scholarly journals MODELING SNOWMELT-RUNOFF UNDER CLIMATE CHANGE SCENARIOS IN THE BEHESHTABAD WATERSHED

2019 ◽  
Vol 65 (3) ◽  
Author(s):  
Mohammad BAGHER RAISI ◽  
Mehdi VAFAKHAH ◽  
Hamidreza MORADI
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios ◽  
Snowmelt Runoff

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.

scholarly journals Climate Change Scenarios and Effects on Snow-Melt Runoff

2020 ◽  
Vol 6 (9) ◽  
pp. 1715-1725 ◽  
Author(s):  
Safieh Javadinejad ◽  
Rebwar Dara ◽  
Forough Jafary
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Annual Runoff ◽  
Hydrologic Model ◽  
Climate Change Scenarios ◽  
Snow Melt ◽  
Snowmelt Runoff ◽  
Rcp Scenarios ◽  
Temperature And Precipitation ◽  
Spring Runoff

Climate change is an important environmental issue, as progression of melting glaciers and snow cover is sensitive to climate alteration. The aim of this research was to model climate alterations forecasts, and to assess potential changes in snow cover and snow-melt runoff under the different climate change scenarios in the case study of the Zayandeh-rud River Basin. Three cluster models for climate change (NorESM1-M, IPSL-CM5A-LR and CSIRO-MK3.6.0) were applied under RCP 8.5, 4.5 and 2.6 scenarios, to examine climate influences on precipitation and temperature in the basin. Temperature and precipitation were determined for all three scenarios for four periods of 2021-2030, 2031-2040, 2041-2050 and 2051-2060. MODIS (MOD10A1) was also applied to examine snow cover using temperature and precipitation data. The relationship between snow-covered area, temperature and precipitation was used to forecast future snow cover. For modeling future snow melt runoff, a hydrologic model of SRM was used including input data of precipitation, temperature and snow cover. The results indicated that all three RCP scenarios lead to an increase in temperature, and reduction in precipitation and snow cover. Investigation in snowmelt runoff throughout the observation period (November 1970 to May 2006) showed that most of annual runoff is derived from snow melting. Maximum snowmelt runoff is generated in winter. The share of melt water in the autumn and spring runoff is estimated at 35 and 53%, respectively. The results of this study can assist water manager in making better decisions for future water supply.

scholarly journals Modelling Snowmelt Runoff under Climate Change Scenarios in an Ungauged Mountainous Watershed, Northwest China

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yonggang Ma ◽  
Yue Huang ◽  
Xi Chen ◽  
Yongping Li ◽  
Anming Bao
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Stream Flow ◽  
Coupled Model ◽  
Northwest China ◽  
Climate Change Scenarios ◽  
Watershed Hydrology ◽  
Climate Data ◽  
Snowmelt Runoff ◽  
The Impact

An integrated modeling system has been developed for analyzing the impact of climate change on snowmelt runoff in Kaidu Watershed, Northwest China. The system couples Hadley Centre Coupled Model version 3 (HadCM3) outputs with Snowmelt Runoff Model (SRM). The SRM was verified against observed discharge for outlet hydrological station of the watershed during the period from April to September in 2001 and generally performed well for Nash-Sutcliffe coefficient (EF) and water balance coefficient (RE). The EF is approximately over 0.8, and the water balance error is lower than ± 10%, indicating reasonable prediction accuracy. The Statistical Downscaling Model (SDSM) was used to downscale coarse outputs of HadCM3, and then the downscaled future climate data were used as inputs of the SRM. Four scenarios were considered for analyzing the climate change impact on snowmelt flow in the Kaidu Watershed. And the results indicated that watershed hydrology would alter under different climate change scenarios. The stream flow in spring is likely to increase with the increased mean temperature; the discharge and peck flow in summer decrease with the decreased precipitation under Scenarios 1 and 2. Moreover, the consideration of the change in cryosphere area would intensify the variability of stream flow under Scenarios 3 and 4. The modeling results provide useful decision support for water resources management.

scholarly journals Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

2021 ◽  
Author(s):  
Rohitashw Kumar ◽  
Saika Manzoor ◽  
Dinesh Kumar Vishwakarma ◽  
N. L. Kushwaha ◽  
Ahmed Elbeltagi ◽  
...  
Keyword(s):  
Climate Change ◽  
Temperature Rise ◽  
Model Performance ◽  
High Elevation ◽  
Himalayan Region ◽  
Coefficient Of Determination ◽  
Climate Change Scenarios ◽  
Snowmelt Runoff ◽  
Mountain Environments ◽  
Elevation Data

The current study was planned to simulate runoff due to the snowmelt in the Lidder River catchment of Himalayan region under climate change scenarios. A basic degree-day model, Snowmelt-Runoff Model (SRM) was utilized to assess the hydrological consequences of change in climate. The SRM model performance during the calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found as 11.7, -10.1, -11.8, 1.96, and 8.6 during 2009-2014, respectively, while the R2 is 0.96, 0.92, 0.95, 0.90, and 0.94, respectively. The Dv and R2 values indicating that the simulated snowmelt runoff has a close agreement with the observed value. The simulated findings were also assessed under the different scenarios of climate change: a) increases in precipitation by +20 %, b) temperature rise of +2 °C, and c) temperature rise of +2 °C with a 20 % increase in snow cover. In scenario "b", the simulated results showed that runoff increased by 53 % in summer (April–September). In contrast, the projected increased discharge for scenarios "a" and "c" was 37 % and 67 %, respectively. In high elevation data-scarce mountain environments, the SRM is efficient in forecasting future water supplies due to the snowmelt runoff.

Simulation of snowmelt-runoff under climate change scenarios in a data-scarce mountain environment

2017 ◽  
Vol 12 (8) ◽  
pp. 910-930 ◽  
Author(s):  
Adnan Ahmad Tahir ◽  
Samreen Abdul Hakeem ◽  
Tiesong Hu ◽  
Huma Hayat ◽  
Muhammad Yasir
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios ◽  
Snowmelt Runoff ◽  
Mountain Environment

Modelling impacts of different climate change scenarios on soil water regime of a Mollisol

2005 ◽  
Vol 33 (1) ◽  
pp. 185-188 ◽  
Author(s):  
Csilla Farkas ◽  
Roger Randriamampianina ◽  
Juraj Majerčak
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Water Regime ◽  
Climate Change Scenarios ◽  
Soil Water Regime

scholarly journals Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

2021 ◽  
Author(s):  
Mark Cooper ◽  
Kai P. Voss-Fels ◽  
Carlos D. Messina ◽  
Tom Tang ◽  
Graeme L. Hammer
Keyword(s):  
Climate Change ◽  
Crop Improvement ◽  
Target Population ◽  
Crop Productivity ◽  
Climate Change Scenarios ◽  
Global Food Security ◽  
Genotype By Environment ◽  
Improvement Strategies ◽  
Yield Gaps ◽  
On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

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