Assessing the hydrological effects of land-use changes on a catchment using the Markov chain and WetSpa models

Abstract

Predicting the effects of land-use (LU) changes and hydrological processes on a rapidly urbanized catchment using the Markov chain and WetSpa models is the main objective of this research. Hourly hydrometeorological data for 2001-2016, land use maps, a digital elevation model (DEM) and soil texture were used as inputs into the models. The simulation results verified some negative impacts of LU changes, such as increases in peak discharge and flow velocity from 2001 to 2032 by 57.1% and 39.4%, respectively. Additionally, the time of concentration decreased from 6 h in 2001 to 5 h in 2016 and to 4 h in 2032. Surface runoff recorded the highest increases by 48.4% and 83.9%, respectively, in 2016 and 2032, compared to 2001. We concluded that the combination of both models is an appropriate tool for predicting the possible effects of LU changes on different hydrological features, which provide vital information for land managers.

A parameter allocation approach for flow simulation using the WetSpa‐Python model

“…Some studies demonstrate how these parameters are manually calibrated (e.g., Kabir et al, 2011; Liu et al, 2006; Zeinivand & De Smedt, 2009, 2010) or calibrated using optimization (e.g., Bahremand & De Smedt, 2008, 2010; Shafii & Smedt, 2009). Automated calibration often results in unreasonable values for one or two model parameters (Karimi et al, 2016; Kavian et al, 2020). One of the parameters that is often associated with an unreasonable value after automated calibration is the maximum active groundwater storage (Zeinivand & De Smedt, 2009).…”

“…The hydraulic conductivity is scaled by a multiplier (K i ) to account for anisotropy of the soil matrix. The scaling factor for interflow (K i ) can be allocated using information given by previous studies and manual calibration within the context of these studies (e.g., Azinmehr et al, 2016; Karimi et al, 2016; Kavian et al, 2020). K i considers the cumulative effects of the soil root system, organic material and preferential drainage on hydraulic conductivity.…”

“…Automated calibration often results in unreasonable values for one or F I G U R E 2 Land cover/use map (a) and soil type map (b) of the Shemshak-Roudak watershed. The soil data were obtained from a map provided by the regional water Organization of Tehran 2015two model parameters (Karimi et al, 2016;Kavian et al, 2020). One of the parameters that is often associated with an unreasonable value after automated calibration is the maximum active groundwater storage (Zeinivand & De Smedt, 2009…”

“…to account for anisotropy of the soil matrix. The scaling factor for interflow (K i ) can be allocated using information given by previous studies and manual calibration within the context of these studies (e.g., Azinmehr et al, 2016;Karimi et al, 2016;Kavian et al, 2020).…”

Evaluation of multi-hazard map produced using MaxEnt machine learning technique

“…Events such as gully erosion, landslides, and floods are physical phenomena, active in geological times but uneven in time and space 14 – 17 . They are considered hazard events, which can be induced by humans or not, but all of them are key global issues threatening human life, resources and goods 18 – 21 . Moreover, they have different drawbacks in various places and because of their correlated subsequences, these catastrophes have contrary long-term effects.…”