1. Introduction
Atmospheric rivers (ARs) are moisture-rich plumes concentrated in the lower part of the troposphere and are identified as transient filamentary features (Zhu and Newell 1998) having a lifetime spanning from several hours to generally 3 days or more (Zhou et al. 2018). Upon landfall, ARs bring intense precipitation in coastal regions (Konrad and Dettinger 2017; Neiman et al. 2008), in the mountainous regions (Chen et al. 2019; Leung and Qian 2009; Nayak et al. 2021; Thapa et al. 2018), and can even infiltrate deep inland through valleys and plains (Mahoney et al. 2016; Nayak et al. 2016; Rutz et al. 2014). When lifted orographically, they are known to deliver copious rainfall at lower altitudes and snowfall at higher altitudes (Neiman et al. 2008). ARs are often related to extreme hazards such as floods, landslides, destructive surface winds, etc. (Paltan et al. 2017; Ralph and Dettinger 2011; Waliser and Guan 2017); when viewed in certain favorable contexts, however, they emerge as essential sources of moisture supply, alleviate droughts, and decrease water scarcity (Dettinger et al. 2011; Dettinger and Cayan 2014).
ARs have been extensively examined in connection with high-impact precipitation events, especially over mountainous regions adjacent to oceans. In the western United States, for example, winter ARs produce twice the precipitation as compared to non-AR storms (Neiman et al. 2008). AR impacts are amplified by the presence of mountains, e.g., the west coast of South America receives ∼15% more of the AR-related precipitation than North America due to the Andes (Viale et al. 2018). In another study on impacts of inland-penetrating ARs in Europe, rainfall and/or snowfall produced by ARs are found to be the major cause of all extreme floodings in the Rhine catchment (Ionita et al. 2020). A study by Paltan et al. (2017) on the impacts of global ARs found that ARs contribute approximately 11% to the annual snowpack in limited areas of the Himalayas. Since ARs are known to deliver snowfall and rainfall depending on the region’s location, topography, and atmospheric conditions, it becomes critical to understand how ARs modify the hydrology of these regions. AR and precipitation connections in the Himalayan basins are understudied. Previous works were focused on improving precipitation observations over Hindu–Kush (HK), Karakoram (KA), and Himalayas and conducted topographic-related precipitation extremes studies (Riley et al. 2021; Palazzi et al. 2013). Recently, a few studies have initiated the exploration of ARs in South Asia. Yang et al. (2018) identified ARs over the Bay of Bengal that landed over coastal India, Bangladesh, Bhutan, Myanmar, and Nepal at 24°N and those that penetrated inland into Eastern Himalayas (EH) at 27°N. They also found that many extreme rainfall events at the foothills of EH are related to ARs. Other studies in South Asia have focused on ARs in the coastal mountainous regions in South India (Dhana Lakshmi and Satyanarayana 2019, 2020). Thapa et al. (2018) identified ARs in Nepal and examined their frequency and contribution to annual and nonmonsoon (October–May) precipitation extremes at six grid cells covering Nepal. However, this study excluded most regions in the two Himalayan basins and did not attempt to identify ARs and assess their impacts on HK, KA, and WH (western Himalayas). Nash and Carvalho (2018) focused on monthly frequencies of ARs in KA, WH, CH (central Himalayas), and EH, while Nash et al. (2022) investigated the synoptic atmospheric patterns and dynamic mechanisms associated with different types of ARs during December–May over High Mountain Asia (HMA). While acknowledging the frequent occurrence of ARs over the Himalayas, the two studies did not consider their hydrologic impacts. This represents an important research problem since estimating the impact of ARs on the hydrology of the two major snow- and glacier-rich basins can help manage water resources in the region that supports the livelihood of roughly a billion people.
The two Himalayan basins, IB and GB observed the largest floods in September 2014 and June 2013, respectively. The September 2014 Kashmir flood killed nearly 460 people (Rehman et al. 2016; Vithalani and Bansal 2017), affected 2.5 million people, destroyed 2.4 million acres of cropland, impacted 4000 villages, and caused an economic loss of over $15 billion (Vithalani and Bansal 2017). The June 2013 Kedarnath flood in Uttarakhand killed roughly 5000 people and caused an economic losses of $400 million (Bhambri et al. 2016). Kedarnath flood was a complex cascading catastrophe, which triggered a Glacier Lake Outburst Flood in the region (Das et al. 2015). Though both floods have been extensively studied, the role of ARs in Kashmir flood has not been ascertained nor there is any assessment of ARs’ impact in the Kedarnath flood. In this study, we show that these events were accompanied by category 5 ARs (Ralph et al. 2019) (Figs. 1a,b) that triggered extreme precipitation, which translated into disastrous floods (Fig. 1 and Fig. S1 in the online supplemental material; further details are given in the results section). These results underscore the impacts of ARs over the Himalayas.
IVT (kg m−1 s−1) for two ARs (a) at 1800 UTC 3 Sep 2014 (AR event: 3–6 Sep) and (b) at 1800 UTC 16 Jun 2013 (AR event: 12–18 Jun); (c),(d) the total accumulated precipitation (mm; using WFDE5) for the AR events. The star and circle (solid) represent the discharge measuring stations in Jhelum River and Chenab River, respectively, in (c), and the star in (d) represents the location of the Tehri Dam at Bhagirathi River. (e),(f) The discharges (×103 m3 s−1) at the gauge stations/dam. In (e) the discharge and very high flood level are shown by dashed lines for at Rasul and by solid lines for Trimmu in IB, and in (f) the discharges are shown by solid line and storage by dashed line at Tehri dam in GB. The basin’s boundary are shown by light gray lines in (a)–(d); boundaries of HK, KA, WH, and CH are shown by black lines (Bolch et al. 2012) in (c).
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0599.1
The objectives of the present study are 1) to estimate the impact of ARs on seasonal and annual total precipitation and extremes in IB and GB, 2) to estimate the impacts of ARs on winter rainfall and snowfall in the mountains of IB and GB, and 3) to identify the major floods related to ARs.
The remainder of the paper is organized as follows. Section 2 provides a brief description of the study area. Section 3 describes in brief the AR database, precipitation, floods, and discharge data used in this study which is followed by a detailed description of methods implemented to quantify the AR impacts on the hydrology of IB and GB. Section 4 presents the relative contribution of ARs to the annual and seasonal precipitation, including precipitation extremes and floods in the basins with discussion on their impacts. The results and discussions of our findings are then summarized and concluded in section 5.
2. Study area
The Himalayas, located in South Asia, extend ∼2500 km from west/northwest to east/southeast across Pakistan, India, China, Nepal, and Bhutan. They are landlocked by the Tibetan Plateau in the north, the HK and KA in the west, the Yunnan Plateau in the east, and the Indo-Gangetic Plains in the south. The high mountains (∼8000 m MSL) impede moist air transported from the tropical waters of South Asia, Atlantic Ocean, and seas in the Middle East and results in precipitation via orographic lift (Figs. 1c,d) (Bookhagen and Burbank 2006). The two basins cover areas of 1 120 000 and 1 087 300 km2, supporting 312.5 and 637 million people, respectively (Azam et al. 2021). They have distinct climatic regimes that are controlled by latitude, altitude, and location relative to the atmospheric flow of the Indian summer monsoon (ISM) and western disturbances (WDs). The climates within these basins show great variation across their geographical extents, which become even more intricate in the upper regions due to the complex mountain terrains. For example, it is arid and semiarid in lower IB (∼200 mm yr−1) and humid to arid in northern IB (1500–2000 mm yr−1) (Laghari et al. 2012; Archer et al. 2010); in GB, western regions are arid to semiarid (350 mm yr−1), eastern regions are typically tropical (hot with abundant precipitation in summer; 2000 mm yr−1), and in the northern parts the climate transition from tropical to alpine (2000–3000 mm yr−1) (Anand et al. 2018).
Of the two precipitation drivers preeminent to Himalayan basins, the ISM contributes 60%–80% of their annual precipitation, though in IB mostly the south-facing WH receives precipitation from the ISM. The ISM is a tropical wind regime that brings precipitation during boreal summer months, set in motion by two intense pressure centers in central and south Asia, related to temperature differences between land and surrounding water bodies. Another driver of precipitation [40% and 25%–35% of the annual precipitation in IB and GB, respectively (Barros et al. 2006; Lang and Barros 2004)] is the WDs (also called upper synoptic systems or extratropical storms), which are active mainly in winter and spring due to the large latitudinal temperature differences in subtropics. WDs deliver precipitation through orographic lift when the upper levels are rich in moisture. They originate in the Middle East seas (Bookhagen et al. 2005; Dimri 2006) and propagate eastward across semiarid and arid lands before reaching these basins. Precipitation is predominantly snowfall above 2500 m MSL (headwater of these basins) and rainfall in the lesser elevation plains (Lang and Barros 2004). The annual precipitation variability during WDs mainly leads to variations in glacier accumulations (Kääb et al. 2012). The accumulated snow and ice every winter in the upper reaches serve as important reservoirs for spring water yield. Meltwaters contribute the highest to the annual streamflow in IB (40.6% in northern IB), while rainwater dominates in GB (66% in northern GB) (Lutz et al. 2014). Snow and ice meltwaters are indispensable for the Himalayan water resources especially when monsoon onset is delayed. Any changes in the seasonal/annual contributions by the dominant precipitation regimes potentially impact roughly a billion inhabitants there (Immerzeel et al. 2020; Pritchard 2019).
3. Materials and methods
a. Data
We used the recently developed European Centre for Medium-Range Forecasts reanalysis version 5 (ERA5) (Hersbach et al. 2020) based AR database for the Himalayas over 1982–2018 (Nayak et al. 2021). This database provides multiple characteristics of AR events including location, intensity (average IVT of AR axes), duration, maximum IVT, and categories of AR events. The ARs are identified if they intersect the detection transect located south of the Himalayas (Fig. 2, green line) and fulfil the AR criteria (see Text S2 for details). The algorithm of Nayak et al. (2021) uses relative IVT thresholds to extract locally extreme IVTs. As in Lavers et al. (2012), only ARs that persist for at least 18 h are considered. Moreover, only the timesteps of ARs without cyclones are retained. A brief description of the regional algorithm used for the identification of ARs in the Himalayas is given in Text S2.
(left) Spatial distribution of annual and seasonal average rainfall (mm yr−1 and mm per season), (center) annual and seasonal average AR-related rainfall (mm yr−1 and mm per season), and (right) the fractional contributions (%) over the period 1982–2016 using WFDE5. The basin outlines and the detection transect are shown by light gray lines and the dark green line, respectively. Note that grid cells with annual (seasonal) average rainfall < 50 mm (<15 mm), shaded in white, are not shown in the fractional contribution.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0599.1
We also used the “tARget” algorithm by Guan and Waliser (2019) to identify ARs in this study region, and we found that nearly 82% of the AR days match (Fig. S10) with the ARs in Nayak et al. (2021); the detailed result based on “tARget” ARs are given in the supplemental material. In the main manuscript, however, we focused on the results obtained using ARs by Nayak et al. (2021) because the algorithm 1) is region specific for the Himalayas, 2) is capable of identifying persistent ARs of low intensities, and 3) excludes all cyclone-related impacts. We also added a section in Text S7a,b to discuss the limitation of the present Nayak et al. (2021) approach.
Hourly rainfall and snowfall flux (aggregated to daily) from Water and Global Change (WATCH) Forcing Data ERA5 (WFDE5; Cucchi 2021; Cucchi et al. 2020) are used to analyze the precipitation patterns over the IB and GB over the period 1982–2018. WFDE5 are recent global meteorological data developed by applying WATCH Forcing Data (WFD) methodology (Cucchi 2021; Cucchi et al. 2020) on ERA5. ERA5 is bias corrected for the number of wet days per month using CRU TS4.03 (Climate Research Unit; Harris et al. 2014) and for the precipitation totals using GPCCv2018 (Global Precipitation Climatological Centre; Schneider et al. 2016), and upscaled to half-degree (0.5° × 0.5°) spatial resolution to conform with CRU resolution. Corrections for unrealistic precipitation phase (rainfall or snowfall) relative to large elevation differences between ERA5 and CRU TS4.03 grids were applied, for example, a phase was changed if at a time step (hourly) the bias-corrected air temperature for a grid at a certain elevation is beyond the respective monthly minimum CRU air temperature records for rainfall and monthly maximum CRU air temperature records for snowfall (Cucchi et al. 2020).
We also used high-resolution daily gridded rainfall observations from India Meteorological Department (IMD; https://dsp.imdpune.gov.in; Pai et al. 2014) with a spatial resolution of 0.25° × 0.25° from 1982 to 2018. These data are developed using gauge station observations interpolated into grids using Shepard’s method (Shepard 1968). In this dataset, the daily rainfall total for any day (say, 5 June 2020) is defined as rainfall collected from 0830 Indian Standard Time (IST) of the previous day (i.e., 4 June 2020) to 0830 IST of that day. Since the AR dates are in coordinated universal time (UTC) and rainfall observations are in IST, to match an AR day we take 1 day ahead in the rainfall day. IMD extent, however, does not include parts of IB and GB outside India, hence results based on IMD rainfall are reported in the supplemental material.
Important details for flood events in India (location, start date, end date, etc.) are retrieved from Dartmouth Flood Observatory’s (DFO) flood database developed by Najibi and Devineni (2018), which is publicly available at https://dataverse.harvard.edu/dataverse/dfo1985to2015 for the 1985–2015 period. These are large flood events (i.e., over 10-year recurrence period) that have been validated using information from various sources and have been mapped for changes in surface water extent during floods by DFO using remote sensing satellites. More information regarding the collection and validation of the flood events can be found in the cited paper and link provided above.
b. AR impacts on annual and seasonal rainfall
A day is considered an AR day if an AR is detected for at least one time step (out of the four available, 0000, 0600, 1200, and 1800 UTC) on that day. For the assessment of AR impacts on precipitation, we defined the regions within 250 km from the AR major axis as regions with AR-affected precipitation, which is consistent with previous studies on AR-related precipitation (Nayak and Villarini 2017, 2018; Mahoney et al. 2016). Hatchett et al. (2017) also used 250 km to link avalanche events to ARs (or AR precipitation) when the AR major axis is found upstream of the events and within 250 km. It has been observed that regions closer to the AR major axes have more rainfall (Nayak et al. 2016), and showed a stronger positive relationship with on-axis AR IVT (Nayak et al. 2016) than rainfall at 300 km away from the axis. Thus, precipitation can be expected to be more surrounding to the AR axes that have higher atmospheric IVT, than the regions which are far from the axis. As mentioned earlier, the Himalayas block and deflect moisture transports, coming from the west, south, and east, therefore ARs will also be deflected from their path on impinging upon the mountains. In addition, since the AR detection transect was defined along the Himalayan foothills, before elevated terrains, the AR axes were not identified beyond the detection transect where AR IVT is dispersed in the Himalayas. To counter this hindering and dispersing effect of the Himalayas, we consider precipitation if it happens during an AR time step within a 350 km band around the detection transect, and only those grids that have IVT above the threshold used to identify ARs as AR-related precipitation. The “350 km” was also considered to include the northern reaches of IB, the HK, and KA. In this way, on a particular AR time step, we assess grids within 250 km from the AR axes and those that have IVT above the threshold within 350 km from the detection transect for AR impacts on precipitation (referred to here as AR grids). Now, when there are more than one AR time steps in an AR day, we compute the union set of AR grids for that day by combining each set of AR grids of each time step. Then the precipitation (daily) in the AR grids during AR days is defined as AR-related precipitation. For this analysis, we did not include one day each before and after an AR event i.e., any possible differences in the lag/lead times of AR-derived precipitation (Shu et al. 2021; Wang et al. 2021), as we cannot exactly determine the AR-related precipitation without knowing the AR axes. Hence, we believe that our estimates of AR contributions to precipitation are conserved. Relative contributions of AR-related precipitation are estimated at annual and seasonal scales, such as winter (December–February), spring (March–May), summer (June–August), and autumn (September–November).
We also compute the coefficient of determination (R2) between precipitation totals and AR-related precipitation for the winter season for HK, KA, WH, and CH, as a measure of ARs’ influence on the interannual variability of winter precipitation in the mountainous regions.
c. AR impacts on extreme rainfall
d. AR impacts on floods
We also assessed the number of flood events reported in these basins that are related to ARs. An AR-related flood event is defined as follows: 1) the flood should have occurred within the AR grids around the AR axis on an AR day, 2) the flood should have happened within 7 days after an AR event to account the time of concentration, and 3) floods that have happened 2 days before the AR event to account for high IVT already present in the region before the AR is identified and for the time steps ARs are present very close to the transect but were not identified by the algorithm.
4. Results and discussion
a. Patterns of annual and seasonal rainfall
The annual rainfall shows significant spatial variation in both basins (Fig. 2a) mainly due to variations in topography and the influence of the main precipitation drivers. Mountainous regions, particularly CH and its eastern foothill, receive the highest rainfall. Seasonal rainfall (Figs. 2d–o) further illustrates remarkable spatial variations and regional distribution of rainfall among the seasons in each basins. In IB, the highest rainfall happens in summer and is limited only to the south-facing WH (Fig. 2j). Rainfall magnitude reduces rapidly from WH toward the plain areas in the south IB and the northern most IB (in KA). This is related to monsoon airflow propagating westward toward northwest India with reduced atmospheric moisture, which is extracted mostly by WH (Fowler and Archer 2006). The rainfall in IB is also brought by interactions between WDs coming from the west and the monsoon propagation from the east, and often such interactions produce extreme rainfall and generate floods (Pai and Bhan 2014). In winter, the highest rainfall (70–240 mm per winter) is in central-north IB (WH and lower HK, Fig. 2d), while rainfall is less than 15 mm per winter in KA (data not shown). This pattern is similarly observed in spring, with an average rainfall of 50–300 mm found only in central-north IB (Fig. 2g), though a large part of western IB receives greater than 50 mm on average. The rainfall in winter and spring is mostly associated with WDs approaching from the west/southwest. In autumn, as in summer, the band that receives the maximum rainfall (200–350 mm per autumn) is to the southeast of WH (Fig. 2m), which is related to moist winds associated with the ISM. GB receives the lowest rainfall, less than 65 mm, in winter except in the north (115–140 mm) (Fig. 2d), and the highest rainfall in summer with 400 mm distributed throughout the basin, the highest is in CH (750–1140 mm, Fig. 2j). In spring, rainfall between 100 and 400 mm is found in the north and east GB, and less than 50 mm is found in the remaining areas of the basin (Fig. 2g). The autumn rainfall (Fig. 2m) bears similar patterns to summer rainfall though the magnitudes are comparatively lower. The east–west rainfall gradients in summer and autumn imitate the monsoon progression across GB basin (in June) (Bookhagen and Burbank 2010) and the monsoon retreat (in October) in autumn. Though some portion of the summer and autumn rainfall is related to cyclones that made landfall at the Indian coastline or those that moved inland from the Arabian Sea and Bay of Bengal during the pre- to postmonsoon (Sattar and Cheung 2019). We found similar spatial patterns for the annual and seasonal rainfall in both basins except for the northern IB when IMD is used; details are provided in the supplemental material (section S6).
b. ARs’ contributions to annual and seasonal rainfall
The spatial patterns of AR-related rainfall are similar to annual rainfall in both basins, albeit with smaller magnitudes. On average, ARs contribute nearly 20%–30% to the annual rainfall in north IB and over isolated patches in south IB (Fig. 2c). In GB, 5%–15% of the annual rainfall happens during ARs, with higher fractions ∼20% in the lower east in response to ARs’ frequent landfall north of the Bay of Bengal during summer and autumn. On average, rainfall in IB (KA, HK, WH, and southern plains) is more influenced by ARs than rainfall in GB. Similar spatial patterns are noted for annual, AR-related rainfall and AR contributions (%) in both basins when IMD rainfall data are used (Fig. S6), and discrepancies were found in the northernmost reaches of IB, where mountain elevation exceeds 3000 m (mostly in KA) (more details are given Text S6).
The highest AR contribution to seasonal rainfall is found in winter (Fig. 2f), contributing up to 50% of comparatively high rainfall (40–120 mm, Fig. 2e) in central IB (WH) and northwest GB (west of CH) and even higher fractions of above 60% for comparatively low rainfall (less than 40 mm) is found in the south and northern reaches of IB. In GB, ARs contribute 50%–60% of winter rainfall in the northwest which gradually reduces to 10% in the south GB. The higher contributions from ARs in the central IB and northwest GB during winter are likely due to winter ARs frequently originating in the Arabian Sea and mostly striking the WH and western part of CH (see Fig. 4 in Nayak et al. 2021). These ARs deposit the bulk of their moisture in the mountains and release additional moisture when deflected along the Himalayan Range. In spring (Fig. 2h), AR-related rainfall is the highest in north IB (mostly in WH and HK), and southeast GB. On average, ARs contribute 15%–30% in IB and 10%–30% in GB, with the highest AR contribution of 35% found in the mountains of IB and northwest GB (Fig. 2i). In summer, ARs have a higher impact on rainfall in the north IB (WH) and in GB (except the west) (Fig. 2k). ARs contribute to 15%–20% of rainfall in IB except in the south, and ∼15% in the north (CH) and southeast of GB (Fig. 2l). For autumn season, ARs contribute nearly 20% in southern GB, while the contribution decreases gradually to 15% in northern GB. In IB, ARs contribute about 5%–40% on average to the autumn rainfall with contributions between 25% and 40% for rainfall in north and southeast IB. Higher fractions of AR contribution for high rainfall are found mainly in the mountain regions, though higher fractions are also observed for comparatively low rainfall (in KA), especially in winter and autumn. We also observed higher fractions to rainfall in southeast lower IB (lowland areas with less than 200 m MSL), which may indicate the preferred passage of ARs along this region.
The higher AR contribution in the regions beyond the WH (i.e., KA and HK) may be related to ARs that penetrate through mountain gaps or cross over the barriers, as observed in mountainous terrains elsewhere (Alexander et al. 2015; Mueller et al. 2017). This implies that moisture from far sources contributes significantly to water inputs in the headwaters of the IB and GB via ARs. Another favorable passage for ARs would be from the southeast of IB (Figs. 2c,f,i,l,o). These observations suggest to a certain extent, that ARs’ impacts on the annual and seasonal rainfall are region and season dependent and are critical 1) for water resource management, particularly in the forecast of ARs, and 2) in the designing of reservoirs capable of accommodating large inflows during AR events and storing stormwater for later uses. As ARs and floods are closely connected, ARs generate large runoffs and trigger floods, and as flood water cannot be stored entirely, they result in structural failure of reservoirs (Henn et al. 2020) and a small net water gain for uses.
c. Impact of ARs on snowfall in IB and GB
The meltwaters from accumulated snowpack and glaciers sustain downstream streamflow in IB and GB during nonwinter seasons. We observe that snow accumulation is relatively lower in summers, especially in the IB, where it occurs only at higher altitudes compared to widespread snow accumulations in winter (Viste and Sorteberg 2015). For this reason, we only selected the winter season for further in-depth analysis of AR impacts on snowfall. Snowfall of more than 200 mm w.e. (water equivalent) is noted along the northern IB (HK and WH) and a smaller region in north GB (western CH), that sharply reduces toward the northern reaches of IB (KA) southern parts of IB and GB (Fig. 3a). The spatial distribution of snowfall is similar to rainfall patterns as in Fig. 2d; however, the rainfall depths are slightly lower in comparison to snowfall. Snowfall of 200–400 mm w.e. is concentrated in the WH and HK at elevations of 3500–6000 m MSL The IB has extensive snow-covered areas compared to GB where snow cover is limited to regions above 1500 m MSL. The AR-related snowfall also shows similar spatial patterns but with slightly lower magnitudes (Fig. 3b). ARs on average contribute 40% to winter snowfall over an extensive area in the mountains in both basins and ∼50% in the northern reaches of IB (Fig. 3c). This contribution is much higher than that obtained by Paltan et al. (2017) in their global study of AR impacts on hydrology including snowfall, which may be due to differences in AR identification algorithm and data used [i.e., WFDE5 (ERA5) versus WFDEI (ERA-Interim)]. The differences in spatial resolution showed ERA5 (bias-corrected with GPCC) provided better precipitation estimates than ERA-Interim (bias-corrected with GPCC) (Cucchi et al. 2020). In the KA, ARs’ contribution to winter snowfall varies from 40% in the central region to 50% in the eastern and western parts, suggesting ARs as the major driver of snowfall variability even at high altitudes, deep in the high-mountain ranges. In northern IB, winter precipitation is mostly attributed to WDs (Palazzi et al. 2013), but it is observed in this study that contributions to the total precipitation are also from ARs, which has not been studied earlier in detail.
(a) Average winter snowfall (mm w.e.), (b) AR-related average winter snowfall (mm), and (c) fractional contribution (%) over IB and GB. The black boundaries in the mountains represent the four mountain ranges considered here. (d) Average winter precipitation in HK, WH, CH, and KA; solid color represents the average winter rainfall (dark blue shades) and snowfall (light blue shades) and the solid color with hatches represents the annual average AR-related precipitation, respectively.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0599.1
In winter, the WDs travel eastward across semiarid to arid land before reaching the IB and northern GB with reduced atmospheric moisture while ARs appear to originate from the Arabian Sea and travel northeastward across southern IB and western GB [see Fig. 4 in Nayak et al. (2021)], possibly with greater moisture content. Strong WDs in winter also favors moisture transport from the Arabian Sea (Dimri 2006; Madhura et al. 2015; Riley et al. 2021) into the KA, HK, and WH and leads to higher precipitation mostly in the form of snow at higher altitudes (see Fig. 2A of Azam et al. 2021). ARs may combine with WDs and intensify the impact on precipitation in these regions, though such relations are yet to be established in these regions. Baudouin et al. (2021) did note the influx of moisture advected from the northern Arabian Sea toward northern IB during WDs that increased the precipitable water on the windward side of the mountains, though the moisture flux from the Arabian Sea shown in their composite maps [in Fig. 10a of Baudouin et al. (2021)] could be moisture related to ARs. However, in other midlatitude regions, cooccurrences of extratropical cyclones (ECs, some WDs have similar dynamics to ECs) and ARs are often highlighted (Ralph et al. 2004; Zhang et al. 2019). AR and snowfall links are also observed on the west coast of the United States, Sierra Nevada mountains (mean elevation of 3478 m) (Hu and Nolin 2019), and the southern Andes Mountains (mean elevation of ∼4000 m) (Saavedra et al. 2020). Curio and Scherer (2016) observed a strong correlation between IVT and winter precipitation in the KA, HK, and WH, including the northern regions of CH.
d. Impact of ARs on winter precipitation in the mountains of IB and GB
Winter precipitation is highest in WH, followed by HK, CH, and KA, roughly half of which is AR related (Fig. 3d). It is interesting to observe that the winter precipitation incisively follows the AR-related snowfall patterns, which highlights the role of ARs in defining precipitation variability. Nearly all the precipitation in KA in winter falls as snow with 75% interannual variability attributable to ARs (Fig. 4d). ARs explain 57% and 63% of the winter snowfall and rainfall variability in HK, respectively, which is critical as HK receives most of its annual precipitation during winter (and also spring). In CH, ARs explain about 42% and 43% of the winter snowfall and rainfall variability, whereas in WH they explain 30% and 59% of snowfall and rainfall variability, respectively (Figs. 4a–h). It can be argued that ARs may also impact the glaciers in HK, KA, WH, and CH by depositing large snow volumes during AR events. Further, strong dependence of total precipitation on AR precipitation is observed in HK [regression slope of 1.24 for snowfall (1.2 for rainfall)] and KA [1.36 (1.2)], while comparatively milder dependencies are noticed in WH [0.56 (0.75)] and CH [0.87 (0.82)]. The above estimates underscore the previously ignored vital role of ARs in winter precipitation over the high-elevation mountain ranges of the Himalayas.
Variability for average winter precipitation totals (y axis) and AR-related precipitation (x axis) for HK, WH, CH, and KA of (left) snowfall and (right) rainfall using WFDE5 (all slopes are statistically significant at 5%).
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0599.1
e. Seasonal rainfall extremes related to ARs
In Fig. 5a, we find that 50%–100% of winter rainfall extremes between 1982 and 2016 in the northern IB and GB (foothills of HK, WH, and CH) are AR related. The highest winter rainfall record happened often in the presence of ARs and these extremes may generate the largest streamflow records in these regions. In spring, 40%–60% of the rainfall extremes in northern IB (along the foothills of HK and WH) and between 10% and 60% in GB (CH and southeast GB) are related to ARs (Fig. 5b). Consistent with Thapa et al. (2018), we observed higher fractions of rainfall extremes are AR related to the nonmonsoon (October–May) particularly in the winter season (DJF) over west Nepal. Smaller fractions of extremes in GB are related to ARs in summer and autumn, though extremes happen throughout the basin. In IB, 20%–50% of extremes are related to ARs in eastern parts, whereas in central and south IB, 20%–40% of extremes are related to ARs (Figs. 5c,d). Similar results are obtained using data from IMD, except that IMD showed more grids in WH and KA that have extremes related to ARs (Figs. S8a,b). Furthermore, we also used a lower threshold of 2 mm for the daily maxima (Fig. S5b), where we see large contributions of ARs (≥50%) to Indus basin in winter and spring seasons and smaller contribution (≥25%) during summer and autumn.
Relative frequency of seasonal rainfall maxima related (%) to ARs for 1982–2016 using WFDE5 for (a) winter, (b) spring, (c) summer, and (d) autumn seasons. The grids shaded in dark gray have either seasonal maximum precipitation less than 40 mm or the number of seasonal maxima is less than 7.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0599.1
f. Floods associated with ARs
Several studies have shown that ARs produce extreme/heavy precipitation that often leads to large-scale flooding (Lavers and Villarini 2013; Leung and Qian 2009) in mountains (Neiman et al. 2008; Thapa et al. 2018) and have large societal impacts (Henn et al. 2020) and economic losses. A total number of 189 flood events are observed during 1985–2015 in the IB (80) and GB (109) (Fig. S9). About 55% of the events in IB and 72.5% in GB (Fig. 6) have occurred during the presence of ARs in the vicinity of the affected area. We find that many long-duration events were associated with AR clusters, which is the successive occurrence of multiple AR events over a region.
Total and AR-related flood events (left y axis) during 1985–2015 in (a) IB and (b) GB. Light gray bars show the total number of floods and dark gray bars show the number of flood events related to ARs. The fractions are shown by dotted line (right y axis).
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0599.1
To further understand the role of ARs in causing floods in Himalayan basins, we study the two most disastrous AR-related floods in IB and GB. The floods chosen are September 2014 Kashmir flood (in IB) and June 2013 Kedarnath flood (in GB). Previous studies have linked the 2013 and 2014 floods to the interaction between the cyclonic/frontal system of WDs and monsoonal flow that developed an intensified convective instability and resulted in extreme precipitation (Ranalkar et al. 2016; Ray et al. 2015; Champati Ray et al. 2016; Yadav et al. 2017). On the contrary, Thapa et al. (2018) noted an AR during the 2013 flood. They stated the importance and utility of monitoring and forecasting AR storms for emergency preparedness. Along those lines, we illustrate and quantify the contribution of ARs to the accumulated precipitation and discharge during these events.
ARs struck Jammu and Kashmir (WH) at 0600 UTC 3 September 2014 and Uttarakhand (CH) at 0600 UTC 12 June 2013 (Fig. 1). On 2–6 September 2014, the WH received precipitation between 350 and 650 mm, which corresponds to ∼34%–40% of the total annual precipitation in the region (Fig. 1c, Fig. S1c). Consistent with other studies (Mishra 2015; Ray et al. 2015; Romshoo et al. 2018), the Kashmir region received ≥400 mm of rainfall that generated a large flood flow in the two major rivers, Jhelum and Chenab. ARs were observed consistently during days of heavy precipitation and during the flood. The discharge at Rasul and Trimmu barrages attained peak inflow of 11 088 and 16 772 m3 s−1 on 6 and 11 September 2014 (Fig. 1e), respectively, which exceeded the corresponding very high flood levels by 4000 m3 s−1. In June 2013, Uttarakhand received 300–650 mm of rainfall (20%–25% of annual precipitation) between 12 and 18 June, and ≥650 mm of rainfall (∼40%–45% of annual precipitation) was observed (Fig. 1d, Fig. S1d) near Haridwar. Moderate precipitation for the first 3 days saturated the soil layers, and the succeeding heavy precipitation on 16–17 June generated substantial streamflow. The inflow in the Bhagirathi River (a tributary of the Ganga River) before 16 June was low, and the Tehri reservoir was almost at its dead storage. The inflow increased rapidly on 16 June and reached a peak discharge of 4220 m3 s−1 on 17 June (peak hourly flow ≥ 7500 m3 s−1; Arora et al. 2018), resulting in flooding (Fig. 1f). During these dates, ARs were identified from 12 to 18 June, which contributed to the high streamflow observed in Tehri Dam during the flood.
These results highlight that ARs are important to hydrologic extremes over the Himalayas. Many areas are vulnerable to AR precipitation and associated impacts in different seasons, which underscores the need for forecasting of ARs. AR forecasts could help in improving seasonal and subseasonal forecasts of winter precipitation over the Himalayas. Numerical weather prediction models (NWPs) are adept in short-range forecasting of individual ARs and AR-related precipitation (Hughes et al. 2014; Huang et al. 2020; Wick et al. 2013; Nayak et al. 2014). When paired with forecasted climate modes, the forecasting of AR frequency could be extended up to 9 months in advance (Baggett et al. 2017; Tseng et al. 2021). At present, NWPs are shown to produce reliable long-range AR forecasts with accurate probability of occurrence and their categories (Lavers et al. 2020). In the Himalayas, however, there is no region-specific study dedicated to AR modeling/forecasting. Nevertheless, we suggest that future research will explore these areas of interest and include AR forecasting in existing NWPs in these regions. In addition to improving models’ output through improved terrain data, observations, and precise initial conditions, forecast can also be enhanced if physical processes are better described, and understanding of how IVT is resolved in models. Nonetheless, we believe that accurate forecasts of ARs may likely be a major breakthrough in hydrometeorological prediction over the Himalayas.
5. Conclusions
Precipitation in the Himalayas is mainly due to the orographic effect of very high mountain ranges that block and uplift the moisture fluxes approaching them. This study aims to quantify the relative contributions of ARs on regional precipitation, seasonal extremes, and AR-related floods in two major basins in the Himalayas–Indus and Ganga basins. ARs are unique synoptic features, responsible for 20%–30% of the total annual rainfall in IB and 5%–20% in GB, though the contributions are spatially heterogeneous. Seasonally, ARs contribute the largest fractions in winter, impacting large areas in IB and northwest GB, followed by autumn, spring, and summer. In three of the seasons (i.e., winter, spring, and autumn), the IB is more influenced by ARs than GB. In winter, the west and north of IB and northern GB receive up to 200 mm w.e. snowfall, of which 40%–50% is attributable to ARs. Since ARs contribute the highest (40%–80%) to rainfall in winter season, we examined further the ARs’ impact on winter precipitation in the mountain ranges of the Himalayas (as rainfall and snowfall totals are higher here compared to plains in the lower IB and GB). We observed that on average ARs contribute more than 50% of the annual winter rainfall and snowfall in HK, KA, WH, and CH, and ARs explained over 75%, 57%, 42%, and 30% of the variability in winter precipitation of KA, HK, CH, and WH, respectively. The streamflow in both IB and GB rely heavily on winter snowfall and ice cover available, and any fluctuation in annual snowfall will impact water availability for the billion inhabitants in the following season (Azam et al. 2021). Other implications of snow cover extent in these mountains are their influence on the strength/delay of the ISM (Dash et al. 2005; Mamgain et al. 2010). It is to be noted, however, that with limited observational stations in these regions, the snowfall estimated by reanalysis products is highly uncertain (Azam et al. 2019).
A major fraction of extreme rainfall in different seasons (especially winter and spring) is associated with ARs over the two Himalayan basins. These extremes have the potential to generate large floods that impact extensive areas and result in large social and economic consequences in these regions, especially for areas in the foothills of the mountains. However, not all ARs lead to floods. Our results revealed that a major fraction (56%–73%) of the floods in the Himalayas during 1985–2018 happened during the presence of ARs. In addition, the two largest floods in these basins further elucidate the importance of ARs on the hydrology and water resources in IB and GB basins.
These extreme events (precipitation and floods) are recurring natural disasters and understanding what generates them could help in improving the decision-making for management, forecasts, and mitigation. These results imply that ARs are key agents delivering water deep inland, more than thousands of kilometers from the Indian coasts, and are the potential mechanisms of flooding in the Himalayan basins. The seasonality of ARs combined with topographic barriers has an important implication on the region’s water availability. Further exploration is needed to understand the impacts of ARs in these basins including ARs that bring warmer storms and lead to more rain instead of snow, and disasters related to avalanches and landslides in the mountains of IB and GB. Based on the importance of ARs to the Himalayan hydrology, it is imperative to channelize research efforts on forecasting of ARs, for example through numerical and statistical approaches, for improving operational forecasts of precipitation and hydrologic extremes.
Acknowledgments.
Munir Ahmad Nayak and Rosa Vellosa Lyngwa gratefully acknowledge the financial support provided by the Science and Engineering Research Board (SERB) of the Department of Science and Technology (DST), Govt. of India, under the Early Career Research (ECR) Award ECR/2017/002782. Mohd. Farooq Azam and Rosa Vellosa Lyngwa gratefully acknowledge the research grants from DST CRG/2020/004877. We acknowledge and thank the European Centre for Medium-Range Weather Forecasts (ECMWF) for keeping the data publicly available. The authors declare no competing interest.
Data availability statement.
The AR dataset is openly published at Zenodo (https://doi.org/10.5281/zenodo.4451901, date of retrieval: 1 February 2021). Precipitation data from IMD are available at https://dsp.imdpune.gov.in (date of retrieval: 9 January 2021) and are available from IMD at minimal costs. Precipitation data from WFDE5 are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.20d54e34?tab=form [date of retrieval: 31 December 2020 (rainfall) and 8 March 2021 (snowfall)]. The daily discharge data in Indus basin are available at https://ffc.gov.pk/annual-flood-reports/ (date of retrieval: 11 February 2021) and for Ganga basin the dam discharge is available at https://indiawris.gov.in/wris/#/Reservoirs (date of retrieval: 13 February 2021). Shapefiles of Indus and Ganga basins are available at https://rds.icimod.org/Home/Data?any=indus%20basin&Category=datasets (date of retrieval: 7 December 2020). The Hindu-Kush Karakoram Himalaya boundary shapefiles are available at https://www.mountcryo.org/login/?signedup=1 (date of retrieval: 31 March 2021). The flood database are available from https://dataverse.harvard.edu/dataverse/dfo1985to2015 (date of retrieval: 18 April 2022).
REFERENCES
Alexander, M. A., J. D. Scott, D. Swales, M. Hughes, K. Mahoney, and C. A. Smith, 2015: Moisture pathways into the U.S. Intermountain West associated with heavy winter precipitation events. J. Hydrometeor., 16, 1184–1206, https://doi.org/10.1175/JHM-D-14-0139.1.
Anand, J., A. K. Gosain, R. Khosa, and R. Srinivasan, 2018: Regional scale hydrologic modeling for prediction of water balance, analysis of trends in streamflow and variations in streamflow: The case study of the Ganga River basin. J. Hydrol. Reg. Stud., 16, 32–53, https://doi.org/10.1016/j.ejrh.2018.02.007.
Archer, D. R., N. Forsythe, H. J. Fowler, and S. M. Shah, 2010: Sustainability of water resources management in the Indus basin under changing climatic and socioeconomic conditions. Hydrol. Earth Syst. Sci., 14, 1669–1680, https://doi.org/10.5194/hess-14-1669-2010.
Arora, H. L., A. K. Singh, and M. Agrawal, 2018: Role of Tehri Dam in prevention of floods. THDC, https://thdc.co.in/content/role-tehri-dam-prevention-floodveling-and-centring.
Azam, M. F., P. Wagnon, C. Vincent, A. L. Ramanathan, N. Kumar, S. Srivastava, J. G. Pottakkal, and P. Chevallier, 2019: Snow and ice melt contributions in a highly glacierized catchment of Chhota Shigri Glacier (India) over the last five decades. J. Hydrol., 574, 760–773, https://doi.org/10.1016/j.jhydrol.2019.04.075.
Azam, M. F., and Coauthors, 2021: Glaciohydrology of the Himalaya-Karakoram. Science, 373, eabf3668, https://doi.org/10.1126/science.abf3668.
Baggett, C. F., E. A. Barnes, E. D. Maloney, and B. D. Mundhenk, 2017: Advancing atmospheric river forecasts into subseasonal-to-seasonal time scales: Forecasting ARs at S2S time scales. Geophys. Res. Lett., 44, 7528–7536, https://doi.org/10.1002/2017GL074434.
Barros, A. P., S. Chiao, T. J. Lang, D. Burbank, and J. Putkonen, 2006: From weather to climate—Seasonal and interannual variability of storms and implications for erosion processes in the Himalaya. Spec. Pap. Geol. Soc. Amer., 398, 17–38, https://doi.org/10.1130/S2006.2398(02).
Baudouin, J.-P., M. Herzog, and C. A. Petrie, 2021: Synoptic processes of winter precipitation in the upper Indus basin. Wea. Climate Dyn., 2, 1187–1207, https://doi.org/10.5194/wcd-2-1187-2021.
Bhambri, R., M. Mehta, D. P. Dobhal, A. K. Gupta, B. Pratap, K. Kesarwani, and A. Verma, 2016: Devastation in the Kedarnath (Mandakini) Valley, Garhwal Himalaya, during 16–17 June 2013: A remote sensing and ground-based assessment. Nat. Hazards, 80, 1801–1822, https://doi.org/10.1007/s11069-015-2033-y.
Bolch, T., and Coauthors, 2012: The state and fate of Himalayan glaciers. Science, 336, 310–314, https://doi.org/10.1126/science.1215828.
Bookhagen, B., and D. W. Burbank, 2006: Topography, relief, and TRMM-derived rainfall variations along the Himalaya. Geophys. Res. Lett., 33, L08405, https://doi.org/10.1029/2006GL026037.
Bookhagen, B., and D. W. Burbank, 2010: Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J. Geophys. Res., 115, F03019, https://doi.org/10.1029/2009JF001426.
Bookhagen, B., R. C. Thiede, and M. R. Strecker, 2005: Abnormal monsoon years and their control on erosion and sediment flux in the high, arid northwest Himalaya. Earth Planet. Sci. Lett., 231, 131–146, https://doi.org/10.1016/j.epsl.2004.11.014.
Champati Ray, P. K., S. L. Chattoraj, M. P. S. Bisht, S. Kannaujiya, K. Pandey, and A. Goswami, 2016: Kedarnath disaster 2013: Causes and consequences using remote sensing inputs. Nat Hazards, 81, 227–243, https://doi.org/10.1007/s11069-015-2076-0.
Chen, X., L. R. Leung, M. Wigmosta, and M. Richmond, 2019: Impact of atmospheric rivers on surface hydrological processes in western U.S. watersheds. J. Geophys. Res. Atmos., 124, 8896–8916, https://doi.org/10.1029/2019JD030468.
Cucchi, M., 2021: Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis. ECMWF Copernicus Rep., 14 pp., https://datastore.copernicus-climate.eu/documents/derived-near-surface-meteorological-variables/C3S_Dataset-PUG_WFDE5_v2.0.pdf.
Cucchi, M., G. P. Weedon, A. Amici, N. Bellouin, S. Lange, H. Müller Schmied, H. Hersbach, and C. Buontempo, 2020: WFDE5: Bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020.
Curio, J., and D. Scherer, 2016: Seasonality and spatial variability of dynamic precipitation controls on the Tibetan Plateau. Earth Syst. Dyn., 7, 767–782, https://doi.org/10.5194/esd-7-767-2016.
Das, S., N. S. Kar, and S. Bandyopadhyay, 2015: Glacial lake outburst flood at Kedarnath, Indian Himalaya: A study using digital elevation models and satellite images. Nat. Hazards, 77, 769–786, https://doi.org/10.1007/s11069-015-1629-6.
Dash, S. K., G. P. Singh, M. S. Shekhar, and A. D. Vernekar, 2005: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia. Climate Dyn., 24 (1), 1–10, https://doi.org/10.1007/s00382-004-0448-3.
Dettinger, M. D., and D. R. Cayan, 2014: Drought and the California delta—A matter of extremes. San Francisco Estuary Watershed Sci., 12, 4, https://doi.org/10.15447/sfews.2014v12iss2art4.
Dettinger, M. D., F. M. Ralph, T. Das, P. J. Neiman, and D. R. Cayan, 2011: Atmospheric rivers, floods and the water resources of California. Water, 3, 445–478, https://doi.org/10.3390/w3020445.
Dhana Lakshmi, D., and A. N. V. Satyanarayana, 2019: Influence of atmospheric Rivers in the occurrence of devastating flood associated with extreme precipitation events over Chennai using different reanalysis data sets. Atmos. Res., 215, 12–36, https://doi.org/10.1016/j.atmosres.2018.08.016.
Dhana Lakshmi, D., and A. N. V. Satyanarayana, 2020: Climatology of landfalling atmospheric rivers and associated heavy precipitation over the Indian coastal regions. Int. J. Climatol., 40, 5616–5633, https://doi.org/10.1002/joc.6540.
Dimri, A. P., 2006: Surface and upper air fields during extreme winter precipitation over the western Himalayas. Pure Appl. Geophys., 163, 1679–1698, https://doi.org/10.1007/s00024-006-0092-4.
FFC-GOP, 2014: Annual flood reports. Federal Flood Commission, Government of Pakistan, accessed 11 February 2021, https://ffc.gov.pk/annual-flood-reports/.
Fowler, H. J., and D. R. Archer, 2006: Conflicting signals of climatic change in the upper Indus basin. J. Climate, 19, 4276–4293, https://doi.org/10.1175/JCLI3860.1.
Guan, B., and D. E. Waliser, 2019: Tracking atmospheric rivers globally: Spatial distributions and temporal evolution of life cycle characteristics. J. Geophys. Res. Atmos., 124, 12 523–12 552, https://doi.org/10.1029/2019JD031205.
Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 dataset. Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711.
Hatchett, B. J., S. Burak, J. J. Rutz, N. S. Oakley, E. H. Bair, and M. L. Kaplan, 2017: Avalanche fatalities during atmospheric river events in the western United States. J. Hydrometeor., 18, 1359–1374, https://doi.org/10.1175/JHM-D-16-0219.1.
Henn, B., K. N. Musselman, L. Lestak, F. M. Ralph, and N. P. Molotch, 2020: Extreme runoff generation from atmospheric river driven snowmelt during the 2017 Oroville Dam spillways incident. Geophys. Res. Lett., 47, e2020GL088189, https://doi.org/10.1029/2020GL088189.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Hu, J. M., and A. W. Nolin, 2019: Snowpack contributions and temperature characterization of landfalling atmospheric rivers in the western Cordillera of the United States. Geophys. Res. Lett., 46, 6663–6672, https://doi.org/10.1029/2019GL083564.
Huang, X., D. L. Swain, D. B. Walton, S. Stevenson, and A. D. Hall, 2020: Simulating and evaluating atmospheric river‐induced precipitation extremes along the U.S. Pacific Coast: case studies from 1980–2017. J. Geophys. Res. Atmos., 125, e2019JD031554, https://doi.org/10.1029/2019JD031554.
Hughes, M., K. M. Mahoney, P. J. Neiman, B. J. Moore, M. Alexander, and F. M. Ralph, 2014: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part II: Sensitivity of modeled precipitation to terrain height and atmospheric river orientation. J. Hydrometeor., 15, 1954–1974, https://doi.org/10.1175/JHM-D-13-0176.1.
Immerzeel, W. W., and Coauthors, 2020: Importance and vulnerability of the world’s water towers. Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y.
Ionita, M., V. Nagavciuc, and B. Guan, 2020: Rivers in the sky, flooding on the ground: The role of atmospheric rivers in inland flooding in central Europe. Hydrol. Earth Syst. Sci., 24, 5125–5147, https://doi.org/10.5194/hess-24-5125-2020.
Kääb, A., E. Berthier, C. Nuth, J. Gardelle, and Y. Arnaud, 2012: Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature, 488, 495–498, https://doi.org/10.1038/nature11324.
Konrad, C. P., and M. D. Dettinger, 2017: Flood runoff in relation to water vapor transport by atmospheric rivers over the western United States, 1949–2015. Geophys. Res. Lett., 44, 11 456–11 462, https://doi.org/10.1002/2017GL075399.
Laghari, A. N., D. Vanham, and W. Rauch, 2012: The Indus basin in the framework of current and future water resources management. Hydrol. Earth Syst. Sci., 16, 1063–1083, https://doi.org/10.5194/hess-16-1063-2012.
Lang, T. J., and A. P. Barros, 2004: Winter storms in the Central Himalayas. J. Meteor. Soc. Japan, 82, 829–844, https://doi.org/10.2151/jmsj.2004.829.
Lavers, D. A., and G. Villarini, 2013: Atmospheric rivers and flooding over the central United States. J. Climate, 26, 7829–7836, https://doi.org/10.1175/JCLI-D-13-00212.1.
Lavers, D. A., G. Villarini, R. P. Allan, E. F. Wood, and A. J. Wade, 2012: The detection of atmospheric rivers in atmospheric reanalyses and their links to British winter floods and the large-scale climatic circulation. J. Geophys. Res., 117, D20106, https://doi.org/10.1029/2012JD018027.
Lavers, D. A., F. M. Ralph, D. S. Richardson, and F. Pappenberger, 2020: Improved forecasts of atmospheric rivers through systematic reconnaissance, better modelling, and insights on conversion of rain to flooding. Commun. Earth Environ., 1, 39, https://doi.org/10.1038/s43247-020-00042-1.
Leung, L. R., and Y. Qian, 2009: Atmospheric rivers induced heavy precipitation and flooding in the western U.S. simulated by the WRF regional climate model. Geophys. Res. Lett., 36, L03820, https://doi.org/10.1029/2008GL036445.
Lutz, A. F., W. W. Immerzeel, A. B. Shrestha, and M. F. P. Bierkens, 2014: Consistent increase in high Asia’s runoff due to increasing glacier melt and precipitation. Nat. Climate Change, 4, 587–592, https://doi.org/10.1038/nclimate2237.
Madhura, R. K., R. Krishnan, J. V. Revadekar, M. Mujumdar, and B. N. Goswami, 2015: Changes in western disturbances over the western Himalayas in a warming environment. Climate Dyn., 44, 1157–1168, https://doi.org/10.1007/s00382-014-2166-9.
Mahoney, K., and Coauthors, 2016: Understanding the role of atmospheric rivers in heavy precipitation in the southeast United States. Mon. Wea. Rev., 144, 1617–1632, https://doi.org/10.1175/MWR-D-15-0279.1.
Mamgain, A., S. K. Dash, and P. P. Sarthi, 2010: Characteristics of Eurasian snow depth with respect to Indian summer monsoon rainfall. Meteor. Atmos. Phys., 110, 71–83, https://doi.org/10.1007/s00703-010-0100-1.
Mishra, A. K., 2015: A study on the occurrence of flood events over Jammu and Kashmir during September 2014 using satellite remote sensing. Nat. Hazards, 78, 1463–1467, https://doi.org/10.1007/s11069-015-1768-9.
Mueller, M. J., K. M. Mahoney, and M. Hughes, 2017: High-resolution model-based investigation of moisture transport into the Pacific Northwest during a strong Atmospheric River event. Mon. Wea. Rev., 145, 3861–3879, https://doi.org/10.1175/MWR-D-16-0466.1.
Najibi, N., and N. Devineni, 2018: Recent trends in the frequency and duration of global floods. Earth Syst. Dyn., 9, 757–783, https://doi.org/10.5194/esd-9-757-2018.
Nash, D., and L. V. Carvalho, 2018: What is the impact of atmospheric rivers on high mountain Asia precipitation? 43rd NOAA Annual Climate Diagnostic and Prediction Workshop, Santa Barbara, CA, NOAA, https://www.nws.noaa.gov/ost/climate/STIP/43CDPW/posters/DNash.jpg.
Nash, D., L. M. V. Carvalho, C. Jones, and Q. Ding, 2022: Winter and spring atmospheric rivers in high mountain Asia: Climatology, dynamics, and variability. Climate Dyn., 58, 2309–2331, https://doi.org/10.1007/s00382-021-06008-z.
Nayak, M. A., and G. Villarini, 2017: A long‐term perspective of the hydroclimatological impacts of atmospheric rivers over the central United States. Water Resour. Res., 53, 1144–1166, https://doi.org/10.1002/2016WR019033.
Nayak, M. A., and G. Villarini, 2018: Remote sensing-based characterization of rainfall during atmospheric rivers over the central United States. J. Hydrol., 556, 1038–1049, https://doi.org/10.1016/j.jhydrol.2016.09.039.
Nayak, M. A., G. Villarini, and D. A. Lavers, 2014: On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States. Geophys. Res. Lett., 41, 4354–4362, https://doi.org/10.1002/2014GL060299.
Nayak, M. A., G. Villarini, and A. A. Bradley, 2016: Atmospheric rivers and rainfall during NASA’s Iowa Flood Studies (IFloodS) campaign. J. Hydrometeor., 17, 257–271, https://doi.org/10.1175/JHM-D-14-0185.1.
Nayak, M. A., M. F. Azam, and R. V. Lyngwa, 2021: ERA5-based database of atmospheric rivers over Himalayas. Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-397.
Neiman, P. J., F. M. Ralph, G. A. Wick, J. D. Lundquist, and M. D. Dettinger, 2008: Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the West Coast of North America based on eight years of SSM/I Satellite observations. J. Hydrometeor., 9, 22–47, https://doi.org/10.1175/2007JHM855.1.
Pai, D. S., and S. C. Bhan, 2014: Monsoon Report 2014. ESSO Doc. ESSO/IMD/Synoptic Met./01(2015)/17, 219 pp., https://www.tropmet.res.in/∼kolli/MOL/Monsoon/year2014/Monsoon-2014-NEW.pdf.
Pai, D. S., M. Rajeevan, O. P. Sreejith, B. Mukhopadhyay, and N. S. Satbhai, 2014: Development of a new high spatial resolution (0.25° × 0.25°) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 65, 1–18, https://doi.org/10.54302/mausam.v65i1.851.
Palazzi, E., J. von Hardenberg, and A. Provenzale, 2013: Precipitation in the Hindu-Kush Karakoram Himalaya: Observations and future scenarios. J. Geophys. Res. Atmos., 118, 85–100, https://doi.org/10.1029/2012JD018697.
Paltan, H., D. Waliser, W. H. Lim, B. Guan, D. Yamazaki, R. Pant, and S. Dadson, 2017: Global floods and water availability driven by atmospheric rivers. Geophys. Res. Lett., 44, 10 387–10 395, https://doi.org/10.1002/2017GL074882.
Pritchard, H. D., 2019: Asia’s shrinking glaciers protect large populations from drought stress. Nature, 569, 649–654, https://doi.org/10.1038/s41586-019-1240-1.
Ralph, F. M., and M. D. Dettinger, 2011: Storms, floods, and the science of atmospheric rivers. Eos, Trans. Amer. Geophys. Union, 92, 265–266, https://doi.org/10.1029/2011EO320001.
Ralph, F. M., P. J. Neiman, and G. A. Wick, 2004: Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98. Mon. Wea. Rev., 132, 1721–1745, https://doi.org/10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2.
Ralph, F. M., J. J. Rutz, J. M. Cordeira, M. Dettinger, M. Anderson, D. Reynolds, L. J. Schick, and C. Smallcomb, 2019: A scale to characterize the strength and impacts of atmospheric rivers. Bull. Amer. Meteor. Soc., 100, 269–289, https://doi.org/10.1175/BAMS-D-18-0023.1.
Ranalkar, M. R., H. S. Chaudhari, A. Hazra, G. K. Sawaisarje, and S. Pokhrel, 2016: Dynamical features of incessant heavy rainfall event of June 2013 over Uttarakhand, India. Nat Hazards, 80, 1579–1601, https://doi.org/10.1007/s11069-015-2040-z.
Ray, K., S. C. Bhan, and B. K. Bandopadhyay, 2015: The catastrophe over Jammu and Kashmir in September 2014: A meteorological observational analysis. Curr. Sci., 109, 580–591, https://doi.org/10.18520/cs%2Fv109%2Fi3%2F580-591.
Rehman, A., L. Jingdong, Y. Du, R. Khatoon, S. A. Wagan, and S. K. Nisar, 2016: Flood disaster in Pakistan and its impact on agriculture growth (a review). J. Econ. Sustainable Dev., 6, 39–42.
Riley, C., S. Rupper, J. W. Steenburg, C. Strong, A. K. Kochanski, and S. Wolvin, 2021: Characteristics of historical precipitation in high mountain Asia based on a 15-year high resolution dynamical downscaling. Atmosphere, 12, 355, https://doi.org/10.3390/atmos12030355.
Romshoo, S. A., S. Altaf, I. Rashid, and R. A. Dar, 2018: Climatic, geomorphic and anthropogenic drivers of the 2014 extreme flooding in the Jhelum basin of Kashmir, India. Geomatics Nat. Hazards Risk, 9, 224–248, https://doi.org/10.1080/19475705.2017.1417332.
Rutz, J. J., W. J. Steenburgh, and F. M. Ralph, 2014: Climatological characteristics of atmospheric rivers and their inland penetration over the western United States. Mon. Wea. Rev., 142, 905–921, https://doi.org/10.1175/MWR-D-13-00168.1.
Saavedra, F., G. Cortés, M. Viale, S. Margulis, and J. McPhee, 2020: Atmospheric rivers contribution to the snow accumulation over the southern Andes (26.5°S–37.5°S). Front. Earth Sci., 8, 261, https://doi.org/10.3389/feart.2020.00261.
Sattar, A. M., and K. K. W. Cheung, 2019: Comparison between the active tropical cyclone seasons over the Arabian Sea and Bay of Bengal. Int. J. Climatol., 39, 5486–5502, https://doi.org/10.1002/joc.6167.
Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, B. Rudolf, and M. Ziese, 2016: GPCC full data reanalysis version 7.0: Monthly land-surface precipitation from rain gauges built on GTS based and historic data. NCAR Research Data Archive, accessed 10 August 2022, https://doi.org/10.5065/D6000072.
Shepard, D., 1968: A two-dimensional interpolation function for irregularly-spaced data. ACM ‘68: Proceedings of the 1968 23rd ACM National Conference, ACM Press, 517–524, https://doi.org/10.1145/800186.810616.
Shu, J., A. Y. Shamseldin, and E. Weller, 2021: The impact of atmospheric rivers on rainfall in New Zealand. Sci. Rep., 11, 5869, https://doi.org/10.1038/s41598-021-85297-0.
Thapa, K., T. A. Endreny, and C. R. Ferguson, 2018: Atmospheric rivers carry nonmonsoon extreme precipitation into Nepal. J. Geophys. Res. Atmos., 123, 5901–5912, https://doi.org/10.1029/2017JD027626.
Tseng, K.-C., and Coauthors, 2021: Are multiseasonal forecasts of atmospheric rivers possible? Geophys. Res. Lett., 48, e2021GL094000, https://doi.org/10.1029/2021GL094000.
Viale, M., R. Valenzuela, R. D. Garreaud, and F. M. Ralph, 2018: Impacts of atmospheric rivers on precipitation in southern South America. J. Hydrometeor., 19, 1671–1687, https://doi.org/10.1175/JHM-D-18-0006.1.
Viste, E., and A. Sorteberg, 2015: Snowfall in the Himalayas: an uncertain future from a little-known past. Cryosphere, 9, 1147–1167, https://doi.org/10.5194/tc-9-1147-2015.
Vithalani, K. R., and N. Bansal, 2017: Causes and effect of Kashmir flood. Int. J. Adv. Res. Ideas Innovations Technol., 3, 853–869, https://www.ijariit.com/manuscripts/v3i6/V3I6-1301.pdf.
Waliser, D., and B. Guan, 2017: Extreme winds and precipitation during landfall of atmospheric rivers. Nat. Geosci., 10, 179–183, https://doi.org/10.1038/ngeo2894.
Wang, T., K. Wei, and J. Ma, 2021: Atmospheric rivers and mei-yu rainfall in China: A case study of summer 2020. Adv. Atmos. Sci., 38, 2137–2152, https://doi.org/10.1007/s00376-021-1096-9.
Wick, G. A., P. J. Neiman, F. M. Ralph, and T. M. Hamill, 2013: Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Wea. Forecasting, 28, 1337–1352, https://doi.org/10.1175/WAF-D-13-00025.1.
WRIS, 2013: India Water Resources Information System. Accessed 13 February 2021, https://www.indiawris.gov.in/wris/#/DataDownload.
Yadav, B. P., N. Kumar, and S. Lotus, 2017: Synoptic & climatological aspects of extreme rainfall over western Himalayas towards end of 2014 southwest monsoon season. Mausam, 68, 597–606, https://doi.org/10.54302/mausam.v68i4.715.
Yang, Y., T. Zhao, G. Ni, and T. Sun, 2018: Atmospheric rivers over the Bay of Bengal lead to northern Indian extreme rainfall. Int. J. Climatol., 38, 1010–1021, https://doi.org/10.1002/joc.5229.
Zhang, Z., F. M. Ralph, and M. Zheng, 2019: The relationship between extratropical cyclone strength and atmospheric river intensity and position. Geophys. Res. Lett., 46, 1814–1823, https://doi.org/10.1029/2018GL079071.
Zhou, Y., H. Kim, and B. Guan, 2018: Life cycle of atmospheric rivers: Identification and climatological characteristics. J. Geophys. Res. Atmos., 123, 12 715–12 725, https://doi.org/10.1029/2018JD029180.
Zhu, Y., and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725–735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.
