scholarly journals Modelling Snowmelt Runoff from Tropical Andean Glaciers under Climate Change Scenarios in the Santa River Sub-Basin (Peru)

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3535
Author(s):  
Elmer Calizaya ◽  
Abel Mejía ◽  
Elgar Barboza ◽  
Fredy Calizaya ◽  
Fernando Corroto ◽  
...  
Keyword(s):  
Climate Change ◽  
Climatic Data ◽  
Climate Change Scenarios ◽  
Snowmelt Runoff ◽  
Snow Covered Area ◽  
Covered Area ◽  
Digital Elevation ◽  
Rcp 8.5 ◽  
Andean Glaciers ◽  
Elevation Model

Effects of climate change have led to a reduction in precipitation and an increase in temperature across several areas of the world. This has resulted in a sharp decline of glaciers and an increase in surface runoff in watersheds due to snowmelt. This situation requires a better understanding to improve the management of water resources in settled areas downstream of glaciers. In this study, the snowmelt runoff model (SRM) was applied in combination with snow-covered area information (SCA), precipitation, and temperature climatic data to model snowmelt runoff in the Santa River sub-basin (Peru). The procedure consisted of calibrating and validating the SRM model for 2005–2009 using the SRTM digital elevation model (DEM), observed temperature, precipitation and SAC data. Then, the SRM was applied to project future runoff in the sub-basin under the climate change scenarios RCP 4.5 and RCP 8.5. SRM patterns show consistent results; runoff decreases in the summer months and increases the rest of the year. The runoff projection under climate change scenarios shows a substantial increase from January to May, reporting the highest increases in March and April, and the lowest records from June to August. The SRM demonstrated consistent projections for the simulation of historical flows in tropical Andean glaciers.

Correction to 'Determination of actual snow-covered area using Landsat TM and digital elevation model data in Glacier National Park, Montana'

Polar Record ◽  
2002 ◽  
Vol 38 (204) ◽  
pp. 53-55 ◽  
Author(s):  
Philip T. Giles
Keyword(s):  
National Park ◽  
Mean Value ◽  
Glacier National Park ◽  
Snow Covered Area ◽  
Covered Area ◽  
Digital Elevation ◽  
Sloping Terrain ◽  
Elevation Model ◽  
Digital Elevation Model Data ◽  
Factor C

AbstractIn the article by Hall and others (1995), a topographic correction factor (C) was developed for estimating actual land area by taking into account the effect of sloping terrain. An error that was made during image processing resulted in values of C being exaggerated. For this note, values of C for the example landscape in Glacier National Park were recalculated, and the results with and without the error are compared. It is shown that the error caused the mean value of C reported for the example landscape to be exaggerated by a factor of 2.62 times.

Determination of actual snow-covered area using Landsat TM and digital elevation model data in Glacier National Park, Montana

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 191-198 ◽  
Author(s):  
Dorothy K. Hall ◽  
James L. Foster ◽  
Janet Y.L. Chien ◽  
George A. Riggs
Keyword(s):  
Snow Cover ◽  
Digital Elevation Model ◽  
National Park ◽  
Glacier National Park ◽  
Modis Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Digital Elevation ◽  
The Future ◽  
Elevation Model

AbstractIn the future, data from the moderate resolution imaging spectroradiometer (MODIS) will be employed to map snow in an automated environment at a resolution of 250 m to 1 km. Using Landsat thematic mapper (TM) data, an algorithm, SNOMAP, has been developed to map snow-covered area. This algorithm will be used, with appropriate modification, with MODIS data following the launch of the first Earth Observing System (EOS) platform in 1998. SNOMAP has been shown to be successful in mapping snow in a variety of areas using TM data. However, significant errors may be present in mountainous areas due to effects of topography. To increase the accuracy of mapping global snow-covered area in the future using MODIS data, digital elevation model (DEM) data have been registered to TM data for parts of Glacier National Park, Montana, so that snow cover on mountain slopes can be mapped. This paper shows that the use of DEM data registered to TM data increases the accuracy of mapping snow-covered area. Using SNOMAP on a subscene within the 14 March 1991 TM scene of northwestern Montana, 215 km2 of snow is mapped when TM data are used alone to map the snow cover. We show that about 1062 km2 of snow are actually present as measured when the TM and DEM data are registered. Approximately five times more snow is present when the effects of topography are considered for this subscene, which is in a rugged area in Glacier National Park. A simple model has been developed to determine the relationship between terrain relief and the amount of correction that must be applied to map actual snow-covered area in Glacier National Park using satellite data alone.

scholarly journals The Impact of Climate Change on Reservoir Inflows Using Multi Climate-Model under RCPs’ Including Extreme Events—A Case of Mangla Dam, Pakistan

2016 ◽  
Author(s):  
Muhammad Babur ◽  
Mukand Singh Babel ◽  
Sangam Shrestha ◽  
Akiyuki Kawasaki ◽  
Nitin Kumar Tripathi
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Swat Model ◽  
Spring Discharge ◽  
Climatic Data ◽  
Climate Change Scenarios ◽  
Global Circulation Models ◽  
Wide Range ◽  
Rcp 8.5 ◽  
The Impact

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

scholarly journals Extracting High Resolution Snow Distribution Information with Inexpensive Autonomous Cameras

10.29007/93gh ◽  
2018 ◽  
Author(s):  
Pauline Millet ◽  
Hendrik Huwald ◽  
Steven V. Weijs
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Time Lapse ◽  
Distortion Correction ◽  
Snow Covered Area ◽  
Covered Area ◽  
Digital Elevation ◽  
Snow Distribution ◽  
Elevation Model ◽  
Time Lapse Photography

This study details a procedure to derive high resolution snow cover information using low-cost autonomous cameras. Images from time lapse photography of target areas are used to obtain temporally resolved binary snow-covered area information. Various image processing steps, such as distortion correction, alignment, projection using the Digital Elevation Model (DEM), and classification using clustering are described. Several innovations, such as matching the mountain silhouette with the DEM, and application of specific filters are described to make this terrestrial remote sensing method generally applicable to derive valuable snow information.

scholarly journals Water Temperature and Hydrological Modelling in the Context of Environmental Flows and Future Climate Change: Case Study of the Wilmot River (Canada)

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2101
Author(s):  
Christian Charron ◽  
André St-Hilaire ◽  
Taha B.M.J. Ouarda ◽  
Michael R. van den Heuvel
Keyword(s):  
Climate Change ◽  
Water Temperature ◽  
Environmental Flows ◽  
Low Flow ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Water Abstraction ◽  
Surface Water Flow ◽  
Modelling Studies ◽  
Rcp 8.5

Simulation of surface water flow and temperature under a non-stationary, anthropogenically impacted climate is critical for water resource decision makers, especially in the context of environmental flow determination. Two climate change scenarios were employed to predict streamflow and temperature: RCP 8.5, the most pessimistic with regards to climate change, and RCP 4.5, a more optimistic scenario where greenhouse gas emissions peak in 2040. Two periods, 2018–2050 and 2051–2100, were also evaluated. In Canada, a number of modelling studies have shown that many regions will likely be faced with higher winter flow and lower summer flows. The CEQUEAU hydrological and water temperature model was calibrated and validated for the Wilmot River, Canada, using historic data for flow and temperature. Total annual precipitation in the region was found to remain stable under RCP 4.5 and increase over time under RCP 8.5. Median stream flow was expected to increase over present levels in the low flow months of August and September. However, increased climate variability led to higher numbers of periodic extreme low flow events and little change to the frequency of extreme high flow events. The effective increase in water temperature was four-fold greater in winter with an approximate mean difference of 4 °C, while the change was only 1 °C in summer. Overall implications for native coldwater fishes and water abstraction are not severe, except for the potential for more variability, and hence periodic extreme low flow/high temperature events.

scholarly journals Climatic suitability predictions for the cultivation of macadamia in Malawi using climate change scenarios.

2021 ◽  
Author(s):  
Emmanuel Junior Zuza ◽  
Yoseph Negusse Araya ◽  
Kadmiel Maseyk ◽  
Shonil A Bhagwat ◽  
Kaue de Sousa ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Food Sources ◽  
Climate Change Scenarios ◽  
Crop Species ◽  
Cascading Effects ◽  
Northern Regions ◽  
Rcp 8.5 ◽  
Model Approach

Climate change is altering suitable areas of crop species worldwide, with cascading effects on people and animals reliant upon those crop species as food sources. Macadamia is one of Malawi's most important and profitable crop species. Here, we used an ensemble model approach to determine the current distribution of macadamia producing areas across Malawi in relation to climate. For future distribution of suitable areas, we used the climate outputs of 17 general circulation models (GCM's) based on two climate change scenarios (RCP 4.5 and RCP 8.5). We found that the precipitation of the driest month and isothermality were the climatic variables that strongly influenced macadamia's suitability in Malawi. These climatic requirements were fulfilled across many areas in Malawi under the current conditions. Future projections indicated that large parts of Malawi's macadamia growing regions will remain suitable for macadamia, amounting to 36,910 km2 (39.1%) and 33,511 km2 (35.5%) of land based on RCP 4.5 and RCP 8.5, respectively. Of concern, suitable areas for macadamia production are predicted to shrink by −18% (17,015 km2) and −22% (20,414 km2) based on RCP 4.5 and RCP 8.5, respectively, with much of the suitability shifting northwards. Although a net loss of area suitable for macadamia is predicted, some currently unsuitable areas will become suitable in the future. Notably, suitable areas will increase in Malawi's central and northern regions, while the southern region will lose most of its suitable areas. In conclusion, our study provides critical evidence that climate change will significantly affect the macadamia sub-sector in Malawi. Therefore area-specific adaptation strategies are required to build resilience.

scholarly journals Utilization of Satellite Snow-Cover Observations for Seasonal Streamflow Estimates in the Western Himalayas

1983 ◽  
Vol 14 (5) ◽  
pp. 257-266 ◽  
Author(s):  
B. Dey ◽  
D. C. Goswami ◽  
A. Rango
Keyword(s):  
Regression Model ◽  
Snow Cover ◽  
Multiple Regression ◽  
Snowmelt Runoff ◽  
Indus River ◽  
Snow Covered Area ◽  
Covered Area ◽  
Measured Flow ◽  
Seasonal Runoff ◽  
Kabul River

The results presented in this study indicate the possibility of seasonal runoff prediction when satellite-derived basin snow-cover data are related to point source river discharge data for a number of years. NOAA-VHRR satellite images have been used to delineate the areal extent of snow cover for early April over the Indus and Kabul River basins in Pakistan. Simple photo-interpretation techniques, using a zoom transfer scope, were employed in transferring satellite snow-cover boundaries onto base map overlays. A linear regression model with April 1 through July 31 seasonal runoff (1974-1979) as a function of early April snow cover explains 73% and 82% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. The correlation between seasonal runoff and snow cover is significant at the 97% level for the Indus River and at the 99% level for the Kabul River. Combining Rango et al.'s (1977) data for 1969-73 with the above period, the April snow cover explains 60% and 90% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. In an attempt to improve the Indus relationship, a multiple regression model, with April 1 through July 31, 1969-79, seasonal runoff in the Indus River as a function of early April snow-covered area of the basin and concurrent runoff in the adjoining Kabul River, explains 79% of the variability in flow. Moreover, a significant reduction (27%) in the standard error of estimate results from using the multi-variate model. For each year of the study period, 1969-79, a separate multiple regression equation is developed dropping the data for the year in question from the data-base and using those for the rest of the years. The snow cover area and concurrent runoff data are then used to estimate the snowmelt runoff for that particular year.The difference between the estimated and observed dircharge values averaged over the 11 year study period is 10%. Satellite derived snow-covered area is the best available input for snowmelt-runoff estimation in remote, data sparse basins like the Indus and Kabul Rivers. The study has operational relevance to water resource planning and management in the Himalayan region.

scholarly journals Maxent modeling for predicting the potential distribution of Arbutus andrachne L. belonging to climate change in Turkey.

2021 ◽  
Vol 48 (2) ◽  
Author(s):  
Ayse Gul Sarikaya ◽  
◽  
Omer K. Orucu ◽  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Potential Distribution ◽  
Climate Change Scenarios ◽  
Small Tree ◽  
Potential Habitat ◽  
Northern Areas ◽  
Maxent Modeling ◽  
Rcp 8.5 ◽  
Turkish Flora

Arbutus andrachne L., the strawberry tree, is an evergreen shrub or small tree in the Turkish flora and has broad uses. The wood is used for decorative purposes, packaging, and manufacturing furniture. The fruits are edible and used in treating many kinds of diseases. However, global warming might affect the abundance of this symbolic plant's distribution, especially at higher latitudes. This study was conducted to determine the expected effects of climate change on A. andrachne. For this purpose, Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 were used to expect climate change scenarios for 2050 and 2070, and potential distribution areas of A. andrachne were presented. The results indicated that the distribution of A. andrachne would decrease in the southern regions of Turkey. However, the spread of the species could be expanded in the western and northern areas. It is also expected that there would be potential habitat losses, which would affect the distribution of A. andrachne.

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