scholarly journals Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change scenarios using SWAT model

2017 ◽  
Author(s):  
Sangchul Lee ◽  
In-Young Yeo ◽  
Ali M. Sadeghi ◽  
Gregory W. McCarty ◽  
Wells D. Hively ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Stream Flow ◽  
Climate Change Scenario ◽  
Swat Model ◽  
Co2 Concentration ◽  
Change Scenario ◽  
Baseline Scenario ◽  
Climate Variability And Change ◽  
Precipitation Increase

Abstract. Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to exacerbate under climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluates the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitive scenarios to assess the individual effects of variations in CO2 concentration (590 and 850 ppm), precipitation increase (11 and 21 %) and temperature increase (2.9 and 5.0 °C), and considered the predicted climate change scenario using five general circulation models (GCMs) under the Special Report on Emissions Scenarios (SRES) A2 scenario. Using SWAT model simulations from 2001 to 2014, as a baseline scenario, the predicted water and nitrate budgets under climate variability and change scenarios were analyzed at multiple temporal scales. Compared to the baseline scenario, precipitation increase of 21 % and elevated CO2 concentration of 850 ppm significantly increased stream flow and nitrate loads by 50 % and 52 %, respectively, while, temperature increase of 5.0 °C reduced stream flow and nitrate loads by 12 % and 13 %, respectively. Under the climate change scenario, annual stream flow and nitrate loads showed an average increase of nearly 40 %, relative to the baseline scenario. Differences in hydrological responses observed from the two watersheds were primarily attributed to contrasting land use and soil characteristics. The watershed with larger percent croplands indicated increased nitrate yield of 0.52 kg N ha−1 compared to the one with less percent croplands under the climate change scenario, due to increased export of nitrate derived from fertilizer. The watershed dominated by poorly-drained soils showed a lower increase in nitrate yield than one dominated by well-drained soils, due to a high potential of nitrate loss in surface runoff and enhanced denitrification. To mitigate increased nitrate loads potentially caused by climate change, the enhanced implementation of conservation practices would be necessary for this region in the future. These findings assist watershed managers and regulators as they seek to establish effective adaptation strategies to mitigate water quality degradation in this region.

scholarly journals Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change using the SWAT model

2018 ◽  
Vol 22 (1) ◽  
pp. 689-708 ◽  
Author(s):  
Sangchul Lee ◽  
In-Young Yeo ◽  
Ali M. Sadeghi ◽  
Gregory W. McCarty ◽  
Wells D. Hively ◽  
...  
Keyword(s):  
Climate Variability ◽  
Elevated Co2 ◽  
Temperature Increase ◽  
Swat Model ◽  
Co2 Concentration ◽  
Baseline Scenario ◽  
Climate Variability And Change ◽  
Hydrological Responses ◽  
Precipitation Increase ◽  
Precipitation And Temperature

Abstract. Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to be exacerbated by climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluated the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using the Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitivity scenarios to assess the individual impacts of variations in CO2 concentration (590 and 850 ppm), precipitation increase (11 and 21 %), and temperature increase (2.9 and 5.0 ∘C), based on regional general circulation model (GCM) projections. Further, we considered the ensemble of five GCM projections (2085–2098) under the Representative Concentration Pathway (RCP) 8.5 scenario to evaluate simultaneous changes in CO2, precipitation, and temperature. Using SWAT model simulations from 2001 to 2014 as a baseline scenario, predicted hydrologic outputs (water and nitrate budgets) and crop growth were analyzed. Compared to the baseline scenario, a precipitation increase of 21 % and elevated CO2 concentration of 850 ppm significantly increased streamflow and nitrate loads by 50 and 52 %, respectively, while a temperature increase of 5.0 ∘C reduced streamflow and nitrate loads by 12 and 13 %, respectively. Crop biomass increased with elevated CO2 concentrations due to enhanced radiation- and water-use efficiency, while it decreased with precipitation and temperature increases. Over the GCM ensemble mean, annual streamflow and nitrate loads showed an increase of ∼ 70 % relative to the baseline scenario, due to elevated CO2 concentrations and precipitation increase. Different hydrological responses to climate change were observed from the two watersheds, due to contrasting land use and soil characteristics. The watershed with a larger percent of croplands demonstrated a greater increased rate of 5.2 kg N ha−1 in nitrate yield relative to the watershed with a lower percent of croplands as a result of increased export of nitrate derived from fertilizer. The watershed dominated by poorly drained soils showed increased nitrate removal due do enhanced denitrification compared to the watershed dominated by well-drained soils. Our findings suggest that increased implementation of conservation practices would be necessary for this region to mitigate increased nitrate loads associated with predicted changes in future climate.

scholarly journals Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1556 ◽  
Author(s):  
Daeeop Lee ◽  
Giha Lee ◽  
Seongwon Kim ◽  
Sungho Jung
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Change Scenario ◽  
Swat Model ◽  
Mekong River ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Runoff Analysis

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.

scholarly journals Change in Stream Flow of Gumara Watershed, Upper Blue Nile Basin, Ethiopia under Representative Concentration Pathway Climate Change Scenario

2020 ◽  
Author(s):  
Gashaw Gismu Chakilu ◽  
Szegedi Sandor ◽  
Turi Zoltan
Keyword(s):  
Climate Change ◽  
Stream Flow ◽  
Climate Change Scenario ◽  
Rainfall Amount ◽  
Change Scenario ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Gumara Watershed ◽  
Rcp 8.5

Climate change plays a pivotal role in the hydrology of tributaries in the upper Blue Nile basin. This study was designed to reveal the extent to which climate change impacts on stream flow of the Gumara watershed under the Representative Concentration Pathway (RCP) climate change scenario. The study considered the RCP 2.6, RCP 4.5 and RCP 8.5 scenarios using the second generation Canadian Earth System Model (CanESM2). The Statistical Downscaling Model (SDSM) was used for calibration and projection of future climatic data of the study area. Soil and Water Assessment Tool (SWAT) model was used for simulation of the future stream flow of the watershed. Result showed that the average temperature will be increasing by 0.84oC, 2.6oC and 4.1oC in the end of this century under RCP 2.6, RCP 4.5 and RCP 8.5 scenarios respectively. The change in monthly rainfall amount showed a fluctuating trend in all scenarios but the overall annual rainfall amount is projected to increase by 8.6%, 5.2% and 7.3% in RCP 2.6, RCP 4.5, and RCP 8.5 respectively. Overall, this study revealed that, due to climate change, the stream flow of the watershed is found to be increasing by 4.06%, 3.26%, and 3.67% under RCP 2.6, RCP 4.5 and RCP 8.5 scenarios respectively.

scholarly journals Assessing the impacts of climate change on dependable flow and potential irrigable area using the SWAT model. The case of Maasin River watershed in Laguna, Philippines

2019 ◽  
Vol 50 (2) ◽  
pp. 88-98
Author(s):  
Lanie A. Alejo ◽  
Victor B. Ella
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Seasonal Changes ◽  
Stream Flow ◽  
Swat Model ◽  
The Philippines ◽  
Rice Production ◽  
Change Scenario ◽  
Irrigation Systems ◽  
Impacts Of Climate Change

Seasonal changes in rainfall and temperature brought about by climate change affect water resources availability for rice production areas. There are currently no published applications of the soil and water assessment tool (SWAT) model on quantified effects of climate variability on irrigation service areas for rice production. The study assessed the impacts of climate change on dependable flow and potential irrigable areas of the Maasin River in Laguna, Philippines. Projected variations of rainfall and temperature in 2020 and 2050 developed using PRECIS model based on special report on emission scenarios were employed. The SWAT model was then used to simulate stream flow for each climate change scenario, from which dependable flows were quantified using flow duration analysis. Diversion water requirements for the rice areas in the watershed were determined using CROPWAT. Based on dependable flows and irrigation demand, the potential irrigable areas were estimated. Calibration and validation of the SWAT model showed satisfactory performance in stream flow simulations. The dependable flow in irrigation systems may decline by more than 50% in 2020 and by as much as 97% in 2050, because of seasonal changes in rainfall. In effect, the potential irrigable area may decrease to less than half of the current service area depending on the level of greenhouse gases emissions. SWAT water balance projections suggest surface runoff during wet seasons and increase annual groundwater recharge are possible sources of supplemental irrigation. Provisions of suitable storage reservoir facilities and groundwater development projects will alleviate water scarce conditions. The study demonstrated a technique that may be applied in other irrigation systems in the Philippines and in other countries to quantify the effects of climate change on dependable flows and potential irrigable areas. It can serve as an input to water resources planning and policy recommendations for climate change adaptation and risk reduction strategies. This technique can also be used to assess water resources in other perennial rivers and its viability for the development of new irrigation systems in the Philippines.

Irish farms under climate change – is there a regional variation on farm responses?

2014 ◽  
Vol 153 (3) ◽  
pp. 385-398 ◽  
Author(s):  
S. SHRESTHA ◽  
M. ABDALLA ◽  
T. HENNESSY ◽  
D. FORRISTAL ◽  
M. B. JONES
Keyword(s):  
Climate Change ◽  
Climate Change Scenario ◽  
Growth Models ◽  
Dairy Farms ◽  
Change Scenario ◽  
Baseline Scenario ◽  
The South ◽  
Forage Maize ◽  
Farm Level ◽  
East Region

SUMMARYThe current paper aims to determine regional impacts of climate change on Irish farms examining the variation in farm responses. A set of crop growth models were used to determine crop and grass yields under a baseline scenario and a future climate scenario. These crop and grass yields were used along with farm-level data taken from the Irish National Farm Survey in an optimizing farm-level (farm-level linear programming) model, which maximizes farm profits under limiting resources. A change in farm net margins under the climate change scenario compared to the baseline scenario was taken as a measure to determine the effect of climate change on farms. The growth models suggested a decrease in cereal crop yields (up to 9%) but substantial increase in yields of forage maize (up to 97%) and grass (up to 56%) in all regions. Farms in the border, midlands and south-east regions suffered, whereas farms in all other regions generally fared better under the climate change scenario used in the current study. The results suggest that there is a regional variability between farms in their responses to the climate change scenario. Although substituting concentrate feed with grass feeds is the main adaptation on all livestock farms, the extent of such substitution differs between farms in different regions. For example, large dairy farms in the south-east region adopted total substitution of concentrate feed while similar dairy farms in the south-west region opted to replace only 0·30 of concentrate feed. Farms in most of the regions benefitted from increasing stocking rate, except for sheep farms in the border and dairy farms in the south-east regions. The tillage farms in the mid-east region responded to the climate change scenario by shifting arable production to beef production on farms.

scholarly journals Simulating Agroforestry Adoption in Rural Indonesia: The Potential of Trees on Farms for Livelihoods and Environment

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 385
Author(s):  
Beatrice Nöldeke ◽  
Etti Winter ◽  
Yves Laumonier ◽  
Trifosa Simamora
Keyword(s):  
Climate Change ◽  
Climate Change Scenario ◽  
Agroforestry System ◽  
Environmental Benefits ◽  
Small Scale ◽  
Change Scenario ◽  
Trees On Farms ◽  
Agroforestry Adoption ◽  
Mitigate Climate Change ◽  
Small Scale Farmers

In recent years, agroforestry has gained increasing attention as an option to simultaneously alleviate poverty, provide ecological benefits, and mitigate climate change. The present study simulates small-scale farmers’ agroforestry adoption decisions to investigate the consequences for livelihoods and the environment over time. To explore the interdependencies between agroforestry adoption, livelihoods, and the environment, an agent-based model adjusted to a case study area in rural Indonesia was implemented. Thereby, the model compares different scenarios, including a climate change scenario. The agroforestry system under investigation consists of an illipe (Shorea stenoptera) rubber (Hevea brasiliensis) mix, which are both locally valued tree species. The simulations reveal that farmers who adopt agroforestry diversify their livelihood portfolio while increasing income. Additionally, the model predicts environmental benefits: enhanced biodiversity and higher carbon sequestration in the landscape. The benefits of agroforestry for livelihoods and nature gain particular importance in the climate change scenario. The results therefore provide policy-makers and practitioners with insights into the dynamic economic and environmental advantages of promoting agroforestry.

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