Impact of ENSO on River Flooding in the Karnali River Basin of Nepal

doi:10.21203/rs.3.rs-5279938/v1

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Impact of ENSO on River Flooding in the Karnali River Basin of Nepal

https://doi.org/10.21203/rs.3.rs-5279938/v1

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El Niño and La Niña, collectively known as the El Niño Southern Oscillation (ENSO), play an important role in river flooding. This study investigates the relationship between ENSO and associated floods in the Karnali River Basin (KRB). A specific focus is placed on a comprehensive analysis of extreme flood events in El Niño and La Niña years, which would deepen the understanding of the influence of ENSO on hydrological and hydrodynamic processes. Precipitation and discharge data from 1962 to 2020 were obtained from the Department of Hydrology and Meteorology (DHM), Government of Nepal. The hydrodynamic model (HEC-HMS & HEC-RAS) was used to simulate the extreme floods of 1983, 2000, 2014 & 2015 at the DHM hydrological station. The year of 2015 is a strong El Niño year, while 1983 and 2014 are La Niña years. In the El Niño year 2015, the annual precipitation was 1190 mm and the average annual discharge was 1130 m³/s. The instantaneous peak discharge for the extreme flood event was 4560 m³/s. However, in the same river basin, the annual precipitation during the La Niña years of 2014 and 1983 was 1413 mm and 1283 mm, respectively. The 1D and 2D HEC-RAS models were used to simulate the flood behaviors and analyze the river channel shift during the extreme flood event in August 2015. At the station in the Chisapani area, the observed peak discharge was 3354 m³/s while the model can well capture the peak flood discharge with a simulated peak of 3365 m³/s. The models further showed that the river channel shifted at 2,000 m intervals along both the river branches during the extreme flood event. This study highlights the impact of ENSO on precipitation and river flooding in Nepal.

ENSO

Precipitation

Flood response

Hydrological Modelling

Karnali River Basin

Nepal

ENSO play a considerable influence on global weather patterns, by exerting complex ocean-atmosphere dynamics (Wang et al. 2023). El Niñoand La Niña are natural cyclical patterns of warming and cooling and ENSO has been recognized as the most pronounced interannual oscillation signal of the climate system (Webster et al. 1998). ENSO is characterized by an increase in sea surface temperatures that trigger extreme variations in precipitation, violent storms, flooding and other notorious events (Adamson 2021). Traditional ENSO events occur on the coast of Peru along the eastern equatorial Pacific, and the SST anomaly gradually extends westward (Huang et al. 1989; Zhang et al. 1999, Feng et al. 2003). Recently, a new type of ENSO event has been observed, marked by the development of its warm/cold center in the central Pacific (Kao et al. 2009). Various studies have highlighted the significance of distinguishing different episodes of SST in the Pacific when discussing ENSO impacts on floods (Zhang et al. 2014, Zhang et al. 2016). Several indices are used to describe ENSO, including the Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), and Sea Surface Temperature rise (NOAA 2002; Shrestha & Kostaschuk 2005). Previous studies (Simpson et al. 1993; Kahya & Dracup 1994; Wijeratne et al. 2023; Kim & An, 2018, Sahu et al. 2012, Nguyen & Li 2014) have focused on leveraging ENSO impact assessments to predict stream flow, providing valuable insights into water flow for periods ranging from 6 to 12 months. The studies have been extended to the diverse effects of ENSO on stream flows in the Ganges River in India(Whitaker et al. 2001) and the Mahaweli catchment in Sri Lanka (Zaroug et al. 2013). While an ENSO impact on precipitation in Nepal has also been identified (Shreevastav 2019), runoff was underestimated if rainfall was uniformly placed over large grid cells. At present, the specific basin-level relationship between ENSO and streamflow dynamics in transboundary rivers remains elusive. This research seeks to fill this gap by examining the effects of high and low (El Niño and La Niña) ENSO events on transboundary river floods, considering both regional and local dynamics.

The Indo-Gangetic Plain (IGP) witnesses extensive inundation, primarily due to inefficient land-use planning, inadequate floodplain management, and insufficient pre-development research (Simpson et al. 1993). Downstream flooding near the Nepal-India border is exacerbated by unauthorized infrastructure construction without proper land assessment, compounded by the absence of cross-drainage passages and embankments on the IGP (Kahya & Dracup 1994). A floodplain assessment identified the downstream region of the Karnali River Basin (KRB) as particularly vulnerable (Dingle et al. 2020; Aryal et al. 2020). El Niño years bring about water scarcity for irrigation, while La Niña years pose heightened flood risks during the monsoon season. Understanding these dynamics and preparing for forecasted ENSO years could significantly benefit society, particularly in areas designated as high-flood-risk zones (Dingle et al. 2020). The influence of ENSO on flood discharge variation (in the downstream KRB, station no. 280) ranges from a minimum of 3354.0 m³/s to a maximum of 23295.3 m³/s, with gauge heights observed ranging from a minimum of 7.93 m to a maximum of 15.2 m from the observed Government of Nepal Department of Hydrology and Meteorology (DHM) data during 1964 to 2020 (Dingle et al. 2020; Aryal et al. 2020). The calculated 150-year return period is shown to be 16.2 year. These findings shed light on how ENSO can influence flood occurrences, offering valuable insights for enhancing flood response strategies. The DHM has established crucial threshold water level gauge heights for warning and danger levels during the monsoon season which is essential for accurate flood prediction and effective mitigation. The severity of downstream flooding, for example highlighted by the discharge recorded on August 15, 2014, at Chisapani gauging station, underscores the importance of precise flood prediction models (Aryal et al. 2020).

This study highlights the importance of combining hydrological and hydrodynamic models (like HEC-HMS and HEC-RAS) with recent field data for accurate flood prediction in response to ENSO events. This is crucial for protecting communities and livelihoods in flood-prone areas. The study emphasizes the vulnerability of mountainous regions to floods during ENSO events (Shrestha et al. 2002). However, studies using HEC-HMS at the basin scale demonstrate a high correlation between estimated and observed rainfall-runoff hydrographs (Hamdan et al., 2021). Similarly, good agreement between observed and satellite-derived precipitation in estimating catchment runoff was found (Belayneh et al. 2020). The KRB is highly prone to flood disasters (Aryal et al. 2020). The main objective of this study was to identify ENSO years and their impact on river flooding in the Karnali River Basin. Specifically, it determines monsoonal precipitation patterns, frequency of flood forecasts, and the relationship between rainfall and discharge influenced by ENSO events in the KRB. During ENSO events, the flood response and IDF curve were developed, emphasizing the impacts of these events on precipitation and flooding in KRB. By examining the interplay between ENSO events and non-ENSO precursors, the research provides a more comprehensive understanding of the ENSO influencing floods in the KRB. The findings underscore the importance of considering regional climate phenomena in developing robust flood response strategies, crucial for safeguarding communities and their livelihoods.

2.1 Study Area

The study area is divided into three regions i.e. the China-exclusive zone covering 3060.1 km², the region from the China border to the Chisapani hydrological station spanning 42890 km², and the total basin study area extending to 46,177.3 km² up to the Indian border. The DHM has established 47 precipitation stations and 1 hydrological station in the KRB. These stations are distributed across elevations ranging from 114 to 7,746m asl in the KRB transboundary river system. Extreme floods and droughts reach their peak during the monsoon months (June to September) in the Karnali River basin of Nepal (DHM 1998). Nineteen hydrometric stations each with a record spanning more than 30 years were selected for the study (Fig. 1).

2.2 Data

Hydro-Meteorological Data

Daily precipitation, temperature, and discharge data for the downstream Karnali River Basin (KRB) were obtained from the Department of Hydrology and Meteorology (DHM), Government of Nepal. This analysis focuses specifically on the downstream area of the KRB (hydrological station No. 280). The annual instantaneous discharge and precipitation are presented in Supplementary Table (I). For the transboundary Karnali River Basin, we analyzed daily observed precipitation and discharge data over a 56-year period (1964–2020). Four significant El Niño events were identified in 1983, 2000, 2014, and 2015 respectively (see Table 4 and methods section 2.3 for criteria). The year 2015 had the lowest recorded precipitation and discharge during the four EL Nino events. Precipitation during the following year (2015 to 2016) varied spatially, with some areas receiving above-average rainfall while others experienced below-average amounts.

Digital Elevation Model (DEM)

For constructing a Digital Elevation Model (DEM), SRTM DEM data were collected from the freely available United States Geological Survey website (https://urs.earthdata.nasa.gov/) with a 30×30m resolution. The collected DEM data served as input for both the HEC-HMS and HEC-RAS models. In HEC-HMS, DEM data helps calculate basin parameters like sub-basin area and reach lengths, while in HEC-RAS, it aids in determining the basin's geometric data needed for simulating extremely high peak flows and ENSO-year events for this research. Information on the selected ENSO-affected event dates, precipitation, and peak flood from the DHM is listed in Table 1.

Table 1

Maximum Precipitation and Discharge obtained from DHM for ENSO year events
Date of Events	Basin max Precipitation (mm)	Location	Peak Flood Discharge (m³/s) (Station no:280)	Remark
9/11/1983	431.0	Katai	21700	Maximum Precipitation 431.0 mm receiving at the elevation of 1388 m station no 205
8/1/2000	240.7	Jajarkot	12500	Maximum Precipitation 240.7 mm receiving station no 404 at elevation 1231 m.
8/15/2014	499.8	Jajarkot	21700	At the elevation of 225 m maximum precipitation is 499.8 mm receiving station no 405
1/17/2015	280.8	Mehalkuna	385	Winter Snow Precipitation 280. 8 mm receiving station no 432 at elevation 1472 m.
8/22/2015	217.6	South Chisapani Karnali	4560	Basin max Precipitation 280.7 mm receiving Surkhet station no 406, elevation 720 m.
1964 to 2020	290.7	KRB	9470	Basin average Precipitation (77.8 mm) and discharge (9470 m³/s)

Daily precipitation and discharge data for the period of 1964–2020 was analyzed using data summarization techniques like crosstabs, along with filtering methods, to identify the maximum and minimum values within the dataset.

ENSO Data

The ENSO data were obtained from the ENSO Index (MEI) values, Southern Oscillation Index (SOI), Sea Surface Temperature (SST), and pressure data of 1000 mb scale downloaded from https://www.cpc.ncep.noaa.gov/data/indices/, https://giovanni.gsfc.nasa.gov/giovanni/, and https://sealevel.jpl.nasa.gov/data/el-nino-la-nina-watch-and-pdo/el-nino-2015/, these data from 1964 to 2020 are depicted in supplementary Table (I)

Auxiliary Data

Data on social incidents, crop failures, water shortages, damage, and loss for 2014 and 2015 were obtained from the Disaster Risk Reduction Portal (DDRRP), Home Ministry of the Government of Nepal (http://drrportal.gov.np/). The data covers the study area encompassing the districts of Rolpa, Rukum, Salyan, Bardiya, Surkhet, Dailekh, Jajarkot, Dolpa, Jumla, Kalikot, Mugu, Humla, Bajura, Bajhang, Doti, Kailali, Dadeldhura, Baitadi, and Darchula during the monsoon season (Table 2) rainy days, while the monsoon season precipitation days are shown in Table 3. For analysis of transboundary precipitation and flood response, the maximum flood event of 2014 and the minimum flood event of 2015 were selected from the events listed in Table 1 and the hydro-meteorological and social loss and dam, age records in Table 2.

Table 2

Incident and loss during 2014 and 2015 obtained from DHM.
Incident by Heavy rainfall storm flood and Landslide	2014	2015
Displaced Shed	11	9
Total people Death	27	23
People Injured	42	13
Estimated Loss in US $	33418979	350000

Table 3

Number of precipitation days in different ENSO-years obtained from DHM.
Duration	No. of rainy days (From May to Oct)	% of monsoon days
Average Days	108	81
Minimum Days	81	60
Maximum Days	134	100
Year-1983	98	73
Year-2000	112	84
Year-2014	110	82
Year-2015	113	84

2.3 Methods

In this study, the basin's characteristics, precipitation depth, river discharge and gauge height (water level) during the extreme flood events were examined. The daily data were used to estimate the flood discharge by HEC- HMS and flood depth by HEC- RAS models. This study emphasizes the significant influence of ENSO events on flood behavior within Nepal's transboundary Karnali River Basin from 1964 to 2015, while focusing on the August 2015 precipitation and flooding events. The methodology employed in this analysis is presented in the following section.in Fig. 2.

The ENSO-related variability used for stream flow (Shrestha & Kostaschuk 2005b), in this research for similar criteria for the temporal analysis of Sea Surface Temperature (SST), Southern Oscillation Index (SOI), Multivariate ENSO Index (MEI), pressure, temperature, precipitation and discharge data in the average of the entire KRB Nepal using following methods are depicted in Table 4.

Table 4

ENSO Running mean and standard deviation criteria
CENSO Index	Criteria/ Source
SST	El Niño (La Niña): Maximum (Minimum) SST > 1°C (< -1°C) standard deviation and SST > 0.5°C (< -0.5°C) for at least 8 months/ (B. Wang et al., 2000)
SOI	El Niño (La Niña): 5-month running mean of SOI< -0.5 (> 0.5) for 5 or more consecutive months between April of the year to March of the following year (C)/ (Whitaker et al., 2001) and (Kiem & Franks, 2001)
MEI	El Niño (La Niña): 5-month running mean of MEI of > 0.5 (< 0.5) for 5 or more consecutive months between April to March of the following year (+) and the peak MEI > 1 (< -1). (Whitaker et al. 2001), (B. Wang et al., 2000) and (Kiem & Franks, 2001)

Running Mean

In this study, the running mean statistical method was used to analyze the long-term rainfall patterns, discharge, and temperature across the entire study area. This technique utilizes time-series data to smooth out short-term fluctuations, thereby accentuating the long-term trends in weather data. In the KRB, this study used average precipitation and discharge data from 1962 to 2020, by applying a three-year running mean to the precipitation data to correlate it with discharge from 1964 to 2020, to identify the long-term ENSO effect. The running mean was calculated by the following methods (Sahu et al, 2012)

$\:{\varvec{R}}_{\varvec{a}\varvec{v}\varvec{g}-\varvec{n}}\:=\left(\frac{\sum\:{\varvec{X}}_{\varvec{s}\varvec{u}{\varvec{m}}_{\varvec{i}}}}{\varvec{n}}\right)$ [1]

where:

$\:{R}_{avg-n}$ is running mean for point n

$\:\sum\:{X}_{su{m}_{i}}$ is sum of the data points within the window (i = n-(w-1) to n)

n is the current data point

w is the window size (number of data points in the subset)

Running Mean SOI:

(SOI) _rm (t) = $\:\frac{1}{\varvec{n}}\:{\varvec{*}\sum\:\varvec{i}=\varvec{t}-\varvec{n}+1)}^{\varvec{t}}\left(\varvec{S}\varvec{O}\varvec{I}\right)\_\varvec{s}\varvec{t}\varvec{d}\left(\varvec{i}\right)$ [2]

t is the current time steps and n is the window size for the running mean. The running mean commonly used values are 3 and 5 months.

2.3.1. Intensity Duration Frequency Analysis (IDF)

The disintegration of daily rainfall into short durations is crucial for hydrological assessments. Following the method recommended by the Indian Meteorological Department, the study utilized a one-third reduction technique after comparing different methods (Rashid et al. 2012b, Rashid et al. 2012a) and proposed a relationship that has been employed for estimating short-duration rainfall in specific regions.

2.3.2. Correlation and regression analysis

In this study for correlation analysis, the dependent variable (Y) and independent variables are (X₁, X₂, X₃ and X₄). In the correlation analysis between ENSO and observed discharge the p-value was less than 0.0001, indicating high statistical significance found in the dependent (discharge) and all four independent variables.

Similarly, in the regression analysis, discharge data served as the dependent variable (Y), while the other four independent variables, such as SST (X₁), pressure (X₂), surface temperature (X₃), and precipitation (X₄), were used as predictors. The results from this analysis allowed us to assess their combined linear relationship with discharge and understand the potential influence of ENSO on the other four independent variables.

2.3.3. Estimated Extreme Discharge (HEC-HMS) Model

This study utilized the Hydrological Engineering Center's software suite: the Hydrological Modeling System (HEC-HMS, Version 4.11) and the River Analysis System (HEC-RAS, new version 6.5) (Basnet and Acharya 2016; Hicks and Peacock 2005; Shrestha et al. 2010; Baniya et al. 2024; Adhikari et al. 2023). They were used to estimate flood event discharge and water level rise at the Karnali Chisapani hydrological station (Kim & An, 2018, Kumar et al., 2012). Specifically, observed event data were used in the HEC-HMS model for calibration and validation in Karnali Chisapani hydrological station. Discharge estimation using HEC HMS has been applied in continuous simulation methods using simple canopy data, temperature, simple surface data and deficit and constant Clark and recession methods from the available precipitation and discharge from 1964 to 2020. For the strong ENSO-year 2015, flood events were selected using the Soil Conservation Service Curve Number (SCS CN) method, for the gauged discharge at Chisapani hydrological station (No. 280). This selected event was simulated using the HEC-HMS hydrological model and the IDF data were calculated for the strong ENSO 2015 event.

2.3.4. Hydro dynamical Modelling (HEC-RAS Model)

The HEC-RAS model involved the preparation of geometric data within the software's RAS mapper module to check channel shifting in the Karnali River (Rakhal et al., 2021). The available data on precipitation and discharge were used in HEC-HMS model and the outputs were plugged into the HEC-RAS model for 1D and 2D flood analysis. The given model used the continuity equation and St. Venant equations for the flood study as follows Chow, V. T. (1964):

$\:\frac{\partial\:\varvec{Q}}{\partial\:\varvec{t}}+\frac{\partial\:\varvec{p}}{\partial\:\varvec{x}}+\frac{\partial\:\varvec{q}}{\partial\:\varvec{y}}=0\:\:\:$ [3]

where, h = water depth (m),

t = time (s),

Q = flow rate (m³/s),

x = distance along the river (m)

Similarly, the movement equation is t rate of change of water depth ($\:\frac{\partial\:\text{Q}}{\partial\:\text{t}}$) at any point, which is equal to the difference between the inflow rate and the outflow rate $\:(\frac{\partial\:\text{Q}}{\partial\:\text{x}}$) can be written as

$\:\frac{\partial\:\mathbf{Q}}{\partial\:\mathbf{t}}+\text{α}\text{}\text{V\:*|V|}+\left(\varvec{g}\mathbf{*}\frac{\partial\:\mathbf{h}}{\partial\:\mathbf{x}}\right)-\left(\varvec{g}\mathbf{*}\varvec{S}\varvec{f}\right)=0$ [4]

where, α = momentum coefficient (dimensionless), V = average flow velocity (m/s), g = acceleration due to gravity (m/s²) and Sf = friction slope (m/m) This equation applies Newton's second law to the water in the control volume. It considers the rate of change of momentum as ($\:\frac{\partial\:\text{Q}}{\partial\:\text{t}}$), friction losses is $\:\text{α}\text{}\text{V\:*|V|}$, pressure forces is $\:\left(g\text{*}\frac{\partial\:\text{h}}{\partial\:\text{x}}\right)$, and gravity forces due to the bed slope is $\:\left(g\text{*}Sf\right)$.

3.1. ENSO-year Event Precipitation and Discharge

The IDF curves were analyzed for six ENSO events between 1983 and 2020 (Appendix III). On average, the basin precipitation was 217.6 mm and the calculated maximum intensity of half-hour (0.5) duration of the event was 121.3 mm/hour. The calculated IDF values were used to simulate 24-hour (one-day) storms. These curves represent the relationships between precipitation intensity, duration, and frequency. Arranged from lowest to highest, they depict the varying precipitation characteristics across the different ENSO events (Fig. 3). The numerical results are presented in Appendix I and Appendix II.

Elevation plays a crucial role in influencing precipitation patterns within the KRB, especially during strong ENSO events like the one in 2015. This relationship is evident in the varying maximum precipitation values recorded at stations with different elevations. The KRB's diverse elevations (from 129 m at Naubasta to 3430 m at Khaptad) contribute to this variation. Chisapani (Karnali), at a relatively low 225 meters, experienced unusually high maximum precipitation (217.6 mm) in 2015, highlighting the potential for intense rain, even at lower elevations. Stations at higher elevations, like Katti (1472m), with its maximum precipitation of 280.8 mm, may experience significant discharge fluctuations due to both elevation and rainfall (Fig. 4A and 4B).

The impact of ENSO on these climatic variables is demonstrated by typical precipitation peaks in July and August. KRB flow values demonstrate a binary pattern, indicating either normal or no flow. Compared to 2014 and 2015, the years 1983 and 2000 exhibit higher flow values (Fig. 5). The hydrograph peak during September in 1983 suggests a more significant flood event, potentially influenced by transboundary effects. In contrast, the flood peak of 2000 may not be due to transboundary effects. The monthly hydrographs offer further insights into how ENSO influences precipitation and flow patterns in the KRB basin (Fig. 5).

3.2 Continuous Flow Modeling

Continuous flow modeling using the running mean precipitation and discharge over 56 years (1964 to 2020) in the KRB, identified a strong El Niño event in 2015, characterized by a negative SST index, below-average precipitation, and decreased base flow discharge. On average, the monsoon duration spans 108 days, constituting 81% of the period from May to October. The range fluctuates from a minimum of 81 days (60%) to a maximum of 134 days (100%). Specific ENSO years further represent this variability; for instance, in the El Niño year of 1983, there were 98 monsoon days (73%), while the La Niña year of 2000 experienced an extended 112 monsoon days (84%). The time series modeling using daily precipitation and discharge data from 1964 to 2020 and based on the Clark recession method, identified a particularly low flow variation in 2015 by continuous simulation (Fig. 6).

The modelling also considered high flow in three different locations, by determining flood response in the 2015 ENSO year, compared to the 1964–2020 average (Fig. 7). In 2015, the ENSO- El Niño year received 79% or less discharge compared to the 1964–2020 average.

3.3 Calibration and Validation

The model's percentage validity in terms of estimated peak flood discharge, compared to observed discharge was − 0.34%. Similarly, the difference between the modeled and observed water levels was − 6.81% (Table 5). The transboundary KRB is snow fed and supported by extensive glaciers (1361 covering 1740 km²) and numerous glacial lakes (907). Studies indicate the river channel shifts approximately every 2000 m in both branches, with more frequent changes on the right side, possibly due to flooding between 2010–2013. Our results indicate a close agreement between the simulated and observed discharge, showing the model is highly satisfactory in calibration and validation periods (Table 5, Fig. 8)

Table 5

Calibration and validation model for peak discharge estimation.
Discharge Calibration with Observed and Model	Observed Hydrological Discharge (m³/s)	Observed Flood Height (m)	Model Discharge (m³/s)	Model Flood Height (m)	% Of Different Discharge	% Of Different Gauge Height
Transboundary Discharge ENSO Year 2015	3354.0	7.93	3365.4	8.47	-0.34	-6.81
Cal Determined Equation y = 0.816x + 692.27

3.4 ENSO Effect on Flow

The lowest minimum discharge was observed in 2015. Similarly, La Niña events were identified from 1967 to 1977, 1981, 1989, 1990, 1999 to 2003, and 2007 to 2014. The peaks or minimum values in SST were noted in 1967, 1979, 1994, 1995, 2004, and 2015–2016. The El Niño event of 2015–2016 correlated with low discharge, while La Niña events were observed during the aforementioned periods (Fig. 9).

Data from KRB (1964–2020) indicates La Niña events in 1983 and 2014 and a strong El Niño in 2015. Specifically, between August 22nd and September 7th, 2015, values were input into the HEC-HMS model, revealing a maximum one-day precipitation of 217.6 mm within a 24-hour period. The derived IDF storm precipitation values for 1-hour, 2-hour, 3-hour, 6-hour, 12-hour, and 24-hour intervals during the El Niño event of 2015 were obtained as 121.3, 76.2, 49.8, 37.0, 22.6, 12.0, and 9.1 mm/hour respectively (Appendix II).

3.5 Correlation and Regression-Identified Flow Data

The seasonal analysis shows a weak positive correlation between January and February precipitation, suggesting a slight increase in February rainfall when January rainfall is higher. Equally, March and July exhibit a relatively weak negative correlation (r=-0.34), meaning higher March precipitation tends to precede lower July precipitation. June and November show a moderate positive correlation (r = 0.39), indicating they tend to experience rainfall increases or decreases together. Similarly, discharge data reveals a moderate positive correlation between January and February (r = 0.60) and a strong positive correlation between June and November (r = 0.78). This suggests a moderate to strong association between precipitation and subsequent discharge (Appendix IV). Analysis of the correlation between the KRB mean observed discharge and various meteorological variables provided several significant insights. Notably, a moderate positive relationship exists between observed discharge and both annual mean SST (X1) and pre-annual mean SST (X2), with correlation coefficients of 0.39 and 0.39, respectively. This suggests that higher sea surface temperatures and annual precipitation levels are associated with increased basin discharge. Conversely, basin average temperature (X3) exhibits a weak negative correlation of -0.16 with discharge, indicating that higher temperatures may slightly reduce observed discharge, possibly due to increased evaporation. The most substantial positive relationship is observed with basin mean precipitation (X4) and discharge, which has a correlation coefficient of 0.59, emphasizing that precipitation is a primary driver of basin discharge. In contrast, modeled discharge demonstrates different correlation patterns with the same variables. The modelled discharge exhibits negligible to weak positive correlations with SST and pre-annual mean SST, suggesting minimal influence from these factors on its output. However, the model is moderately responsive to basin average temperature (0.43) and strongly influenced by basin mean precipitation (0.740) (Appendix V) In seasonal analysis during the monsoon season, mean precipitation (X₄) and SST were found to have a highly significant relationship with discharge. In other seasons (MAM: March-April-May, and ON: October-November), the correlations between mean precipitation and SST with discharge were moderate, indicating that both factors contribute to discharge variability, albeit to a lesser extent compared to the dry and monsoon seasons (Table 6).

Table 6

Seasonal Correlation (r) analysis
Seasonal Discharge (Y)	Correlation r	Coffee. determination. r ²	Significance f	SST (X1) P-value	Pres (X2) P-value	Temp (X3) P-value	PPT (X4) Mean P-value
DJF	0.0500	0.0025	0.998	0.864	0.820	0.878	0.865
MAM	0.3563	0.1270	0.126	0.114	0.215	0.401	0.339
JJAS	0.6649	0.4421	0.000	0.635	0.417	0.003	0.0002
ON	0.2774	0.0770	0.374	0.252	0.134	0.684	0.882

3.6 Frequency Analysis Using Gumbel Extreme Flood

The HEC-HMS model was employed to simulate the maximum discharge and gauge height at the Chisapani gauging station, employing the Gumbel distribution method (Table 7).

Table 7

Gumbel frequency distribution in different return periods Chisapani station.
Return Period (Yrs)	Discharge (m³/s)	Gage Height (m)
2	8894.5	10.0
5	12411.7	11.5
10	14740.3	12.5
25	17682.6	13.8
50	19865.3	14.8
100	22031.9	15.7
150	23295.3	16.2

The observed discharge at the gauge was 3354.00 m³/s at Hydrological Station No. 280 in the Karnali Basin's Chisapani area during the strong ENSO year 2015. The HEC-HMS corresponding simulation of the peak flood discharge was 3365.38 m³/s.

3.7 Flood Effect Analysis Using (HEC-RAS) Model

Using both 1D and 2D HEC-RAS model analysis revealed that river channel shifts occurred at 2,000-meter intervals along both branches of the Karnali River with a higher frequency observed on the right bank. Consequently, the both river banks were investigated further using the 1D model, which effectively analyzed flooding scenarios across the right bank, center, and left bank of the river, allowing the prediction of river channel flood velocity and channel shifts during the 2015 ENSO event. Similarly, the 2D model was employed to examine river channel shifts at 2,000m intervals, confirming that the right bank was more prone to these shifts. The 2D model, applied temporally and spatially, provided a comprehensive forecast of channel flood response on both sides of the river (Fig. 9).

The study explores the impacts of the El Niño-Southern Oscillation (ENSO) on river flooding in the Karnali River Basin (KRB) by examining precipitation patterns, flood response, and associated hydrodynamic processes. The study makes significant strides in understanding these complex interactions which are crucial for effective water resource management and flood risk assessment in the region. The Karnali River catchment area spans 42890 km², and encompasses the West Seti and Bheri tributaries (Adhakari et al. 2013). In this study, the six main ENSO events from 1983–2020 and their relationship with precipitation and discharge was evaluated (Appendix III). The daily intensity of precipitation from 1964 to 2020 and hourly intensity of the precipitation from 1983 to 2020 with ENSO were also evaluated using IDF curves (Appendix I&II). IDF data and ENSO impacts on river systems can play an important role for people planting cash crops i.e., farming, downstream of Karnali hydrological station towards the Indian border (Masood & Takeuchi, 2012, Costabile et al. 2021,Pandit et al. 2023, Adhikari & Panthee 2020). During La Niña years, the basin's annual precipitation fluctuated, with 1413 mm in 1983, 1596 mm in 2000, and 1283 mm in 2014. In contrast, during the El Niño year of 2015, the precipitation was lower, at 1190 mm. The precipitation characteristics differed significantly across different elevations, ranging from 225 m asl to 1472 masl (Fig. 4). During the 2014 La Niña event, the maximum observed precipitation was 499.8 mm at an elevation of 225 meters. In the KRB, monsoon precipitation plays an important role in shaping precipitation patterns, especially during strong ENSO events. The total (monsoon season) precipitation in the basin during ENSO years was 960 mm in 1983, 1290 mm in 2000, 972 mm in 2014, and 747 mm in 2015.

The KRB exhibits significant hydrological variation due to its diverse elevation range, from 129 m at Naubasta to 3430 m at Khaptad. Flow values were higher in 1983 and 2000 compared to 2014 and 2015. The Gumbel frequency method was used to analyze precipitation and extreme flood data (Adhikari et al., 2021; Suhaiza Selaman et al., 2007; Pepin et al., 2015). Monthly hydrographs of precipitation and discharge during four ENSO events (1983, 2000, 2014, and 2015) reveal that ENSO has a significant impact on precipitation and discharge, especially during the monsoon season (Fig. 3), which is particularly sensitive to ENSO influences (Geng et al., 2023). During the events in 1983 and 2000, the instantaneous peak discharge reached 21,790 m³/s, while in the strong El Niño year of 2015, the observed peak discharge at Hydrological Station No. 280 in Chisapani was 2545 m³/s. The annual mean discharge in Chisapani from 1964 to 2020 was 1361 m³/s, with peak modeled discharge in the Karnali River estimated at 29,910 m³/s, and a 100-year flood depth of 23 m (Aryal et al., 2019). Data from 1964 to 2020 show an annual maximum observed discharge of 1796 m³/s, an average of 1361 m³/s, and an instantaneous maximum of 21,700 m³/s, with a minimum discharge of 2365 m³/s and a standard deviation of 269 m³/s.

The study focuses on the importance of the Soil Conservation Service (SCS) method in evaluating rainfall duration and flood response by considering terrain variations, soil types, and land use (Jothityangkoon et al., 2013; Nishio & Mori, 2015). This approach was successfully applied using the HEC-HMS and HEC-RAS models for rainfall-runoff estimation and flood simulation (Muhammad, 2016) and has been effectively used in previous studies (Hussein et al., 2022; Zema et al., 2017; Masood & Takeuchi, 2012). The SCS approach enhances the estimation of flood dynamics, offering in-depth insights into the connections between ENSO events and significant floods, including rare catastrophic flood events that occur every 600–800 years (Raj et al., 2020; Shrestha & Kostaschuk, 2005b; Abdolrahimi, 2016). The relationship between sea surface temperature (SST) and significant ENSO effects on the 1D and 2D HEC-RAS models revealed that river channel shifts occurred at 2,000-meter intervals along both branches, with a higher frequency on the right bank. Hence, the analysis initially focused on the right bank using the 1D model, while the HEC-RAS 2D model was applied to examine both sides of the river spatially. The 1D model effectively analyzed flooding scenarios across the right bank, center, and left bank, predicting flood velocity and channel shifts during the 2015 ENSO event. This approach, consistent with previous studies (Masood & Takeuchi, 2012), was crucial in forecasting flood discharge and assessing future flood risks. Additionally, the study found moderate to strong positive correlations between precipitation and discharge, with a moderate correlation observed in January and February, and a strong correlation in June and November, indicating a direct relationship between seasonal precipitation and discharge patterns. The regression analysis of ENSO events, focusing on basin hydro-meteorological parameters, reveals mean precipitation (X4) and SST as the most influential factors (Supplementary Appendix V). Mean precipitation (X4) shows a strong relationship with discharge, with a coefficient of determination (R²) of 0.44, explaining approximately 44.21% of discharge variability. While SST has a lower R² value of 0.13, it continues to play a significant role, especially in seasonal discharge variations. During the dry season (December-February), SST exhibit weak correlations with discharge (R² = 0.003) and mean precipitation (R² = 0.05). However, during the monsoon season (June-September), both mean precipitation and SST show strong correlations with discharge, with R² values of 0.44 and 0.67, respectively.

This study analyzes the anomalies of crucial parameters using a three-year running mean method, to assess ENSO impacts on discharge. These parameters include the SST index (Wijeratne et al. 2023), annual pressure (mb), annual rainfall (mm), annual temperature (°C), and annual discharge (mm) from 1964 to 2020. These variables exhibit fluctuations characteristic of El Niño (E), La Niña (L), and Neutral (N) ENSO phases (Kiem & Franks, 2001; Whitaker et al. 2001). Similar to "elevation-dependent warming” in mountain regions of the world(Pepin et al., 2015) and hydrological databases(Santos et al. 2017) HESSD offers further insights into elevation dependent phenomena. This methodology accurately predicted flood discharge, velocity, and the extent of inundation in settlements along the river, helping to assess and mitigate flood risks in the KRB during ENSO events which demonstrated increased flood discharge during high return periods (Table 7). The application of both 1D and 2D models reinforces the critical need for spatial and temporal flood forecasting, particularly in ENSO-affected years. The 1D model was utilized to analyze flooding scenarios on the right bank, center, and left bank (Masood & Takeuchi 2012). The 2D model was employed temporally and spatially on both sides of the river channel to forecast flood response. This methodology accurately predicted flood discharge, velocity, and inundation in the settlements (Maharjan et al .2023).

The study faces several constraints that limit the scope and accuracy of its findings. One significant limitation is the relatively small number of monitoring stations used in the analysis. The limited spatial distribution of these stations restricts the identification of core regions within the basin and impacts the accuracy of the data. Moreover, the short length of the data records, combined with the inter-annual variability of El Niño and La Niña events, makes it challenging to conclusively determine the influence of ENSO on hydrodynamic processes in the KRB. the, Moreover, we acknowledge the importance of exploring atmospheric influences beyond ENSO, as demonstrated recent studies which used non-ENSO precursors to significantly improve Asian summer monsoon predictability (Wang et al. 2023; Raj et al. 2020). The calibration and validation show that the model is highly satisfactory. During the ENSO year, 2015, discharge was also calibrated (Fig. 7). Our findings demonstrate significant ENSO effects on KRB discharge and suggest further investigation into future projections using climate prediction data. This study provides valuable insights into the complex interactions between ENSO events and river flooding in the KRB. Despite several limitations, the findings highlight the importance of continued research and the development of predictive models to enhance water resource management and mitigate flood risks.

This study investigates the impact of ENSO events, particularly focusing on ENSO year 2015, on extreme flood discharge responses in Karnali River Basin through a comprehensive approach employing intensity-duration-frequency (IDF) analysis, correlation/regression analysis and hydrological and hydrodynamic modeling (using both 1D and 2D HEC-RAS models). Between 1964 and 2020, the maximum peak flood discharge magnitude was observed to be 21,700 m³/s and the minimum was 4560 m³/s during the strong ENSO-year 2015. On the same days, basin precipitation exceeded 100 mm at two stations (Rampur 111 mm and Kola Gaun 200 mm). In Karnali Basin's Chisapani area, the observed discharge at the gauge was 3354.00 m³/s during a strong El Niño-Southern Oscillation (ENSO) year 2015. The model simulation of the peak flood discharge was 3365.38 m³/s. Analysis of daily precipitation data collected from DHM showed that there were 113 precipitation days, contributing 84% of the total precipitation during the monsoon period in the study area. The 1D and 2D HEC-RAS models analysis revealed that river channel shifts occurred at 2,000 m intervals along both branches of the river. It provided a comprehensive forecast of flood response on both sides of the river channel during the ENSO year. Comparing flood discharge and gauge height across different years, along with flood inundation extents, depths, and velocities, revealed the relationship of the ENSO effect to precipitation and flood response. The findings indicate a highly significant relationship between observed and modelled discharge in ENSO years for the monsoon season SST, precipitation, and discharge data. These findings are helpful in supporting future planning and decision-making in three countries for managing water resources and development, anticipating future strong ENSO and El Niño events.

Funding declaration: There was no funding available

Author Contribution

All Authors review the manuscript

Acknowledgments

This first author is supported by the President’s International Fellowship Initiative (PIFI) visiting scientist grant for the Chinese Academy of Science's (CAS) international talent (2024VEA0001) and also supported by the Third Xinjiang Scientific Expedition Program (Grant No. 2021xjkk0806). The authors would also like to express their gratitude to the Department of Hydrology and Meteorology (DHM), Government of Nepal, for their data support. The authors are also grateful to the College of Applied Sciences-Nepal (CAS), Institute of Science and Technology (IoST), Tribhuvan University (TU), Nepal.

Data Availability:

The data is not made openly accessible. It is available for interested researchers upon request from the first and corresponding author.

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Impact of ENSO on River Flooding in the Karnali River Basin of Nepal

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Abstract

Figures

1. Introduction

2. Study Area Data and Methods

2.1 Study Area

2.2 Data

2.3 Methods

2.3.1. Intensity Duration Frequency Analysis (IDF)

2.3.2. Correlation and regression analysis

2.3.3. Estimated Extreme Discharge (HEC-HMS) Model

2.3.4. Hydro dynamical Modelling (HEC-RAS Model)

3. Results

3.1. ENSO-year Event Precipitation and Discharge

3.2 Continuous Flow Modeling

3.3 Calibration and Validation

3.4 ENSO Effect on Flow

3.5 Correlation and Regression-Identified Flow Data

3.6 Frequency Analysis Using Gumbel Extreme Flood

3.7 Flood Effect Analysis Using (HEC-RAS) Model

4. Discussion

5. Conclusion

Declarations

Author Contribution

Acknowledgments

Data Availability:

References

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Supplementary Files

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