The purpose of this study is to understand atmospheric factors, which cause mudflow variability on interannual and longer timescales, from local to synoptic scales. In a first step, historical data of mudflow occurrences in Uzbekistan provided by the Centre of Hydrometeorological Service of the Republic of Uzbekistan (Uzhydromet) for more than 140 years were analysed. During the investigation period a total of about 3000 mudflow events were observed with about 21 events per year on average. The majority of mudflows occur during the advection of westerly airflow when moist air from central and southern Europe reaches Uzbekistan. This synoptic weather type (SWT) can be related to one of the 15 primary synoptic circulation types over central Asia (CA) and Uzbekistan, which were subjectively derived by Bugayev and Giorgio in the 1930s and 1940s. To understand the main atmospheric regimes steering the variability in mudflow occurrences, we additionally applied an objective classification following the circulation weather type (CWT) approach. By means of the CWT approach, we found that on mudflow days the frequencies of cyclonic (C), westerly (W), south-westerly (SW) and north-westerly (NW) stream flows are increased in comparison to the climatological frequency of the occurrence of these circulation weather patterns. Results confirm that CWT westerly airflow initiates relatively more mudflow events comparing to other CWTs in the study area. An integrated approach of the CWT classification and an antecedent daily rainfall model are combined together in logistic regression analysis to construct a mudflow-triggering precipitation threshold for every CWT class. In general W, SW and C weather types require less antecedent rainfall to trigger mudflow occurrences in the study area. This technique is thus shown to be applicable to coarse-resolution climate model diagnostics.
Mudflows are amongst the most damaging and deadly natural hazards in Uzbekistan. Data from the Centre of Hydrometeorological Service of the Republic of Uzbekistan (Uzhydromet) suggest that mudflows were responsible for over 38 deaths and damaged approximately 3000 households and 5000 ha of agricultural crops over the decade (2005–2014) in Uzbekistan (Table A1 in the Appendix). However, the incidence of damage may be much larger as these events commonly occur in mountainous areas, in incised valleys and in areas of otherwise low relief.
Mudflow occurrences for the years 2005–2014 in areas with a high probability of mudflow passage in Uzbekistan include the following: Zerafshan basin (blue dots) in the central part of the country; Fergana Valley (red dots) in the east; Chirchik–Akhangaran basin (orange) in the north-east; Surkhandar'ya (green) and Kashkadar'ya (violet) river basins in the south of Uzbekistan. The map also represents political administrative divisions and administrative centres/cities of the country.
Hungr et al. (2014) suggest the term mudflow as a very rapid,
sometimes extremely rapid, surging flow of saturated plastic soil in a steep
channel involving significantly greater water content relative to the source
material. In the river basins of Uzbekistan, mudflows generally occur during periods of intense rainfall or rapid snowmelt. The consistency of the
mudflow is mainly water and mud (liquidity index
The period of historically documented mudflow events confirms that the areas with a high passage of mudflow occurrences in Uzbekistan can be divided into five regions: Fergana Valley, the Zerafshan basin included in Zerafshan Valley, and the Surkhandar'ya, Kashkadar'ya and Chirchik–Akhangaran river basins (Fig. 1). From a geological point of view, mountain ranges (western part of the Pamir–Alai mountain system and western Tien Shan) of the study area are mainly composed of Palaeozoic limestones, granites, schists, marbles, sandstones, conglomerates and partly igneous rocks (Petrov et al., 2017) as well as shale and loess deposits forming weak surfaces of the low-relief hillslopes, especially in Fergana Valley, that are frequently prone to sliding due to the interaction with water.
Precipitation is an important mudflow trigger (Huggel et al., 2012) in Uzbekistan; however, snow cover and glaciers in mountain regions (Petrov et al., 2017), slope instability and temperature (Huggel et al., 2010) are additional factors. Other main factors such as antecedent rainfall (Glade et al., 2000; Sidle and Ochiai, 2006) and rapid snowmelt (Kim et al., 2004) may further reduce the slope stability, thereby enhancing potential of mud and debris flow occurrences.
Rainfall records in high mountain regions are limited, reflecting the limited number of meteorological stations in Uzbekistan; thus there are gaps in the rainfall and mudflow data due to missing spatial and temporal information. Furthermore, orographic effects on precipitation may not be captured adequately by a single (or a few) rain gauge (Huggel et al., 2012). In the Alps, precipitation is generally observed in mid- to high-elevation areas (Buzzi et al., 1998; Huggel et al., 2012), similar to the mountain ranges (Tien Shan, Alai and Pamir) in central Asia (CA); however, mudflows usually occur in lower to intermediate elevations. Reviews on precipitation thresholds triggering landslides indicate the magnitude of an extreme event (Glade, 1998; Guzzetti et al., 2008) depends on the rainfall intensity and duration (Caine, 1980), the local climate and orographic precipitation (Buzzi et al., 1998; Gheusi and Davies, 2004), the geomorphologic structure of the area (Rosi et al., 2016), soil characteristics (Yamao et al., 2016), and land use (Sidle and Ochiai, 2006; Gravina et al., 2017).
Seasonal variations in precipitation and an earlier snowmelt are assumed to be the main factors for changes in the seasonality of mudflow occurrences in Uzbekistan. On this basis, the goals of this paper are
to assess the link between the potential effects of synoptic conditions and
the occurrence of extreme hydro-meteorological mudflow episodes in
Uzbekistan; to establish an objective method for airflow classification of synoptic
conditions that will be applicable to a large set of atmosphere–ocean
general circulation model (AOGCM) and regional climate model (RCM) members
to investigate key factors of climate change impact on precipitation over
Uzbekistan and CA; to compare and validate this with pre-existing subjective classification
approaches; to identify thresholds of mudflow triggers taking into account the
antecedent precipitation; and to validate the streamflow dependencies of these thresholds.
Section 2 describes available climatological data as well as the methodology of this paper. The main results presented in Sect. 3 focus on climatological and statistical characteristics of mudflow occurrences recorded for more than 140 years in Uzbekistan, thus making these data accessible for the international scientific community for the first time. As an analysis of mudflow conditions in Uzbekistan is widely missing in scientific peer-reviewed international literature, this paper starts with a respective overview of local observational data and peer-reviewed material from non-English literature. Further, we introduce a subjective method to classify synoptic conditions of CA and Uzbekistan as well as an objective approach known as circulation weather types (CWTs) to identify the major weather types leading to the formation of mudflows in the study area in Sect. 4. Precipitation thresholds triggering mudflow occurrences in Uzbekistan are quantified and discussed in Sect. 5. The main conclusions and discussion are summarized in Sect. 6.
Monthly distribution of mudflow events in five regions in Uzbekistan.
The research approach consists of a five-step strategy, which is described in Fig. 2. The first component involves examining the historical data of mudflow occurrences in Uzbekistan and their characteristics on a longer timescale. Secondly, empirically developed local synoptic weather types (SWTs) are manually assigned to the observed mudflow occurrences. The objective CWT approach is therefore used to identify the atmospheric circulation and its relationship with the observed precipitation and their joint impact on mudflow occurrences in the study area. The fourth step is to evaluate the precipitation threshold triggering mudflow events in Uzbekistan using an empirical–statistical antecedent daily rainfall model (ADRM) (Glade et al., 2000). A detailed description of this approach as well as the datasets used for this component are given in the methods and Sect. 5 of this paper. Finally, the objective CWT method and the statistical ADRM are combined to estimate weather types which are most likely to trigger mudflow occurrences in the study area.
Methodological flow chart of the investigation process presented in this paper.
The desired outcome of this study is to eventually select representative weather types which can then be applied to AOGCM and RCM. That way the influence of precipitation patterns on mudflow occurrences can be studied under climate change scenarios across Uzbekistan and CA in further studies.
The investigation is based upon two categories of datasets: ground observation and reanalysis. Observed daily meteorological variables recorded by Uzhydromet, such as precipitation and temperature from the four meteorological stations (Gallyaaral, So'x, Chimgan and Mingchukur) located in the mountains and the foothills with high mudflow passages were used to produce respective climatologies (Table 2).
In addition, historical data of Uzhydromet regarding mudflow occurrences in Uzbekistan over the period 1870–2014 were analysed. The national-scale mudflow database includes information such as the name of the water stream, location, date of passage, the potential reason for the formation of the mudflow, a rough estimate of the volume and major damages.
Data of the daily mean synoptic situation or local classification of synoptic weather types (SWTs), which are available at Library Services and the Archive Department of Uzhydromet as catalogues of six-hourly (00:00, 06:00, 12:00, 18:00 GMT) data manually derived from synoptic charts, were calculated to produce relative outputs regarding mudflow-inducing weather situations. The period considered in this study is the warm seasons (March–August) of 1984–2013.
In order to assess potential climatic drivers over Uzbekistan, daily mean
lower atmospheric flow in 700 hPa geopotential height (GPH) fields by the
European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim
reanalysis (Dee et al., 2011), spanning the period of 1984–2013, was used to estimate
large-scale atmospheric circulation. This gridded dataset has 0.75
We select the stations Gallyaaral (574 m a.s.l.) located in the Zerafshan basin, So'x (1200 m a.s.l.) in Fergana Valley, Chimgan (1620 m a.s.l.) in the Chirchik–Akhangaran basin and Mingchukur (2132 m a.s.l.) representing both the Kashkadar'ya and Surkhandar'ya basins (Fig. 9b, c, Table 2) in order to investigate regional and local characteristics of the rainfall over each basin with high mudflow passages and its adjustment areas. The investigation area is limited to rain gauges located in the mountain areas, based on the assumption that each station represents hydro-climatologic conditions of the basin well, and at the same time station data on the rainfall accumulation process can capture mudflows within the radius of roughly 100 km. Regional and local mudflow data were extracted from the national-scale database for the warm seasons (March–August) of 1984–2013 to evaluate the relationship between mudflows and synoptic- and large-scale meteorological patterns. There were more than 300 days with mudflow episodes during the investigation period. Each mudflow event was placed within five areas based on information regarding the geographical name of the evidence.
Investigation of atmospheric circulation over CA first started in 1921 in the Turkestan synoptic-meteorological institute in Tashkent (Aksarin and Inagamova, 1993). The complexity and diversity of the regional atmospheric circulation was identified soon after Bugayev and Giorgio developed and simplified a model of airflow advection to explain synoptic differences in the 1930s and 1940s (Giorgio and Bugayev, 1936; Bugayev et al., 1957; Aksarin and Inagamova, 1993). The founders of the Central Asian Tashkent Institute of Weather Forecasters, Bugayev and Giorgio, had supervised research on synoptic meteorology and the impact of orographic factors on CA's climate for many years. In 1947 scientists published the first findings of the statistical characteristics of synoptic situations over the region for the cold period in the newsletters published by the USSR Academy of Science (Sarimsakov et al., 1947). After a decade, researchers summarized the studies on the atmospheric circulation classification scheme for CA and published it as a fundamental monograph, describing the main atmospheric patterns as 11 SWTs over CA (Bugayev et al., 1957). This monograph is still being used as the main literature and guidelines on synoptic conditions in CA countries mainly in Uzbekistan (Gavkhar Mamadjanova's personal experience, Aizen et al., 2004). Figure 3 provides a basic illustrative view of SWTs classified by Bugayev et al. (1957) emphasizing the air mass source regions and their path to CA. In the early 1960s, researchers at the Scientific Institute of Hydrometeorological Service of Uzbek SSR (known today as Uzbekistan) updated Bugayev and Giorgio's classification from 11 to 15 types by including additional weather classes. Subjectively classified weather types for all the years, except 2013–2014, over CA and Uzbekistan have been published as registers of the daily sequence of SWTs in Ilinova (1968), Voynova and Inagamova (1982), and Inagamova (1993, 2013). Table A2 provides comprehensive information for these 15 primary SWTs, describing weather conditions on a synoptic scale in CA and particularly in Uzbekistan. Figure 4 shows the long-term seasonal distribution (1935–2014) of SWTs within CA and Uzbekistan.
The main characteristics of the meteorological stations in Uzbekistan used in this study (source: Uzhydromet)
Scheme of synoptic weather types in central Asia and
Uzbekistan during the cold
We have used this subjective scheme in order to manually evaluate the relationship between synoptic weather patterns and recorded mudflow occurrences in five areas with high mudflow passages in Uzbekistan. Therefore, a SWT database was created based on SWT daily sequence sources contained in the previously published literature. In addition, the daily mean SWT sequence for summer (March–August) of 1984–2013 was extracted from the database to assess the association between mudflow days and frequency of synoptic-scale circulation.
The classification of daily flow patterns was carried out using the CWT approach. It
was developed by Jenkinson and Collison (Jones et al., 1993) based
on the original Lamb weather types for the British Isles (Lamb, 1972).
The basic method and details of the scheme were provided by Jones et al. (1993). The objective CWT scheme makes use of three basic variables that
define the circulation features at the surface over the study region:
direction of mean flow (
This objective approach was successfully applied mainly in Europe (Trigo and DaCamara, 2000; Donat et al., 2010; Jones et al., 2013; Ramos et al., 2015), as well as other parts of the world, e.g. in Armenia (Gevorgyan, 2013) and in Saudi Arabia (Kenawy et al., 2014). Recently, this method was used by Reyers et al. (2013) to determine present-day and future high-resolution rainfall distributions in the Aksu River basin (on the southern slopes of the Tien Shan, CA). To our knowledge this study is the first one that has applied the CWT approach to the territory of Uzbekistan. The aim of applying this method is to estimate the impact of airflow on precipitation patterns and its association with extreme mudflow episodes in Uzbekistan. Furthermore, the CWT scheme allows us to define atmospheric flow regimes objectively for application to Coupled Model Intercomparison Project Phase 5 (CMIP5) model data.
Frequency distributions of daily synoptic weather types by Bugayev's classification during the cold (September–February) and warm (March–August) seasons in the period of 1935–2014 years. Definitions of SWTs can be seen in Table A2.
In order to estimate the precipitation threshold causing mudflow events in the study area, a combination of an empirical and a statistical model has been applied: (1) ADRM as the relationship between antecedent rainfall conditions prior to an actual “rainstorm event” and the rainstorm magnitude itself (Glade et al., 2000); (2) a logistic regression model (LRM) as the relationship between an outcome (dependent or response) variable and a set of independent (predictor or explanatory) variables (Hosmer and Lemeshow, 2000).
The ADRM introduced by Crozier and Eyles (1980) defines landslide-triggering rainfall conditions for the Otago Peninsula during 1977–1978.
This model was applied in many parts of the world to obtain the threshold
probabilities of landslide occurrences on the basis of precipitation
conditions (e.g. for New Zealand by Glade et al., 2000; for Portugal by Zêzere and Rodrigues, 2002, and Zêzere et al.,
2005;
for São Miguel Island (Azores) by Marques et al., 2008; for Bangladesh by
Khan et al., 2012; for China by Bai et al., 2014). The advantage of this model lays in the substitution of soil moisture
storage levels by daily precipitation data. In the absence of real-time soil
moisture measurements, this model allows the prediction of the probability of
landslides (Glade et al., 2000). The ADRM (Crozier and Eyles, 1980) is expressed by the following formula:
In the first step, daily rainfall totals of 30 years (1984–2013) recorded in
four representative stations (Gallyaaral, Chimgan, Mingchukur and So'x) were
used to assess the average probability of mudflow-triggering thresholds. The
empirical model analysis consists of calculating the antecedent precipitation
for 10 consecutive days. Daily rainfall observations provided by Uzhydromet
were used in this study and a period of 24 h was taken between 08:00 of the
previous day to 08:00 LT (GMT
In the next step, a LRM was developed in order to estimate the relationship
among mudflow occurrences and daily rainfall and antecedent rainfall
value. The LRM was run in the freely available and open-source R software environment (Elsner and Jagger, 2013; R Core Team, 2017). Mudflow occurrence was treated as a dependent variable
and 10 days of antecedent rainfall index and the rainfall value on the day
with the mudflow event were used as independent variables. The values of the
variables were the input data for the LRM algorithm to calculate the
precipitation threshold equation and plot probability (
Finally, we integrated the results from the two different investigation strands to estimate each weather type as a proxy for the triggering mudflows by applying a combination of CWT, ADRM and LRM to produce the precipitation threshold for each CWT class.
In general, the climate in Uzbekistan is continental and semi-arid with hot
and dry summers and cold winters, sometimes severe with snowfall. Due to its
geographic location (between 37
The 30-year means (1984–2013) of monthly temperature
(
Chub (2007) confirms that the long-term climatology based on
50 stations' data (some series reach back to 1881) in Uzbekistan shows that
the mean air temperature in July varies from 26
The air temperature in the piedmont areas at an altitude from 300–400 to
600–1000 m is notably warmer during the cold season of the year and cooler in
summers than the plain areas of the country. With increasing altitude in the
mountainous regions, the air temperature drops 0.6
The amount and distribution of precipitation, as well as its seasonal variability, greatly depends on the geographical location of the area, topographic features and the general characteristics of the atmospheric circulation. In fact, several authors have identified moist air from the Atlantic Ocean, the Mediterranean Sea and the Persian Gulf as the main large-scale or regional climate factor for the precipitation regime in the country (Bugayev, 1946; Small et al., 1999; Inagamova et al., 2002; Chub, 2007; Schiemann et al., 2008).
The average precipitation distribution in Uzbekistan has a sharp contrast between the plain and mountain areas. Mean annual precipitation in major parts of the plains or deserts and dry steppes (Ustyurt Plateau, Kyzylkum Desert, Karshi, Dalverzin and Golodnaya steppes) is about 80–200 mm. However, precipitation can be significantly greater in some piedmont areas and the mountains, particularly in the north-east and the south-east of the country. In fact, precipitation in areas with an elevation between 600 and 1000 m or piedmont areas (Tien Shan and Gissar–Alai mountain ranges) can reach up to 500 mm; above 1000 m elevation the annual totals may exceed 500 mm. On some hillsides, especially the western slopes of the Tien Shan, it may even be greater than 2000 mm (Chub, 2007).
Generally, the precipitation regime in Uzbekistan reveals a seasonal character with wet conditions from October to May and a dry season with little or almost no rainfall during the summer (Fig. 5). Heavy precipitation events frequently occur during the rainy season, especially in March and April. August represents the driest month with the minimum amount of rainfall throughout the year.
Variability in mudflow events in Uzbekistan (1870–2014). Vertical bars present the mudflow observations for each year. The mean annual mudflow count (21) is indicated in a solid continuous horizontal line (pink). Curves (red, blue, green) have been fitted to the distribution for illustrative purposes and denote the 5-, 11- and 21-year rates of mudflow occurrences.
The earliest mudflow event induced by snowmelt and avalanches in the Akhangaran River basin was recorded in March 1870 (Lyakhovskaya, 1989; Chub et al., 2007). The archive data of mudflow occurrences in Uzbekistan since then, the Soviet period and after have been collected and published as catalogues by Uzhydromet.
In this study, we investigated century-long time series of the annual distribution of mudflows to identify key factors contributing to extreme mudflow occurrences. During the observation period (1870–2014) more than 3000 mudflow events have been identified in the river basins in the piedmont areas of Uzbekistan. Table 1 provides comprehensive information on the monthly distribution of mudflows in five regions of the country.
Mudflows with various magnitudes developing on the slopes of the study area appear several times during the year with an average number of 21 extremes per year (Table 1). The highest number of events was observed in 1930 with 167 mudflows followed by 161 mudflows in 1931, 144 episodes in 1963 and 108 events in 2012 (Fig. 6). An interesting point is that the 5-, 11- and 21-year moving averages of mudflow events features a periodicity of approximately 30 years, which repeated the highest peaks in the 1930s, 1960s and 1990s (Fig. 6). The last peak period of mudflow activity occurred in the 2010s. This signature of potential natural variability will pose an additional challenge for investigation of mudflow cycles and their variability under climate change conditions. However, according to Chub (2007), apart from the natural causes the number of recorded mudflows can increase due to several factors which often interact to generate mudflow occurrences in Uzbekistan. Socio-economic factors such as residential and industrial activities below unstable hillslopes accelerate soil creep, and accumulated materials in channels decrease the roughness condition by overloading with fills, which can potentially increase the probability and impact of mudflows.
More than 90 % of all recorded mudflows were associated with extreme precipitation events, hail and sleet, whereas 6 % of mudflow episodes were observed during intensive snowmelt events induced by respective temperature and precipitation changes (Chub et al., 2007). Glaciers melting due to increasing temperatures and mountain lake outbursts (Petrov et al., 2017) and dam failures has been suggested as a possible trigger of further minor mudflows (1.4 %) in the study area. Approximately 80 % of all recorded mudflow episodes with different origins occurred during the period of April–June (Fig. 7).
Records confirm that mudflow events often affected the Fergana Valley, indicating the highest event frequency during the investigation period of 12 mudflow occurrences per year on average (see Table 1). Salikhova (1975) and Lyakhovskaya (1989) interpreted that due to its topographic feature, the Fergana Valley was susceptible to constant mudflow passages. Geologically the uplands in the Fergana Valley are mostly covered with loess loam, which minimizes water infiltration and makes it receptive for even a small surface runoff to flush off the soil easily. This process contributes to soil erosion, which ends with the formation of mudflow episodes almost every year from March until August in the valley. In contrast to the Fergana Valley, the geological structure of the mountain areas for the following regions, namely the Zerafshan, Surkhandar'ya and Kashkadar'ya basins, is formed of effusive or volcanic rocks of the Palaeozoic age, which may cause more debris flows rather than mudflow events. Geomorphologic factors can be useful to determine the general susceptibility of various lithologies to landsliding in specific region (Sidle and Ochiai, 2006).
Monthly mudflow frequencies (bars) for the years 1870–2014. Values over the bars indicate the percentage of mudflow occurrences in a given month.
Frequency of mudflows under the synoptic weather types (SWTs) over
Uzbekistan during 1984–2013 (March–August):
There are more than 450 streams located in the river basins in the mountain and foothill areas in Uzbekistan and it is fairly common to observe extreme mudflow occurrences in multiple streams on the same day (Chub et al., 2007). For instance, on 15 April 1964 only in the Samarkand province (Zerafshan Valley) were 22 mudflow episodes recorded in a single day (Lyakhovskaya, 1989). During the period spanning from 1870 to 2014 up to 24 passages of flows were observed on 18 May 1991 in many parts of the country.
During the investigation period between 1984 and 2013, there were more than 300 days with mudflow occurrences in Uzbekistan. Figure 8 shows the frequencies of mudflow days for each SWT in five regions (Zerafshan, Fergana, Chirchik–Akhangaran, Kashkadar'ya and Surkhandar'ya) with high mudflow passage. According to the results, the majority of mudflows occur during the advection of airflow from the west (SWT 10) and a low level of small barometric gradient (SWT 13) in the study area. However, SWT 13 is the most frequent weather type in Fergana Valley (Fig. 8b) as the interaction of frontal circulation with orography as well as the associated effects of condensation and evaporation are assumed to determine the formation of low-level fronts and small-scale rainbands (Buzzi et al., 1998; Inagamova et al., 2002) there. The stationary cyclone type (SWT 8) is the second most frequent SWT triggering mudflow events, although in some regions it is not as prominent. Purely anticyclonic weather types (SWT 9, 9a, 9b) constitute less than 15 % of all events (Fig. 8) even though this weather type is the most frequent per year on average (Fig. 4). Cyclones propagating from the south-west (SWTs 1 and 2) towards the study area, advection from the north and north-west (SWTs 6 and 5), a high level of small barometric gradient (SWT 12) and synoptic wave activity on a cold front (SWT 7) also contribute to significantly unstable weather conditions inducing mud and debris flows (Fig. 8). While the majority of SWTs were associated with regional- or local-scale precipitation patterns, the summer thermal low (SWT 11) was not (Fig. 8b, c, d). Observations confirmed this SWT has triggered mudflows with origins of snow and glaciers melting due to increasing surface temperatures.
Contribution of CWT classes to the observed precipitation over the
stations Gallyaaral in the Zerafshan basin
Box plots show daily precipitation (1984–2013) for each CWT class at four representative stations, namely Gallyaaral (Zerafshan basin), Chimgan (Chirchik–Akhangaran basin), Mingchukur (Kashkadar'ya and Surkhandar'ya basins) and So'x (Fergana Valley). The blue and red lines represent percentiles 0.90 and 0.95 of the precipitation for each class. The panels have different scales.
In order to assess the impact of each CWT class on mudflow-triggering precipitation regimes, long-term daily circulation types and the corresponding daily values of precipitation were analysed.
Figures 10 and 11 show the seasonal distribution of the relative frequency
of the number of days and precipitation values for each CWT class during
1984–2013 for the four stations in total. The column graphs in Fig. 10
highlight the frequency of each weather class (CWT days, %) as well as
the relative contribution of each weather type to the total recorded
rainfall values (total precipitation, %) and the average daily
precipitation for each CWT (mm day
It is worth noting that on average the large-scale atmospheric circulation
over Uzbekistan and central Asia is mainly dominated by the W weather type throughout the year (Fig. 10). The frequencies of the W type
show the highest value between 22 % and 38 %, depending on the season. The
percentage of precipitation during the W days ranges from 35 % to 75 %
of the total annual precipitation for each station. The spatial distribution of
daily average precipitation up to 3–7 mm day
C and A weather types feature almost similar frequencies (18 %) in summer; however, C flow
contributes roughly 4 times more of the annual precipitation (up to
27 %) and daily rainfall values (13 mm day
SW flow occurs from 6 % to 13 % during the
year and contributes 10 %–22 % of the precipitation totals (5 % in Fergana
Valley). Seasonal distribution of weather types associated with
E, SE and S flows is up to 1.3 %
per season and fairly variable throughout the year and produces little
or almost no rainfall, i.e. 0.2 %–0.5 % or less than 1 mm day
In comparison to other stations, the NE and N weather types have different precipitation patterns at So'x station, Fergana Valley, during the warm season of the year (Figs. 10d and 11d). This is due to the location and orographic pattern of the area (Fig. 9c), which makes these weather types some of the wettest airflows throughout the year with frequent floods and mudflow occurrences.
Frequency of CWT (700 GPH) climatology for the period
March–August, 1984–2013 (red bars), and mudflow days (blue bars) that occurred
in the
Zerafshan basin
The impact of small-scale orographic features on weather types and rainfall distribution is assumed to be one of the reasons for the notable seasonal variabilities in the undefined weather type throughout the year. For illustrative purposes, an ERA-Interim orography map confirming this has been included (Fig. 9c). An important inclusion in this study was the CWT evaluation, which highlighted the spatial distribution of precipitation in Uzbekistan on a synoptic scale.
Anomaly of mudflow days for every CWT class (grey bars, grey
axis, %) and CWT classes for mudflow days (red line, red axis, %) for
the March–August period between 1984 and 2013 in five regions:
This section presents the analysis of the relationship between CWT classes
and mudflow occurrences in the investigation regions of Uzbekistan. Figure 12
shows a comparison between mudflow events with CWT daily frequencies in
March–August, 1984–2013. For consistency, the central grid point (40
Figure 13 confirms that anomalies compared to the average occurrence of mudflow days for the C, W and SW class are higher in comparison to normal CWT climatology during the warm phases of 1984–2013. The A weather pattern has a noticeable decline of mudflow frequencies compared to the climatological mean for each CWT class. NW flow is also attributed to the mudflow trigger weather class as it was accompanied by heavy rainfall induced extreme mudflow events, particularly in the Chirchik–Akhangaran and Surkhandar'ya basins and slightly in Fergana Valley. During the investigation period, mudflow events in the study area were found to be highly unlikely for E, SE and S, thus representing missing values in the figure.
An antecedent daily rainfall model applied to the
representative stations Gallyaaral
It is noteworthy here that the variability in C, W, SW and NW days in comparison to all CWT days again shows increased trends in mudflow probability and corroborated all results mentioned above. Therefore, it can be concluded that appreciable weather classes (C, W, SW and NW) are the main contributors to observed mudflow occurrences for the study area and are in agreement with the recorded precipitation distribution patterns.
The model results (Fig. 14) show that minimum and maximum threshold
boundaries with different probabilities of occurrence exist, but not all
rainfall values among the thresholds are associated with mudflow episodes.
A daily rainfall value
According to Fig. 14, there is a 10 % probability of mudflow events if the antecedent rainfall value reaches 40 mm at the Gallyaaral station, approximately 60 mm at Mingchukur and 90 mm at Chimgan (Table 3). Interestingly, there is always a chance that a rainfall event of sufficient magnitude could induce mudflows, even when the antecedent index is lower than the levels above. The results indicate that a weather type with a high level of relative moisture may provide sufficient rainfall to trigger floods and mudslides even when the cumulative rainfall value is close to 0 or soil moisture storage is in deficit. In contrast, after a long period of accumulation of antecedent rainfall, which weakens the slope gradually, the slope becomes more susceptible to the lower value of rainfall on a given day and this could trigger a mudflow event.
Threshold probabilities (10 %, 50 % and 90 %) inducing
mudflow events at four stations (Gallyaaral, Chimgan, Mingchukur and So'x);
Some mudflow events were recorded when the rainfall level and antecedent rainfall index showed less than 10 mm. This could possibly be induced by snowmelt due to a joint occurrence of sudden temperature rises and rainfall. Conversely, local heavy rainfall events in the areas adjacent to the stations could induce flows and mudslides in the river catchment and hilly areas.
Figure 14d shows that the So'x station area located in the Fergana Valley is more susceptible to extreme mudflow events, indicating that mudflow events are also influenced by the geomorphologic structure of the area. The 0.1 probability threshold indicates that 10 mm of rainfall with antecedent conditions of less than 30 mm can trigger flash floods or mudflows in So'x. This means that the threshold varies in space and it is important to consider the regional characteristics of the research area whilst applying the ADRM.
Table 4 provides logistic regression equations for the data from the four stations, which can be used to estimate the rainfall thresholds with different probabilities of mudflow occurrences. For Chimgan station a cubic regression and for Mingchukur a quadratic equation with probability curves proved to be the best fit; however, probability envelopes of the linear regression worked satisfactorily for the data of the other stations, namely Gallyaaral and So'x. Associated values of a chi-squared test represented in Table 4 show the significance of model fitting for the station data.
In this section, the ADRM fit for each CWT class is examined in order to
identify precipitation thresholds triggering mudflow events under each
weather type for the four stations (Gallyaaral, Chimgan, Mingchukur and
So'x) located in areas with a high probability of mudflow events in
Uzbekistan. For this purpose, all rainfall days with an amount of
Rainfall threshold probability equations of mudflow occurrences in
selected areas (
The probability envelopes on C, W and SW days at all stations show consistently positive results, each resembling a regional overlaid threshold to trigger an extreme mudflow event. Following the above airflows, the antecedent rainfall index associated with the A circulation has a sufficient magnitude to trigger mudflows even when the antecedent index and the rainfall value are less than the regional threshold for the Gallyaaral, Chimgan and So'x stations (Fig. 15). It is assumed that the A hybrids, mainly anticyclonic westerly (ACW) and anticyclonic south-westerly (ACSW), initiate significantly more mudflow probability than purely A flow. Another interesting observation is that the overlaid threshold probabilities for mudflow events under the NW airflow in Chimgan, So'x and Mingchukur indicate that similar or lower values of antecedent and daily rainfall records of regional probability can trigger mudflow occurrence there. The NE (except in Fergana Valley, Fig. 15), E, SE and S flows had little or no precipitation to affect mudflow in the study area. Threshold probability tests computed for rainfall data for each CWT for four individual stations over the period from 1984 to 2013 are given in Table A3.
Threshold probabilities initiating mudflow occurrences for each CWT class at the stations Gallyaaral, Chimgan, Mingchukur and So'x (panel columns) for the period of March–April 1984–2013. The black dots are days without mudflow, the green triangles are days with probable mudflow and the red triangles are days with initiated mudflow occurrences in the study area. Red lines and curves indicate the 0.1, 0.5 and 0.9 probability thresholds triggering mudflow occurrences for each CWT class.
This study attempts to identify more sensitive weather types that trigger mudflow events in Uzbekistan, using the CWT approach and an ADRM. The relative importance of each CWT to induce mudflows varies considerably and includes antecedent rainfall index and the daily precipitation value. Results from this study confirm that W, SW, C, NW and the A hybrids (associated with W and SW flows) are the main drivers of the interannual variability in precipitation patterns and are responsible for the rainfall-induced mudflow cases, depending on the region, in Uzbekistan on a synoptic scale. This confirms the core findings of the synoptic classification by Bugayev. The CWT objective approach proves to be a useful tool to address questions of anthropogenic climate change with model data.
Extreme precipitation events in Uzbekistan are responsible for about 90 % of documented historical mudflows, especially in the warm season (March–August), for the years 1870–2014. What are the main precipitation-supporting weather types inducing mudflows in the study area? In the present study, mudflows and their relationship with precipitation and weather types were investigated using multiple and coherent systematic approaches for Uzbekistan. This is especially important as only a few studies have investigated atmospheric circulation conditions and precipitation variability on different spatial scales over Uzbekistan. The principal findings of this study are as follows.
Advection of moist and relatively cold air from the west, as classified in
the SWT classification by Bugayev et al. (1957), was
revealed as the dominant synoptic situation inducing mudflows. This result
is consistent with the findings of Aizen et al. (2004), thus
confirming that westerly advection is the predominant synoptic-scale driver
for precipitation climatology in central Asia. This analysis was performed by
adopting a manual assessment methodology of mudflow-generating weather
conditions on a synoptic scale based on SWT catalogues. Historical synoptic
charts have not yet been fully digitized by Uzhydromet; thus manual assessment
was the preferred methodology. Even though assessment of weather situations by means of SWT may identify
plausible synoptic-scale flow characteristics triggering mudflows, this
subjective approach does not consider any mesoscale conditions over the
study area. Therefore, additional information of smaller-scale features such
as terrain-induced flow modulations (Stucki et al., 2012) merits
further analysis. The relationships and related variables explaining the spatial distribution
of precipitation, obtained with an objective CWT approach, defined the four
weather classes west (W), south-west (SW), cyclonic (C) and
north-west (NW) as the main drivers of precipitation characteristics on
a regional scale. This allows a positive evaluation of the CWT method in
principle. Furthermore, CWT findings are in line with results from Reyers
et al. (2013), who evaluated spatial patterns and annual cycles of
precipitation using the CWT scheme for CA. Interestingly, the westerly
airflow in Reyers et al. (2013) was split into two subgroups,
as W1 (distinct zonal flow) and W2 (negative 700 hPa GPH gradient). NE
and E as well as SE and S were combined. In general, it was
found that probability of precipitation was much higher for C, CWT W2, N and
SW airflows during the summer. However, the highest rainfall probability and
precipitation amount was attributed to the rare CWT NE–E weather type
(Reyers et al., 2013). In our study as well, NE weather type,
despite its low frequency for the selected grid point, revealed a high
probability of precipitation patterns that could trigger mudflow events in
Fergana Valley (Figs. 10–11d, 12b, 13b, 15). Fergana Valley, which has a
better representation of topography, presumably makes a case of particular
interest with the findings in previous studies. According to Schiemann et al. (2008) it can be assumed that on smaller
spatial scales, the influence of topography on precipitation climatology
over CA is paramount. Small et al. (1999) and Reyers et al. (2013) both confirm this. In general, the ability of CWT to describe the large-scale circulations in
the
lower troposphere over Uzbekistan was satisfactory. However,
it produces a fairly high frequency of undefined weather classes showing a
strong seasonality and, particularly during summer, relatively large amounts
of precipitation. It could possibly be attributed to the impact of mesoscale
features such as orographically induced lifting and frontogenesis and thus
variations in atmospheric dynamics (Buzzi et al., 1998). Gevorgyan (2013) argued that the objective circulation classification
scheme does not always identify frontal processes over mountain regions
correctly, which makes the objective scheme less suitable to classify
differences in the atmospheric circulation in areas with complex topography
or when the sub-synoptic scale is effected by the orography in elevated
areas, even though the scheme is suitable for large-scale circulation
patterns. By means of the antecedent rainfall model, we could identify regional
differences in the probabilities of precipitation thresholds causing mudflow
events. However, sparse data on actual mudflows and uncertainty over
probable mudflow occurrences could be the main factors regarding the
uncertainty in model building (Glade et al., 2000).
Nevertheless, our identified thresholds deliver reasonable and well-justified results and form a benchmark for any further study for this
region. Our antecedent daily rainfall model (ADRM) results further
corroborate findings by Trofimov (2006), who used Pearson correlations
to calculate rainfall thresholds for triggering mudflows in Fergana Valley.
For the So'x area, he suggested a 0.5 probability of mudflow events in the
case that daily precipitation reaches 22.3 mm. This value falls in the lower
envelope of the 0.5 probability threshold identified for Fergana
Valley in our study (Fig. 14). However, regional thresholds cannot be
applied easily to neighbouring areas due to additional natural variables
influencing mudflows such as geologic, geomorphologic and hydrologic factors
as well as slope aspect and land use. A combination of three statistical approaches (CWT, ADRM and LRM) revealed
that when the W, C and SW directional flow classes occurred over Uzbekistan,
the higher precipitation amount (a characteristic of these directional
flows) and antecedent rainfall could trigger mudflow episodes and increase
their magnitude and probability.
Thus, it can be concluded that the CWT approach and ADRM produced robust results, despite the orographic influence on the study area and the limited data on mudflow timing and precipitation intensity. Future investigation will focus on regional downscaled seasonal and annual precipitation, with observed data for each CWT, preferably W, C, SW and NW stream flows, to identify key factors of future precipitation distributions and to discover how this will affect mudflow occurrences on a longer timescale and how it will be impacted by climate change.
For access and general information on the reanalysis dataset
used in this study see
Mudflow disasters causing fatalities and other relative damages over the period of 2005–2014 in Uzbekistan (data source: Uzhydromet)
Synoptic weather types (SWTs) of central Asia and general weather characteristics over the region and in Uzbekistan. SWTs 1–9, 9a and 10–11 were classified by Bugayev et al. (1957); types 9b and 12–15 were added later by the researchers of the Hydrometeorological Scientific Institute in Uzbekistan.
Continued.
Threshold probability (10 %, 50 % and 90 %) values
initiating mudflow episodes for each CWT for individual stations in
Uzbekistan;
CWT south-west
The research objective was proposed by GCL and GM. GM performed all data analysis and visualization and wrote the initial manuscript with input from all authors. SW contributed to the CWT algorithm and computational experiments. MAW constructed the LRM algorithm and edited the paper. GCL supervised and directed the research and contributed to the analysis and design of the findings of this work and participated in the revision process of the paper.
The authors declare that they have no conflict of interest.
We are grateful to Uzhydromet for sharing the mudflow and daily meteorological data. We are also grateful to ECMWF for granting access to the ERA-Interim reanalysis data. Gavkhar Mamadjanova expresses her gratitude to Islamic Development Bank (IDB) for awarding her with a PhD scholarship. Sections 3 and 4.1 of this paper are fragments from the MSc thesis of Gavkhar Mamadjanova at the National University of Uzbekistan (NUUz). Gavkhar Mamadjanova expresses her sincere gratitude to the late Professor Gennady N. Trofimov at the Department of Geography, NUUz, and to Boris K. Tsarev at Uzhydromet for their guidance and encouragement throughout and after her MSc program. Gavkhar Mamadjanova also sincerely appreciates Irina B. Zaytseva, Gulnoza Khamdamova and Natalya Panteeva from Uzhydromet for their timely inputs with regards to data clarification. Gavkhar Mamadjanova's special thanks are extended to Nicolas Kirchner Bossi and Mohammad Alharbi (University of Birmingham) for their assistance with programming. The authors would like to thank the two anonymous reviewers for their insightful and constructive comments, which greatly improved the quality of the article. Edited by: Ricardo Trigo Reviewed by: two anonymous referees