The Indus is the westernmost of the major rivers of South Asia. It has a total length of 3200 km and drains parts of China, India, Afghanistan and Pakistan [1
], in particular providing Pakistan’s main water source. The Indus basin’s water resources have been estimated to support 215 million people with an average per capita annual water availability of 1329 m
]. The Indus originates from Tibetan Plateau and drains into the Arabian Sea, and the basin as a whole covers a latitude range of about 24
N and longitude range of about 66
E (Figure 1
). The northern or upper part of the basin includes high mountains of the Himalaya, Karakoram, and Hindu Kush mountain ranges, whereas much of its southern or lower part is flat lowland. Temperatures range from below freezing at high elevations to above 40
C in spring and summer at low elevations. Located at the margin of the South Asia Summer Monsoon region, much of the Indus basin is relatively arid. Particularly the upper part of the basin receives a substantial share of precipitation in winter, during westerly disturbances [3
]. Some 85% of the Indus’ flow is in the summer, when both monsoon rain and mountain glacier melt peak [1
Quantifying historic precipitation change is key for understanding variability and change in the water budget over the Indus basin. Climate model simulations mostly project that anthropogenic global warming would lead to increased precipitation in the region over the coming decades, but decreased snowfall at high elevations in winter and increased evaporation, which could lead to reduced reliability of water resources [7
]. Moreover, there is high uncertainty owing to divergent behavior of climate models and limited ground observations [9
]. Sparsity and discontinuity of climate and hydrologic observations make it difficult to evaluate the accuracy of hydrologic and climate models and create gaps in the scientific understanding needed to provide evidence for policy decisions [12
]. Globally, there is recognition of an increased need for employing statistical analyses to provide better information about long-term changes and variability of precipitation as a result of the changing climate and consequent greater exposure to risks such as droughts and floods [14
Most previous work on precipitation trends in the Indus basin has focused only on portions of the basin, defined by political or hydrologic boundaries. For example, Archer and Fowler [15
] examined precipitation records of varying lengths from 17 stations in the Upper Indus basin, and found no significant precipitation trend from 1895–1999 overall, but increases in some stations over the most recent decades of 1961–1999. Bhutiyani et al. [16
] examined station precipitation at three stations in Jammu and Kashmir and Himachal Pradesh states, India, finding negative trends in precipitation over 1866–2006. Khattak et al. [17
] found no definite pattern in precipitation over the period 1967–2005 over 20 stations in Pakistan’s portion of the Upper Indus basin. Similarly, investigations of station precipitation records in Pakistan’s lower and middle Indus basin [18
] and Swat river subbasin [19
] found insignificant trends over 40–50 year periods. A study of 53 meteorological stations over the China–Pakistan Economic Corridor during 1980–2016 found mixed trends by season and altitude, with little change in mean precipitation overall and most of the seasonal changes not showing statistical significance [20
]. Chevuturi et al. [21
] studied trends in precipitation based on station measurements and different gridded data sets at one location, Leh, Jammu and Kashmir, India. Iqbal et al. [22
] found no trend in annual precipitation over northern Pakistan using the Asian Precipitation–Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) gridded precipitation data set for 1951–2007. Using Global Precipitation Climatology Centre (GPCC) gridded data over Pakistan for 1961–2010, Ahmed et al. [23
] found that many grid cells over the northern part of the country had increasing trends, but that fewer grid cells had significant increases after adjusting for temporal autocorrelation. Trends in heavy summer monsoon precipitation were also investigated in relation to the devastating 2010 floods in Pakistan [24
]. Multi-century hydroclimate variations have been assessed for parts of South Asia using tree-ring and other proxies [25
]. In the Upper Indus basin, tree rings were used to reconstruct streamflow for the period 1452–2008, showing that flows over the recent 1988–2008 period were historically high and that multi-decade periods of particularly low flow occurred in the 16th and 17th Centuries [27
]. Hunt, K.M.R. et al. [28
] argued based on simulations with an ensemble of climate models that low winter solar insolation in the mid-Holocene led to smaller meridional temperature gradients, a less intense subtropical westerly jet in the midlatitude winter troposphere, and therefore less frequent and intense westerly disturbances over the Indus basin and less winter precipitation, along with more summer precipitation.
In the present work, the main goal is to estimate precipitation amounts and trends over the Indus basin based on available station data. To achieve this, we compared current versions of different global or regional precipitation data sets that cover the basin with generally incomplete available station records within the basin on the monthly timescale. Attention was paid to each data set’s accuracy in representing precipitation totals, seasonal distribution, and interannual variability. In particular, we assessed whether a data set’s precipitation trend is inconsistent with the station measurements, which could manifest as a time-varying bias between the two data sets. Two main timescales were considered for trend estimation: the past 100–150 years, over which station observations have been made, and the past ∼40–60 years, roughly corresponding to the availability of information on precipitation from satellite remote sensing and from global weather observations. Our hypothesis is that careful comparison with available station observations can narrow the range in precipitation amount and trend from that found in different data sets and thus provide more reliable information for assessing climatic change and its hydrologic impacts. The methods developed could also be applied to study trends either in more detail over parts of the Indus basin or in other basins where there is currently high uncertainty.
Our comparison of precipitation data sets with station observations over the Indus basin suggests that GPCC is the best-performing long-term data set. Consistent with our study, Ahmed et al. [82
] found that, compared with other station-based gridded precipitation data sets, including APHRODITE V1101 and CRU, GPCC was better correlated with station data in arid southwestern Pakistan. Adnan et al. [83
] concluded that “GPCC data are very close to real-time station data and hence may be used in the absence of station data in Pakistan”.
The TMPA remote sensing based data set performed comparably well to GPCC, although it only begins in 1998. TMPA could be more accurate than GPCC for areas without stations, such as the high mountains in the far north of the basin, since it can draw on global remote sensing coverage. It also has the advantage of updating more quickly than the GPCC Full Data Product that we investigated.
The new APHRODITE V1901 was shown to be successful at removing the low bias from which APHRODITE V1101 suffered, although its representation of precipitation seasonality and interannual variability was not improved. Similarly, the new IMERG appeared not to perform better than TMPA, despite higher spatial resolution, and suffered from a high bias.
Out of the satellite-period reanalyses, MERRA-2 underestimated precipitation over the Indus basin, whereas the others (JRA-55 and ERA-5) overestimated it. Globally, the assimilation of precipitation observations reduces the MERRA-2 precipitation by almost 30% compared with that simulated by the underlying climate model, with particularly large-amplitude changes over mountain areas, including the Himalayas [63
]. While these observation-based corrections were found to reduce precipitation biases globally for MERRA-2, they may worsen errors in places like the Indus basin where the little gauge data available in near real time may not be regionally representative. In fact, a comparison of MERRA-2 precipitation with GPCP found that MERRA-2 generally underestimates precipitation over the Indus basin (and much of adjoining southern Asia) in both winter and summer [63
]. It was concluded that “better-quality precipitation products” available in near real time “are needed to improve the land surface precipitation and thus the terrestrial water budget in forthcoming reanalysis datasets” [63
]. Until such improved near real-time precipitation products are made available, precipitation climatologies, such as the GPCC Climatology, could at least be used to correct the mean bias in precipitation for areas such as the Indus basin. Another recent study of precipitation products [13
] also showed that MERRA-2 tended to have lower precipitation around the Indus watershed than APHRODITE and TMPA, while ERA-5 had higher precipitation, although that study did not evaluate how these products compared to station observations.
GPCC, supported by CRU, showed a significant increase in precipitation over the Indus basin since the end of the 19th Century. The long-term reanalyses showed, by contrast, a large decrease in precipitation over the same period, which was not supported by observations. Ferguson and Villarini [84
] showed that the earlier version of 20CR-2c included pronounced artificial inhomogeneities in many grid cells that were consequences of inhomogeneities in the surface pressure data and ocean boundary conditions. Another factor in the poor performance of 20CR-2c in capturing precipitation amounts and trends may be its low spatial resolution compared to the other reanalyses, which would affect the representation of surface properties such as topography, with dramatic consequences on climate simulation in mountainous regions, and would require coarse physical parameterization of atmospheric processes such as convective precipitation [85
]. We found that the newer CERA-20C performs better than 20CR-2c at matching station precipitation data but shows an equally strong negative precipitation trend, suggesting that its climate trends may also not be reliable in this region. On the other hand, the shorter-term reanalyses that use upper-air and satellite data showed reasonable precipitation trends, despite evidence of some time-dependent bias as well as substantial mean biases.
Several studies have found that, for many high-elevation subwatersheds of the Indus, station-based gridded data sets tend to underestimate precipitation amounts. Glaciers and snow-covered mountain areas are more important contributors to streamflow for the Indus compared to other major South Asian rivers; while on average amounting to no more than a few percent of basinwide precipitation, glacier melt is disproportionally important in providing water for dry seasons and periods [87
]. Immerzeel et al. [88
] attempted to infer the extent of underestimation using glacier water balance calculations and streamflow data, although uncertainties in other water balance terms make such assessments imprecise. Dahri et al. [89
] used precipitation data from high-altitude stations that have not been previously available for inclusion in gridded datasets along with estimates of precipitation over glaciers from water balance studies to refine the estimated precipitation pattern over the upper Indus basin. For example, their study identifies a precipitation maximum in the extreme north of the Indus watershed (the central Karakoram range in the northwest corner of Shyok basin) that is not seen in GPCC (Figure 4
, top) or other station-based gridded data sets, presumably due to lack of station data. Particularly with adjustment for gauge undercatch, the improved precipitation climatology was more consistent with measured streamflows across the upper Indus basin [90
]. To address this, developers of gridded data sets should seek more precipitation data from high altitudes, such as those identified by Dahri et al. [89
], and may need to work at higher spatial resolution to better represent altitude effects on precipitation before averaging to the desired data set resolution.
JRA-55 and ERA5’s predecessor ERA-Interim were noted for not underestimating precipitation at high elevations [88
], but our comparison with station observations showed that JRA-55 and ERA5 do overestimate precipitation where stations are located, while the MERRA-2 reanalysis underestimates precipitation in the basin compared to station observations. Based on these findings, we recommend that climate patterns derived from reanalyses be used with caution over the Indus basin for applications where ground-based validation is not available.
Our method of combining gridded data sets with station observations could be applied to estimate precipitation amounts and trends in other areas where these are poorly known. For example, for the Congo basin in Equatorial Africa, Washington et al. [92
] found large differences in precipitation distribution between different reanalyses and climate models, and suggested that, in the absence of a dense station network, a short intensive observation campaign that included upper-air radiosonde profiles could constrain moisture transport in the region. Nicholson et al. [93
] compiled and analyzed station observations over Equatorial Africa to analyze precipitation patterns and trends, noting that the number of operative stations declined since a peak in the 1960s and 1970s (similar to the situation in the Indus basin as represented in GPCC and other gridded data sets). These authors derived an improved precipitation climatology and reconstruction scheme based on principal components from this earlier data, and confirmed earlier reports of a decline in precipitation in much of the Congo basin over 1985–2012.
According to GPCC, precipitation increased ∼15% in the Indus basin over 1891–2016. This increase, all else being equal, will help glaciers in the basin maintain their masses, unlike areas such as the Andes and Equatorial Africa where decreasing precipitation has contributed to glacier loss [94
]. It is larger than the global precipitation increase since ∼1900, which has amounted to only a few percent [95
The GPCC data set suggests that precipitation in the Indus basin has increased throughout the year, with the exception of early winter (December–January). However, increases attained statistical significance for the months of June, October and November, immediately before and after the summer monsoon (Figure 3
). Given the seasonal differences in regional circulation, the causes of these trends are likely to be complex. An analysis of summer monsoon precipitation over 1901–2014 found a a significant decreasing trend over northeast India coupled with increases along the South Asia monsoon’s western margin and changes in Indian Ocean sea surface temperatures, corresponding to a westward shift of 2–3
in the monsoon flow system [97
]. It is unclear how this shift relates to the overall weakening trend in the South Asian Summer Monsoon since the 1950s that has been attributed to increased aerosol loading along with land-use change over the Indian subcontinent [98
]. The non-significant decreases in precipitation found for December and January are consistent with regional modeling analyzed by Rajbhandari et al. [2
], where, forced by rising greenhouse gas concentrations, near-future (2011–2040) precipitation over the Indus basin was projected to increase compared to a baseline period (1961–1990) overall and in summer (June to September), but not to increase in winter (December to February).
Based on satellite observations, most of South Asia, including the Indus basin, showed a greening trend over 1982–2014. This greening trend is attributable to higher soil moisture particularly over drier parts of the region, and reflects an increase in precipitation over drier areas even while overall South Asia summer monsoon strength declined [100
], although, for Nepal, which is just east of the Indus basin (but has more precipitation), greening was associated primarily with increasing atmospheric CO
and not with precipitation change [101
]. To better understand the implications of complex climate changes for water supply and disaster risks in the region, the impact of global and regional forcings and dynamics on climate, surface hydrology, and vegetation need to be modeled on regional and basin scales, constrained by observed trends in precipitation (such as those assessed here) and in other water flows and stocks.
In addition to the long-term increasing trend, GPCC shows large year-to-year variation in precipitation (Figure 2
). For summer monsoon precipitation, it is possible to connect interannual variation with sea surface temperature modes, including those associated with the El Niño Southern Oscillation (ENSO) [103
]. ENSO is also associated with the timing of monsoon onset [104
]. The November–April westerly precipitation regime is also closely linked to moisture transport from the Indian Ocean [105
] and relates in complex ways to several Northern Hemisphere modes of climate variability, as well as to ENSO [106
]. Both observations and global climate models show that interannual and decadal variability in winter/spring precipitation over the upper Indus basin can be correlated with specific Pacific Ocean sea surface temperature modes, particularly ENSO and the Pacific Decadal Oscillation [108
]. We hope to further explore the interannual predictability of precipitation in the Indus basin and applications to streamflow prediction in future work.