The upper Indus River basin is characterized by biseasonal heavy precipitation falling on the foothills of major mountain ranges (Hindu Kush, Karakorm, Himalayas). Numerical studies have confirmed the importance of topography for the triggering of precipitation and investigated the processes responsible for specific events, but a systematic and cross-seasonal analysis has yet to be conducted. Using ERA5 reanalysis data and statistical methods, we show that more than 80% of the precipitation variability is explained by southerly moisture transport at 850 and 700 hPa, along the Himalayan foothills. We conclude that most of the precipitation is generated by the forced uplift of a cross-barrier flow. This process explains both wet seasons, despite different synoptic conditions, but is more important in winter. The precipitation signal is decomposed into the contribution of each altitude and each variable (wind and moisture), which exhibit different seasonality. The winter wet season is dominated by moisture transport at higher altitude, and is triggered by an increase in wind. By contrast, the summer wet season is explained by an increase in moisture at both altitudes, while wind is of secondary importance. Selected CMIP6 climate models are able to represent the observed links between precipitation and southerly moisture transport, despite important seasonal biases that are due to a misrepresentation of the seasonality in the magnitude of the southerly wind component.

Abstract. Large uncertainty remains about the amount of precipitation falling in the Indus River basin, particularly in the more mountainous northern part. While rain gauge measurements are often considered as a reference, they provide information for specific, often sparse, locations (point observations) and are subject to underestimation, particularly in mountain areas. Satellite observations and reanalysis data can improve our knowledge but validating their results is often difficult. In this study, we offer a cross-validation of 20 gridded datasets based on rain gauge, satellite, and reanalysis data, including the most recent and less studied APHRODITE-2, MERRA2, and ERA5. This original approach to cross-validation alternatively uses each dataset as a reference and interprets the result according to their dependency on the reference. Most interestingly, we found that reanalyses represent the daily variability of precipitation as well as any observational datasets, particularly in winter. Therefore, we suggest that reanalyses offer better estimates than non-corrected rain-gauge-based datasets where underestimation is problematic. Specifically, ERA5 is the reanalysis that offers estimates of precipitation closest to observations, in terms of amounts, seasonality, and variability, from daily to multi-annual scale. By contrast, satellite observations bring limited improvement at the basin scale. For the rain-gauge-based datasets, APHRODITE has the finest temporal representation of the precipitation variability, yet it importantly underestimates the actual amount. GPCC products are the only datasets that include a correction factor of the rain gauge measurements, but this factor likely remains too small. These findings highlight the need for a systematic characterisation of the underestimation of rain gauge measurements.

The development, floruit and decline of the urban phase of the Indus Civilisation (c.2600/2500-1900 BC) provide an ideal opportunity to investigate social resilience and transformation in relation to a variable climate. The Indus Civilisation extended over most of the Indus River Basin, which includes a mix of diverse environments conditioned, among other factors, by partially overlapping patterns of winter and summer precipitation. These patterns likely changed towards the end of the urban phase (4.2 ka BP event), increasing aridity. The impact of this change appears to have varied at different cities and between urban and rural contexts. We present a simulation approach using agent-based modelling to address the potential diversity of agricultural strategies adopted by Indus settlements in different socio-ecological scenarios in Haryana, NW India. This is an ongoing initiative that consists of creating a modular model, Indus Village, that assesses the implications of trends in cropping strategies for the sustainability of settlements and the resilience of such strategies under different regimes of precipitation. The model aims to simulate rural settlements structured into farming households, with sub-models representing weather and land systems, food economy, demography, and land use. This model building is being carried out as part of the multi-disciplinary TwoRains project. It brings together research on material culture, settlement distribution, food production and consumption, vegetation and paleoenvironmental conditions.

Climate models are capable of producing features similar to tropical cyclones, but typically display strong biases for many of the storm physical characteristics due to their relatively coarse resolution compared to the size of the storms themselves. One strategy that has been adopted to circumvent this limitation is through the use of a hybrid downscaling technique, wherein a large set of synthetic tracks are created by seeding disturbances in the large-scale environment. Here, we evaluate the ability of this technique at reproducing many of the characteristics of the recent North Atlantic hurricane activity as well as its sensitivity to the choice of the reanalysis dataset used as boundary conditions. In particular, we show that the geographical and intensity distributions are well reproduced, but that the technique has difficulty capturing the large difference in activity observed between the most recent active and quiescent phase. Although the signal is somewhat reduced compared to observation, the technique also detects a significant decrease in the intensification rate of hurricanes near the coastal US during the active phase compared to the quiescent phase. Finally, the influence of the El Niño Southern Oscillation on hurricane activity is generally well captured as well, but the technique fails to reproduce the increase in activity over the western part of the basin during Modoki El Niños.