Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future

JOURNAL OF ENVIRONMENTAL MANAGEMENT

卷:298

Article

Janizadeh, Saeid¹  Pal, Subodh Chandra²  Saha, Asish²  Chowdhuri, Indrajit²  Ahmadi, Kourosh³  Mirzaei, Sajjad¹  Mosavi, Amir Hossein^4,5  Tiefenbacher, John P.⁶ 

[1] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn & Sci, Tehran 14115111, Iran

[2] Univ Burdwan, Dept Geog, Burdwan, W Bengal, India

[3] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Forestry, Tehran 14115111, Iran

[4] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary

[5] J Selye Univ, Dept Informat, Komarno 94501, Slovakia

[6] Texas State Univ, Dept Geog, San Marcos, TX 78666 USA

关键词: Climate change;    Ensemble approach;    Flood risk;    Land-use change;    Machine learning;   

DOI  :  10.1016/j.jenvman.2021.113551

来源: Elsevier

【 摘 要 】

The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 flood and non-flood locations were identified and mapped. Twenty flood-risk factors were selected to model flood risk using several machine learning techniques: conditional inference random forest (CIRF), the gradient boosting model (GBM), extreme gradient boosting (XGB) and their ensembles. To investigate the future (year 2050) effects of changing climates and changing land use on future flood risk, a general circulation model (GCM) with representative concentration pathways (RCPs) of the 2.6 and 8.5 scenarios by 2050 was tested for impacts on 8 precipitation variables. In addition, future land uses in 2050 was prepared using a CA-Markov model. The performances of the flood risk models were validated with Receiver Operating Characteristic-Area Under Curve (ROC-AUC) and other statistical analyses. The AUC value of the ROC curve indicates that the ensemble model had the highest predictive power (AUC = 0.83) and was followed by GBM (AUC = 0.80), XGB (AUC = 0.79), and CIRF (AUC = 0.78). The results of climate and land use changes on future flood-prone areas showed that the areas classified as having moderate to very high flood risk will increase by 2050. Due to the changes occurring with land uses and in climates, the area classified as moderate to very high risk increased in the predictions from all four models. The areal proportion classes of the risk zones in 2050 under the RCP 2.6 scenario using the ensemble model have changed of the following proportions from the current distribution Very Low =-12.04 %, Low =-8.56 %, Moderate = +1.56 %, High = +11.55 %, and Very High = +7.49 %. The RCP 8.5 scenario has caused the following changes from the present percentages: Very Low =-14.48 %, Low =-6.35 %, Moderate = +4.54 %, High = +10.61 %, and Very High = +5.67 %. The results of current and future flood risk mapping can aid planners and flood hazard managers in their efforts to mitigate impacts.

【 授权许可】

Free