Predicting sediment yield and transport dynamics of a cold climate region watershed in changing climate

Shrestha, Narayan Kumar; Wang, Junye

doi:10.1016/j.scitotenv.2017.12.347

Cited by 78 publications

(32 citation statements)

References 37 publications

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“…All sectors depending on water availability are now vastly exposed to the climate change impacts [5]. Hence, climate change can also impact sediment export [6,7], crop yield and biomass [8,9], water quality [10], etc. All these are driven by streamflow.…”

Section: Introductionmentioning

confidence: 99%

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

et al. 2018

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This research investigated the effect of climate change on the two main river basins of Senegal in West Africa: the Senegal and Gambia River Basins. We used downscaled projected future rainfall and potential evapotranspiration based on projected temperature from six General Circulation Models (CanESM2, CNRM, CSIRO, HadGEM2-CC, HadGEM2-ES, and MIROC5) and two scenarios (RCP4.5 and RCP8.5) to force the GR4J model. The GR4J model was calibrated and validated using observed daily rainfall, potential evapotranspiration from observed daily temperature, and streamflow data. For the cross-validation, two periods for each river basin were considered: 1961-1982 and 1983-2004 for the Senegal River Basin at Bafing Makana, and 1969Makana, and -1985Makana, and and 1986Makana, and -2000 for the Gambia River Basin at Mako. Model efficiency is evaluated using a multi-criteria function (F agg ) which aggregates Nash and Sutcliffe criteria, cumulative volume error, and mean volume error. Alternating periods of simulation for calibration and validation were used. This process allows us to choose the parameters that best reflect the rainfall-runoff relationship. Once the model was calibrated and validated, we simulated streamflow at Bafing Makana and Mako stations in the near future at a daily scale. The characteristic flow rates were calculated to evaluate their possible evolution under the projected climate scenarios at the 2050 horizon. For the near future (2050 horizon), compared to the 1971-2000 reference period, results showed that for both river basins, multi-model ensemble predicted a decrease of annual streamflow from 8% (Senegal River Basin) to 22% (Gambia River Basin) under the RCP4.5 scenario. Under the RCP8.5 scenario, the decrease is more pronounced: 16% (Senegal River Basin) and 26% (Gambia River Basin). The Gambia River Basin will be more affected by the climate change.

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Section: Introductionmentioning

confidence: 99%

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

et al. 2018

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“…This may overcome the weakness of hydrological effects in the above BGC-AGC models. The Soil Water Assessment Tool (SWAT) and the Variable Infiltration Capacity (VIC) are two typical of watershed models [31,[87][88][89]. The SWAT is a land surface-atmosphere interactions model that has its origin in hydrological science, while the VIC is a process-based hydrologic model that considers the energy and water balance across the land surface [31,90,91].…”

Section: Watershed-scale Modelingmentioning

confidence: 99%

“…Digital Elevation Model (DEM), land use, and soil are used for generation of hydrological response units (HRUs), which includes unique combinations of land use, soil and slope. It is widely used in assessing the quality and quantity of surface and ground water, soil erosion prevention and control as well as in predicting the environmental impact of land use, land management practices, non-point pollutant sources and climate change [30,87,89].…”

Section: Soil and Water Assessment Tool (Swat)mentioning

confidence: 99%

Effects of Grazing Management on Spatio-Temporal Heterogeneity of Soil Carbon and Greenhouse Gas Emissions of Grasslands and Rangelands: Monitoring, Modelling and Upscaling

Wang¹

2019

Preprint

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The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental sustainability. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how grazing lands can be better monitored, assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on impacts of grazing on ecosystem goods and services, identifies current knowledge gaps, and formulates a plan forward. We review the impacts of two contrasting grazing systems, continuous and multi-paddock rotational grazing, on soil carbon (C), nutrient cycling and greenhouse gas emissions (GHGs). We then extend our review to explore challenges of incorporating spatial heterogeneity and temporal variability into monitoring and modelling C and nutrient cycling in grazing lands. We revisit two process-based models (i.e., DNDC and DayCent) and two watershed models (i.e., SWAT and VIC) widely used to simulate C, nutrient and water cycles of these lands. Finally we identify research directions for improving the knowledge base which is essential to conserve grazing lands and maintain their ecosystem goods and services.

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“…Advancements in computational power over recent decades, however, has resulted in the creation of innovative techniques for estimating sediment load such as machine learning algorithms that can estimate suspended sediment yield without solving the governing hydraulic equations [42,43]. In addition to these methods, empirical models such as the Soil Water Assessment Tool (SWAT), which is based on the Universal Soil Loss Equation (USLE) [44,45], and physically based models such as the Water Erosion Prediction Project (WEPP) model [46,47] have also been used. However, most of these existing models require either substantial amounts of data or training in order to properly utilize them.…”

Section: Introductionmentioning

confidence: 99%

A Regression-Based Prediction Model of Suspended Sediment Yield in the Cuyahoga River in Ohio Using Historical Satellite Images and Precipitation Data

Ampomah

Hosseiny

Zhang

et al. 2020

Water

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Urbanization typically results in increased imperviousness which alters suspended sediment yield and impacts geomorphic and ecological processes within urban streams. Therefore, there is an increasing interest in the ability to predict suspended sediment yield. This study assesses the combined impact of urban development and increased precipitation on suspended sediment yield in the Cuyahoga River using statistical modeling. Historical satellite-based land-cover data was combined with precipitation and suspended sediment yield data to create a Multiple Linear Regression (MLR) model for the Cuyahoga watershed. An R2 value of 0.71 was obtained for the comparison between the observed and predicted results based on limited land-use and land-cover data. The model also shows that every 1 mm increase in the mean annual precipitation has the potential to increase the mean annual suspended sediment yield by 860 tons/day. Further, a 1 km2 increase in developed land area has the potential to increase mean annual suspended sediment yield by 0.9 tons/day. The framework proposed in this study provides decision makers with a measure for assessing the potential impacts of future development and climate alteration on water quality in the watershed and implications for stream stability, dam and flood management, and in-stream and near-stream infrastructure life.

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Predicting sediment yield and transport dynamics of a cold climate region watershed in changing climate

Cited by 78 publications

References 37 publications

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

Effects of Grazing Management on Spatio-Temporal Heterogeneity of Soil Carbon and Greenhouse Gas Emissions of Grasslands and Rangelands: Monitoring, Modelling and Upscaling

A Regression-Based Prediction Model of Suspended Sediment Yield in the Cuyahoga River in Ohio Using Historical Satellite Images and Precipitation Data

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