Hydrological Response to Agricultural Land Use Heterogeneity Using Variable Infiltration Capacity Model

Srivastava, Ankur; Kumari, Nikul; Maza, Minotshing

doi:10.1007/s11269-020-02630-4

Cited by 94 publications

(37 citation statements)

References 45 publications

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“…Vegetation can have a significant effect on hydrological fluxes due to variations in the physical characteristics of the land surface, soil, and vegetation; such as the roughness, albedo, infiltration capacity, root depth, architectural resistance, leaf area index (LAI), and stomatal conductance [20,21]. Setyorini et al [22] utilized SWAT model calibrated with the LULC map obtained from supervised classification of Landsat images to evaluate the impact of LULC changes and climatic variables on hydrological parameters.…”

Section: Introductionmentioning

confidence: 99%

Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

Demirel

Newton

2021

Water

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Effective management of water resources entails the understanding of spatiotemporal changes in hydrologic fluxes with variation in land use, especially with a growing trend of urbanization, agricultural lands and non-stationarity of climate. This study explores the use of satellite-based Land Use Land Cover (LULC) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

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Section: Introductionmentioning

confidence: 99%

Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

Demirel

Newton

2021

Water

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“…The land use maps contained three non-agricultural classes, namely settlements, water, and fallow (barren) land, and five agricultural land use types: cotton, rice, wheat-fallow, wheat-rice, and wheat-other crops (Figure 6). 31.8%, and that of rice also decreased from 5.8% to 2.0%, whereas fallow areas increased from 13.2% to 27.3%. The percentage of the wheat-only class increased slightly from 1.4% to 3.4% because the reduced water supply in 2008 limited the cultivation of the second crop after wheat.…”

Section: Land Usementioning

confidence: 91%

Afforestation of Degraded Croplands as a Water-Saving Option in Irrigated Region of the Aral Sea Basin

Kumar

Khamzina

Knöfel

et al. 2021

Water

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Climate change is likely to decrease surface water availability in Central Asia, thereby necessitating land use adaptations in irrigated regions. The introduction of trees to marginally productive croplands with shallow groundwater was suggested for irrigation water-saving and improving the land’s productivity. Considering the possible trade-offs with water availability in large-scale afforestation, our study predicted the impacts on water balance components in the lower reaches of the Amudarya River to facilitate afforestation planning using the Soil and Water Assessment Tool (SWAT). The land-use scenarios used for modeling analysis considered the afforestation of 62% and 100% of marginally productive croplands under average and low irrigation water supply identified from historical land-use maps. The results indicate a dramatic decrease in the examined water balance components in all afforestation scenarios based largely on the reduced irrigation demand of trees compared to the main crops. Specifically, replacing current crops (mostly cotton) with trees on all marginal land (approximately 663 km2) in the study region with an average water availability would save 1037 mln m3 of gross irrigation input within the study region and lower the annual drainage discharge by 504 mln m3. These effects have a considerable potential to support irrigation water management and enhance drainage functions in adapting to future water supply limitations.

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“…Changes in vegetal cover often lead to change inbehaviour of landslide (Glade 2003). There is a strong linkage between surface runoff and the agricultural land use (Srivastava et al 2020). Modification in agricultural land use results in alteration in infiltration and surface runoff and thereby triggers landslide occurrence.Eight LULC classes such as water body, dense forest, open forest, built-up, agriculture, scrub land, barren land and shifting cultivation were considered (Fig.…”

Section: Land Use and Land Cover Mapmentioning

confidence: 99%

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process

Panigrahi

et al. 2021

Preprint

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Landslides are one of the most extensive and destructive geological hazards in the globe. Tripura, a north-eastern hilly state of India experiences landslides almost each year during monsoon season causing casualty and huge economic losses. Hence it is required to assess the landslide susceptibility of the area that would support in short and long term planning and mitigation. Analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon and post-monsoon were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during monsoon season and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The output of this study may be referred by the engineers and planners for the assessment, control and mitigation of landslides and development of basic infrastructure in the state.

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Hydrological Response to Agricultural Land Use Heterogeneity Using Variable Infiltration Capacity Model

Cited by 94 publications

References 45 publications

Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

Afforestation of Degraded Croplands as a Water-Saving Option in Irrigated Region of the Aral Sea Basin

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process

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