Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities

Abstract

Recent evidence of regional climate change impacts on hydrological cycle directed us to study the floods in a high elevated and rapidly urbanized river basin, the Kabul River basin (KRB), Pakistan, which is susceptible to frequent flooding. Therefore, we analyzed the changes in flood regime at various spatial and temporal scales and their possible causes, which is accomplished by using flood indicators, trend analysis, change point analysis, and hydrological modeling. The results showed that the northern and northwestern parts of the KRB were more exposed to flood hazard than the southern parts under long-term scenario (1961/64-2015). However, after the change points, the flood risk decreased in the northern and increased in the southern regions. This spatial shift increased the vulnerability of population to the flood hazard, because the majority of population resides in the southern region. The extreme precipitation has also increased, especially the maximum one-day rainfall and maximum five-day rainfall throughout the basin. Particularly, the major cause of the decrease in different flood indicators in the northern parts of the KRB is the corresponding decrease in the annual and monsoonal rainfall and corresponding positive mass balance of glaciers in the northern region after the occurrence of change point in flood regime. However, the major cause of the increase in flood hazard on the southern part of the KRB is associated with maximum five-day rainfall. A 68% variability of annual maximum flood for the Kabul River at Nowshera and an 84% variability of annual maximum flood for Bara River at Jhansi post are explained by maximum five-day rainfall. In addition, a considerable decrease in forests (–5.21%) and increase in the urban area (88.26%) from 1992–2015 also amplifies the risk of higher flood peaks. The results of hydrological modeling suggest that the six-hourly flood peak increased by 6.85% (1992–2010) and 4.81% (2010–2015) for the extreme flood of 2010 for the Kabul River at Nowshera. The flood peak per decade will increase by 8.6%, as compared to the flood peak under the land use scenario of 2010. Therefore, consideration of proper land use planning is crucial for sustainable flood management in the KRB.

A novel hybrid framework to model the relationship of daily river discharge with meteorological variables

“…The Kabul River begins at the Unai pass base from the Hindu Kush mountains in Afghanistan, flowing toward the east and spanning 700 km to drain into the Indus River of Pakistan (Mehmood et al 2021).…”

“…Thus, it is essential to carefully manage and plan water resources to reduce loss of life and property damage caused by drought, floods, or heat waves (Ali, Shahbaz 2020; Mangukiya et al 2022). Climate changes influence the hydrological cycle globally; the resulting variations in weather and climate have increased the risks of drought and floods because weather changes, variations in precipitation, peak flows, and extreme temperatures have impacts on river discharge (Mehmood et al 2021). The amount of discharge generated from a catchment depends on various factors such as duration, meteorological variables, velocity, and water level (Gleason et al 2014;Saidi et al 2018;Malik et al 2020).…”

“…Although data-driven (statistical and machine learning) models are applied to predict river discharge, there is no single model that can predict river discharge without bias or with utmost certainty (Mehmood et al 2021). Literature shows that researchers have developed hybrid models by combining two or more techniques to improve the prediction ability of the models (Shabbir et al 2024).…”

Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM

“…Researchers have considered the VAR model with different orders for multivariate time series models and ARIMA models for univariate time series models [11][12][13]. Multivariate time series analysis (MVTSA) introduces a way to observe the relationship of a group of variables over time [14], thus making use of all possible information such as correlation [15].…”

Evaluating the Hydrologic Risk of n-Year Floods According to RCP Scenarios

“…To reduce the risk and effects of flooding, it is necessary to identify the flood-prone areas in current and predicted environmental conditions [4][5][6][7][8]. Recent studies related to climate change identified changes of flow regime, trend, and frequency of flood as well as investigated their causes through hydrological modeling for various spatio-temporal scales [9][10][11][12][13].…”