scholarly journals Land-use based modeling approach for determining freshwater nitrate loadings from small agricultural watersheds

2020 ◽  
Author(s):  
Pierre Grizard ◽  
Kerry T. B. MacQuarrie ◽  
Yefang Jiang
Keyword(s):  
Land Use ◽  
Prince Edward Island ◽  
Agricultural Land ◽  
Latin Hypercube Sampling ◽  
Potato Production ◽  
Lumped Parameter Model ◽  
Model Parameters ◽  
Agricultural Watersheds ◽  
Lumped Model ◽  
Transport Delay

Abstract Nitrate released from a variety of land-use activities is a major factor in the degrading conditions observed in many watersheds and estuaries. In this research a spatially lumped model is developed to estimate annual nitrate loads and concentrations from over 100 small watersheds in the Canadian province of Prince Edward Island (PEI). Nitrate source concentrations are associated with major land-use categories, and nitrate attenuation, based on the width of riparian zones, and transport delay due to groundwater residence time are simulated. To investigate the uncertainty of the results, model parameters were selected using a Latin hypercube sampling method. Nitrate concentrations from 12 watersheds were used for model calibration (R2 = 0.91), while 118 other watersheds were used for verification purposes (R2 = 0.82). Overall, the lumped parameter model is shown to be a useful tool for simulating annual nitrate loadings from agricultural watersheds when detailed spatiotemporal agricultural land-use data are available. For PEI the model results indicate that nitrate loadings to estuaries are strongly related to agricultural land, especially the land area in potato production.

scholarly journals A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions

2018 ◽  
Vol 15 (9) ◽  
pp. 2909-2930 ◽  
Author(s):  
Sebastian Lienert ◽  
Fortunat Joos
Keyword(s):  
Land Use ◽  
Carbon Emissions ◽  
Latin Hypercube Sampling ◽  
Historical Period ◽  
Model Parameters ◽  
Dynamic Global Vegetation Model ◽  
Order Of Magnitude ◽  
The Difference ◽  
Wood Harvest ◽  
Global Vegetation

Abstract. A dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC). Processes featured in DGVMs include parameters which are prone to substantial uncertainty. To cope with these uncertainties Latin hypercube sampling (LHS) is used to create a 1000-member perturbed parameter ensemble, which is then evaluated with a diverse set of global and spatiotemporally resolved observational constraints. We discuss the performance of the constrained ensemble and use it to formulate a new best-guess version of the model (LPX-Bern v1.4). The observationally constrained ensemble is used to investigate historical emissions due to LULCC (ELUC) and their sensitivity to model parametrization. We find a global ELUC estimate of 158 (108, 211) PgC (median and 90 % confidence interval) between 1800 and 2016. We compare ELUC to other estimates both globally and regionally. Spatial patterns are investigated and estimates of ELUC of the 10 countries with the largest contribution to the flux over the historical period are reported. We consider model versions with and without additional land-use processes (shifting cultivation and wood harvest) and find that the difference in global ELUC is on the same order of magnitude as parameter-induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.

EFFECTS OF AGRICULTURAL LAND USE ON POTENTIAL SHEET EROSION LOSSES IN SOUTHERN ONTARIO

1976 ◽  
Vol 56 (4) ◽  
pp. 443-451 ◽  
Author(s):  
L. J. P. VAN VLIET ◽  
G. J. WALL ◽  
W. T. DICKINSON
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Cropping Systems ◽  
Agricultural Land Use ◽  
Agricultural Watersheds ◽  
Row Crops ◽  
Southern Ontario ◽  
Climatic Regions ◽  
Permanent Pasture ◽  
Sheet Erosion

Potential sheet erosion losses from 13 agricultural watersheds were investigated to assess the effects of agricultural land use on soil erosion levels. The agricultural watersheds, ranging in size from 19 to 54 km2, were representative of the predominant livestock and cropping management systems in the major physiographic and climatic regions of Southern Ontario. Average annual sheet erosion losses were estimated on a field basis using the universal soil loss equation. Rainfall intensity and soil and topographic variability were considered in deriving mean annual soil losses for a range of field crops. Crops yielding the highest annual sheet erosion loss estimates were row crops, such as horticultural cash crops (9.1 ton/ha/yr), beans (7.6 ton/ha/yr), and continuous corn (6.7 ton/ha/yr). Erosion losses from crops in rotations with grasses or legumes (corn, small grains, meadow) and from tobacco were determined to be approximately one half of values obtained for continuous row cropping systems. Lowest sheet erosion losses occurred on permanent pasture and woodlands (<0.5 ton/ha/yr). Results of this study suggest that on a regional basis, the intensively farmed cash crop areas have higher erosion potentials than areas with rotational cropping systems. Watersheds with a low potential for sheet erosion often are dominated by livestock operations, where a large percentage of the area is represented by grasses or legumes and permanent pasture.

Modelling land use/cover change scenarios in a transboundary catchment

2020 ◽  
Author(s):  
Stanley Chasia ◽  
Luke Olang ◽  
Lewis Sitoki ◽  
Mathew Hernnergger
Keyword(s):  
Land Use ◽  
Population Density ◽  
Land Use Change ◽  
Physical Environment ◽  
Agricultural Land ◽  
Slow Growth ◽  
Model Parameters ◽  
Rock Outcrops ◽  
Barren Land ◽  
Growth Scenario

&lt;p&gt;Changes in land use/cover are among the most important anthropogenic transformation on the physical environment affecting proper functioning of the earth system. Hitherto, land characterization has often been studied using archived satellite data products to understandd trends in space and time. However, due to future uncertainties in land use change in developing countries and the associated impacts on the physical environment, there is need to model these changes at a local scale. A modelling framework to simulate empirically quantified relations between land use and its driving factors was used in the Sio-Malaba-Malakisi catchment between Kenya and Uganda. Changes for the catchment were simulated for a period of 30 years (2017 &amp;#8211; 2047) using model parameters that define location characteristics, spatial policies, area restrictions, land use demand and conversion elasticity settings. Elevation, slope, population density, soil organic carbon, soil CEC and precipitation were potential factors selected to evaluate the suitability of devoting a grid cell to a land use type using a stepwise regression model. The scenarios evaluated include first growth, slow growth and an urbanization scenario. The high ROC value in all statistical tests (&gt;0.72) indicated that the spatial distribution of some land use types in the basin could be explained by the selected driving variables. In a fast growth scenario (under policy restriction), areas under open soil and shrubland would be converted to cropland when demand for cash crop goes up in the region. Areas under open trees and marshland outside protected zones, would be converted to agricultural land while barren land with rock outcrops would remain largely unchanged over the period. In a slow growth scenario, expansion of the area under cropland would follow historical trend at 1.25% growth per annum. Marshland areas unsuitable for agricultural expansion are projected to remain the same. In an urbanization scenario, built-up areas would increase steadily at &gt;1% per annum especially in areas earmarked for infrastructural development. In all the scenarios explored, topography, precipitation, soil characteristics and population density were identified as the key drivers of land use change. Results of this study would enhance the understanding of the complexities in projecting future land cover changes and provide baseline data for supporting ongoing soil and land management programs in a data scarce area.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key words:&lt;/strong&gt; Land use change; CLUE-S model; Scenario analysis; Sio-Malaba-Malakisi catchment; Transboundary basin&lt;/p&gt;

scholarly journals Parametric uncertainty or hydrological changes?

2014 ◽  
Vol 364 ◽  
pp. 134-139
Author(s):  
F. Viola ◽  
L. V. Noto ◽  
D. Pumo
Keyword(s):  
Land Use ◽  
Model Calibration ◽  
Time Window ◽  
Time Windows ◽  
Climatic Variability ◽  
Parametric Uncertainty ◽  
Model Parameters ◽  
Lumped Model ◽  
Daily Streamflow ◽  
Random Variability

Abstract. The model calibration is the way of hydrologists for searching also a physical interpretation of complex interactions acting within a basin. Actually, it can be frequently noticed how model calibration performed on a given time-window may converge to a point in the parameter space that could be distant from another obtainable calibration of the model in the same basin but considering a different time window. Is that again parametric uncertainty or does the trajectory in the parametric space relate about to a slow hydrological basin change? This paper depicts a possible path for detecting changes' signatures in a streamflow time series. In particular, the paper seeks to draw a way to discern the random variability over different time-windows of the calibrated model parameters set from that induced by the variation in time of some boundary conditions and external forcings. To this purpose, we will refer to a conceptual lumped model for simulating daily streamflow, the EHSM (EcoHydrological Streamflow Model), and to a hypothetical case study. The selected hydrological model requires a total of seven parameters, some of which can be easily related to land use, while others rely on climate variables. The calibration of the EHSM parameters with regard to different time-windows and the analysis of potential impacts of the anthropic variation in land use and/or climatic variability on the calibrated parameters set, will support our investigation.

scholarly journals Evaluation of the effect of land use change on runoff using supervised classified satellite data

2019 ◽  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Surface Runoff ◽  
Agricultural Land ◽  
Swat Model ◽  
Model Parameters ◽  
Time Interval ◽  
Efficiency Coefficient ◽  
Runoff Simulation ◽  
Maximum Probability

<p>The main objective of this study was to determine the effect of land use change on runoff in Chenar Rahdar watershed. Land use map of the studied basin was determined using Landsat satellite imagery for 2004 and 2015 using ENVI software. After applying the necessary corrections to the images and field surveys to take the educational points, supervised classification technique and maximum probability algorithm were applied to mapping land use change in the study area. According to results, 6 classes of land use were investigated (bare land, rain fed land, forest land, water agriculture land, rangelands and urban lands). In this study, 21 model parameters were calibrated with monthly runoff using 2004-2012 data and validated using 2012-2015 data. The efficiency coefficient for calibration and validation were between 0.88 and 0.94, respectively. The land use changes trend within the time interval showed that the highest percentage of incremental changes is related to urban lands with 108.45%, whereas, the highest decline was observed for agricultural land with 12.46%. In order to investigate the effect of land use change on surface runoff, different land use maps were applied to SWAT model, supposing constant condition for other parameters of the model. The results show that surface runoff increased by 11%, in 2015 compared to 2004. Comprehensive water management can reduce surface runoff in the watershed. The results showed that if all uncertainties were minimized, the calibrated SWAT model can give acceptable runoff simulation results regarding the land use change. These results can be useful for water and environmental resource managers.</p>

scholarly journals A Bayesian Ensemble Data Assimilation to Constrain Model Parameters and Land Use Carbon Emissions

2018 ◽  
Author(s):  
Sebastian Lienert ◽  
Fortunat Joos
Keyword(s):  
Land Use ◽  
Carbon Emissions ◽  
Latin Hypercube Sampling ◽  
Historical Period ◽  
Model Parameters ◽  
Dynamic Global Vegetation Model ◽  
Order Of Magnitude ◽  
The Difference ◽  
Wood Harvest ◽  
Global Vegetation

Abstract. A dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC). Processes featured in DGVMs include parameters which are prone to substantial uncertainty. To cope with these uncertainties Latin Hypercube Sampling (LHS) is used to create a 1000-member perturbed parameter ensemble, which is then evaluated with a diverse set of global and spatio-temporaly resolved observational constraints. We discuss the performance of the constrained ensemble and use it to formulate a new best-guess version of the model (LPX-Bern v1.4). The observationally constrained ensemble is used to investigate historical emissions due to LULCC (ELUC) and their sensitivity to model parametrization. We find a global ELUC estimate of 158 (108, 211) PgC (median and 90 % confidence interval) between 1800 and 2016. We compare ELUC to other estimates both globally and regionally. Spatial patterns are investigated and estimates of ELUC of the ten countries with the largest contribution to the flux over the historical period are reported. We consider model versions with and without additional land-use processes (shifting cultivation and wood harvest) and find that the difference in global ELUC is on the same order of magnitude as parameter induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.

Climate Change and Land Use Change of Rural Households in The Red River Delta, Vietnam

2013 ◽  
pp. 79-94
Author(s):  
Ngoc Luu Bich
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Agricultural Land ◽  
Rural Households ◽  
River Delta ◽  
Red River ◽  
Red River Delta ◽  
Sea Levels ◽  
Main Production ◽  
Farming Households

Climate change (CC) and its impacts on the socio-economy and the development of communities has become an issue causing very special concern. The rise in global temperatures, in sea levels, extreme weather phenomena, and salinization have occurred more and more and have directly influenced the livelihoods of rural households in the Red River Delta – one of the two regions projected to suffer strongly from climate change in Vietnam. For farming households in this region, the major and traditional livelihoods are based on main production materials as agricultural land, or aquacultural water surface Changes in the land use of rural households in the Red River Delta during recent times was influenced strongly by the Renovation policy in agriculture as well as the process of industrialization and modernization in the country. Climate change over the past 5 years (2005-2011) has started influencing household land use with the concrete manifestations being the reduction of the area cultivated and the changing of the purpose of land use.

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