Southern Malawi lies towards the southern incipient end of the EARS Western Branch, where it channels the Shire River from Lake Malawi to its confluence with the Zambezi River (Dulanya, 2017; Ivory et al., 2016). This portion of the EARS is typically considered to represent the divergent boundary between the Rovuma and Nubia plates (Fig. 1a; Saria et al., 2013; Stamps et al., 2008, 2018, 2020). However, recent seismotectonic analysis suggests that the Nubia Plate can be further divided by the Lower Zambezi and Luangwa rifts into the San and Angoni plates, with the EARS in Malawi forming the Angoni–Rovuma plate boundary (Fig. 1a; Daly et al., 2020). EARS activity in southern Malawi is unlikely to have initiated prior to the mid-Pliocene (∼ 4.5 Ma) onset of sediment accumulation in Lake Malawi's south basin (Delvaux, 1995; McCartney and Scholz, 2016; Scholz et al., 2020) and almost certainly not before the Oligocene (23–25 Ma) age of the Rungwe Volcanic Province (RVP) in southern Tanzania (Mesko, 2020; Mortimer et al., 2016; Roberts et al., 2012). The RVP, 700 km to the north (Fig. 1a), marks the closest surface volcanism to southern Malawi; hence, this rift section is considered to be amagmatic.

Like elsewhere in the Western Branch, the EARS in southern Malawi follows Proterozoic orogenic belts and can be divided along strike into a number of 50–150 km long linked basins (Ebinger, 1989). Immediately south of Lake Malawi, the EARS bifurcates around the Shire Horst within the NW–SE trending Makanjira Graben before following an arcuate bend in regional Proterozoic fabrics to form the NNE–SSW trending Zomba Graben (Fig. 2; Dulanya, 2017; Fullgraf et al., 2017; Laõ-Dávila et al., 2015; Wedmore et al., 2020a; Williams et al., 2019). Along strike to the south, the EARS then intersects the Lower Shire Basin, a reactivated Karoo-age (i.e. Permo-Triassic) basin (Castaing, 1991; Chisenga et al., 2019; Habgood, 1963; Habgood et al., 1973; Wedmore et al., 2020b), before bending around the Nsanje Horst to link up with the Urema Graben in Mozambique (Bloomfield, 1958; Steinbruch, 2010). Daly et al. (2020) proposed that the Lower Shire Basin also extends to the west along the Mwanza Basin into Mozambique where it links with the Lower Zambezi Rift and forms the San–Angoni plate boundary (Fig. 1a).

Prior to this study, the only systematic active fault mapping in southern Malawi had been conducted by Chapola and Kaphwiyo (1992) and, for the Lower Shire Basin, by Castaing (1991). These maps were subsequently incorporated by Macgregor (2015) into EARS-scale maps, and later into the Global Earthquake Model Global Active Fault Database (Styron and Pagani, 2020). However, the faults are mapped at a coarse scale (Fig. 2a), and this database does not include active faults traces identified in legacy geological maps (Bloomfield, 1965; Bloomfield and Garson, 1965; Habgood et al., 1973; Walshaw, 1965) and high-resolution digital elevation models (Hodge et al., 2019, 2020; Wedmore et al., 2020a, b).

There are no known historical accounts of surface-rupturing earthquakes in southern Malawi, although a continuous written record only extends to ca. 1870 (Pike, 1965; Stahl, 2010). However, in northern Malawi, the previously unrecognised St Mary Fault exhibited surface rupture following the 2009 Karonga earthquakes, a sequence consisting primarily of four shallow (focal depths < 8 km) MW 5.5–5.9 events over a 13 d period (Fig. 1b; Biggs et al., 2010; Gaherty et al., 2019; Hamiel et al., 2012; Kolawole et al., 2018b; Macheyeki et al., 2015).

The International Seismological Centre (ISC) record for Malawi is complete from 1965 to present for events with MW>4.5 (Figs. 1b, 2a; Hodge et al., 2015), with the largest event in this record being the 1989 MW 6.3 Salima earthquake (Jackson and Blenkinsop, 1993). Notably, seismicity in Malawi is commonly observed to depths far greater (30–35 km; Craig et al., 2011; Delvaux and Barth, 2010; Jackson and Blenkinsop, 1993) than would be expected for continental crust of typical composition and geothermal gradient (10–15 km). Thick cold anhydrous lower crust (Craig et al., 2011; Jackson and Blenkinsop, 1997; Njinju et al., 2019; Nyblade and Langston, 1995), localised weak viscous zones embedded within strong lower crust (Fagereng, 2013), and/or volumes of mafic material in the lower crust (Shudofsky et al., 1987) that are velocity weakening at temperatures < 700 ∘C (Hellebrekers et al., 2019) have been proposed as explanations for this unusually deep seismicity.

Earthquake focal mechanism stress inversions that encompass events from across Malawi indicate a normal fault stress state (i.e. vertical maximum principal compressive stress) with an ENE–WSW to E–W trending minimum principal compressive stress (σ3, Fig. 1b; Delvaux and Barth, 2010; Ebinger et al., 2019; Williams et al., 2019). This σ3 orientation is comparable to the σ3 direction inferred from regional joint orientations (Williams et al., 2019) and the geodetically derived extension direction between the Nubia and Rovuma plates (Fig. 1b; Saria et al., 2014; Stamps et al., 2018, 2020).

Using instrumental catalogues, probabilistic seismic hazard analysis (PSHA) finds that there is a 10 % probability of exceeding 0.15 g peak ground acceleration in the next 50 years in southern Malawi (Midzi et al., 1999; Poggi et al., 2017). Through the SMAFD and SMSSD, we outline how geological and geodetic data can be collated and assessed so that they may also be incorporated into PSHA in southern Malawi.

There are many inherent limitations in mapping active faults. Even in countries with well-developed databases, such as Italy and New Zealand, their success in accurately predicting the locations of future surface-rupturing earthquakes is, at best, mixed (Basili et al., 2008; Nicol et al., 2016a). An active fault might not be recognised because evidence of previous surface rupture is subsequently buried, eroded (Wallace, 1980), or the fault itself is blind (e.g. Quigley et al., 2012), which in turn depends on earthquake magnitude, focal depth, thickness of the seismogenic crust, and the local geology. Furthermore, although active and inactive faults are typically differentiated by the age of the most recent earthquake, the precise maximum age that is used to define “active” varies between different active fault databases depending on the regional strain rate (i.e. plate boundary vs. stable craton) and the prevalence of youthful sediments (Clark et al., 2012; Jomard et al., 2017; Langridge et al., 2016; Machette et al., 2004). Indeed, it may not always be possible to reliably determine if an exposed fault has been recently active or not (Cox et al., 2012; Nicol et al., 2016a).

Each of these issues has relevance to mapping active faults in southern Malawi. Firstly, active faults may be buried by sediments deposited due to tectonic subsidence (Gawthorpe and Leeder, 2000) and/or by regular (10–100 ka) climate-driven ∼ 100 m scale fluctuations in the level of Lake Malawi, which would likely flood the Zomba and Makanjira basins (Ivory et al., 2016; Lyons et al., 2015; Wedmore et al., 2020a). Alternatively, the relatively thick (30–35 km) seismogenic crust in southern Malawi means that even moderate–large earthquakes (MW>6) do not necessarily result in surface rupture, as illustrated by the MW 6.3 Salima earthquake (Gupta, 1992; Jackson and Blenkinsop, 1993). Finally, except for studies around Lake Malombe (Van Bocxlaer et al., 2012), there is no chronostratigraphic control for this section of the EARS to help differentiate between inactive and active faults (Dulanya, 2017; Wedmore et al., 2020a).

Thus, for the SMAFD, we define active faults based on evidence of activity within the current tectonic regime. Such an approach has been advocated elsewhere in the EARS (Delvaux et al., 2017) and in other areas with low levels of seismicity, few paleoseismic studies, and/or where there are faults that are favourably oriented for failure in the current stress regime but that have no definitive evidence of recent activity (Nicol et al., 2016a; De Pascale et al., 2017; Villamor et al., 2018). In practice, this means that faults will be included in the SMAFD if they can be demonstrated to have been active during East African rifting. This evidence can vary from the accumulation of post-Miocene hanging-wall sediments to the presence of a steep fault scarp, offset alluvial fans, and/or knickpoints in rivers that have migrated only a short vertical distance (< 100 m) upstream (Hodge et al., 2019, 2020; Jackson and Blenkinsop, 1997; Wedmore et al., 2020a). We note that the absence of post-Miocene sediments in the hanging wall of a normal fault does not necessarily imply that it is inactive, if, for example, faults are closely spaced across strike so that sediments are eroded during subsequent footwall uplift of an interior normal fault (e.g. Chirobwe–Ncheu Fault, Fig. 3c; see also Mortimer et al., 2016; Muirhead et al., 2016). In these cases, if there is other evidence of recent activity (e.g. scarp, triangular facets), these faults are still included.

For the sake of completeness, major faults that control modern-day topography but that do not fit the criteria of being active (e.g. Karoo faults) were mapped separately (Fig. 2a). However, this map is not necessarily complete for all other faults in southern Malawi, and we also cannot definitively exclude the possibility that some of these faults are still active although they display no evidence for it. The relatively broad definition of an active fault may also mean that some inactive faults are included in the SMAFD. However, in applying the opposite approach (i.e. requiring an absolute age for the most recent activity on a fault) there is a greater risk that faults mistakenly interpreted to be inactive subsequently rupture in a future earthquake (Litchfield et al., 2018; Nicol et al., 2016a).

Following the active fault definition and synthesis of the datasets described above, faults in southern Malawi are mapped following the approach outlined for the Global Earthquake Model Global Active Fault Database (GAF-DB) where each fault constitutes a single continuous GIS feature (Styron and Pagani, 2020). Therefore, the SMAFD differs from other active fault databases where each distinct geomorphic (i.e. traces) or geometric (i.e. sections) part of a fault is mapped as a separate GIS feature (Christophersen et al., 2015; Machette et al., 2004).

The attributes associated with each fault in the SMAFD are listed and briefly described in Table 1. These resemble the attributes in the GEM GAF-DB that describe a fault's geomorphic attributes and confidence that it is still active (Styron and Pagani, 2020). To incorporate the multidisciplinary approach that we have used to map faults in southern Malawi, we also include a “Location method” attribute, which details how the fault was mapped (Table 1). Some fault attributes used in the GEM GAF-DB, such as slip rates, are not included in the SMAFD, as these data have not been collected in southern Malawi. We instead derive these attributes as outlined in Sect. 4 and incorporate them separately into the SMSSD (Table 2). However, within each database, a numerical ID system is used make the two databases compatible (Tables 1, 2).

Faults may rupture both along their entire length and in smaller individual-section ruptures that are often bounded by changes in fault geometry (DuRoss et al., 2016; Goda et al., 2018; Gómez-Vasconcelos et al., 2018; Hodge et al., 2015; Iezzi et al., 2019; Valentini et al., 2020). Therefore, the basic GIS feature in the SMSSD is a fault section, where individual faults from the SMAFD may be divided into multiple sections by bends in their fault trace (Fig. 2d; DuRoss et al., 2016; Jackson and White, 1989; Wesnousky, 2008; Zhang et al., 1991). Along-strike minima in fault displacement (e.g. scarp or knickpoint height) may also be indicative of segmentation (Willemse, 1997), but these do not always coincide with geometrical complexities in southern Malawi (Fig. 4; Hodge et al., 2018a, 2019; Wedmore et al., 2020a, b). This may indicate that deeper structures, not visible in the surficial fault geometry, are also influencing fault segmentation (Wedmore et al., 2020b). Therefore, where along-strike scarp height measurements exist, these local minima are also used to define fault sections (Figs. 2d, 4).

Faults that are closely spaced across strike but are not physically connected may also rupture together through “soft linkages” (Childs et al., 1995; Wesnousky, 2008; Willemse, 1997; Zhang et al., 1991). In the SMSSD, we follow empirical observations and Coulomb stress modelling that suggests that normal fault earthquakes may rupture across steps whose width is < 20 % of the combined length of the interacting sections, up to a maximum separation of 10 km (Biasi and Wesnousky, 2016; Hodge et al., 2018b), and we use this as a criteria to assign whether two en echelon faults in the SMSSD may rupture together.

A number of geometrical attributes are then assigned to both individual sections and whole faults in the SMSSD (Table 2). Section length (Lsec) is defined as the straight-line distance between section end points (Fig. 4b). This approach avoids the difficulty of measuring the length of fractal features, and it accounts for the hypothesis that small-scale (less than kilometre-scale) variations in fault geometry in southern Malawi may represent only near-surface complexity (depths < 5 km) and that the faults are relatively planar at depth (Hodge et al., 2018a). However, it only provides a minimum estimate of section length. For segmented faults in the SMSSD, fault length (Lfault) is the sum of Lsec, otherwise Lfault is the distance between its tips (Fig. 4b). As each GIS feature in the SMSSD represents a distinct earthquake source, we consider that Lsec and/or Lfault must be > ∼ 5 km, except in the case of linking sections that rupture only in whole-fault ruptures. (Christophersen et al., 2015).

In southern Malawi, fault dip is either unknown or uncertain, because fault planes are rarely exposed, surface processes affect scarp angle (Hodge et al., 2020), and/or dip at depth is not constrained. This difficulty in measuring fault dip is common, and dip has been parameterised using a range of reasonable values in these cases (Christophersen et al., 2015; Langridge et al., 2016; Styron et al., 2020). Thus, we assign minimum, intermediate, and maximum dip values in the SMSSD of 40, 53, and 65∘ respectively, which encapsulate dip estimates from field data in southern Malawi (Hodge et al., 2018a; Williams et al., 2019), and earthquake focal mechanisms (Biggs et al., 2010; Ebinger et al., 2019), seismic reflection data (Mortimer et al., 2007; Wheeler and Rosendahl, 1994), and aeromagnetic surveys (Kolawole et al., 2018a) elsewhere in Malawi.

It is typically assumed that fault width (W) can be estimated by projecting the difference in lower and upper seismogenic depth into fault dip (δ), with the assumption that faults are equidimensional up to the point where W is limited by the thickness of the seismogenic crust (Christophersen et al., 2015): 1W=Lfault,whereLfault≤zsin⁡δ;zsin⁡δ,whereLfault>zsin⁡δ

In southern Malawi, both seismogenic thickness, z (30–35 km; Jackson and Blenkinsop, 1993; Craig et al., 2011), and δ (40–65∘, as justified above) are poorly constrained, so a range of W values must be considered. Furthermore, ruptures unlimited by z are not necessarily equidimensional (Leonard, 2010; Wesnousky, 2008). Therefore, in the SMSSD, we estimate W from an empirical scaling relationship between fault length and W (Leonard, 2010): 2W=C1Lfaultβ, where Lfault>5 km, and C1 and β are empirically derived constants that are equal to 17.5 and 0.66 respectively for interplate dip-slip earthquakes (Leonard, 2010). As shown in Fig. 5c, when applying Eq. (2), estimates of W in the SMSSD are consistent with (1) observations of >1 length-to-width ratios for dip-slip earthquakes (Fig. 5c) and (2) the thick seismogenic crust in East Africa (i.e. W∼40 km, Fig. 5c; Craig et al., 2011; Ebinger et al., 2019; Jackson and Blenkinsop, 1993; Lavayssière et al., 2019; Nyblade and Langston, 1995).

For a narrow amagmatic continental rift such as the EARS in southern Malawi, the first step to estimate slip rates is to divide the rift along its axis into its basins (Fig. 2b); within each basin, the mapped faults are then divided into border and intra-basin faults. We define border faults geometrically, as a fault located at the edge of the rift with the implicit assumption that all other mapped active faults are intra-basin faults (Fig. 2d; Ebinger, 1989; Gawthorpe and Leeder, 2000; Muirhead et al., 2019; Wedmore et al., 2020b). These geometric definitions have no direct implications for how displacement is partitioned among border and intra-basin faults.

The slip rate for each fault or fault section i is then estimated using the following equation: 3Slipratei=αbfvcos⁡θ(i)-ϕnbfcos⁡δ,for border faultsαifvcos⁡θ(i)-ϕnifcos⁡δ,for intra-basin faults Here, θ(i) is the fault or fault section slip azimuth, v and φ are the respective horizontal rift extension rate and azimuth, α is a weighting applied to each fault depending on whether it is a border (αbf) or intra-basin (αif) fault, and it is divided by the number of mapped border faults (nbf) or intra-basin faults (nif) in each basin (Fig. 6). Although Eq. (3) is specific for rifts, it could be adapted in other tectonic settings where there is an a priori understanding of the rate and distribution of regional strain – for example, to distribute regional strain between the basal detachment and thrust ramps in a fold and thrust belt (Poblet and Lisle, 2011), to distribute regional strain between multiple subparallel faults in a strike-slip system, or to assess more complex strain partitioning between kinematically distinct fault populations in transtensional or transpressional systems (Braun and Beaumont, 1995).

The distribution of v between border (αbf) and intra-basin faults (αif) in an amagmatic narrow rift depends on factors such as total rift extension (Ebinger, 2005; Muirhead et al., 2016, 2019), rift obliquity (Agostini et al., 2011b), hanging-wall flexure (Muirhead et al., 2016; Shillington et al., 2020), lower crustal rheology (Heimpel and Olson, 1996; Wedmore et al., 2020a), and whether border faults have attained their maximum theoretical displacement (Accardo et al., 2018; Olive et al., 2014; Scholz and Contreras, 1998). In some incipient rifts like southern Malawi, extensional strain is observed to be localised (∼ 80 %–90 %) on its border faults (Muirhead et al., 2019; Wright et al., 2020). Furthermore, evidence from boreholes and topography indicates that border faults in southern Malawi have relatively small throws (< 1000 m, Fig. S1), which combined with its thick seismogenic crust indicates that the flexural extensional strain on its intra-basin faults is likely to be negligible (Billings and Kattenhorn, 2005; Muirhead et al., 2016; Wedmore et al., 2020a). However, detailed analysis of fault scarp heights across the Zomba Graben indicates that ∼ 50 % of extensional strain is currently distributed onto its intra-basin faults (Wedmore et al., 2020a). To account for this uncertainty in the SMSSD, lower, intermediate, and upper estimates of αbf are set to 0.5, 0.7, and 0.9 respectively (Fig. 6). As αif=1-αbf, respective lower, intermediate, and upper estimates are 0.1, 0.3, and 0.5 (Fig. 6).

Where distinct intra-basin faults kinematically interact across steps, we consider these as one fault in Eq. (3), as this equation accounts for strain across, not along, the rift. For the Mwanza and Nsanje basins, no intra-basin faults are identified (Fig. 2b), so all the extension strain is assigned to their border faults (i.e. αbf=1). In the case of the Nsanje Basin, however, this is extension is divided into increments of 30 %, 50 %, and 70 % between the Nsanje Fault and a border fault identified 25 km along strike in Mozambique (Fig. S1; Macgregor, 2015) to estimate its lower, intermediate, and upper slip rate.

In the SMSSD, the horizontal extension rate, v, is taken from the plate motion vector between the Rovuma and Nubia plates at the centre of each individual basin (Table 3, Figs. 1b, S1) using the Euler poles reported by Saria et al. (2013). We use the Euler pole (as defined by a location and rotation rate) and the uncertainties associated with the Euler pole (defined by an error ellipse; Fig. A1) to calculate the plate motion and the plate motion uncertainty between the Rovuma and Nubia plates for each basin (Table 3, Fig. 1b) following the methods outlined in Robertson et al. (2016). With this approach, the lower bound of v is negative (i.e. the plate motion is contractional; Table 3). However, the topography and seismicity of southern Malawi clearly indicate that it is not a contractional regime nor is it a stable craton. Therefore, a lower bound of 0.2 mm/yr horizontal extension is assigned in the SMSSD, which is considered the minimum strain accrual that is measurable using geodesy (Calais et al., 2016). There are no geodetic constraints for the extension rate across the Mwanza Basin as it lies along the poorly defined Angoni–San plate boundary (Daly et al., 2020). Therefore, we assign this basin an extension rate of 0.2–1 mm/yr. This reflects the smaller escarpment height along its border fault (250 m vs. ∼ 750 m; Fig. 2b) relative to the Lower Shire Basin, which indicates a slower average extension rate over geological time.

The rift extension azimuth (φ) in southern Malawi is derived from a regional focal mechanism stress (073∘±012∘, Fig. 1b; Delvaux and Barth, 2010; Ebinger et al., 2019; Williams et al., 2019), as there is considerable uncertainty in this parameter from geodesy (Table 3; Saria et al., 2013). Faults in southern Malawi are considered to be normal (Delvaux and Barth, 2010; Hodge et al., 2015; Williams et al., 2019). Therefore, the slip azimuth (θ(i)) is the dip direction of each fault or fault section, where it is then projected into φ in Eq. (3). Although this sets up an apparent inconsistency in which variably striking faults accommodate normal dip-slip under a uniform extension direction, this phenomena that can be explained by lateral heterogeneity in the lower crust in southern Malawi (Corti et al., 2013; Philippon et al., 2015; Wedmore et al., 2020a; Williams et al., 2019). To account for the uncertainty in φ, upper and lower extension rates are obtained by varying φ±012∘ depending on the fault's dip direction (e.g. upper slip rate estimates for NE and NW dipping fault are estimated with φ set to 061 and 085∘ respectively). An example of these slip rate calculations for the central section of the Chingale Step fault is provided in Fig. 7.

We estimate earthquake magnitudes in the SMSSD by applying empirically derived scaling relationships between fault length and earthquake magnitude. Scaling relationships between fault length and average single-event displacement (D‾) can then be combined with slip rate estimates to calculate earthquake recurrence intervals (R) through the relationship R=D‾ / slip rate (Wallace, 1970). To select an appropriate set of earthquake-scaling relationships for the SMSSD, we consider three previously reported regressions and apply them to its mapped faults: (1) between normal fault length and MW (Wesnousky, 2008), (2) interplate dip-slip fault length and MW (Leonard, 2010), and (3) fault area and MW (Wells and Coppersmith, 1994) where A is calculated using W derived from Eq. (1).

We find that although generally comparable for MW<7.5, the Wells and Coppersmith (1994) regression overestimates magnitudes relative to Leonard (2010) (Fig. 5d). This likely reflects the discrepancy in W between applying Eq. (1) and the Leonard (2010) regression (Eq. 2; Fig. 5c; Sect. 4.1). The Wesnousky (2008) regression overestimates magnitudes for MW<6.9 relative to Leonard (2010) equations and underestimates them at larger magnitudes (Fig. 5d). This may reflect that the Wesnousky (2008) regression is derived from only six events, and these events show a poor correlation between length and MW (Pearson's regression coefficient = 0.36). Given these considerations, the Leonard (2010) regressions are used in the SMSSD. Furthermore, these regressions are used to estimate W (Sect. 4.1) and are self-consistent when estimating MW and D‾ from Lfault, which is not necessarily true for the other cases.

Thus, MW and D‾ are estimated in the SMSSD by 4MW(i)=52log⁡Lsec+32log⁡C1+log⁡C2μ-9.091.5for individual-section ruptures and52log⁡Lfault+32log⁡C1+log⁡C2μ-9.091.5for whole-fault ruptures, and5logD‾(i)=56log⁡Lsec+12log⁡C1+log⁡C2μfor individual-section ruptures and56log⁡Lfault+12log⁡C1+log⁡C2μfor whole-fault ruptures, where μ is the shear modulus (3.3×1010 Pa), C1 is as defined for Eq. (2), and C2 is another constant derived by Leonard (2010). Both constants are varied between the full range of values derived in a least square analysis (Leonard, 2010) to obtain lower, intermediate, and upper estimates of MW and D‾ (Figs. 6, 7). Following Eq. (5), recurrence intervals R(i) can be calculated as follows: 6R(i)=D‾(i)Slip rate(i), where upper estimates of R are calculated by dividing the upper estimate of D‾ by the lowest estimate of fault or section slip rate and vice versa (Fig. 6). An example of these earthquake source calculations for the central section of the Chingale Step fault is provided in Fig. 7.

The SMAFD contains 23 active faults across five EARS basins. The northernmost faults lie in the NW–SE trending Makanjira Graben, a full graben where two border faults, the Makanjira and Chirobwe–Ncheu, clearly define either side of the rift (Fig. 8a). Four intra-basin faults are identified, with, two of them, the Bilila–Mtakataka and Malombe faults, exhibiting steep scarps (Hodge et al., 2018a, 2019). In particular, one-dimensional diffusional models of scarp degradation suggest that the Bilila–Mtakataka Fault scarp formed within the past 10 000 years (Hodge et al., 2020). The Malombe Fault forms a ∼ 500 m high escarpment that bounds the Shire Horst and divides post-Miocene deposits in the Makanjira Graben across strike (Fig. 8a; Hodge et al., 2019; Laõ-Dávila et al., 2015).

Along strike to the south, the NNE–SSW trending Zomba Graben contains a prominent border fault, the Zomba Fault, on its eastern edge, and three well-defined intra-basin fault scarps in its interior (Fig. 8b; Bloomfield, 1965; Wedmore et al., 2020a). The western edge of the Zomba Graben grades onto the Kirk Plateau where there are several deeply incised N–S trending valleys that have been previously mapped as “rift valley faults” (Fig. 8b; Bloomfield and Garson, 1965). However, only one of these faults has an active scarp and accumulated post-Miocene sediments (the Lisungwe Fault; Wedmore et al., 2020a). In addition, the Wamkurumadzi Fault, which lies to the west of the Lisungwe, is also included in the SMAFD – albeit with low confidence – as evidence of recent activity is noted by Bloomfield and Garson (1965), and any recent sediments may have been eroded by the Wamkurumadzi River that flows along its base. Given the complex topography and ambiguity on fault activity, we tentatively interpret these faults as intra-basin faults in the SMSSD and note that the western Zomba Graben should be a priority area for future fault mapping.

The floor of the NW–SE trending Lower Shire Basin lies at an elevation 350 m lower than the floor of the Zomba Graben. Between these two EARS sections basement is exposed, and there is no evidence of tectonic activity that falls within the SMAFD definition of an active fault. Gravity surveys and topographic data indicate that the Lower Shire Basin exhibits a half-graben structure, with the Thyolo Fault bounding it to the northeast (Fig. 8d; Chisenga et al., 2019; Wedmore et al., 2020b). A number of intra-basin faults have been identified in the hanging wall of the Thyolo Fault (Chisenga et al., 2019), although none are identified in the Nsanje and Mwanza basins (Fig. 8d, e).

By implementing a logic tree approach to assess uncertainty in the SMSSD, three values (lower, intermediate, and upper) are derived for each calculated attribute (Table 2, Fig. 6). However, it is implicit that the upper and lower values have a low probability as they require a unique, and possibly unrealistic, combination of parameters. Therefore, we primarily report values obtained from applying the intermediate branches in the logic tree but discuss the uncertainties in Sect. 5.4.

Although the SMAFD contains 23 active faults, these are further subdivided into 74 sections in the SMSSD – of which 13 are linking sections. Section lengths (Lsec) range between 0.7 and 62 km, whereas fault lengths (Lfault) vary from 6.2 to 144 km (Fig. 5a, Table 4). The highest slip rates are estimated to be on the Thyolo and Zomba faults (intermediate estimates of 0.6–0.8 mm/yr). On intra-basin faults in the SMSSD, intermediate slip rate estimates are 0.05–0.1 mm/yr (Fig. 9). Slip rates tend to be relatively fast in the Makanjira Graben (Fig. 9c), as the extension rate is higher (Table 3), and its NNW–SSE striking faults are more optimally oriented to the regional extension direction (Fig. 2). The difference between upper and lower slip rate estimates in the SMSSD logic tree is 2 orders of magnitude: ∼ 0.05–5 mm/yr for the border faults and ∼ 0.005–0.5 mm/yr on the intra-basin faults (Fig. 9).

For whole-fault ruptures along border faults, intermediate estimates of earthquake recurrence intervals (R) are between 2000 and 5000 years, whereas they are between 10 000 and 30 000 years for intra-basin whole-fault ruptures (Fig. 10a–c). Considerable uncertainty exists in these values, with the upper and lower estimates for R varying from 102 to 105 years and from ∼ 103 to 106 years for border and intra-basin whole-fault ruptures respectively (Fig. 10a–c). Furthermore, if these faults rupture in individual sections, R may be reduced by up to an order of magnitude (Fig. 10d–f). Intermediate estimates of earthquake magnitudes range from MW 5.4 to MW 7.2 for individual-section ruptures and from MW 5.6 to MW 7.8 for faults that rupture their entire length (Table 4, Fig. 11b). The SMSSD also includes one example where multiple en echelon faults, the Panga Fault system (Fig. 2d), could rupture together given the constraints outlined in Sect. 4.1.

It is possible that slip rate estimates in the SMSSD are effectively upper bounds, as some proportion of the geodetically derived rift extension may be accommodated by aseismic creep or along unrecognised faults. With regards to aseismic creep, the discrepancy between geodetic and seismic moment rates in Malawi implies that its faults are strongly coupled (Ebinger et al., 2019; Hodge et al., 2015). This is also consistent with the velocity-weakening behaviour of some samples from the rift in deformation experiments at lower crustal pressure–temperature conditions (Hellebrekers et al., 2019).

Conversely, the possible inclusion of inactive faults in the SMAFD and SMSSD would mean that individual fault slip rates may be lower bounds. Without paleoseismic investigations and dating of offset surfaces in southern Malawi, it is difficult to test this point. Nevertheless, reactivation analysis that encompasses the range of fault orientations in southern Malawi indicates that these faults are favourably oriented in the current stress field (Williams et al., 2019). Therefore, even faults that have been inactive for a considerable time (up to the entire age of the EARS) could still theoretically be reactivated. We also note that slip rates of intra-basin faults in the North Basin of Lake Malawi over the last 75 ka (0.15–0.7 mm/yr; Shillington et al., 2020) are within the range of estimates of intra-basin faults in the SMSSD (Fig. 9).

Upper and lower estimates of R differ by up to 3 orders of magnitude in the SMSSD (Fig. 10). To investigate these uncertainties, we performed a multiparameter sensitivity analysis following the methods presented in Box et al. (1978) and Rabinowitz and Steinberg (1991). Full details of this analysis are given in Appendix A. In summary, seven parameters that contribute to uncertainty in R for the central section of the Chingale Step fault are considered (Table 5). By exploring all possible combinations in which these seven parameters are set at their upper or lower estimates, 128 (i.e. 27) different values of R can be calculated. However, we instead considered 64 parameter combinations that were chosen following a fractional factorial design (Table S1; Box et al., 1978). In this way, parameter combinations that offer little insight into how a system works are omitted, thereby increasing the efficiency of this analysis at minimal cost to its validity (Rabinowitz and Steinberg, 1991). From these combinations, the natural log of the average value of R when a parameter (k) is set at its upper (ln⁡R‾(k+)) and lower (ln⁡R‾(k-)) value is calculated and the difference between these values defines the parameter effect (A; Rabinowitz and Steinberg, 1991): 7A=ln⁡R‾k+-ln⁡R‾k-

This analysis indicates that R is most sensitive to uncertainties in the partitioning of strain between border and intra-basin faults in the rift (i.e. αif/nif), the rift extension rate (v), and the C2 parameter in Eq. (5), and it is least sensitive to uncertainties in the rift's extension azimuth and the C1 parameter in Eq. (5) (Table 5). If, however, v and its associated uncertainties were estimated using a different Nubia–Rovuma Euler pole solution (Fig. A1; Table 3; Stamps et al., 2008), R estimates are least sensitive to v and most sensitive to C2 (Table 5). There are no interaction effects between two separate parameters that may influence their effect on R (Table S2).

The existence of active faults within southern Malawi poses a significant risk to the 7.75 million people living in this region (Malawi National Statistics Office, 2018) as well as those living adjacent to the rift in northern Mozambique (Fig. 11a). Furthermore, with population growth at an annual rate of 2.7 % in southern Malawi (Malawi National Statistics Office, 2018), this risk will increase over the coming decades. The rapidly growing city of Blantyre (population of 800 000; Malawi National Statistics Office, 2018), which is in the footwall of both the relatively fast slipping (intermediate estimates ∼ 0.8 mm/yr) Zomba and Thyolo faults is at a particularly high risk (Fig. 11a).

Intermediate estimates in the SMSSD for MW 5.4–7.8 earthquakes and fault recurrence intervals (R) of 103–104 years (Fig. 11) imply that southern Malawi's seismic hazard is characterised by infrequent large-magnitude events. Indeed, faults in this region may host earthquakes comparable to the largest historical continental normal fault earthquakes (∼ MW 7.5; Valentini et al., 2020); although relatively rare, >150 km long normal faults have been mapped elsewhere, and these would be capable of even larger events (Styron and Pagani, 2020).

One of the purposes of collating the SMSSD was to identify current knowledge gaps in our understanding of active faulting and seismic hazard in southern Malawi. Our sensitivity analysis (Sect. 5.4) indicates that the two biggest factors contributing to uncertainty in R in the SMSSD is related to our understanding of the distribution and rate of extension (v) in southern Malawi (Table 5). In particular, there is considerable uncertainty in the position of the Nubia–Rovuma Euler pole (Fig. A1; Saria et al., 2013), and we would not expect such large differences between upper and lower fault slip rate estimates by following our systems-based approach elsewhere. Although the uncertainties associated with v in the SMSSD could be reduced if an alternative solution for the Nubia–Rovuma Euler pole was applied (Fig. A1, Tables 5, S2; Stamps et al., 2008), this solution uses fewer Global Positioning System (GPS) sites and a shorter position time series (Saria et al., 2013). Therefore, in the short term, the best refinements to R estimates may come from new regional geodetic data and further high-resolution topographic analysis (e.g. Daly et al., 2020; Stamps et al., 2020; Wedmore et al., 2020a).

Directly measuring on-fault slip rates and paleoseismicity would provide more robust R estimates than the model-derived estimates in SMSSD. However, careful site selection would be required for these analyses in southern Malawi because of its potential for large (∼ 10 m) single-event displacements (Hodge et al., 2020). Furthermore, these investigations carry large inherent uncertainties in low-strain-rate regions like southern Malawi if only a few earthquakes are sampled, as these events may be temporally clustered (Nicol et al., 2006, 2016b; Pérouse and Wernicke, 2017; Taylor-Silva et al., 2020).

When considering how different rupture magnitude estimates in the SMSSD influence R, the main source of uncertainty is the C2 parameter from the Leonard (2010) regressions (Table 5). This factor controls the amount of displacement for a given rupture area (Leonard, 2010). It is therefore likely related to earthquake stress drops, and uncertainty in C2 in southern Malawi will only be reduced by recording more events here or in similar tectonic environments – i.e. normal fault earthquakes in regions with low (∼ 1–10 mm/yr) extension rates and thick (20–35 km) seismogenic crust.