Soil Erosion in the United States. Present and Future (2020-2050)
- URL: http://arxiv.org/abs/2207.06579v1
- Date: Thu, 14 Jul 2022 00:46:37 GMT
- Title: Soil Erosion in the United States. Present and Future (2020-2050)
- Authors: Shahab Aldin Shojaeezadeh, Malik Al-Wardy, Mohammad Reza Nikoo,
Mehrdad Ghorbani Mooselu, Mohammad Reza Alizadeh, Jan Franklin Adamowski,
Hamid Moradkhani, Nasrin Alamdari, Amir H. Gandomi
- Abstract summary: We estimate/predict soil erosion rates by water erosion using three alternative scenarios across the contiguous United States.
The baseline model ( 2020) estimates soil erosion rates of 2.32 Mg ha 1 yr 1 with current agricultural conservation practices.
The soil erosion forecast for 2050 suggests that all the climate and LULC scenarios indicate either an increase in extreme events or a change in the spatial location of extremes.
- Score: 8.729045594301041
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Soil erosion is a significant threat to the environment and long-term land
management around the world. Accelerated soil erosion by human activities
inflicts extreme changes in terrestrial and aquatic ecosystems, which is not
fully surveyed/predicted for the present and probable future at field-scales
(30-m). Here, we estimate/predict soil erosion rates by water erosion, (sheet
and rill erosion), using three alternative (2.6, 4.5, and 8.5) Shared
Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP)
scenarios across the contiguous United States. Field Scale Soil Erosion Model
(FSSLM) estimations rely on a high resolution (30-m) G2 erosion model
integrated by satellite- and imagery-based estimations of land use and land
cover (LULC), gauge observations of long-term precipitation, and scenarios of
the Coupled Model Intercomparison Project Phase 6 (CMIP6). The baseline model
(2020) estimates soil erosion rates of 2.32 Mg ha 1 yr 1 with current
agricultural conservation practices (CPs). Future scenarios with current CPs
indicate an increase between 8% to 21% under different combinations of SSP-RCP
scenarios of climate and LULC changes. The soil erosion forecast for 2050
suggests that all the climate and LULC scenarios indicate either an increase in
extreme events or a change in the spatial location of extremes largely from the
southern to the eastern and northeastern regions of the United States.
Related papers
- MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge [5.554201560484389]
Agricultural managed aquifer recharge (Ag-MAR) is proposed to recharge the aquifer by artificially flooding agricultural lands using surface water.
Current Ag-MAR scheduling does not take into account complex environmental factors such as weather and soil oxygen.
This paper proposes MARLP, the first end-to-end data-driven control system for Ag-MAR.
arXiv Detail & Related papers (2024-07-01T06:36:40Z) - OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning [50.365198230613956]
Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities.
We propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023.
arXiv Detail & Related papers (2024-05-12T09:32:40Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Assessing of Soil Erosion Risk Through Geoinformation Sciences and
Remote Sensing -- A Review [0.0]
The main goal of the chapter is to review different types and structures erosion models as well as their applications.
Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment.
arXiv Detail & Related papers (2023-10-12T15:53:47Z) - Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan.
textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes.
Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
arXiv Detail & Related papers (2023-09-24T19:57:22Z) - Remote estimation of geologic composition using interferometric
synthetic-aperture radar in California's Central Valley [1.5677136474147644]
Land in California's Central Valley is sinking at a rapid rate due to groundwater pumping.
In this study, we aim to identify specific regions with different temporal dynamics of land displacement.
Then, we aim to remotely estimate geologic composition using interferometric synthetic aperture radar (InSAR)-based land deformation data.
arXiv Detail & Related papers (2022-12-04T23:06:14Z) - Multi-scale Digital Twin: Developing a fast and physics-informed
surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination.
We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO)
In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z) - Learning to forecast vegetation greenness at fine resolution over Africa
with ConvLSTMs [2.7708222692419735]
We use a Convolutional LSTM (ConvLSTM) architecture to address this task.
We predict changes in the vegetation state in Africa using Sentinel-2 satellite NDVI, having ERA5 weather reanalysis, SMAP satellite measurements, and topography.
Ours results highlight how ConvLSTM models can not only forecast the seasonal evolution of NDVI at high resolution, but also the differential impacts of weather anomalies over the baselines.
arXiv Detail & Related papers (2022-10-24T23:03:36Z) - From Static to Dynamic Prediction: Wildfire Risk Assessment Based on
Multiple Environmental Factors [69.9674326582747]
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States.
We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
arXiv Detail & Related papers (2021-03-14T17:56:17Z) - EarthNet2021: A novel large-scale dataset and challenge for forecasting
localized climate impacts [12.795776149170978]
Large Earth observation datasets now enable us to create machine learning models capable of translating coarse weather information into high-resolution Earth surface forecasts.
We define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts.
We introduce EarthNet 2021, a new curated dataset containing target-temporal Sentinel 2 satellite imagery at 20 m resolution, matched with high-resolution topography and mesoscale (1.28 km) weather variables.
arXiv Detail & Related papers (2020-12-11T11:21:00Z) - Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
We find a third intermediate stable state in one of the two climate models we consider.
The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
arXiv Detail & Related papers (2020-10-20T15:31:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.