Anticipating of Potential Climate and Land Use Change Impacts on Floods: A Case Study of the Lower Nam Phong River Basin

Kuntiyawichai, Kittiwet; Sri-Amporn, Winai; Wongsasri, Sarayut; Chindaprasirt, Prinya

doi:10.3390/w12041158

Cited by 9 publications

(12 citation statements)

References 30 publications

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“…Panda et al [49] in their study observed that the Mahanadi basin is characterized by a tropical monsoon (June to September) climate and more than 80% of the annual runoff occurs during the monsoon season. Moreover, these high streamflow events or floods [50,51] have a tremendous impact on the local population and ecosystem. Hence, to cope with this obvious impact of floods in the basin area, the study of climate variability impact on river water streamflow has become the need of the hour.…”

Section: Seasonality Of Daily Streamflowmentioning

confidence: 99%

Impact of Indo-Pacific Climate Variability on High Streamflow Events in Mahanadi River Basin, India

Sahu

Panda

Nayak

et al. 2020

Water

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The potential impact of climate variability on the hydrological regime in the Mahanadi river basin is of great importance for sustainable water resources management. The impact of climate variability on streamflow is analyzed in this study. The impact of climate variability modes on extreme events of Mahanadi basin during June, July, and August (JJA), and September, October, and November (SON) seasons were analyzed, with daily streamflow data of four gauge stations for 34 years from 1980 to 2013 found to be associated with the sea surface temperature variations over Indo-Pacific oceans and Indian monsoon. Extreme events are identified based on their persistent flow for six days or more, where selection of the stations was based on the fact that there was no artificially regulated streamflow in any of the stations. Adequate scientific analysis was done to link the streamflow variability with the climate variability and very significant correlation was found with Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO), El Nino Modoki Index (EMI), and Indian monsoon. Agriculture covers major portion of the basin; hence, the streamflow is very much essential for agriculture as well as population depending on it. Any disturbances in the general flow of the river has subjected an adverse impact on the inhabitants’ livelihood. While analyzing the correlation values, it was found that all stations displayed a significant positive correlation with Indian Monsoon. The respective correlation values were 0.53, 0.38, 0.44, and 0.38 for Andhiyarkore, Baronda, Rajim, and Kesinga during JJA season. Again in the case of stepwise regression analysis, Monsoon Index for the June, July, and August (MI-JJA) season (0.537 for Andhiyarkore) plays significant role in determining streamflow of Mahanadi basin during the JJA season and Monsoon Index for July, August, and September (MI-JAS) season (0.410 for Baronda) has a strong effect in affecting streamflow of Mahanadi during the SON season. Flood frequency analysis with Weibull’s plotting position method indicates future floods in the Mahanadi river basin in JJA season.

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Section: Seasonality Of Daily Streamflowmentioning

confidence: 99%

Impact of Indo-Pacific Climate Variability on High Streamflow Events in Mahanadi River Basin, India

Sahu

Panda

Nayak

et al. 2020

Water

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“…CN III = 23CN II 10 + 0.13CN II (3) where CN I is the runoff curve number value of each LULC type of AMC-I, CN II is the runoff curve number value of each LULC type of AMC-II, and CN III is the runoff curve number value of each LULC type of AMC-III (Table 2). In practice, the three runoff curve numbers of the hydrologic soil group of each LULC type were separately applied to estimate the potential maximum storage under three different AMCs using Equation (4).…”

Section: Suitable Amc For Surface Runoff Estimation Using the Scs-cn Methodsmentioning

confidence: 99%

“…In general, the primary cause of flooding is heavy rainfall [3]. However, many other causes are also due to human activities, such as land degradation; deforestation of catchment areas, urban growth, and increased population along riverbanks [4][5][6]; poor land use planning, zoning, and control of flood plain development; poor drainage, particularly in cities; and insufficient management of discharge from river reservoirs [7].…”

Section: Introductionmentioning

confidence: 99%

“…The specific objectives of the study were (1) to classify LULC data in 2001, 2010, and 2019 using the random forest classifier, (2) to predict LULC change in two periods (2002-2009 and 2011-2018) using the CLLUE-S model, (3) to estimate surface runoff between 2001 and 2019, (4) to optimize and map LULC allocation for flood mitigation under three rainfall conditions, and (5) to evaluate economic and ecosystem service values and change for the most suitable LULC allocation for flood mitigation.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Land Use and Land Cover Allocation for Flood Mitigation Using Land Use Change and Hydrological Models with Goal Programming, Chaiyaphum, Thailand

Phinyoyang

Ongsomwang

2021

Land

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Floods represent one of the most severe natural disasters threatening the development of human society worldwide, including in Thailand. In recent decades, Chaiyaphum province has experienced a problem with flooding almost every year. In particular, the flood in 2010 caused property damage of 495 million Baht, more than 322,000 persons were affected, and approximately 1046.4 km2 of productive agricultural area was affected. Therefore, this study examined how to optimize land use and land cover allocation for flood mitigation using land use change and hydrological models with optimization methods. This research aimed to allocate land use and land cover (LULC) to minimize the surface for flood mitigation in Mueang Chaiyaphum district, Chaiyaphum province, Thailand. The research methodology consisted of six stages: data collection and preparation, LULC classification, LULC prediction, surface runoff estimation, the optimization of LULC allocation for flood mitigation and mapping, and economic and ecosystem service value evaluation and change. According to the results of the optimization and mapping of suitable LULC allocation to minimize surface runoff for flood mitigation in dry, normal, and wet years using goal programming and the CLUE-S model, the suitable LULC allocation for flood mitigation in 2049 under a normal year could provide the highest future economic value and gain. In the meantime, the suitable LULC allocation for flood mitigation in 2049 under a drought year could provide the highest ecosystem service value and gain. Nevertheless, considering future economic and ecosystem service values and changes with surface runoff reduction, the most suitable LULC allocation for flood mitigation is a normal year. Consequently, it can be concluded that the derived results of this study can be used as primary information for flood mitigation project implementation. Additionally, the presented conceptual framework and research workflows can be used as a guideline for government agencies to examine other flood-prone areas for flood mitigation in Thailand.

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“…A modeling method relies on Multi-Layer Perceptron (MLP) neural network and Markov Chain (MC), in which the MLP was trained to model land use transitions through creating transition potential maps. The transition potential maps were created based on a set of descriptive environmental variables called drivers, i.e., agricultural areas, distance to urban areas, rivers, roads, as well as altitude, slope, and aspect of land [18]. Thereafter, the Markov Chain method was then applied to process the transition maps for the prediction process [19], based on the past trends of land use changes from 2010 to 2015.…”

Section: Projection Of Future Land Use Changementioning

confidence: 99%

Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand

Kuntiyawichai

Wongsasri

2021

Water

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The Lam Phaniang River Basin is one of the areas in Northeast Thailand that experiences persistent drought almost every year. Therefore, this study was focused on the assessment of drought severity and vulnerability in the Lam Phaniang River Basin. The evaluation of drought severity was based on the Drought Hazard Index (DHI), which was derived from the Standardized Precipitation-Evapotranspiration Index (SPEI) calculated for 3-month (short-term), 12-month (intermediate-term), and 24-month (long-term) periods. Drought vulnerability was assessed by the Drought Vulnerability Index (DVI), which relied on water shortage, water demand, and runoff calculated from the WEAP model, and the Gross Provincial Product (GPP) data. A drought risk map was generated by multiplying the DHI and DVI indices, and the drought risk level was then defined afterwards. The CNRM-CM5, EC-EARTH, and NorESM1-M global climate simulations, and the TerrSet software were used to evaluate the potential impacts of future climate under RCPs 4.5 and 8.5, and land use during 2021–2100, respectively. The main findings compared to baseline (2000–2017) revealed that the average results of future rainfall, and maximum and minimum temperatures were expected to increase by 1.41 mm, and 0.015 °C/year and 0.019 °C/year, respectively, under RCP 4.5 and by 2.72 mm, and 0.034 °C/year and 0.044 °C/year, respectively, under RCP 8.5. During 2061–2080 under RCP 8.5, the future annual water demand and water shortage were projected to decrease by a maximum of 31.81% and 51.61%, respectively. Obviously, in the Lam Phaniang River Basin, the upper and lower parts were mainly dominated by low and moderate drought risk levels at all time scales under RCPs 4.5 and 8.5. Focusing on the central part, from 2021–2040, a very high risk of intermediate- and long-term droughts under RCPs 4.5 and 8.5 dominated, and occurred under RCP 8.5 from 2041–2060. From 2061 to 2080, at all time scales, the highest risk was identified under RCP 4.5, while low and moderate levels were found under RCP 8.5. From 2081–2100, the central region was found to be at low and moderate risk at all time scales under RCPs 4.5 and 8.5. Eventually, the obtained findings will enable stakeholders to formulate better proactive drought monitoring, so that preparedness, adaptation, and resilience to droughts can be strengthened.

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Anticipating of Potential Climate and Land Use Change Impacts on Floods: A Case Study of the Lower Nam Phong River Basin

Cited by 9 publications

References 30 publications

Impact of Indo-Pacific Climate Variability on High Streamflow Events in Mahanadi River Basin, India

Impact of Indo-Pacific Climate Variability on High Streamflow Events in Mahanadi River Basin, India

Optimizing Land Use and Land Cover Allocation for Flood Mitigation Using Land Use Change and Hydrological Models with Goal Programming, Chaiyaphum, Thailand

Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand

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