Influence of the Indian Ocean Dipole (IOD) on Hydro-climate variability in Dwangwa River Basin, Malawi

Influence of the Indian Ocean Dipole (IOD) on Hydro-climate variability in Dwangwa River Basin, Malawi | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of the Indian Ocean Dipole (IOD) on Hydro-climate variability in Dwangwa River Basin, Malawi Aubren C. Chirwa, Cosmo Ngongondo, Ephraim Vunain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4252531/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Large-scale climate processes such as the Indian Ocean Dipole (IOD) have significant roles in modulating rainfall and hydrological systems. Understanding such processes can inform adaptation measures for climate change and variability, as well as water resource management and planning. This study investigated the impact of IOD on rainfall and discharge variability in the Dwangwa River Basin (DRB) in Malawi, a key inflow to Lake Malawi. Specifically, the study analysed annual rainfall variability trends from 1985 to 2015 using the Coefficient of Variation (CV) and the annual Precipitation Concentration Index (PCI). The significance and direction of rainfall and discharge trends were quantified using the Mann-Kendall trend test at the α = 0.05 significance level. To evaluate the association between rainfall and IOD, the Pearson product moment used three IOD phases: positive, negative, and neutral. Simple linear regression was utilised to check the response of the river during the concerned IOD phases. The study found CVs below 30%, typical of climates with moderate monthly rainfall variability. The PCI ranged from 20–30%, suggesting a strongly seasonal and highly variable temporal intra-annual rainfall distribution in the DRB. Moreover, the Mann-Kendall test statistics showed insignificant annual rainfall trends. Further, the findings demonstrated an insignificant negative correlation between rainfall and positive IOD, with rainfall increases associated with negative IOD, whereas positive IOD is associated with decreased river discharge. Consequently, El Niño and a positive IOD could cause DRB to have low water availability. Indian Ocean Dipole Precipitation Concentration Index Rainfall Variability Dwangwa River Basin Malawi Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1.0 Introduction Large scale climate processes play key roles in catchment-level hydrological processes. Studies conducted in various parts of the world have shown that the Indian Ocean Dipole (IOD), once such a large-scale climate process, has a modulating effect on hydroclimatic regimes [ 1 ]. Earlier, El Niño and Southern Oscillation (ENSO) received greater attention as the most significant Sea Surface Temperature (SST) that influences climate in many parts of the world [ 2 ]. However, comparative studies have become more prevalent since the discovery of the IOD as one of the maritime drivers of climate variability. For instance, some studies, e.g., Amirudin et al. [ 3 ], used composite partial correlation techniques and found that the IOD plays a similar role in modifying rainfall as the ENSO in Southeast Asia. In the same region, Zhang et al. [ 4 ] examined the significance of IOD and ENSO in modifying summer precipitation in Eastern China using partial correlation and composite analysis. It was demonstrated that both IOD and ENSO foster anticyclonic circulation across the western North Pacific (WNP) throughout the summer. This displays the triggering mechanics of both phenomena in influencing local rainfall. The vulnerability of Africa to climate change has led to a growth in studies on the implications of IOD on rainfall regimes. Recent studies by Jiang et al. [ 5 ] used the same method as demonstrated by Zhang et al. [ 4 ] to study the impact of IOD on rainfall in Central Equatorial Africa (CEA) during September–December (SOND). The study displayed a strong positive relationship between positive IOD during SOND and CEA rainfall. Yet in the interior of Africa, Bethel and Dusabe [ 6 ] found no clear correlation between rainfall and IOD in Burundi, Rwanda, or Uganda using the information flow method. Hirons and Turner [ 7 ] used the MetUM model to study the effect of IOD on the inter-annual variability of the East African short rains (October to December). Results confirmed a positive correlation between anomalous equatorial easterly flow across the Indian Ocean Basin and wetter East Africa. Earlier, Behera [ 8 ] studied the significant influence of the Indian Ocean Dipole on the East African Short Rains using the Coupled Global Circulation Model (CGCM). The results align with the current literature, which indicates that the IOD influences short-term rainfall in a similar capacity as ENSO. In the southern region of Africa, Manatsa and Mukwada [ 9 ] offered different implications for rainfall resulting from the introduction of IOD mechanics. The Principal Component Analysis (PCA) was utilised to study ENSO and IOD in Zimbabwe's homogeneous rainfall. The results displayed pure IOD composites as having the strongest mechanisms for suppressing rainfall compared to pure ENSO. However, in most southern African literature, ENSO has received more attention than IOD [ 10 ]–[ 13 ]. The number of studies that have explicitly examined how IOD affects the temporal-spatial rainfall variability of nearby landmasses is still relatively small, e.g. [ 9 ] and [ 14 ]. Equally, in Malawi, studies linking the phenomenon with rainfall are scarce. Available studies have shown rainfall variability across the nation's time scales linked to global circulations [ 15 ]. Recent studies have shown that the evaporation of open water has a greater influence on rainfall than covariates such as IOD and ENSO (Ngongondo et al. 2020). Contrary to this, Kumbuyo et al. (2015) applied the Pearson correlation coefficient method and established that summer rainfall over Malawi has a strong and significant correlation with the Indian Ocean SST as compared to the Atlantic and Pacific Ocean SSTs. Furthermore, the study showed that most stations in the northern region had a more significant correlation with the IOD than those in the central and southern regions. However, the extent to which the IOD has influenced hydro-climate processes in catchments in Malawi has not been fully documented. This necessitates extensive research involving additional smaller drainage basins and robust techniques. The Dwangwa River Basin (DRB) is one of the key inflows of Lake Malawi; hence, it is crucial to appreciate its dynamics in relation to global circulation. The main objective of this study is to examine the relationship between rainfall variability and IOD in the Dwangwa River Basin in Malawi. In particular, the study: (i) evaluated the observed rainfall for evidence of climate variability in the Dwangwa River Basin; (ii) assessed the impact of IOD on the rainfall regime of the Dwangwa River Basin; and (iii) examined the response of the discharge of the Dwangwa River Basin during IOD events. Thus, this study expands on earlier efforts that strived to understand important global forces in the basins in Malawi. The study findings provide some evidence on the drivers of key climate forcings, such as IOD, on major river basins flowing into Lake Malawi. A better understanding of maritime mechanisms would increase the accuracy and reliability of weather forecasts aimed at promoting adaptive mechanisms for climate change, water resource management, and environmental planning. 2.0 Methodology 2.1 Study Area The study was undertaken in the Dwangwa River Basin (DRB) in Malawi, Southern Africa (Fig. 1 ). The DRB covers a catchment area of 7,768 km 2 before it drains into Lake Malawi [ 18 ]. It is located in the central region of Malawi at an altitude between 500 and 1500 m above mean sea level. The DRB has a mean annual rainfall of 902 mm and an annual runoff of 12% [ 18 ]. It is designated as Water Resources Unit (WRA) 6, according to the National Water Resources Master Plan [ 18 ]. The upper course of the basin in the west and central parts is the Kasungu plain, which is gently undulating with altitudes between 975 and 1300 m above sea level [ 19 ]. A number of smaller rivers drain into the main river system before inflowing into Lake Malawi (Fig. 1 ). 2.2 Data Collection 2.2.1 Hydro-Meteorological data The DRB is located in a region where hydro-meteorological data is scarce [ 20 ]. The study used climate data from Kasungu, Dwangwa, Kaluluma, and Mwimba climate stations (Fig. 1 ) for the period 1985–2015 obtained from the Malawi Department of Climate Change and Meteorological Services (DCCMS). The DCCMS data was of relatively high quality and contained no gaps, suggesting some level of inspection before being distributed. However, the selection of the stations was based on the World Meteorological Organisation's (WMO) recommendation that a minimum of 30 years of data is sufficient for analysing trends in hydro-climatic data [ 20 ]. In addition, the Malawi Ministry of Water and Sanitation, through the Department of Water Resources (DWR), provided average monthly river discharge data for the upper and lower Dwangwa River Basins from 1985 to 2009. The data were from two gauging stations at Kwengwere upper catchment (6C1) and Dwangwa S53 (6D10) lower catchment (Fig. 1 ). Unlike the DCCMS data, the discharge data had prolonged periods (2009–2015) of missing data. Average monthly river discharge data for the upper and lower Dwangwa River Basin was collected from the Ministry of Irrigation and Water-Malawi, Department of Water Resources, from 1985 to 2009. Data was not available at Dwangwa Road Bridge S53 station in 1985. The period from 2009 to 2015 had the longest period of missing data; hence, it was discarded for analysis. Many studies have recommended that for better spatial variability in hydrological analysis, data spanning more than 20 years can be desirable for trend analysis [ 21 ]. In a case where missing data exists in the basin, missing flows can be estimated using nearby rivers that have data [ 22 ]. Therefore, the study used regression analysis to fill in the missing data. The method has been successfully utilised in hydrological studies [ 22 ]. At both discharge gauging stations, data from 1986 to 1989 was complete. Thus, this period was utilised in generating the equation for filling data gaps. There were big gaps between the series on both stations up until 2015. In other situations, data from one gauging station was used due to prolonged missing data at the other stations (alternatively). Therefore, for the hydrological response, the study used 24 years because of the long period of missing data in some years. Despite the availability of numerous methods for filling in missing hydrological data, there is generally no single method that can be considered universally best [ 22 ]. Further, it was mentioned that each method has its own advantages and disadvantages, depending on the characteristics of the data set. However, other factors, for instance, distance between stations, aerial coverage of each gauging stage, length of gap, the season, the climatic region, or the availability and data characteristics of the records, have significant influences on hydrological data estimates [ 22 ]. In this regard, the study used simple linear regression (Eq. 1 ). $$y={B}_{0}+{B}_{1}X+\in$$ 1 \(\varvec{y}\) is the predicted value of the dependent variable \(\left(\varvec{y}\right)\) in the case of the station with missing data. \({\varvec{B}}_{0}\) is the intercept, the predicted value of y when the X is 0. \({\varvec{B}}_{1}\) is the regression coefficient-how much we expect \(\varvec{y}\) to change as \(\varvec{X}\) increases (Eq. 1 ). X is the independent variable which is the Station of Dwangwa Road Bridge S53. The R square was chosen as the explanation for the model's best fit when determining the dependent variables [ 23 ]. Consider a model where the R2 value is 70%. Here r squared meaning would be that the model explains 70% of the fitted data in the regression model. Generally, when the R square of 2 value is high, it suggests a better fit for the model [ 24 ]. Thus, the study found R square of 0.76 data was considered to be the best fit (Fig. 2 ). best fit of the model. The r square of 0.75 was assumed the best fit hence it was utilised for calculating missing values. 2.2.2 Indian Ocean Dipole data The study used the Dipole Mode Index (DMI) to quantify the Indian Ocean temperature anomalies. The DMI has been used in several studies; e.g., the intensity of the IOD is given by the anomalous SST gradient between the western equatorial Indian Ocean Dipole and the south-eastern equatorial Indian Ocean [ 8 ], [ 25 ], [ 26 ]. When the DMI is positive, the phenomenon is known as a positive IOD event, and when it is negative, it is a negative IOD event (Yamagata et al. 2003). The Dipole Mode Index data was obtained from the Tokyo Climate Centre and the World Meteorological Organisation (WMO) Regional Climate Centre in RA II (ASIA). Data is available at https://ds.data.jma.go.jp/tcc/tcc/products/elnino/index/iod_index.html . In the period from October to December, a three-month average was determined. The standard deviation of ± 0.5 was the accepted yardstick for declaring ± IOD [ 8 ]. Therefore, four years had positive IOD: 1994, 1997, and 2015 (Fig. 2 ). Negative IOD occurred in 1996, 1998, 2005, and 2010. Ultimately, the neutral IODs occurred in 1986, 2000, 2002, 2004, and 2009 (Fig. 2 ). 2.3 Data Quality Control The quality of data for meteorological variables such as rainfall continues to be a challenge in data-scarce regions such as Malawi [ 20 ]. However, the DCCMS undertakes some data quality control checks before archiving their data [ 20 ]. In this study, we checked for randomness in the rainfall data series as part of the enhancement of data quality. The procedure ensures that data is independent and identically distributed, as required by some of the analysis procedures, such as trend tests [ 20 ]. 2.3.1 Test for randomness and persistence There are various methods used to determine the randomness and consistency of the data. These include median crossing, serial autocorrelation analysis, turning points, rank difference, and autocorrelation [ 20 ]. This study adopted the use of serial autocorrelation analysis to check for randomness and persistence. It has been successfully used in many studies of temporal and spatial rainfall trend analysis in order to check if the data is influenced by previous rainfall data, e.g., [ 27 ] and [ 20 ]. Serial autocorrelation analysis correlates a time series dataset with itself at different time lags. Autocorrelation was performed on the total annual rainfall and river discharge data of individual stations in the catchment [ 27 ]. This procedure determines whether the preceding data series has an impact on the rainfall data series. Programme R was used following Eq. ( 2 ). $${r}_{k}=\frac{{\sum }_{i=1}^{N-k}({x}_{i }-m)({x}_{i+k}-m)}{{\sum }_{i=1}^{N}{(x}_{i}-m{)}^{2}}$$ 2 Where r k is the lag-k autocorrelation coefficient, m is the mean value of a time series x i , is the number of observations, and is the time lag. If the calculated r k is not significant at the 5% level, then the Mann-Kendall test can be applied to the original values of the time series [ 27 ]. Where the calculated autocorrelation is significant, the data sets were smoothed using a 3-year moving average to reveal more persistent trends [ 27 ]. The smoothed time series may be obtained as follows (Eq. 2 ): $${\overline{y}}_{t}=\frac{{y}_{t }+{y}_{t-1}{+}_{\dots ..}+{y}_{t-n-1}}{n}$$ 3 Where \(\varvec{y}\) is the variable (such as rainfall), \(\varvec{t}\) is the current time (such as the current month) and \(n\) is the number of times in average. 2.4 Inter and intra-annual rainfall Variability Annual and monthly mean rainfall data were used to calculate the basin-wide. The Precipitation Concentration Index (PCI) and Coefficient Variation (CV) were used as descriptors of rainfall variability in the DRB. PCI was used to evaluate the varying weight of monthly rainfall relative to the total amount of rainfall [ 27 ]. This enabled an understanding of the monthly heterogeneity of rainfall amounts. A modified version of PCI by Oliver (1980), cited in [ 27 ], and was applied (Eq. 4 ). $$PCI=100\times \frac{{\sum }_{i=1}^{12}{P}_{i}^{2}}{({\sum }_{i=1}^{n}{P}_{i}{)}^{2}}$$ 4 \({p}_{i}\) is the rainfall amount of the month, calculated for each year PCI values below 10 indicate a uniform monthly rainfall distribution in the year, whereas PCI values of 11 to 20 indicate seasonality in rainfall distribution. Values above 20 correspond to climates with substantial monthly variability in rainfall amounts [ 16 ]. A higher PCI value indicates that precipitation is more concentrated in a few rainy months during the year, and vice versa. The rainfall variability for representative meteorological stations was determined by calculating the coefficient of rainfall variation (CV) as the ratio of the standard deviation (SD) to the mean rainfall in a given period (CV%) when expressed as a percentage [ 20 ]. Eq. 5 presents the calculation of CV. $$CV=\frac{\sigma }{x}$$ 5 Where \(\sigma =\) Standard Deviation; \(x\) = mean based on the values of CV has classified the rainfall variability of an area as; CV 30% high variable. 2.5 Temporal trends Approaches used for detecting a trend in the time series can be either parametric or non-parametric. The non-parametric Mann-Kendall (MK) test statistic was used to determine the direction and significance of temporal trends of the total annual and monthly rainfall (i.e., from November to April and May to October) [ 16 ]. The test has been widely applied in various trend detection studies, including those of Nsubuga et al. [ 27 ], and it is recommended by the World Meteorological Organisation (WMO) for trend analysis of hydro-climatic data [ 20 ]. Firstly, it is a non-parametric test and does not require the data to be normally distributed. Secondly, the test has low sensitivity to abrupt breaks due to inhomogeneous time series, such that it is insensitive to missing data and outliers [ 20 ]. Therefore, the test is considered very robust for non-normally distributed data series, such as rainfall [ 28 ]. Programme R was utilised in the analysis of trends using Eq. 6 . $$S=s={\sum }_{k=1}^{n-1}{\sum }_{j=k+1}^{n}sgn({X}_{j}-{X}_{k})$$ 6 $$Sgn({X}_{j}-{X}_{k}=\left\{\begin{array}{c}+1, if\left({X}_{j}-{X}_{k}\right)>0\\ 0,if \left({X}_{j}-{X}_{k}\right)=0\\ -1, if\left({X}_{j}-{X}_{k}\right)<0\end{array}\right.$$ 7 Where and are sequential precipitation values in the month, whereas a positive value is an indicator of an increasing (upward) trend and a negative value is an indicator of a decreasing (downward) trend. The slope of a linear trend is estimated with the non-parametric Sen’s slope estimator’s method [ 21 ] references it therein. It is the best method to detect trends because it is not affected by outliers or missing data. The slope of n pairs of data points was estimated using Sen’s estimator, calculated as $${ Q}_{i}=\frac{{x}_{j}-{x}_{k}}{j-k} for i=1\dots N$$ 8 Where and are data values at time, respectively. The median of these values is Sen’s estimator slope. The presence of a statistically significant trend is evaluated using the value [ 21 ]. To decide whether the null hypothesis is to be accepted or rejected, a test statistic is computed with a critical value obtained from a set of statistical tables. The null hypothesis is rejected if the absolute value of is greater than Z1-a/2 , and then the trend is considered significant, where Z 1 − a/2 is obtained from the standard normal distribution [ 29 ]. 2.6 Relationship between IOD and Rainfall The study used Pearson product moment correlation (r) (Eq. 9 ). This method describes the relationship between two variables [ 30 ]. The variables data points determine the direction or sign of the correlation coefficient value. The correlation coefficient is often denoted by rho and its sample estimate ranges from plus one to minus one (r = ± 1) [ 30 ]. A positive value near ± 1 is described as having very strong positive/negative correlation [ 30 ]. $$r=\frac{{\sum }_{i=1}^{N}({x}_{i}-\stackrel{-}{x}\left)\right({y}_{i}-\stackrel{-}{y})}{\sqrt{{\sum }_{i=1}^{N}({x}_{i}-\stackrel{-}{x}{)}^{2}{\sum }_{i=1}^{N}({y}_{i}-\stackrel{-}{y}{)}^{2}}}$$ 9 Where and are the averages of time series and, respectively [ 31 ]. The correlation coefficient r d escribes the degree of closeness to a linear relationship between two variables x and y [ 31 ]. The value of r varies from − 1 for a "perfect "out-of-phase correlation to + 1 for a "perfect" in-phase correlation, and the coefficient of zero indicates that no linear relationship exists. To test whether the correlation is significant, the null hypothesis that the correlation is zero and the alternative hypothesis that the correlation is nonzero were assumed. If the null hypothesis was valid, the relevant test variable ( t ) from Eq. ( 9 ) was the realisation of student ( t ) random variables with a mean of zero and n degrees of freedom [ 31 ]. Based on this information, p -values were computed; p < 0.05 impelled the probability of discarding the null hypothesis, and vice versa. The t -test statistics were used as given in Eq. ( 10 ). $$t=\sqrt[r]{\frac{n-2}{1-{r}^{2}}}$$ 10 2.7 River discharge response to IOD To determine whether there was an increase or decrease in discharge during the IOD anomalies in the basin, the study used the Simple Linear Regression method (Eq. 11). This method has performed very well in similar studies for finding the linear relationship between variables. It mostly relies on the assumption of linearity in the relationships between variables. \({Y}_{i}={\beta }_{0}+{\beta }_{1}{X}_{i}\) +ε (11) \({\text{w}\text{h}\text{e}\text{r}\text{e} \varvec{Y}}_{\varvec{i}}\) is dependent variable in this case it is river discharge from DRB. \({\varvec{\beta }}_{0}\) is the intercept, the predicted value of \({\varvec{Y}}_{\varvec{i}}\) when the value of \({\varvec{X}}_{\varvec{i}}\) is 0. \({\varvec{\beta }}_{1}\) is the regression coefficient of how much we expect \({\varvec{Y}}_{\varvec{i}}\) to change as \(\varvec{X}\) increases [ 32 ] \({\varvec{X}}_{\varvec{i}}\) is the independent variable in this case Dipole Mode Index (DMI) (the variable we expect is influencing \({\varvec{Y}}_{\varvec{i}}\) ). ε is the error of estimate, or how much variation there is in our estimate of the regression coefficient . 3.0 Results and Discussions 3.1 Hydro-meteorological characteristics of Dwangwa River Basin The highest discharge of 59 m 3 /s and 55 m 3 /s was found at S53 and Khwengwere, respectively (Fig. 3 ). The highest mean monthly discharge in the two gauging stations was in February (Fig. 3 ). The river recession starts in March, when the cold, dry season is approaching. The peaked discharge displays that the catchment witnessed an increase in rainfall in November. Therefore, from May to October tends to be a period of no rainfall, displaying the typical rainfall pattern of Malawi [ 28 ]. Figure 6 Mean monthly discharge of Dwangwa basin from 1985 to 2009. 3.2 Rainfall Variability The average CV for the Dwangwa basin catchment is 0.24, indicating that rainfall does not vary greatly from year to year (Table 1 ). All the stations had CVs less than 30% (Table 1 ). The smallest CV in the basin of the Dwangwa River demonstrates that rainfall is not greatly variable over the years. Nsubuga et al. [ 27 ] noted that areas with a CV higher than 30% are likely to have more frequent and severe droughts and floods throughout the year. However, stations in the basin had a PCI (mean) of 20–30%, indicating that intra-annual rainfall is highly variable, both temporally and seasonally (Table 1 ). The results demonstrate that in most years, rainfall is not reliable. The seasonality could be influenced by the station in Dwangwa, which lies in the Lake Shore areas sharing higher rainfall with Nkhata- Bay. The finding agrees with other studies that demonstrated that central Malawi has more variability in rainfall than southern Malawi [ 20 ], [ 28 ]. Table 1 summaries of the PCI and CV in DRB between 1981 and 2015 Station Annual PCI (%) CV Annual Mean (mm) Dwangwa 23 0.25 1316 Kasungu 30 0.21 772 Kaluluma 22 0.27 944 Mwimba 25 0.21 826 3.2 Serial autocorrelation The data was subjected to serial autocorrelation in order to check its persistence. The idea was to treat the rainfall data so that it could not be associated with the previous data in the catchment. The autocorrelation results in Fig. 3 displayed that the data is persistently free; hence, the Mann-Kendall trend test was applied directly (Fig. 3 ). 3.3 Temporal and Spatial trend Annual rainfall exhibited a range of temporal and spatial trends (Fig. 4). Although the rainfall trend results were not statistically significant at the 0.05 significant level, all of the stations registered declining trends in annual rainfall (Fig. 4b). However, Kaluluma displayed an insignificant increase in rainfall in the basin (Fig. 4). This could be because the station is part of the northern region's rainfall-bearing system, which receives comparatively more rainfall than the central region. Despite the interesting trend in annual rainfall at Kaluluma station, the basin-wide annual average rainfall displayed an insignificant (0.05) decrease trend (Fig. 4). This demonstrates that the Dwangwa River Basin's annual rainfall trend is declining. These findings agree with the study of Tadeyo et al. [ 28 ], which presented a decrease in rainfall trend in most areas of Malawi. The decrease in rainfall patterns could be associated with changes in climate globally. The country-based studies [ 20 ], [ 28 ], and [ 33 ] revealed that the majority of Malawi's annual rainfall series are trending downward, though most of them presented insignificant patterns. This demonstrates that the DRB is consistent with the observed decreasing trends in rainfall. The existing pattern of rainfall could be intensifying due to anthropogenic activities, which are contributing to the already fragile climatic systems. Figure 4 Mann kendall rainfall for: annual rainfall series Kasungu (a), Kaluluma (b), Dwangwa (c), Mwimba (d), areal annual average rainfall in Dwangwa River Basin (e). 3.4 Correlation between Dipole Mode Index (DMI) and rainfall The results of the correlation between DMI and rainfall are presented in Table ii. With the exception of Kasungu station, all the other stations displayed an insignificant positive correlation with a negative IOD in the basin (0.9) (Table 2 ). In addition, all the stations exhibited a strong negative, insignificant correlation with positive IOD (-0.7) (Table 3 ). Munthali et al. [ 33 ] report that a negative IOD is associated with droughts and low rainfall. Thus, these results revealed that during the warm phase in the Indian Ocean, the drainage basin experiences low rainfall. This is contrary to the East African scenario, where the region receives a higher amount of rainfall during a positive IOD [ 8 ], [ 25 ]. This presents the opposite results from what is observed in the drainage basin. Negative IOD was shown to be a contributor to increased rainfall in the basin. Positive IOD, however, was found to be associated with low rainfall. Again, during the neutral phase, the Dwangwa River basin is connected with an insignificant positive correlation with IOD (Table 4 ). This can be regarded as a transition time between negative IOD and positive IOD. This period contributes to increased rainfall in the basin. This study suggests that IOD has an overwhelming impact on the rainfall regime in the basin. The study agrees with the findings of Kumbuyo et al. [ 17 ], who found summer rainfall in Malawi to be strongly correlated with the Indian Ocean SST compared to the Atlantic and Pacific Ocean SSTs. During a negative phase, waters in the eastern Indian Ocean (near Indonesia) are warmer than normal, and the western Indian Ocean (near Africa) are cooler than normal [ 25 ], [ 8 ]. It is these low sea surface temperatures in the Indian Ocean near Africa that influence rainfall variability in the DRB. This is primarily attributed to the IOD capability of modifying the Walker circulation over the tropical Indian Ocean and moisture in the middle troposphere over Central Equatorial Africa in countries including the Congo, Tanzania, and Western Kenya [ 5 ]. Therefore, rainfall could be influenced by the trigger of walker circulation due to the intensification of cold temperatures along the coast of east Africa. Table 2 Correlation between Rainfall and DMI during Negative IOD in DRB Variables IOD (-) Kasungu Dwangwa Mwimba Kaluluma Basin Wide IOD (-) 1 0.947 0.525 0.115 0.748 0.883 Kasungu 0.947 1 0.226 0.308 0.613 0.689 Dwangwa 0.525 0.226 1 -0.548 0.707 0.862 Mwimba 0.115 0.308 -0.548 1 -0.559 -0.255 Kaluluma 0.748 0.613 0.707 -0.559 1 0.851 Basin Wide 0.883 0.689 0.862 -0.255 0.851 1 Values in bold are different from 0 with a significance level at alpha = 0.05 Table 3 Correlation between Rainfall and DMI during Positive IOD in DRB Variables IOD (+) Kasungu Dwangwa Mwimba Kaluluma Basin Wide IOD (+) 1 0.880 -0.956 -0.526 -0.652 -0.702 Kasungu 0.880 1 -0.981 -0.866 -0.934 -0.956 Dwangwa -0.956 -0.981 1 0.753 0.846 0.881 Mwimba -0.526 -0.866 0.753 1 0.988 0.975 Kaluluma -0.652 -0.934 0.846 0.988 1 0.998 Basin Wide -0.702 -0.956 0.881 0.975 0.998 1 Values in bold are different from 0 with a significance level alpha = 0.05 Table 4 Correlation between Rainfall and DMI during Neutral Phase in DRB Variables IOD (o) Kasungu Dwangwa Mwimba Kaluluma Basin Wide IOD (o) 1 0.235 0.747 -0.689 0.908 0.756 Kasungu 0.235 1 0.803 -0.512 0.020 0.809 Dwangwa 0.747 0.803 1 -0.655 0.491 0.991 Mwimba -0.689 -0.512 -0.655 1 -0.750 -0.714 Kaluluma 0.908 0.020 0.491 -0.750 1 0.544 Basin Wide 0.756 0.809 0.991 -0.714 0.544 1 Value in bold are different from 0 with a significance level alpha = 0.05 3.5 River discharge response to IOD The study revealed mixed responses between discharge and IOD in the DRB. A negative association was presented in discharge during positive IOD (Fig. 5 ). While negative IOD contributed to an increase in discharge in DRB (Fig. 6 ). However, during the neutral phase, the catchment had mixed results in the upper and lower catchments (Fig. 7). The upper catchment at Kwengwere is associated with an increase in river discharge during the neutral phase. Yet discharge at S53 displayed a slight decrease in river discharge (Fig. 7). The difference could be emanating from the differences in local conditions and the onset of rainfall in the basin. While a negative IOD would induce water availability. The studies by Kumbuyo et al. [ 17 ] displayed that the northern areas of Malawi are strongly influenced by the SST Indian Ocean dipole, whereas the central and southern areas are strongly linked to the SST in the subtropical Indian Ocean. Dwangwa River Basin being at the transition zone would have mixed results at some phases, as displayed in Fig. 7. Figure 7. (e) and (f) regression during the neutral IOD 4.0 Conclusion and Recommendation The current study investigated the impact of IOD on rainfall variability in DRB. Specifically, it looked at the variability of rainfall using the CV and PCI, examined rainfall trends, and finally connected the relationship between IOD under three phases (positive, negative, and neutral). The results suggest that rainfall does not vary greatly from year to year in the basin. Moreover, the basin exhibited less inter-annual variability in all months. Additionally, the stations in the basin had a precipitation concentration index (mean) between 20% and 30%, indicating that the temporal intra-annual rainfall distribution is highly variable. Moreover, the rainfall trend displayed an insignificant decrease annually. Again, the results showed that positive IOD is negatively correlated with rainfall in the catchment, though it is insignificant. This contributes to low river discharge during the phase. Therefore, a positive IOD phase could potentially affect the availability of water in the catchment. Furthermore, there was an insignificant positive correlation between negative IOD and rainfall in the basin, suggesting that a negative IOD is associated with increased rainfall in the DRB. Consequently, this triggers an increase in river discharge in the basin. The study has demonstrated diverse results on the role that large-scale circulation drivers play in modulating the hydro-climate variability. Therefore, the present study would recommend the following for further studies: The studies related to the impact of global circulation seem to be different in each country; further studies should target local areas. Yet again, studies on the impact of the Indian Ocean Dipole on climate and hydrological response remain insufficient. Therefore, studies should look at the periodicities of IOD in relation to hydro-meteorological parameters for modelling and prediction. Declarations Statements & Declarations The study received financial support through the second authors from the projects: (i) NORHED II Climate Change and Ecosystems Management in Malawi and Tanzania (#63826) at the University of Malawi; and (ii) the World Bank supported the Centre for Resilient Agri-Food Systems (CRAFS) at the University of Malawi under the ACE 2 Project. We sincerely acknowledge this support. Competing Interests The authors neither have conflict of interest nor competing interests when developing this paper. Contributing authors All authors contributed to the study from developing the research concept and design. The planning, data collection and analysis were performed by Aubren C. Chirwa, Cosmo S. Ngongondo and Ephraim Vunain. The first draft of the manuscript was written by Aubren C. Chirwa then it was improved and approved by Cosmo S. Ngongondo and Ephraim Vunain. Data availability The Dipole Mode Index data is available at https://ds.data.jma.go.jp/tcc/tcc/products/elnino/index/iod_index.html from the Tokyo Climate Centre World Meteorological Organisation (WMO) Regional Climate Centre in RA II (ASIA). Rainfall data is available Malawi Department of Climate Change and Meteorological Services (DCCMS). River discharge data is available at the Ministry of Irrigation and Water-Malawi, Department of Water Resources. Author Contribution All authors contributed to the study from developing the research concept and design. The planning, data collection and analysis were performed by Aubren C. Chirwa, Cosmo S. Ngongondo and Ephraim Vunain. The first draft of the manuscript was written by Aubren C. Chirwa then it was improved and approved by Cosmo S. Ngongondo and Ephraim Vunain. References C. C. Ibebuchi, ‘Circulation Patterns Linked to the Positive Sub-Tropical Indian Ocean Dipole’, Adv. Atmos. 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Kazumba, ‘Estimation of Missing River Flow Data for Hydrologic Analysis: The Case of Great Ruaha River Catchment’, Hydrol. Curr. Res., vol. 09, no. 02, 2018, doi: 10.4172/2157-7587.1000299 . M. Keer, H. Lohiya, and S. Chouhan, ‘Goodness of Fit for Linear Regression using R squared and Adjusted R-Squared’, Int. J. Res. Publ. Rev. J. homepage com , vol. 4, no. 3, pp. 2431–2439, 2023, [Online]. Available: www.ijrpr.com T. Chai and R. R. Draxler, ‘Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature’, Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014, doi: 10.5194/gmd-7-1247-2014 . S. K. Behera, J. J. Luo, and T. Yamagata, ‘Unusual IOD event of 2007’, Geophys. Res. Lett., vol. 35, no. 14, pp. 1–5, 2008, doi: 10.1029/2008GL034122 . T. Yamagata, S. K. Behera, S. A. Rao, Z. Guan, K. Ashok, and H. N. Saji, ‘Comments on “Dipoles, Temperature Gradients, and Tropical Climate Anomalies” [4]’, Bull. Am. Meteorol. Soc., vol. 84, no. 10, pp. 1418–1422, 2003, doi: 10.1175/BAMS-84-10-1418 . F. N. W. Nsubuga, J. M. Olwoch, C. J. W. de Rautenbach, and O. J. Botai, ‘Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda’, Theor. Appl. Climatol., vol. 115, no. 1–2, pp. 53–71, 2014, doi: 10.1007/s00704-013-0864-6 . E. Tadeyo, D. Chen, B. Ayugi, and C. Yao, ‘Characterization of spatio-temporal trends and periodicity of precipitation over Malawi during 1979–2015’, Atmosphere (Basel). , vol. 11, no. 9, pp. 1–17, 2020, doi: 10.3390/ATMOS11090891 . H. B. Gebremichael, G. A. Raba, K. T. Beketie, G. L. Feyisa, and T. Siyoum, ‘Changes in daily rainfall and temperature extremes of upper Awash Basin, Ethiopia’, Sci. African, vol. 16, p. e01173, 2022, doi: 10.1016/j.sciaf.2022.e01173 . F. Zinzendoff Okwonu, B. Laro Asaju, and F. Irimisose Arunaye, ‘Breakdown Analysis of Pearson Correlation Coefficient and Robust Correlation Methods’, IOP Conf. Ser. Mater. Sci. Eng., vol. 917, no. 1, 2020, doi: 10.1088/1757-899X/917/1/012065 . B. J. Sitienei, S. G. Juma, and E. Opere, ‘On the use of regression models to predict tea crop yield responses to climate change: A case of Nandi East, Sub-County of Nandi County, Kenya’, Climate , vol. 5, no. 3, 2017, doi: 10.3390/cli5030054 . A. R. Nugroho, I. Tamagawa, and M. Harada, ‘The relationship between river flow regimes and climate indices of ENSO and IOD on code river, southern Indonesia’, Water (Switzerland) , vol. 13, no. 10, pp. 1–14, 2021, doi: 10.3390/w13101375 . G. Munthali, W. Gumindoga, R. C. G. Chidya, M. Malota, and H. Muhoyi, ‘Spatial and temporal variation in rainfall and streamflow – Dzalanyama Catchment, Malawi’, Water Pract. Technol., vol. 17, no. 5, pp. 1035–1045, 2022, doi: 10.2166/wpt.2022.045 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2024 Reviews received at journal 30 Apr, 2024 Reviews received at journal 25 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviews received at journal 23 Apr, 2024 Reviewers agreed at journal 21 Apr, 2024 Reviewers agreed at journal 20 Apr, 2024 Reviewers invited by journal 19 Apr, 2024 Editor assigned by journal 16 Apr, 2024 Submission checks completed at journal 16 Apr, 2024 First submitted to journal 11 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4252531","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291697815,"identity":"c104ca28-9475-4bc0-a5e6-099c29367a96","order_by":0,"name":"Aubren C. Chirwa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFACHjCZwAYmK4AEM3MDCVoenAFpYSRSC4hgfNgGpvBr4Z+Re+zDxx12eXzSZww/JM6rjeZvB2r5UbENpxaJG3nJM2eeSS5m48sxlkjcdjx3xmHGBsaeM7dxW3Mjx5iZt+1AYhsP7waglmO5DUAtzIxtuLXIg7T8hWjZ/CNxzrHc+YS0GIC0MEK0bJNIbKjJ3UBIi+GZd8mMvW3JQC383ywSjh3I3QjUchCfX+SO5x5m+Nlmlzi/hy355o+autx55w8ffPCjAo/30cBhMHmAaPVAUEeK4lEwCkbBKBghAACxUV0CCN2FjwAAAABJRU5ErkJggg==","orcid":"","institution":"Nkhoma University","correspondingAuthor":true,"prefix":"","firstName":"Aubren","middleName":"C.","lastName":"Chirwa","suffix":""},{"id":291697817,"identity":"f8257f7b-dcbb-4fa8-9fe6-e7e742147691","order_by":1,"name":"Cosmo Ngongondo","email":"","orcid":"","institution":"University of Malawi","correspondingAuthor":false,"prefix":"","firstName":"Cosmo","middleName":"","lastName":"Ngongondo","suffix":""},{"id":291697818,"identity":"1b22cbc4-d332-4831-966a-c2cd2d0510e2","order_by":2,"name":"Ephraim Vunain","email":"","orcid":"","institution":"University of Malawi","correspondingAuthor":false,"prefix":"","firstName":"Ephraim","middleName":"","lastName":"Vunain","suffix":""}],"badges":[],"createdAt":"2024-04-11 12:48:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4252531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4252531/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55001721,"identity":"b8ef2c59-e99a-4567-9ad4-9fb0e56c3671","added_by":"auto","created_at":"2024-04-19 18:38:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71560,"visible":true,"origin":"","legend":"\u003cp\u003eFig 1. Map of Malawi showing the Dwangwa River Basin. The main river system, river gauging stations, and rainfall stations are displayed in the basin.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/9fa7715f63bd75b3f9307073.jpg"},{"id":55004461,"identity":"f4532b66-cadc-4603-8200-aad1126f1f0b","added_by":"auto","created_at":"2024-04-19 18:46:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150841,"visible":true,"origin":"","legend":"\u003cp\u003eFig 2. Regression of S53 by Kwengwere during (1986-1989). The r square was used to show the best fit of the model. The r square of 0.75 was assumed the best fit hence it was utilised for calculating missing values.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/570436f482de2573ce39827a.jpg"},{"id":55004462,"identity":"317dd53f-ed57-4ef5-9132-6e8704ae1508","added_by":"auto","created_at":"2024-04-19 18:46:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38397,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 2. OND seasonal mean SST anomalies averaged normalized by their own standard deviation. Lines indicate 0.5 standard deviation of SST anomalies (1985-2015).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/4c362a28c30674d17cfb96ab.jpg"},{"id":55001729,"identity":"33620733-d8b6-4851-88f2-413b0ebfd0fc","added_by":"auto","created_at":"2024-04-19 18:38:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154048,"visible":true,"origin":"","legend":"\u003cp\u003eFig 6 Mean monthly discharge of Dwangwa basin from 1985 to 2009.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/67b5069259b18eb30d6d76c6.jpg"},{"id":55001727,"identity":"4e939f35-52fe-4fae-b731-040ae3c6dcf2","added_by":"auto","created_at":"2024-04-19 18:38:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":79486,"visible":true,"origin":"","legend":"\u003cp\u003eFig 3. Autocorrelation of Kasungu (a), Mwimba (b), Kaluluma (c), Dwangwa (d) and basin wide (e). At zero lag, there is high correlation\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/dc4e27aa486e523c00a89ada.jpg"},{"id":55001728,"identity":"dc170f58-c8b9-4f51-a87e-13463fe39f73","added_by":"auto","created_at":"2024-04-19 18:38:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":120982,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 4 Mann kendall rainfall for: annual rainfall series Kasungu (a), Kaluluma (b), Dwangwa (c), Mwimba (d), \u0026nbsp;areal annual average rainfall in Dwangwa River Basin (e).\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/11f66ef633ba1f9dd486d110.jpg"},{"id":55001722,"identity":"3641aae9-c308-4f4b-9442-7fb3dce8a727","added_by":"auto","created_at":"2024-04-19 18:38:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":31262,"visible":true,"origin":"","legend":"\u003cp\u003eFig 5. (a) and (b) regression during positive IOD\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/2a97397620f680185fabec23.jpg"},{"id":55001724,"identity":"d0073a71-ef1d-4b0c-b209-0144d83d27d7","added_by":"auto","created_at":"2024-04-19 18:38:24","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":35414,"visible":true,"origin":"","legend":"\u003cp\u003eFig 6. (c) and (d) regression during negative IOD\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/d3bbf4292df74ac705f38327.jpg"},{"id":55001726,"identity":"3653ba61-fccf-4d04-b1ea-eb6d4ee42a21","added_by":"auto","created_at":"2024-04-19 18:38:24","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":39616,"visible":true,"origin":"","legend":"\u003cp\u003eFig 7. (e) and (f) regression during the neutral IOD\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/d8b962c5dcfd3536f6ca7228.jpg"},{"id":55005901,"identity":"d5befcd1-6469-46ff-bce0-73efe263e36b","added_by":"auto","created_at":"2024-04-19 18:54:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":823704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4252531/v1/76505858-4296-4fec-8752-8c9c675fec42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of the Indian Ocean Dipole (IOD) on Hydro-climate variability in Dwangwa River Basin, Malawi","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eLarge scale climate processes play key roles in catchment-level hydrological processes. Studies conducted in various parts of the world have shown that the Indian Ocean Dipole (IOD), once such a large-scale climate process, has a modulating effect on hydroclimatic regimes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Earlier, El Ni\u0026ntilde;o and Southern Oscillation (ENSO) received greater attention as the most significant Sea Surface Temperature (SST) that influences climate in many parts of the world [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, comparative studies have become more prevalent since the discovery of the IOD as one of the maritime drivers of climate variability. For instance, some studies, e.g., Amirudin et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], used composite partial correlation techniques and found that the IOD plays a similar role in modifying rainfall as the ENSO in Southeast Asia. In the same region, Zhang et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] examined the significance of IOD and ENSO in modifying summer precipitation in Eastern China using partial correlation and composite analysis. It was demonstrated that both IOD and ENSO foster anticyclonic circulation across the western North Pacific (WNP) throughout the summer. This displays the triggering mechanics of both phenomena in influencing local rainfall.\u003c/p\u003e \u003cp\u003eThe vulnerability of Africa to climate change has led to a growth in studies on the implications of IOD on rainfall regimes. Recent studies by Jiang et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] used the same method as demonstrated by Zhang et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] to study the impact of IOD on rainfall in Central Equatorial Africa (CEA) during September\u0026ndash;December (SOND). The study displayed a strong positive relationship between positive IOD during SOND and CEA rainfall. Yet in the interior of Africa, Bethel and Dusabe [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] found no clear correlation between rainfall and IOD in Burundi, Rwanda, or Uganda using the information flow method. Hirons and Turner [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] used the MetUM model to study the effect of IOD on the inter-annual variability of the East African short rains (October to December). Results confirmed a positive correlation between anomalous equatorial easterly flow across the Indian Ocean Basin and wetter East Africa. Earlier, Behera [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] studied the significant influence of the Indian Ocean Dipole on the East African Short Rains using the Coupled Global Circulation Model (CGCM). The results align with the current literature, which indicates that the IOD influences short-term rainfall in a similar capacity as ENSO.\u003c/p\u003e \u003cp\u003eIn the southern region of Africa, Manatsa and Mukwada [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] offered different implications for rainfall resulting from the introduction of IOD mechanics. The Principal Component Analysis (PCA) was utilised to study ENSO and IOD in Zimbabwe's homogeneous rainfall. The results displayed pure IOD composites as having the strongest mechanisms for suppressing rainfall compared to pure ENSO. However, in most southern African literature, ENSO has received more attention than IOD [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The number of studies that have explicitly examined how IOD affects the temporal-spatial rainfall variability of nearby landmasses is still relatively small, e.g. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEqually, in Malawi, studies linking the phenomenon with rainfall are scarce. Available studies have shown rainfall variability across the nation's time scales linked to global circulations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recent studies have shown that the evaporation of open water has a greater influence on rainfall than covariates such as IOD and ENSO (Ngongondo et al. 2020). Contrary to this, Kumbuyo et al. (2015) applied the Pearson correlation coefficient method and established that summer rainfall over Malawi has a strong and significant correlation with the Indian Ocean SST as compared to the Atlantic and Pacific Ocean SSTs. Furthermore, the study showed that most stations in the northern region had a more significant correlation with the IOD than those in the central and southern regions.\u003c/p\u003e \u003cp\u003eHowever, the extent to which the IOD has influenced hydro-climate processes in catchments in Malawi has not been fully documented. This necessitates extensive research involving additional smaller drainage basins and robust techniques. The Dwangwa River Basin (DRB) is one of the key inflows of Lake Malawi; hence, it is crucial to appreciate its dynamics in relation to global circulation. The main objective of this study is to examine the relationship between rainfall variability and IOD in the Dwangwa River Basin in Malawi. In particular, the study: (i) evaluated the observed rainfall for evidence of climate variability in the Dwangwa River Basin; (ii) assessed the impact of IOD on the rainfall regime of the Dwangwa River Basin; and (iii) examined the response of the discharge of the Dwangwa River Basin during IOD events. Thus, this study expands on earlier efforts that strived to understand important global forces in the basins in Malawi. The study findings provide some evidence on the drivers of key climate forcings, such as IOD, on major river basins flowing into Lake Malawi. A better understanding of maritime mechanisms would increase the accuracy and reliability of weather forecasts aimed at promoting adaptive mechanisms for climate change, water resource management, and environmental planning.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe study was undertaken in the Dwangwa River Basin (DRB) in Malawi, Southern Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The DRB covers a catchment area of 7,768 km\u003csup\u003e2\u003c/sup\u003e before it drains into Lake Malawi [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It is located in the central region of Malawi at an altitude between 500 and 1500 m above mean sea level. The DRB has a mean annual rainfall of 902 mm and an annual runoff of 12% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It is designated as Water Resources Unit (WRA) 6, according to the National Water Resources Master Plan [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The upper course of the basin in the west and central parts is the Kasungu plain, which is gently undulating with altitudes between 975 and 1300 m above sea level [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A number of smaller rivers drain into the main river system before inflowing into Lake Malawi (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Hydro-Meteorological data\u003c/h2\u003e \u003cp\u003eThe DRB is located in a region where hydro-meteorological data is scarce [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The study used climate data from Kasungu, Dwangwa, Kaluluma, and Mwimba climate stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for the period 1985\u0026ndash;2015 obtained from the Malawi Department of Climate Change and Meteorological Services (DCCMS). The DCCMS data was of relatively high quality and contained no gaps, suggesting some level of inspection before being distributed. However, the selection of the stations was based on the World Meteorological Organisation's (WMO) recommendation that a minimum of 30 years of data is sufficient for analysing trends in hydro-climatic data [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, the Malawi Ministry of Water and Sanitation, through the Department of Water Resources (DWR), provided average monthly river discharge data for the upper and lower Dwangwa River Basins from 1985 to 2009. The data were from two gauging stations at Kwengwere upper catchment (6C1) and Dwangwa S53 (6D10) lower catchment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Unlike the DCCMS data, the discharge data had prolonged periods (2009\u0026ndash;2015) of missing data. Average monthly river discharge data for the upper and lower Dwangwa River Basin was collected from the Ministry of Irrigation and Water-Malawi, Department of Water Resources, from 1985 to 2009. Data was not available at Dwangwa Road Bridge S53 station in 1985.\u003c/p\u003e \u003cp\u003eThe period from 2009 to 2015 had the longest period of missing data; hence, it was discarded for analysis. Many studies have recommended that for better spatial variability in hydrological analysis, data spanning more than 20 years can be desirable for trend analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In a case where missing data exists in the basin, missing flows can be estimated using nearby rivers that have data [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, the study used regression analysis to fill in the missing data. The method has been successfully utilised in hydrological studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. At both discharge gauging stations, data from 1986 to 1989 was complete. Thus, this period was utilised in generating the equation for filling data gaps. There were big gaps between the series on both stations up until 2015. In other situations, data from one gauging station was used due to prolonged missing data at the other stations (alternatively). Therefore, for the hydrological response, the study used 24 years because of the long period of missing data in some years.\u003c/p\u003e \u003cp\u003eDespite the availability of numerous methods for filling in missing hydrological data, there is generally no single method that can be considered universally best [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Further, it was mentioned that each method has its own advantages and disadvantages, depending on the characteristics of the data set. However, other factors, for instance, distance between stations, aerial coverage of each gauging stage, length of gap, the season, the climatic region, or the availability and data characteristics of the records, have significant influences on hydrological data estimates [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this regard, the study used simple linear regression (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$y={B}_{0}+{B}_{1}X+\\in$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\varvec{y}\\)\u003c/span\u003e \u003c/span\u003e is the predicted value of the dependent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\varvec{y}\\right)\\)\u003c/span\u003e\u003c/span\u003e in the case of the station with missing data.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\varvec{B}}_{0}\\)\u003c/span\u003e \u003c/span\u003e is the intercept, the predicted value of \u003cb\u003ey\u003c/b\u003e when the \u003cb\u003eX\u003c/b\u003e is 0. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{B}}_{1}\\)\u003c/span\u003e\u003c/span\u003e is the regression coefficient-how much we expect \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{y}\\)\u003c/span\u003e\u003c/span\u003e to change as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{X}\\)\u003c/span\u003e\u003c/span\u003e increases (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cb\u003eX\u003c/b\u003e is the independent variable which is the Station of Dwangwa Road Bridge S53. The R square was chosen as the explanation for the model's best fit when determining the dependent variables [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consider a model where the R2 value is 70%. Here r squared meaning would be that the model explains 70% of the fitted data in the regression model. Generally, when the R square of 2 value is high, it suggests a better fit for the model [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, the study found R square of 0.76 data was considered to be the best fit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ebest fit of the model. The r square of 0.75 was assumed the best fit hence it was utilised for\u003c/p\u003e \u003cp\u003ecalculating missing values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Indian Ocean Dipole data\u003c/h2\u003e \u003cp\u003eThe study used the Dipole Mode Index (DMI) to quantify the Indian Ocean temperature anomalies. The DMI has been used in several studies; e.g., the intensity of the IOD is given by the anomalous SST gradient between the western equatorial Indian Ocean Dipole and the south-eastern equatorial Indian Ocean [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. When the DMI is positive, the phenomenon is known as a positive IOD event, and when it is negative, it is a negative IOD event (Yamagata et al. 2003). The Dipole Mode Index data was obtained from the Tokyo Climate Centre and the World Meteorological Organisation (WMO) Regional Climate Centre in RA II (ASIA).\u003c/p\u003e \u003cp\u003eData is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ds.data.jma.go.jp/tcc/tcc/products/elnino/index/iod_index.html\u003c/span\u003e\u003cspan address=\"https://ds.data.jma.go.jp/tcc/tcc/products/elnino/index/iod_index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. In the period from October to December, a three-month average was determined. The standard deviation of \u0026plusmn;\u0026thinsp;0.5 was the accepted yardstick for declaring\u0026thinsp;\u0026plusmn;\u0026thinsp;IOD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, four years had positive IOD: 1994, 1997, and 2015 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Negative IOD occurred in 1996, 1998, 2005, and 2010. Ultimately, the neutral IODs occurred in 1986, 2000, 2002, 2004, and 2009 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Quality Control\u003c/h2\u003e \u003cp\u003eThe quality of data for meteorological variables such as rainfall continues to be a challenge in data-scarce regions such as Malawi [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the DCCMS undertakes some data quality control checks before archiving their data [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, we checked for randomness in the rainfall data series as part of the enhancement of data quality. The procedure ensures that data is independent and identically distributed, as required by some of the analysis procedures, such as trend tests [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Test for randomness and persistence\u003c/h2\u003e \u003cp\u003eThere are various methods used to determine the randomness and consistency of the data. These include median crossing, serial autocorrelation analysis, turning points, rank difference, and autocorrelation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This study adopted the use of serial autocorrelation analysis to check for randomness and persistence. It has been successfully used in many studies of temporal and spatial rainfall trend analysis in order to check if the data is influenced by previous rainfall data, e.g., [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Serial autocorrelation analysis correlates a time series dataset with itself at different time lags. Autocorrelation was performed on the total annual rainfall and river discharge data of individual stations in the catchment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This procedure determines whether the preceding data series has an impact on the rainfall data series. Programme R was used following Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${r}_{k}=\\frac{{\\sum }_{i=1}^{N-k}({x}_{i }-m)({x}_{i+k}-m)}{{\\sum }_{i=1}^{N}{(x}_{i}-m{)}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cb\u003er\u003c/b\u003e\u003csub\u003e\u003cb\u003ek\u003c/b\u003e\u003c/sub\u003e is the \u003cb\u003elag-k\u003c/b\u003e autocorrelation coefficient, \u003cb\u003em\u003c/b\u003e is the mean value of a time series \u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e, is the number of observations, and is the time lag. If the calculated r\u003csub\u003ek\u003c/sub\u003e is not significant at the 5% level, then the Mann-Kendall test can be applied to the original values of the time series [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Where the calculated autocorrelation is significant, the data sets were smoothed using a 3-year moving average to reveal more persistent trends [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The smoothed time series may be obtained as follows (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\overline{y}}_{t}=\\frac{{y}_{t }+{y}_{t-1}{+}_{\\dots ..}+{y}_{t-n-1}}{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{y}\\)\u003c/span\u003e\u003c/span\u003e is the variable (such as rainfall), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{t}\\)\u003c/span\u003e\u003c/span\u003e is the current time (such as the current month) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e is the number of times in average.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Inter and intra-annual rainfall Variability\u003c/h2\u003e \u003cp\u003eAnnual and monthly mean rainfall data were used to calculate the basin-wide. The Precipitation Concentration Index (PCI) and Coefficient Variation (CV) were used as descriptors of rainfall variability in the DRB. PCI was used to evaluate the varying weight of monthly rainfall relative to the total amount of rainfall [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This enabled an understanding of the monthly heterogeneity of rainfall amounts. A modified version of PCI by Oliver (1980), cited in [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and was applied (Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$PCI=100\\times \\frac{{\\sum }_{i=1}^{12}{P}_{i}^{2}}{({\\sum }_{i=1}^{n}{P}_{i}{)}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({p}_{i}\\)\u003c/span\u003e \u003c/span\u003e is the rainfall amount of the month, calculated for each year PCI values below 10 indicate a uniform monthly rainfall distribution in the year, whereas PCI values of 11 to 20 indicate seasonality in rainfall distribution. Values above 20 correspond to climates with substantial monthly variability in rainfall amounts [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A higher PCI value indicates that precipitation is more concentrated in a few rainy months during the year, and vice versa. The rainfall variability for representative meteorological stations was determined by calculating the coefficient of rainfall variation (CV) as the ratio of the standard deviation (SD) to the mean rainfall in a given period (CV%) when expressed as a percentage [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Eq.\u0026nbsp;\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the calculation of CV.\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$CV=\\frac{\\sigma }{x}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sigma =\\)\u003c/span\u003e\u003c/span\u003eStandard Deviation; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x\\)\u003c/span\u003e\u003c/span\u003e= mean based on the values of CV has classified the rainfall variability of an area as; CV\u0026thinsp;\u0026lt;\u0026thinsp;20% less variable, CV 20%-30% moderate variable and CV\u0026thinsp;\u0026gt;\u0026thinsp;30% high variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Temporal trends\u003c/h2\u003e \u003cp\u003eApproaches used for detecting a trend in the time series can be either parametric or non-parametric. The non-parametric Mann-Kendall (MK) test statistic was used to determine the direction and significance of temporal trends of the total annual and monthly rainfall (i.e., from November to April and May to October) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The test has been widely applied in various trend detection studies, including those of Nsubuga et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and it is recommended by the World Meteorological Organisation (WMO) for trend analysis of hydro-climatic data [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Firstly, it is a non-parametric test and does not require the data to be normally distributed. Secondly, the test has low sensitivity to abrupt breaks due to inhomogeneous time series, such that it is insensitive to missing data and outliers [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, the test is considered very robust for non-normally distributed data series, such as rainfall [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Programme R was utilised in the analysis of trends using Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$S=s={\\sum }_{k=1}^{n-1}{\\sum }_{j=k+1}^{n}sgn({X}_{j}-{X}_{k})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$Sgn({X}_{j}-{X}_{k}=\\left\\{\\begin{array}{c}+1, if\\left({X}_{j}-{X}_{k}\\right)\u0026gt;0\\\\ 0,if \\left({X}_{j}-{X}_{k}\\right)=0\\\\ -1, if\\left({X}_{j}-{X}_{k}\\right)\u0026lt;0\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere and are sequential precipitation values in the month, whereas a positive value is an indicator of an increasing (upward) trend and a negative value is an indicator of a decreasing (downward) trend. The slope of a linear trend is estimated with the non-parametric Sen\u0026rsquo;s slope estimator\u0026rsquo;s method [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] references it therein. It is the best method to detect trends because it is not affected by outliers or missing data. The slope of n pairs of data points was estimated using Sen\u0026rsquo;s estimator, calculated as\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${ Q}_{i}=\\frac{{x}_{j}-{x}_{k}}{j-k} for i=1\\dots N$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere and are data values at time, respectively. The median of these values is Sen\u0026rsquo;s estimator slope. The presence of a statistically significant trend is evaluated using the value [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To decide whether the null hypothesis is to be accepted or rejected, a test statistic is computed with a critical value obtained from a set of statistical tables. The null hypothesis is rejected if the absolute value of is greater than \u003cb\u003eZ1-a/2\u003c/b\u003e, and then the trend is considered significant, where Z\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;a/2\u003c/sub\u003e is obtained from the standard normal distribution [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Relationship between IOD and Rainfall\u003c/h2\u003e \u003cp\u003eThe study used Pearson product moment correlation (r) (Eq.\u0026nbsp;\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This method describes the relationship between two variables [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The variables data points determine the direction or sign of the correlation coefficient value. The correlation coefficient is often denoted by rho and its sample estimate ranges from plus one to minus one (r\u0026thinsp;=\u0026thinsp;\u0026plusmn;\u0026thinsp;1) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A positive value near \u0026plusmn;\u0026thinsp;1 is described as having very strong positive/negative correlation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$r=\\frac{{\\sum }_{i=1}^{N}({x}_{i}-\\stackrel{-}{x}\\left)\\right({y}_{i}-\\stackrel{-}{y})}{\\sqrt{{\\sum }_{i=1}^{N}({x}_{i}-\\stackrel{-}{x}{)}^{2}{\\sum }_{i=1}^{N}({y}_{i}-\\stackrel{-}{y}{)}^{2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere and are the averages of time series and, respectively [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The correlation coefficient \u003cb\u003er\u003c/b\u003e \u003cem\u003ed\u003c/em\u003eescribes the degree of closeness to a linear relationship between two variables \u003cb\u003ex\u003c/b\u003e and \u003cb\u003ey\u003c/b\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The value of \u003cb\u003er\u003c/b\u003e varies from \u0026minus;\u0026thinsp;1 for a \"perfect \"out-of-phase correlation to +\u0026thinsp;1 for a \"perfect\" in-phase correlation, and the coefficient of zero indicates that no linear relationship exists. To test whether the correlation is significant, the null hypothesis that the correlation is zero and the alternative hypothesis that the correlation is nonzero were assumed. If the null hypothesis was valid, the relevant test variable \u003cem\u003e(\u003c/em\u003e\u003cb\u003et\u003c/b\u003e\u003cem\u003e)\u003c/em\u003e from Eq.\u0026nbsp;(\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) was the realisation of student \u003cem\u003e(\u003c/em\u003e\u003cb\u003et\u003c/b\u003e\u003cem\u003e)\u003c/em\u003e random variables with a mean of zero and \u003cem\u003en\u003c/em\u003e degrees of freedom [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Based on this information, \u003cem\u003ep\u003c/em\u003e-values were computed; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 impelled the probability of discarding the null hypothesis, and vice versa. The \u003cb\u003et\u003c/b\u003e-test statistics were used as given in Eq.\u0026nbsp;(\u003cspan refid=\"Equ10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$t=\\sqrt[r]{\\frac{n-2}{1-{r}^{2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7 River discharge response to IOD\u003c/h2\u003e \u003cp\u003eTo determine whether there was an increase or decrease in discharge during the IOD anomalies in the basin, the study used the Simple Linear Regression method (Eq.\u0026nbsp;11). This method has performed very well in similar studies for finding the linear relationship between variables. It mostly relies on the assumption of linearity in the relationships between variables.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Y}_{i}={\\beta }_{0}+{\\beta }_{1}{X}_{i}\\)\u003c/span\u003e \u003c/span\u003e+ε (11)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{w}\\text{h}\\text{e}\\text{r}\\text{e} \\varvec{Y}}_{\\varvec{i}}\\)\u003c/span\u003e \u003c/span\u003e is dependent variable in this case it is river discharge from DRB. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{\\beta }}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the intercept, the predicted value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{Y}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003ewhen the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{X}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e is 0. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{\\beta }}_{1}\\)\u003c/span\u003e\u003c/span\u003e is the regression coefficient of how much we expect \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{Y}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e to change as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{X}\\)\u003c/span\u003e\u003c/span\u003e increases [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{X}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e is the independent variable in this case Dipole Mode Index (DMI) (the variable we expect is influencing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{Y}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e ). \u003cb\u003eε\u003c/b\u003e is the error of estimate, or how much variation there is in our estimate of the regression coefficient .\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results and Discussions","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Hydro-meteorological characteristics of Dwangwa River Basin\u003c/h2\u003e\n \u003cp\u003eThe highest discharge of 59 m\u003csup\u003e3\u003c/sup\u003e/s and 55 m\u003csup\u003e3\u003c/sup\u003e/s was found at S53 and Khwengwere, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest mean monthly discharge in the two gauging stations was in February (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The river recession starts in March, when the cold, dry season is approaching. The peaked discharge displays that the catchment witnessed an increase in rainfall in November. Therefore, from May to October tends to be a period of no rainfall, displaying the typical rainfall pattern of Malawi [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e Mean monthly discharge of Dwangwa basin from 1985 to 2009.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Rainfall Variability\u003c/h2\u003e\n \u003cp\u003eThe average CV for the Dwangwa basin catchment is 0.24, indicating that rainfall does not vary greatly from year to year (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). All the stations had CVs less than 30% (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The smallest CV in the basin of the Dwangwa River demonstrates that rainfall is not greatly variable over the years. Nsubuga et al. [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] noted that areas with a CV higher than 30% are likely to have more frequent and severe droughts and floods throughout the year. However, stations in the basin had a PCI (mean) of 20\u0026ndash;30%, indicating that intra-annual rainfall is highly variable, both temporally and seasonally (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The results demonstrate that in most years, rainfall is not reliable. The seasonality could be influenced by the station in Dwangwa, which lies in the Lake Shore areas sharing higher rainfall with Nkhata- Bay. The finding agrees with other studies that demonstrated that central Malawi has more variability in rainfall than southern Malawi [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003esummaries of the PCI and CV in DRB between 1981 and 2015\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual PCI (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnnual Mean (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDwangwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKasungu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaluluma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMwimba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Serial autocorrelation\u003c/h2\u003e\n \u003cp\u003eThe data was subjected to serial autocorrelation in order to check its persistence. The idea was to treat the rainfall data so that it could not be associated with the previous data in the catchment. The autocorrelation results in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displayed that the data is persistently free; hence, the Mann-Kendall trend test was applied directly (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Temporal and Spatial trend\u003c/h2\u003e\n \u003cp\u003eAnnual rainfall exhibited a range of temporal and spatial trends (Fig.\u0026nbsp;4). Although the rainfall trend results were not statistically significant at the 0.05 significant level, all of the stations registered declining trends in annual rainfall (Fig.\u0026nbsp;4b). However, Kaluluma displayed an insignificant increase in rainfall in the basin (Fig.\u0026nbsp;4). This could be because the station is part of the northern region\u0026apos;s rainfall-bearing system, which receives comparatively more rainfall than the central region. Despite the interesting trend in annual rainfall at Kaluluma station, the basin-wide annual average rainfall displayed an insignificant (0.05) decrease trend (Fig.\u0026nbsp;4). This demonstrates that the Dwangwa River Basin\u0026apos;s annual rainfall trend is declining. These findings agree with the study of Tadeyo et al. [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], which presented a decrease in rainfall trend in most areas of Malawi. The decrease in rainfall patterns could be associated with changes in climate globally. The country-based studies [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], and [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] revealed that the majority of Malawi\u0026apos;s annual rainfall series are trending downward, though most of them presented insignificant patterns. This demonstrates that the DRB is consistent with the observed decreasing trends in rainfall. The existing pattern of rainfall could be intensifying due to anthropogenic activities, which are contributing to the already fragile climatic systems.\u003c/p\u003e\n \u003cp\u003eFigure 4 Mann kendall rainfall for: annual rainfall series Kasungu (a), Kaluluma (b), Dwangwa (c), Mwimba (d), areal annual average rainfall in Dwangwa River Basin (e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Correlation between Dipole Mode Index (DMI) and rainfall\u003c/h2\u003e\n \u003cp\u003eThe results of the correlation between DMI and rainfall are presented in Table ii. With the exception of Kasungu station, all the other stations displayed an insignificant positive correlation with a negative IOD in the basin (0.9) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, all the stations exhibited a strong negative, insignificant correlation with positive IOD (-0.7) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Munthali et al. [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] report that a negative IOD is associated with droughts and low rainfall. Thus, these results revealed that during the warm phase in the Indian Ocean, the drainage basin experiences low rainfall. This is contrary to the East African scenario, where the region receives a higher amount of rainfall during a positive IOD [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. This presents the opposite results from what is observed in the drainage basin. Negative IOD was shown to be a contributor to increased rainfall in the basin. Positive IOD, however, was found to be associated with low rainfall. Again, during the neutral phase, the Dwangwa River basin is connected with an insignificant positive correlation with IOD (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This can be regarded as a transition time between negative IOD and positive IOD. This period contributes to increased rainfall in the basin.\u003c/p\u003e\n \u003cp\u003eThis study suggests that IOD has an overwhelming impact on the rainfall regime in the basin. The study agrees with the findings of Kumbuyo et al. [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], who found summer rainfall in Malawi to be strongly correlated with the Indian Ocean SST compared to the Atlantic and Pacific Ocean SSTs. During a negative phase, waters in the eastern Indian Ocean (near Indonesia) are warmer than normal, and the western Indian Ocean (near Africa) are cooler than normal [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is these low sea surface temperatures in the Indian Ocean near Africa that influence rainfall variability in the DRB. This is primarily attributed to the IOD capability of modifying the Walker circulation over the tropical Indian Ocean and moisture in the middle troposphere over Central Equatorial Africa in countries including the Congo, Tanzania, and Western Kenya [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, rainfall could be influenced by the trigger of walker circulation due to the intensification of cold temperatures along the coast of east Africa.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation between Rainfall and DMI during Negative IOD in DRB\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIOD (-)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKasungu\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDwangwa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMwimba\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKaluluma\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBasin Wide\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIOD (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKasungu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDwangwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMwimba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaluluma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasin Wide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eValues in bold are different from 0 with a significance level at alpha\u0026thinsp;=\u0026thinsp;0.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation between Rainfall and DMI during Positive IOD in DRB\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIOD (+)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKasungu\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDwangwa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMwimba\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKaluluma\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBasin Wide\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIOD (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKasungu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDwangwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMwimba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaluluma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasin Wide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eValues in bold are different from 0 with a significance level alpha\u0026thinsp;=\u0026thinsp;0.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation between Rainfall and DMI during Neutral Phase in DRB\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIOD (o)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKasungu\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDwangwa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMwimba\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKaluluma\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBasin Wide\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIOD (o)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKasungu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDwangwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.991\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMwimba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaluluma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasin Wide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.991\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eValue in bold are different from 0 with a significance level alpha\u0026thinsp;=\u0026thinsp;0.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 River discharge response to IOD\u003c/h2\u003e\n \u003cp\u003eThe study revealed mixed responses between discharge and IOD in the DRB. A negative association was presented in discharge during positive IOD (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). While negative IOD contributed to an increase in discharge in DRB (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). However, during the neutral phase, the catchment had mixed results in the upper and lower catchments (Fig.\u0026nbsp;7). The upper catchment at Kwengwere is associated with an increase in river discharge during the neutral phase. Yet discharge at S53 displayed a slight decrease in river discharge (Fig.\u0026nbsp;7). The difference could be emanating from the differences in local conditions and the onset of rainfall in the basin. While a negative IOD would induce water availability. The studies by Kumbuyo et al. [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] displayed that the northern areas of Malawi are strongly influenced by the SST Indian Ocean dipole, whereas the central and southern areas are strongly linked to the SST in the subtropical Indian Ocean. Dwangwa River Basin being at the transition zone would have mixed results at some phases, as displayed in Fig.\u0026nbsp;7.\u003c/p\u003e\n \u003cp\u003eFigure 7. (e) and (f) regression during the neutral IOD\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4.0 Conclusion and Recommendation","content":"\u003cp\u003eThe current study investigated the impact of IOD on rainfall variability in DRB. Specifically, it looked at the variability of rainfall using the CV and PCI, examined rainfall trends, and finally connected the relationship between IOD under three phases (positive, negative, and neutral). The results suggest that rainfall does not vary greatly from year to year in the basin. Moreover, the basin exhibited less inter-annual variability in all months. Additionally, the stations in the basin had a precipitation concentration index (mean) between 20% and 30%, indicating that the temporal intra-annual rainfall distribution is highly variable. Moreover, the rainfall trend displayed an insignificant decrease annually.\u003c/p\u003e \u003cp\u003eAgain, the results showed that positive IOD is negatively correlated with rainfall in the catchment, though it is insignificant. This contributes to low river discharge during the phase. Therefore, a positive IOD phase could potentially affect the availability of water in the catchment. Furthermore, there was an insignificant positive correlation between negative IOD and rainfall in the basin, suggesting that a negative IOD is associated with increased rainfall in the DRB. Consequently, this triggers an increase in river discharge in the basin. The study has demonstrated diverse results on the role that large-scale circulation drivers play in modulating the hydro-climate variability. Therefore, the present study would recommend the following for further studies: The studies related to the impact of global circulation seem to be different in each country; further studies should target local areas. Yet again, studies on the impact of the Indian Ocean Dipole on climate and hydrological response remain insufficient. Therefore, studies should look at the periodicities of IOD in relation to hydro-meteorological parameters for modelling and prediction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements \u0026amp; Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received financial support through the second authors \u0026nbsp;from the projects: (i)\u0026nbsp;NORHED II\u0026nbsp;Climate Change and Ecosystems Management in Malawi and\u0026nbsp;Tanzania (#63826) at the University of Malawi; and\u0026nbsp;(ii) the World Bank supported the Centre for Resilient Agri-Food Systems (CRAFS) at the University of Malawi under the ACE 2 Project.\u0026nbsp;We sincerely acknowledge this support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors neither have conflict of interest nor competing interests when developing this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributing authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study from developing the research concept and design. The planning, data collection and analysis were performed by Aubren C. Chirwa, Cosmo S. Ngongondo and Ephraim Vunain. The first draft of the manuscript was written by Aubren C. Chirwa then it was improved and approved by Cosmo S. Ngongondo and Ephraim Vunain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Dipole Mode Index data is available at https://ds.data.jma.go.jp/tcc/tcc/products/elnino/index/iod_index.html from the Tokyo Climate Centre World Meteorological Organisation (WMO) Regional Climate Centre in RA II (ASIA). Rainfall data is available Malawi Department of Climate Change and Meteorological Services (DCCMS). River discharge data is available at the Ministry of Irrigation and Water-Malawi, Department of Water Resources.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study from developing the research concept and design. The planning, data collection and analysis were performed by Aubren C. Chirwa, Cosmo S. Ngongondo and Ephraim Vunain. The first draft of the manuscript was written by Aubren C. Chirwa then it was improved and approved by Cosmo S. Ngongondo and Ephraim Vunain.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eC. C. Ibebuchi, \u0026lsquo;Circulation Patterns Linked to the Positive Sub-Tropical Indian Ocean Dipole\u0026rsquo;, Adv. Atmos. Sci., vol. 40, no. 1, pp. 110\u0026ndash;128, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00376-022-2017-2\u003c/span\u003e\u003cspan address=\"10.1007/s00376-022-2017-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. 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Technol., vol. 17, no. 5, pp. 1035\u0026ndash;1045, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2166/wpt.2022.045\u003c/span\u003e\u003cspan address=\"10.2166/wpt.2022.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-atmosphere","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Atmosphere](https://www.springer.com/journal/44292)","snPcode":"44292","submissionUrl":"https://submission.nature.com/new-submission/44292","title":"Discover Atmosphere","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Indian Ocean Dipole, Precipitation Concentration Index, Rainfall Variability, Dwangwa River Basin, Malawi","lastPublishedDoi":"10.21203/rs.3.rs-4252531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4252531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge-scale climate processes such as the Indian Ocean Dipole (IOD) have significant roles in modulating rainfall and hydrological systems. Understanding such processes can inform adaptation measures for climate change and variability, as well as water resource management and planning. This study investigated the impact of IOD on rainfall and discharge variability in the Dwangwa River Basin (DRB) in Malawi, a key inflow to Lake Malawi. Specifically, the study analysed annual rainfall variability trends from 1985 to 2015 using the Coefficient of Variation (CV) and the annual Precipitation Concentration Index (PCI). The significance and direction of rainfall and discharge trends were quantified using the Mann-Kendall trend test at the α\u0026thinsp;=\u0026thinsp;0.05 significance level. To evaluate the association between rainfall and IOD, the Pearson product moment used three IOD phases: positive, negative, and neutral. Simple linear regression was utilised to check the response of the river during the concerned IOD phases. The study found CVs below 30%, typical of climates with moderate monthly rainfall variability. The PCI ranged from 20\u0026ndash;30%, suggesting a strongly seasonal and highly variable temporal intra-annual rainfall distribution in the DRB. Moreover, the Mann-Kendall test statistics showed insignificant annual rainfall trends. Further, the findings demonstrated an insignificant negative correlation between rainfall and positive IOD, with rainfall increases associated with negative IOD, whereas positive IOD is associated with decreased river discharge. Consequently, El Ni\u0026ntilde;o and a positive IOD could cause DRB to have low water availability.\u003c/p\u003e","manuscriptTitle":"Influence of the Indian Ocean Dipole (IOD) on Hydro-climate variability in Dwangwa River Basin, Malawi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:38:19","doi":"10.21203/rs.3.rs-4252531/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-04T04:35:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-30T07:34:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-25T19:04:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96a400fb-b3e5-4bbc-84fe-e151f16da048","date":"2024-04-24T13:25:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-23T19:03:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8352080a-a89c-4cb3-a1db-ccecafa4583b","date":"2024-04-21T17:50:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2bd78c55-bacb-4771-9fb4-fd0b51291751","date":"2024-04-20T09:49:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-19T11:01:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T05:59:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-16T05:58:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Atmosphere","date":"2024-04-11T12:46:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-atmosphere","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Atmosphere](https://www.springer.com/journal/44292)","snPcode":"44292","submissionUrl":"https://submission.nature.com/new-submission/44292","title":"Discover Atmosphere","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2473f91-2047-48da-9fd2-940b83d94519","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-28T17:38:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:38:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4252531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4252531","identity":"rs-4252531","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}