were integrated to prepare the Support practice (P) factor. These factors were combined to assess soil erosion in the study area. The estimated soil erosion ranges between 0-25407 tons/hectare/year, with a mean soil loss of 230 tons/hectare/year. The erosion zonation map was then prepared and was classified as very low, low, moderate, high, and very high erosion. 22 % of the district was affected by low to moderate erosion while 67 % area is affected by very high erosion. The areas having more rainfall and steep slopes are more exposed to erosion hazards. Therefore, Erosion control activities are essential in those areas where erosion is high to assure a viable ecosystem.

3D city model for monitoring flash flood risks in Salalah, Oman

This paper investigates the issues and challenges in using a 3D city model for monitoring flash flood risks in Salalah (Oman) designed using the City Geography Markup Language (CityGML) standards version 2.0. The investigation was made based on the fact that 2D and 2.5D GIS solutions cannot analyse flood complex problems inside the urban area. Hence, the study sought to reduce time and effort for the decision-makers by proposing a 3D city model for flood risk management. The study used geospatial tools and databases such as ArcGIS, Watershed Modelling System (WMS), FME, PostgreSQL-PostGIS, and 3D City Database (3DCityDB) to generate the 3D model and to test the capability of establishing a unified geospatial data structure including the 3D city objects, hydrological data, and geological data. The findings showed the importance of addressing flood risks data and arranging it in the 3D geodatabase. It was also revealed that establishing a 3D city model based on the CityGML standard requires homogenised definitions and standards for city objects (surface and subsurface) as well as hydrological data.

Assessment of groundwater potential zone for sustainable water resource management in south-western part of Birbhum District, West Bengal

Water is an indispensable natural resource for survival of any species in the globe. For centuries, civilization has flourished on the bank of river based on the easy availability of water. Groundwater is one of the prime sources of freshwater supply, but, its occurrence and spatial distribution are highly uneven and affected by several surface and subsurface features. Development of several geospatial tool based on the remote sensing and GIS in recent time helps immensely for delineation and management of this precious resource. The present study has been undertaken in south-western part of Birbhum district, West Bengal with an objective to delineate groundwater potential zone using multiple criteria decision analysis and GIS. Seven thematic layers concerning with geology, geomorphology, hydrology, land use land cover and edhaphic factor have been employed in this study with proper weightage depending on their role in groundwater formation to identify the groundwater potential zone. By using analytical hierarchy process, whole study area has been classified into four zones ranging from excellent to poor. Primary field data and secondary data about depth of groundwater have been compared with the result to make it authentic. The result shows that the southern part composed of alluvial plain has the excellent potential compared to the northern lateritic and pediment part where groundwater potential is moderate. Eventually, few recommendation and suggestion have been framed for sustainable water resource management that will help the researchers, planners and other decision-makers for judicious exploration and management of the groundwater resource in the study area.

Investigating groundwater recharge potential of Notwane catchment in Botswana using geophysical and geospatial tools

Study regionThis study is conducted over the Notwane Catchment Area (NCA) in Southeastern Botswana using geospatial and geophysical tools. Study focusGeospatial and geophysical tools, which provide spatial information for both the surface and subsurface have been widely used in groundwater exploration because of their capability in detecting physical properties that are related to groundwater phenomena. In this research, relevant remotely sensed data including aeromagnetic data and satellite images were used to identify potential groundwater recharge zones within the NCA as a contribution towards improved water resources management amid the changing climate. The multi-disciplinary approach used could offer a more reliable way to identify potential recharge zones, particularly in semi-arid hard rock terrains. New hydrological insights for the regionAssessment of groundwater recharge potential looking at remotely sensed data revealed that built-up areas expanded significantly between 2009 and 2019 with implications for run-off and flooding. Through aeromagnetic data interpretation of Euler deconvolution solutions and apparent susceptibility computations, weathered zones and geologic lineaments, mainly dykes and faults, indicating areas with increased secondary porosity were identified. Consequently, the groundwater recharge optimization model was used to determine areas with higher recharge potential which lie on the central part of the NCA, northeastern margin and the southernmost part bounded by topographical relief of hills with thick vegetation. These areas are characterized by approximately 200 m depth of weathering and predominantly east-west structural features.

Abstract WP55: Stroke Center Accessibility Study In The U.s. Using Geospatial Analysis And Machine Learning

Background: Several accrediting bodies certify the level of stroke care hospitals provide. The Joint Commission on Hospital Accreditation (JC) is the largest accrediting body in the United States. There is no open source Geographic Information Systems (GIS) dataset showing the distribution of JC accredited centers by ZIP code. Objective: to create a stroke center accessibility and stroke center desert system using geospatial analysis and machine learning which provides real-time assessment of stroke center availability, distribution and access to care. Method: Geospatial data layers of JC accredited stroke centers were compiled using data sources including U.S. Census Bureau and CDC. Map layers corresponding to the levels of JC accredited stroke hospitals geolocated using ZIP code were created as follows: 1) Acute Stroke Ready 2) Primary 3) Thrombectomy Capable 4) Comprehensive Stroke Center. A GIS dataset displaying stroke mortality by region was obtained from the ArcGIS Living Atlas. Stroke center deserts are analyzed using a 4.5 hour drive map along with population and diversity. Machine learning models were implemented to estimate stroke mortality as a function of distance to care centers and capability levels of the stroke centers. Results: Stroke centers are highly concentrated within large urban centers. There are geographic regions that have poor access to stroke centers. Such regions include the Gulf Coast States of Louisiana, Mississippi, and Alabama that have large areas with poor stroke center acces while having some of the highest stroke mortality in the country. (Figure 1) Conclusion: There are regional variations in stroke center availability. There are certain regions with high stroke mortality with very little stroke center access. Geospatial AI tools can be utilized to improve stroke systems of care.

Water Erosion Monitoring and Prediction in Response to the Effects of Climate Change Using RUSLE and SWAT Equations: Case of R’Dom Watershed in Morocco

Soil erosion is an increasingly issue worldwide, due to several factors including climate variations and humans’ activities, especially in Mediterranean ecosystems. Therefore, the aim of this paper is: (i) to quantify and to predict soil erosion rate for the baseline period (2000–2013) and a future period (2014–2027), using the Revised Universal Soil Loss Equation (RUSLE) and the Soil and Water Assessment Tool (SWAT) model in the R’Dom watershed in Morocco, based on the opportunities of Remote Sensing (RS) techniques and Geographical Information System (GIS) geospatial tools. (ii) we based on classical statistical downscaling model (SDSM) for rainfall prediction. Due to the lack of field data, the model results are validated by expert knowledge. As a result of this study, it is found that both agricultural lands and bare lands are most affected by soil erosion. Moreover, it is showed that soil erosion in the watershed was dominated by very low and low erosion. Although the area of very low erosion and low erosion continued to decrease. Hence, we hereby envisage that our contribution will provide a more complete understanding of the soil degradation in this study area and the results of this research could be a crucial reference in soil erosion studies and also may serve as a valuable guidance for watershed management strategies.