Examining lag time using the landscape, pedoscape and lithoscape metrics of catchments

Amiri, Bahman Jabbarian; Gao, Junfeng; Fohrer, Nicola; Adamowski, Jan

doi:10.1016/j.ecolind.2019.03.050

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…To conduct this study, secondary data [24] were applied. The digital map of land use/land cover was acquired from the European Union's Earth Observation Program for year 2012.…”

Section: Datamentioning

confidence: 99%

Modeling the Soil Erosion Regulation Ecosystem Services of the Landscape in Polish Catchments

Istanbuly

Dostál

Amiri

2021

Water

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In this study, the soil erosion regulation ecosystem services of the CORINE land use/ land cover types along with soil intrinsic features and geomorphological factors were examined by using the soil erosion data of 327 catchments in Poland, with a mean area of 510 ± 330 km2, applying a multivariate regression modeling approach. The results showed that soil erosion is accelerated by the discontinuous urban fabric (r = 0.224, p ≤ 0.01), by construction sites (r = 0.141, p ≤ 0.05), non-irrigated arable land (r = 0.237, p ≤ 0.01), and is mitigated by coniferous forest (r = −0.322, p ≤ 0.01), the clay ratio (r = −0.652, p ≤ 0.01), and the organic content of the soil (r = −0.622, p ≤ 0.01). The models also indicated that there is a strong relationship between soil erosion and the percentage of land use/land cover types (r2 = [0.62, 0.82, 0.83, 0.74]), i.e., mixed forest, non-irrigated arable land, fruit trees and berry plantations, broad-leaf forest, sport and leisure facilities, construction sites, and mineral extraction sites. The findings show that the soil erosion regulation ecosystem service is sensitive to broadleaf forests, rainfed agriculture, soil water content, terrain slope, drainage network density, annual precipitation, the clay ratio, the soil carbon content, and the degree of sensitivity increases from the broadleaf forest to the soil carbon content.

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“…To conduct this study, secondary data [24] were applied. The digital map of land use/land cover was acquired from the European Union's Earth Observation Program for year 2012.…”

Section: Datamentioning

confidence: 99%

Modeling the Soil Erosion Regulation Ecosystem Services of the Landscape in Polish Catchments

Istanbuly

Dostál

Amiri

2021

Water

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“…All datasets were transformed to the same daily and height formats by dividing the discharge volume of a given catchment by its area (Sheikh Goodarzi 2013). Actual evapotranspiration was calculated using Hargreaves's potential evapotranspiration and crop coefficients, which have been used in previous studies (Allen et al 1998;Verstraeten et al 2005;Amiri et al 2016;Amiri et al 2019).…”

Section: Data Requirementsmentioning

confidence: 99%

“…Distributed and semi-distributed hydrologic models explicitly consider spatial variability, which lumped models neglect (Khakbaz et al 2012). Distributed modeling requires extensive data (Amiri et al 2019); thus it can only be used for watersheds with sufficient available data. Generating surface flow components relies on hydro-climatic data as well as hydraulic attributes of watersheds (Reed et al 2004).…”

Section: Introductionmentioning

confidence: 99%

“…This study used the Tank model, which takes advantage of conceptual model capabilities (Ekenberg 2016;Patil and Stieglitz 2014;Haberlandt and Radtke 2014). Elimination of the complexity of conceptualizing hydrological responses, acquiring acceptable results, and minimum data requirements are the advantages of the lumped models that motivate hydrologic modelers to consider these models as efficient and parsimonious platforms, especially in data deficit conditions (Amiri et al 2019;Sheikh Goodarzi, Navardi et al 2018). On the other hand, the Tank model, which was introduced by Sugawara et al (1984) in Japan, is one of the most frequently used conceptual models (Franchini and Pacciani, 1991;Aqili et al 2016;Sugiura et al 2016;Ou et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Performance of a Hydrological Tank Model at Various Spatial Scales

Goodarzi

Amiri

Azarneyvand

et al. 2020

JWMM

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In this study we investigated the performance of the Tank hydrologic model in predicting rainfall-runoff using a descriptive-analytical approach, including objective error measures and flow signatures (mean annual specific runoff, mean flow duration curves, normalized low flow and high flow indexes, coefficient of variation, and hydrograph flashiness). Because hydrological processes vary at different scales, three watersheds located in three significantly different environments, which are examples of small (15 km 2), medium (215 km 2) and large (542 km 2) watersheds, were selected to evaluate the performance of the hydrologic model. We found that Neka (the small watershed) has a large water budget with a steep flow slope, including both extremely low flow (lowest) and high flow (highest), a very high variation, and a flashy hydrograph (0.21 ≤ HF ≤ 0.41). Ghezel-Ozan (the medium watershed) also has a large water budget with a fairly steep flow slope, an extremely low flow and a moderate high flow, a moderate variation, and a very steady hydrograph (0.01 ≤ HF ≤0.02). Babolroud (the large watershed) possesses a moderate water budget with a moderate flow slope, a below-average low flow, a moderate high flow, an average variation, and a fairly flashy hydrograph (0.22 ≤ HF ≤ 0.28). The quality of rainfall-runoff modeling was good to satisfactory for the medium and large watersheds as shown by the two error metrics (1.32 ≤ RMSE ≤ 2.58, 0.61 ≤ NSE ≤ 0.8). However, the small watershed performance, especially in the validation period, was unsatisfactory (4.66 ≤ RMSE ≤ 5.33, 0.21 ≤ NSE ≤ 0.51). The limitations identified should be considered for medium watersheds. The Tank model is highly recommended for use in hydrologic predictions of large watersheds.

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“…The relationship between change in land use/land cover and river water quality variables were well-documented in the studies, e.g., see: [14][15][16], in which the influence of the spatial patterns of land use/land cover on river water quality was addressed. Land use/land cover change due to socio-economic development is one of the significant inevitable consequences that cause the landscape to be fragmented into natural and man-made patches [17].…”

Section: Introductionmentioning

confidence: 99%

Influences of Landscape Configuration on River Water Quality

Aalipour

Antczak

Dostál

et al. 2022

Forests

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The present study investigated the effects of changes in landscape configuration on river water quality, which is calculated by chemical export coefficients, using spatial data onto 31 catchments in the southwestern part of the Caspian Sea basin by applying stepwise multivariate regression models. The water quality modeling has been carried out applying the chemical export coefficients of sulfate, bicarbonate, chlorine, calcium, magnesium, and sodium, and eight landscape metrics (including interspersion juxtaposition index, percentage of like adjacencies, aggregation index, clumpiness index, normalized landscape shape index, patch cohesion index, landscape division index, and splitting index), by which landscape configuration is analyzed. The results indicated that the sulfate (0.25 ± 0.33 gr ha−1yr−1), bicarbonate (0.61 ± 0.87 gr ha−1 yr−1), chlorine (0.17 ± 0.23 gr ha−1 yr−1), calcium (0.16 ± 0.21 gr ha−1 yr−1), magnesium (0.05 ± 0.07 gr ha−1 yr−1), and sodium (0.16 ± 0.21 gr ha−1 yr−1) are annually exported from the study catchments into the rivers. The change in landscape configuration has significantly explained the chemical export coefficients of sulfate, bicarbonate, chlorine, calcium, magnesium, and sodium. The findings showed the cohesion and coherence of the permanently irrigated land patches resulting in the discontinuity of the broad-leaved forest and grassland ecosystems degraded river water quality.

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Examining lag time using the landscape, pedoscape and lithoscape metrics of catchments

Cited by 20 publications

References 21 publications

Modeling the Soil Erosion Regulation Ecosystem Services of the Landscape in Polish Catchments

Modeling the Soil Erosion Regulation Ecosystem Services of the Landscape in Polish Catchments

Assessing the Performance of a Hydrological Tank Model at Various Spatial Scales

Influences of Landscape Configuration on River Water Quality

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