Land use land cover detections using MODIS MCD12Q1 V6.1 and ESRI Sentinel-2 datasets in the Lake Chamo catchment DOI Creative Commons
Agegnehu Kitanbo Yoshe

H2Open Journal, Journal Year: 2024, Volume and Issue: 8(1), P. 20 - 41

Published: Dec. 19, 2024

ABSTRACT Understanding the change dynamics of land use and cover (LULC) has a critical influence on hydrological characteristics watershed, economic development, ecological variation, climate changes, been used to resolve current dilemmas between land, water, energy, food sector. It is also essential as observed reflects status environment provides input parameters for sustainable natural resource management optimization. The Chamo catchment undergone large in LULC which increased soil erosion lake sedimentation. In this paper, long-term variations were evaluated using MODIS ESRI Sentinel-2 datasets. As result, significant variation was study area from 2001 2022. Spatial temporal two Based MODIS, grassland dominant class, whereas ESRI, rangeland cropland LULC. result policy-makers stakeholders water management, maintenance, climatic adoption pathways. findings provided evidence that are effective datasets detecting be applied different areas.

Language: Английский

Monitoring and forecasting water erosion in response to climate change effects using the integration of the global RUSLE/SDR model and predictive models DOI Creative Commons

Belhaj Fatima,

Hlila Rachid,

Abdeldjalil Belkendil

et al.

Applied Soil Ecology, Journal Year: 2025, Volume and Issue: 206, P. 105910 - 105910

Published: Jan. 28, 2025

Language: Английский

Citations

10

Projected climate change impacts on streamflow in the Upper Oum Er Rbia Basin, Upstream of the Ahmed El Hansali Dam, Morocco DOI Creative Commons
Tarik El Orfi,

Mohamed El Ghachi,

Sébastien Lebaut

et al.

Environmental Challenges, Journal Year: 2025, Volume and Issue: 18, P. 101101 - 101101

Published: Feb. 3, 2025

Language: Английский

Citations

1

Spatio-temporal distribution and prediction of agricultural and meteorological drought in a Mediterranean coastal watershed via GIS and machine learning DOI
Siham Acharki, Sudhir Kumar Singh, Edivando Vítor do Couto

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2023, Volume and Issue: 131, P. 103425 - 103425

Published: June 1, 2023

Language: Английский

Citations

19

Evaluation of soil texture classification from orthodox interpolation and machine learning techniques DOI Creative Commons
Lei Feng, Umer Khalil, Bilal Aslam

et al.

Environmental Research, Journal Year: 2023, Volume and Issue: 246, P. 118075 - 118075

Published: Dec. 28, 2023

The current investigation examines the effectiveness of various approaches in predicting soil texture class (clay, silt, and sand contents) Rawalpindi district, Punjab province, Pakistan. employed techniques included artificial neural networks (ANNs), kriging, co-kriging, inverse distance weighting (IDW). A total 44 specimens from depths 10-15 cm were gathered, then hydrometer method was adopted to measure their texture. map grain sets formulated ArcGIS environment, utilizing distinct interpolation approaches. MATLAB software used evaluate gradient fraction, latitude longitude, elevation, fragments points proposed an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), root square (RMSE), utilized precision intended techniques. In assessing size spatial dissemination clay, sand, ANN superior compared weighting. Still, less than a 50% observed using this examination, IDW had inferior other results demonstrated that practices produced acceptable can be for future research. Soil is among most central variables manipulate agriculture plans. prepared maps exhibiting groups are imperative crop yield pastoral scheduling.

Language: Английский

Citations

15

Comparing the ability of different remotely sensed evapotranspiration products in enhancing hydrological model performance and reducing prediction uncertainty DOI Creative Commons
Soufiane Taia, Andrea Scozzari,

Lamia Erraioui

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102352 - 102352

Published: Nov. 2, 2023

The mitigation of uncertainties in the identification natural systems is a fundamental aspect development hydrological models, and represents major challenge for improvement modelling techniques. In particular, calibration models based on streamflow measurements at outlet catchment exposed to significant sources uncertainty, such as impact landscape features runoff generation. Remote sensing-based actual evapotranspiration (AET) data can be incorporated with improve model accuracy reduce uncertainty modelling, resulting enhancement performance. selection right AET dataset crucial task, front availability multi-source datasets that differ methods, parameters, spatiotemporal resolution. Despite existence few studies proposing usage remote data, there lack systematic comparisons between different products, terms performance modelling. This paper aims compare efficacy products improving simulation responses, both single multi-variable scenarios. this investigation, Soil Water Assessment Tool (SWAT) was calibrated observed by experimenting eight datasets. findings our study suggest incorporation process significantly enhance reliability predictions. Thus, proposed approach contribute effectiveness quantitative tool management water resources. Another finding solely yields reasonable results streamflow, which an advantageous promising feature ungauged basins.

Language: Английский

Citations

12

Predicting precipitation and NDVI utilization of the multi-level linear mixed-effects model and the CA-markov simulation model DOI Creative Commons

Fatima Belhaj,

Hlila Rachid,

Abdessalam Ouallali

et al.

Climate Services, Journal Year: 2025, Volume and Issue: 38, P. 100554 - 100554

Published: March 10, 2025

Language: Английский

Citations

0

ANALYSIS OF SWAT+ MODEL PERFORMANCE: A COMPARATIVE STUDY USING DIFFERENT SOFTWARE AND ALGORITHMS DOI
Samanta Tolentino Cecconello, Danielle de Almeida Bressiani, Maria Cândida Moitinho Nunes

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106425 - 106425

Published: March 1, 2025

Language: Английский

Citations

0

Sensitivity of Swat Model Parameters for Modeling Streamflow at Bajulmati Watershed, Situbondo—East Java, Indonesia DOI
Aldi Ainun Habibi, Gusfan Halik, Retno Utami Agung Wiyono

et al.

Lecture notes in civil engineering, Journal Year: 2025, Volume and Issue: unknown, P. 553 - 561

Published: Jan. 1, 2025

Language: Английский

Citations

0

Assessing climate change-driven social flood exposures and flood damage to residential areas in the Solo River basin of Indonesia DOI
Badri Bhakta Shrestha, Mohamed Rasmy, Tomoki Ushiyama

et al.

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(2)

Published: Feb. 24, 2025

Language: Английский

Citations

0

The Impact of soil data on SWAT modeling: Effects, requirements, and future directions DOI Creative Commons
Yassine Bouslıhım, Mohamed Ouarani, Soufiane Taia

et al.

Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. 2694 - 2694

Published: April 1, 2025

Language: Английский

Citations

0