scholarly journals Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2014
Author(s):  
Celina Aznarez ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Juan Pablo Pacheco ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ecosystem Services ◽  
Water Resources ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Erosion Control ◽  
Sensing Data ◽  
Extreme Precipitation Events ◽  
The Impact

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

scholarly journals Coupling Remote Sensing and Hydrological Model for Evaluating the Impacts of Climate Change on Streamflow in Data-Scarce Environment

2021 ◽  
Vol 13 (24) ◽  
pp. 14025
Author(s):  
Fazlullah Akhtar ◽  
Usman Khalid Awan ◽  
Christian Borgemeister ◽  
Bernhard Tischbein
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Water Resources ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Climate Change Scenarios ◽  
Impact Of Climate Change ◽  
Streamflow Data ◽  
The Impact ◽  
Impacts Of Climate Change

The Kabul River Basin (KRB) in Afghanistan is densely inhabited and heterogenic. The basin’s water resources are limited, and climate change is anticipated to worsen this problem. Unfortunately, there is a scarcity of data to measure the impacts of climate change on the KRB’s current water resources. The objective of the current study is to introduce a methodology that couples remote sensing and the Soil and Water Assessment Tool (SWAT) for simulating the impact of climate change on the existing water resources of the KRB. Most of the biophysical parameters required for the SWAT model were derived from remote sensing-based algorithms. The SUFI-2 technique was used for calibrating and validating the SWAT model with streamflow data. The stream-gauge stations for monitoring the streamflow are not only sparse, but the streamflow data are also scarce and limited. Therefore, we selected only the stations that are properly being monitored. During the calibration period, the coefficient of determination (R2) and Nash–Sutcliffe Efficiency (NSE) were 0.75–0.86 and 0.62–0.81, respectively. During the validation period (2011–2013), the NSE and R2 values were 0.52–0.73 and 0.65–0.86, respectively. The validated SWAT model was then used to evaluate the potential impacts of climate change on streamflow. Regional Climate Model (RegCM4-4) was used to extract the data for the climate change scenarios (RCP 4.5 and 8.5) from the CORDEX domain. The results show that streamflow in most tributaries of the KRB would decrease by a maximum of 5% and 8.5% under the RCP 4.5 and 8.5 scenarios, respectively. However, streamflow for the Nawabad tributary would increase by 2.4% and 3.3% under the RCP 4.5 and 8.5 scenarios, respectively. To mitigate the impact of climate change on reduced/increased surface water availability, the SWAT model, when combined with remote sensing data, can be an effective tool to support the sustainable management and strategic planning of water resources. Furthermore, the methodological approach used in this study can be applied in any of the data-scarce regions around the world.

scholarly journals Impact of Disturbances on the Carbon Cycle of Forest Ecosystems in Ukrainian Polissya

Forests ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 337 ◽  
Author(s):  
Petro Lakyda ◽  
Anatoly Shvidenko ◽  
Andrii Bilous ◽  
Viktor Myroniuk ◽  
Maksym Matsala ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ecosystem Services ◽  
Land Cover ◽  
Forest Ecosystems ◽  
Remote Sensing Data ◽  
Forest Carbon ◽  
Land Cover Changes ◽  
Salvage Logging ◽  
Sensing Data ◽  
The Impact

Climate change continues to threaten forests and their ecosystem services while substantially altering natural disturbance regimes. Land cover changes and consequent management entail discrepancies in carbon sequestration provided by forest ecosystems and its accounting. Currently there is a lack of sufficient and harmonized data for Ukraine that can be used for the robust and spatially explicit assessment of forest provisioning and regulation of ecosystem services. In the frame of this research, we established an experimental polygon (area 45 km2) in Northern Ukraine aiming at estimating main forest carbon stocks and fluxes and determining the impact caused by natural disturbances and harvest for the study period of 2010–2015. Coupled field inventory and remote sensing data (RapidEye image for 2010 and SPOT 6 image for 2015) were used. Land cover classification and estimation of biomass and carbon pools were carried out using Random Forest and k-Nearest Neighbors (k-NN) method, respectively. Remote sensing data indicates a ca. 16% increase of carbon stock, while ground-based computations have shown only a ca. 1% increase. Net carbon fluxes for the study period are relatively even: 5.4 Gg C·year−1 and 5.6 Gg C C·year−1 for field and remote sensing data, respectively. Stand-replacing wildfires, as well as insect outbreaks and wind damage followed by salvage logging, and timber harvest have caused 21% of carbon emissions among all C sources within the experimental polygon during the study period. Hence, remote sensing data and non-parametric methods coupled with field data can serve as reliable tools for the precise estimation of forest carbon cycles on a regional spatial scale. However, featured land cover changes lead to unexpected biases in consistent assessment of forest biophysical parameters, while current management practices neglect natural forest dynamics and amplify negative impact of disturbances on ecosystem services.

Mapping, Monitoring, Forecasting and Assessing the Impact of Climate Change in Groundwater Systems in Ireland

2021 ◽  
Author(s):  
Joan Campanyà i Llovet ◽  
Ted McCormack ◽  
Damien Doherty ◽  
Philip Schuler ◽  
Monika Kabza ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Groundwater Resources ◽  
Remote Sensing Data ◽  
National Scale ◽  
Sensing Data ◽  
Impact Of Climate Change ◽  
Groundwater Systems ◽  
The Impact ◽  
Flood Maps

<p>In recent years Ireland has experienced significant and unprecedented flooding events, such as groundwater floods, that extended up to hundreds of hectares during the winter flood season, lasting for weeks to months, and affecting many rural communities in Ireland. In response to the serious flooding of winter 2015-2016, specifically related to groundwater, Geological Survey Ireland (GSI) initiated a project (GWFlood, 2016-2019), in collaboration with Trinity College Dublin (TCD) and Institute of Technology Carlow (ITC), to investigate the drivers, map and numerically model the extent of groundwater flooding in Ireland. Through this project, the use of remote sensing data, Sentinel-1 satellite imagery from the European Space Agency Copernicus program, was key to overcome the practical limitations of establishing and maintaining a national field-based monitoring network. The main outputs for this project included: 1) a national historic groundwater flood map, 2) a methodology for hydrograph generation using satellite images, and 3) predictive groundwater flood maps for Ireland.</p><p>Subsequently GSI started a new project (GWClimate, 2020-2022), in collaboration with ITC, to monitor floods in Ireland using remote sensing data, to enable short-term forecasting groundwater floods at a national scale, and to evaluate the potential that climate change may have on Irish groundwater resources, both in terms of flooding and drought issues. The GWClimate project is enhancing the tools developed by GWFlood in order to deliver: 1) seasonal flood maps for Ireland, 2) near-real time satellite-based hydrographs, 3) groundwater flood forecasting tools, and 4) maps evaluating the impact of climate change in groundwater systems in Ireland. The outputs of this project will contribute to monitor and quantify the impacts of flooding in Ireland at a national scale, improve the national capacity to understand how groundwater resources respond to climatic stresses, and improve the reliability of adaptation planning and predictions in the groundwater sector.</p><p>Data and maps from GWClimate and GWFlood projects are available at: 1) https://gwlevel.ie, and 2) https://www.gsi.ie/en-ie/programmes-and-projects/groundwater/activities/groundwater-flooding/gwflood-project-2016-2019/Pages/default.aspx</p>

scholarly journals Applications of Satellite Remote Sensing to Water Governance and the Mitigation of Climate Change Effects

2020 ◽  
Author(s):  
Ilham Ali ◽  
Jay Famiglietti ◽  
Jonathan McLelland
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Remote Sensing ◽  
Water Stress ◽  
Satellite Remote Sensing ◽  
Water Governance ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Climate Change Effects ◽  
Water Issues

Water stress in both surface and groundwater supplies is an increasing environmental and sustainable management issue. According to the UN Environment Program, at current depletion rates almost half of the world's population will suffer severe water stress by 2030. This is further exacerbated by climate change effects which are altering the hydrologic cycle. Understanding climate change implications is critical to planning for water management scenarios as situations such as rising sea levels, increasing severity of storms, prolonged drought in many regions, ocean acidification, and flooding due to snowmelt and heavy precipitation continue. Today, major efforts towards equitable water management and governance are needed. This study adopts the broad, holistic lenses of sustainable development and water diplomacy, acknowledging both the complex and transboundary nature of water issues, to assess the benefits of a “science to policy” approach in water governance. Such negotiations and frameworks are predicated on the availability of timely and uniform data to bolster water management plans, which can be provided by earth-observing satellite missions. In recent decades, significant advances in satellite remote sensing technology have provided unprecedented data of the Earth’s water systems, including information on changes in groundwater storage, mass loss of snow caps, evaporation of surface water reservoirs, and variations in precipitation patterns. In this study, specific remote sensing missions are surveyed (i.e. NASA LANDSAT, GRACE, SMAP, CYGNSS, and SWOT) to understand the breadth of data available for water uses and the implications of these advances for water management. Results indicate historical precedent where remote sensing data and technologies have been successfully integrated to achieve more sustainable water management policy and law, such as in the passage of the California Sustainable Groundwater Management Act of 2014. In addition, many opportunities exist in current transboundary and interstate water conflicts (for example, the Nile Basin and the Tri-State Water Wars between Alabama, Georgia, and Florida) to integrate satellite-remote-sensed water data as a means of “joint-fact finding” and basis for further negotiations. The authors argue that expansion of access to satellite remote sensing data of water for the general public, stakeholders, and policy makers would have a significant impact on the development of science-oriented water governance measures and increase awareness of water issues by significant amounts. Barriers to entry exist in accessing many satellite datasets because of prerequisite knowledge and expertise in the domain. More user-friendly platforms need to be developed in order to maximize the utility of present satellite data. Furthermore, sustainable co-operations should be formed to employ satellite remote sensing data on a regional scale to preempt problems in water supply, quantity, and quality.

scholarly journals ANALYSIS OF THE IMPACT OF OIL PRODUCT SPILLS ON NATURAL OBJECTS BASED ON SENTINEL-2 REMOTE SENSING DATA IN EASTERN SIBERIA

2021 ◽  
Vol 6 ◽  
pp. 24-31
Author(s):  
Dmitry A. Baikin
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
Remote Sensing Data ◽  
Oil Products ◽  
Eastern Siberia ◽  
Sensing Data ◽  
Sensing System ◽  
Natural Objects ◽  
The Impact ◽  
Sentinel 2

The article analyzes the impact of oil spills on natural objects according to the remote sensing system Sentinel-2 in Eastern Siberia. Remote sensing data analysis is used to detect traces of oil products in the accident area. Conclusions about the usage of Sentinel-2 data for detecting traces of oil products were made.

scholarly journals Hydrological Alteration Index as an Indicator of the Calibration Complexity of Water Quantity and Quality Modeling in the Context of Global Change

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 115 ◽  
Author(s):  
Roxelane Cakir ◽  
Mélanie Raimonet ◽  
Sabine Sauvage ◽  
Javier Paredes-Arquiola ◽  
Youen Grusson ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Water Resources ◽  
Stream Water ◽  
Swat Model ◽  
Water Yield ◽  
Observation Data ◽  
Hydrological Alteration ◽  
Water Quality And Quantity ◽  
The Impact

Modeling is a useful way to understand human and climate change impacts on the water resources of agricultural watersheds. Calibration and validation methodologies are crucial in forecasting assessments. This study explores the best calibration methodology depending on the level of hydrological alteration due to human-derived stressors. The Soil and Water Assessment Tool (SWAT) model is used to evaluate hydrology in South-West Europe in a context of intensive agriculture and water scarcity. The Index of Hydrological Alteration (IHA) is calculated using discharge observation data. A comparison of two SWAT calibration methodologies are done; a conventional calibration (CC) based on recorded in-stream water quality and quantity and an additional calibration (AC) adding crop managements practices. Even if the water quality and quantity trends are similar between CC and AC, water balance, irrigation and crop yields are different. In the context of rainfall decrease, water yield decreases in both CC and AC, while crop productions present opposite trends (+33% in CC and −31% in AC). Hydrological performance between CC and AC is correlated to IHA: When the level of IHA is under 80%, AC methodology is necessary. The combination of both calibrations appears essential to better constrain the model and to forecast the impact of climate change or anthropogenic influences on water resources.

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