Wet Spells Research Articles

Role of midtropospheric heating and surface forcing on monsoon intraseasonal transitions

AbstractMonsoon intraseasonal transitions are intrinsically chaotic; hence, their predictability is limited. In the past the majority of studies were conducted solely keeping in account of dynamic prospective like boreal summer intraseasonal oscillation or Madden–Julian oscillation; however its thermodynamic aspect got limited attention. The current study looks at the internal thermodynamic mechanisms pertaining to monsoon intraseasonal transition using two independent sets of reanalysis data (European Centre for Medium‐range Weather Forecasts Reanalysis version 5 and Indian Monsoon Data Assimilation and Analysis), along with the observed cloud type from Met Office Integrated Data Archive System Land and Marine surface station data. A lead–lag composite analysis shows that the midtropospheric warming by diabatic heating is a thermal control to restrict the deepening of convective cloud (cumulonimbus) as the wet spell progresses. This midtroposhperic heating cuts off the formation of deep convective clouds and results in the formation of relatively shallow clouds, such as stratocumulus and stratus. On the other hand, surface forcing during dry spells humidifies the troposphere by detrainment from the shallow convection with the formation of cumulus and cumulus congestus clouds. This facilitates the preconditioning of the atmosphere for deep convection and results in the onset of wet spells by the formation of cumulonimbus clouds. The comprehensive approach used in this work may further help in identifying the limitation of the predictability in monsoon transition period from the numerical weather prediction models.

Literature Review on Stochastic Modeling of Wet and Dry Spells

With increasing concerns about climate change and its profound implications for water resources, stochastic modeling of wet and dry spells has emerged as a critical domain within hydrology and climatology, providing insight into the temporal patterns of precipitation and drought occurrences. This review of the literature synthesizes recent advances in the modeling of wet and dry spells using stochastic methods, focusing on publications from 2000 to 2024. The review examines various modeling approaches, including Markov chain models, mixed probability models, non-homogeneous models, and time series approaches. Key findings indicate that Markov chain models are effective in simulating the sequential occurrence of wet and dry spells, with higher-order variants addressing issues of overdispersion. Mixed probability models excel at representing heterogeneity and extremes in precipitation data, especially in regions with distinct wet and dry seasons. Non-homogeneous models are valuable for understanding the temporal dynamics and irregularities of dry spells, revealing significant shifts in their frequency and duration over time. These can be complemented by time-series methods to highlight long-term trends and seasonal variations. The review underscores the importance of regional specificity in modeling approaches to accurately capture local climatic and geographical variability. Recommendations for future research include the integration of hybrid models that combine the strengths of various approaches to improve predictive precision, focusing on the impacts of climate change, and conducting cross-regional comparisons to generalize findings and enhance the robustness of the models.

Projected changes in wet and dry extremes in the CMIP6 multi-model ensemble over the IGAD region of Eastern Africa

Wet and dry days, as well as wet and dry spells, are crucial pieces of information for rain-fed agriculture, food security, and numerous socioeconomic activities in East Africa. This study examines the projected changes in wet/dry days and wet/dry spells using data from the Multi-Model Ensemble (MME) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Statistical criteria and thresholds are used to project the changes in wet and dry days, as well as wet and dry spells, under the shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the annual, March–May (MAM), June–September (JJAS), and October-December (OND) seasons. The analysis indicates that the CMIP6 models generally overestimate the number of wet days (wet spells) across all seasons and scenarios at the 1 mm threshold. A projected 10–20% increase in total rainfall over the IGAD region is driven by an increase in rainfall intensity and the number of wet days under all scenarios during the near (2021–2050) and far (2071–2100) futures. A decrease in wet days and spells (increase in dry days and spells) is projected over South Sudan in JJAS, while an increase in wet spells is projected over Kenya, Somalia, and Sudan in OND during the near and far future compared to the baseline period. By the end of the century, wet days are projected to increase by approximately 20–30%, and dry spells are projected to decrease by 10–20% under the SSP1-2.6 and SSP5–8.5 scenarios. The EnsMean 10 CMIP6 tends to smooth out the extremes (wet or dry biases), creating a result that may appear more accurate but does not reflect the extremes or variations projected by individual models over South Sudan, Uganda, Kenya and Ethiopia. The projected patterns of wet and dry spells under SSP1-2.6 highlight the importance of mitigation efforts to limit temperature rise to below 1.5°. It is necessary to model how shifts in extreme dry and wet conditions will likely affect climate-sensitive sectors, such as agriculture and food security, in order to make well-informed decisions.

Wet and dry spell induced changes in the soil CO2 effluxes of Pine and Oak ecosystems of Central Himalaya: a comparative assessment for monsoon and winter seasons.

Soil efflux of CO2( ) is known to be dependent on natural drying and rewetting of the soil. Although the central Indian Himalayan region is predominantly occupied with two ecosystems, i. e. Pine (Pinus roxburghii) and Oak (Quercus leucotrichophora), differences in their dynamics and responses of to varying wet and dry spells were hardly known. To address this knowledge gap, this study provides a comparative assessment of variability from Pine and Oakecosystems of central Himalaya as a response to rainfall induced wet and dry spells of monsoon and winter seasons. The data presented in this study are collected for242 days of 2021-22 that include monsoon and winter seasons from a Pine and an Oak sites. The mean sof Pine and Oak sites are found to be 3.95(± 0.02) and 3.61(± 0.01) μmol.m-2.s-1, respectively. We find that the winter reduction in the in comparison to monsoon at the Pine site (78%) is more substantial than at Oak site (64.6%). The cross wavelet spectra of and monsoon rainfall amount at the Oak site, unlike the Pine site, indicate a negative relationship. The rainfall spell duration and amount of monsoon wet spells are noted to have an inverse relationship with at both sites, although, increasing rainfall spell duration in winter is noted to increase at Pine and Oak sites. Similarly, increasing is observed with increasingdry spells of monsoon at both sites. Results of this study indicate that in comparison to Oak, variability at Pine ecosystem is primarily driven by abiotic factors wherein wet spell is a major determinant.

Amplified photosynthetic responses to drought events offset the positive effects of warming on arid desert plants

Ongoing warming will influence plant photosynthesis via thermal effects and by enhancing water deficit. As the primary limiting factor for the growth and development of plants in arid deserts, water may alter the potential warming effects on plant photosynthesis and lead to increased uncertainty in plant dynamics. Here, we used open-top chambers (OTCs) to evaluate the impacts of in situ warming (+0.5 and +1.5 °C) on the photosynthesis and growth of two representative desert plants, Artemisia ordosica and Grubovia dasyphylla, from wet to dry spells. The plant traits associated with photosynthetic diffusive and biochemical processes were also measured to explore the underlying mechanisms involved. We found that warming significantly increased the net photosynthetic rate (Anet) during wet spells under 1.5 °C warming in both plants, while only increased that of A. ordosica under 0.5 °C warming. During dry spells, Anet decreased both in A. ordosica and G. dasyphylla, with the rates of declining being 48 % and 41 %, respectively, higher than control under warming. Consequently, warming significantly amplified photosynthetic responses to drought events, which offset the positive warming effects during wet spells and led to unchanged plant biomass in both species. Besides, alterations in plant traits tended to be associated with positive warming effects during wet spells, and the negative effects of drought were mainly due to stomatal limitation. Our results emphasised that the potential benefits of warming during wet spells may be reversed during drought events. Thus, the adverse effects of ongoing warming on desert productivity may increase during dry spells in growing seasons and during dry years.

Exploring the patterns of dry and wet spells: a case study of Ethekwini District Municipality, KwaZulu Natal, South Africa

Exploring the patterns of dry and wet spells: a case study of Ethekwini District Municipality, KwaZulu Natal, South Africa

Rain Microphysics Associated with Continental, Oceanic, and Orographic Regions of India during Wet and Dry Spells

Rain Microphysics Associated with Continental, Oceanic, and Orographic Regions of India during Wet and Dry Spells

Regional characterization of meteorological and agricultural drought in Baluchistan province, Pakistan.

Drought is a complex natural hazard that occurs when a region experiences a prolonged period of dry conditions, leading to water scarcity and negative impacts on the environment. This study analyzed the recurrence of drought and wet spells in Baluchistan province, Pakistan. Reconnaissance Drought Index (RDI), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI) were used to analyze droughts in Baluchistan during 1986-2021. Statistical analysis i.e. run theory, linear regression, and correlation coefficient were used to quantify the trend and relationship between meteorological (RDI, SPEI) and agricultural (VCI) droughts. The meteorological drought indices (1, 3, 6, and 12-month RDI and SPEI) identified severe to extreme drought spells during 1986, 1988, 1998, 2000-2002, 2004, 2006, 2010, 2018-2019, and 2021 in most meteorological stations (met-stations). The Lasbella met-station experienced the most frequent extreme to severe droughts according to both the 12-month RDI (8.82%) and SPEI (15.38%) indices. The Dalbandin met-station (8.34%) follows closely behind for RDI, while Khuzdar (5.88%) comes in second for the 12-month SPEI. VCI data showed that Baluchistan experienced severe to extreme drought in 2000, 2001, 2006, and 2010. The most severe drought occurred in 2000 and 2001, affecting 69% of the study region. A positive correlation was indicated between meteorological (RDI, SPEI) and agricultural drought index (VCI). The multivariate indices can provide valuable knowledge about drought episodes and preparedness to mitigate drought impacts.

Data assimilation with machine learning for constructing gridded rainfall time series data to assess long-term rainfall changes in the northeastern regions in India

ABSTRACT Data scarcity and unavailability of observed rainfalls in the northeastern states of India limit prediction of extreme hydro-climatological changes. To fill this gap, a data assimilation approach has been applied to re-construct accurate high-resolution gridded (5 km2) daily rainfall data (2001–2020), which include seasonality assessment, statistical evaluation, and bias correction. Random forest (RF) and support vector regression were used to predict rainfall time series, and a comparison between machine learning and data assimilation-based gridded rainfall data was performed. Five gridded rainfall datasets, namely, Indian Monsoon Data Assimilation and Analysis (IMDAA) (12 km2), APHRODITE (25 km2), India Meteorological Department (25 km2), PRINCETON (25 km2), and CHIRPS (25 and 5 km2), have been utilized. For re-constructed rainfall datasets (5 km2), the comparative seasonality and change assessment have been performed with respect to other rainfall datasets. CHIRPS and APHRODITE datasets have shown better similarities with IMDAA. The RF and assimilated rainfall (AR) have superiority based on bias and extremity, and AR data were recognized as the best accurate data (>0.8). Precipitation change analysis (2021–2100) performed utilizing the bias corrected and downscaled CMIP6 datasets showed that the dry spells will be enhanced. Considering the CMIP6 moderate emission scenario, i.e., SSP245, the wet spell will be enhanced in future; however, when considering SSP585 (representing the extreme worst case), the wet spells will be decreased.

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

Elevation dependent precipitation changes in historical and future times in the Himalayan Upper Ganga Basin, India

This study examines long-term (1991–2092) precipitation trends in the Upper Ganga Basin (UGB) across multiple elevations, using historical data (1991–2022) and future Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model datasets (2022–2092). By identifying the top five best-fit models from 29 climate model datasets, this study assesses historical patterns and project future changes in the Near-Future-Term (NFT) (2023–2056) and Far-Future-Term (FFT) (2061–2092) under moderate and extreme scenarios. Based on the evaluation of 29 CMIP6 climate models precipitation products, five models namely: BCC-CSM2-MR, CMCC-CM2-SR5, INM-CM5–0, KIOST-ESM, and NESM3 were selected as the best models describing precipitation over UGB and future precipitation of these models were bias corrected using linear scaling bias correction approach. Also it was observed that in the UGB region usually the climate models are much more reliable in capturing the precipitation pattern in lower and mid elevation regions as compared to higher-elevation regions. Further analysis of best five climate models' future precipitation indicates a substantial increase in wet spell occurrences in the south-western part of UGB, while the north-western and central regions are expected to see a rise in dry spell counts, although dry spell increase is insignificant. The mid-elevation range from 2200 to 4050 m which historically receives the most precipitation and is expected to continue experiencing the highest volume of precipitation in the future. The lower elevation range (287–1500 m) has anticipated the most substantial increase in future precipitation. Analysis suggests a future rise in low precipitation events (2.5 mm/day to 10 mm/day) at high elevations (4050 m to 7399 m) and an increase in events exceeding 10 mm/day in mid to low elevations (287 m to 4050 m). Although an increase in low precipitation events is expected across the entire UGB, it is particularly noteworthy that the region between 287 m to 4050 m is likely to experience a greater prevalence of these events. Historically, high elevations witnessed most of the years with over 20 days of annual snowfall. However, future scenarios indicate a prominent shift, predominantly anticipating <20 days per year of snowfall, signifying a significant change in snowfall patterns within the UGB. An analysis of snowfall patterns within the UGB indicates that historically, in the elevation range of 1500 m to 2200 m, some years have experienced 3–7 days of snowfall per year, however, under the SSP245 (NFT) scenario, this is projected to decrease to 2–5 days per year. Conversely, in scenarios SSP245 (FFT), SSP585 (NFT), and SSP585 (FFT), it is anticipated that there will be no snowfall in this elevation range in the future.

Rising risks of hydroclimatic swings: A large ensemble study of dry and wet spell transitions in North America

Hydroclimatic swings between dry and wet spells are increasing globally, raising concerns due to their severe impacts on society, agriculture, infrastructure systems, and water resource management. These extremes and their projected changes are traditionally assessed in isolation, which can underestimate associated risks and future resilience and adaptation plans. This study investigates climate change projections of such lagged compound dry and wet spells across North America using a single model initial-condition large ensemble (SMILE). Three dry-wet spell indices are merged into an integrated indicator to provide a comprehensive perspective on changing risks. We apply an ensemble pooling approach using the Canadian Regional Climate Model version 4 Large Ensemble (CanRCM4-LE) to enhance sample size for index estimation. Results suggest that hydroclimatic swings across North America are expected to become more frequent and intensified in a warmer climate. Trends of spatial fraction and aggregation during transitions between dry and wet spells indicate future water resource management challenges. Hot spots for intensified transitions with higher frequency, such as Northern Central America, and the southern part of Eastern and Western North America, including Mexico and the state of California, overlap with larger spatial extent and higher aggregation during transitions. The seasonal analysis of spatial characteristics indicates winter may become wetter and summer drier at higher warming levels, potentially intensifying transitions between dry and wet extremes. Clustered hydroclimatic swings may lead to severe environmental, hydrological, and socio-economic consequences, necessitating appropriate mitigation and adaptation measures.

Global Flash Droughts Characteristics: Onset, Duration, and Extent at Watershed Scales

AbstractAddressing impacts of flash droughts (FDs) on the water‐food nexus requires a understanding of FD mechanisms and drivers at the watershed level. Examining climatic drivers, dry and wet spell lengths from 1980 to 2019, we analyzed FD spatial and temporal characteristics, emphasizing areal extent, onset time, and duration. Our findings reveal substantial variations in FDs among different watersheds. Notably, watersheds in the Southern Hemisphere are witnessing expanding, faster‐developing, and longer‐lasting FDs, aligning with climate variations in precipitation and temperature. Additionally, at the watershed scale, the onset and duration of FDs are more influenced by the intensity (magnitude and variability) of climatic drivers than the average length of wet and dry periods. FD‐extents, however, correlate with both climatic conditions and wet and dry periods, underscoring watershed connectivity. Ultimately, our results underscore the necessity for research to comprehend the interplay between FDs and watershed characteristics and how it manifests in overall water resource management.

Dry and Wet Spells in Poland in the Period 1966–2023

The aim of this study is to present the spatial and temporal variability of the frequency of dry and wet days and dry and wet spells against the background of changes in precipitation and atmospheric circulation. The study is based on daily precipitation totals from 46 meteorological stations in Poland from 1966 to 2023. Additionally, seven circulation indices were used, namely GBI, NAO, AO, EA, EA/WR, SCAND, and AMO. Dry days are defined as days without precipitation. Wet days are days with at least 1 mm of precipitation. It was shown that dry spells are much more common than wet spells, are longer, and cover larger areas. Long-term changes in the annual characteristics of dry and wet days and spells are not statistically significant. Only the length of the most extended dry spell in the year increases. However, there are significant changes in their annual cycles. Spring is drier; in summer, precipitation decreases in the south and increases in the north; November and December, symbols of gloomy rainy weather, are increasingly drier; and rainy weather has shifted to January and February. The impact of circulation varies according to the season, with the NAO, AO, SCAND, and GBI indices having the greatest influence.

Rainfall Variability and Crop Planning of Dahod: A Case study from Tribal District in Gujarat, India

India is an agrarian economy mostly depends on monsoon for its crop success. The onset and cessation of monsoon along with unpredictability in weather conditions, reflects the statistics in foodgrain production in this region. Planning of crop requires details on period of dry and wet spell of an area. The present study takes into account Markov chain model for rainfall probability analysis. The mean annual rainfall of Dahod district is 1073 mm with 59 rainy days. Only 41% shows rainfall above normal. 95% of annual rainfall is contributed by South-west monsoon whereas only 4% was contributed by North-east monsoon followed by 0.9% and 0.2% in summer and winter season. Analyzed data (1998-2019) reveals that monsoon start effectively from 25th SMW and cease on 40th SMW. The average monsoon period is of 16 weeks, i.e., 112 days. Initial, conditional and consecutive probability of dry and wet week indicates a shift in weather pattern around 20th SMW, transition of dry to wet weeks. More than 75% of wet probability can be observed from 27th to 35th SMW. Onwards 40th SMW, likelihood of P(D/D) keeps increasing up to 52 SMW. P(W/W) probability more than 75% can be observed from 27th to 32nd SMW. The district’s priority lies in poultry farming and vegetable cultivation along with sustaining soybean and chickpea. The cropping pattern of majority of district falls in maize-maize, maize-chickpea, maize-wheat-green gram, soybean-wheat-green gram and pigeon pea- groundnut, respectively. The area frequently experiences extended dry spells between monsoon. To address these challenges, crop diversification and efficient irrigation practices are prioritized.

An assessment of Indian Summer Monsoon (ISM) characteristics observed in continuous Radiosonding from New Delhi, National Capital Region (NCR-Delhi)

The geographical location of the New Delhi, National Capital Region (NCR) within the Northern Gangetic Plains is influenced by the Indian Summer Monsoon (ISM) and its associated weather. Monsoon weather is observed by the appearance of organised clouds, an outbreak of rainfall, and humid conditions. The current study examines pre-monsoon and monsoon weather conditions, together with intra-monsoonal variations (viz. wet, dry, and withdrawal spells) in wind structure, thermodynamic parameters, precipitation, and the structure of the Monsoon Boundary Layer (MBL). Investigations are also made into the macrophysical aspects of the monsoonal clouds/ weather systems that initiate and maintain ISM characteristics over the region. Moisture availability in the boundary layer carried by the low-level monsoon winds has a substantial role in forming monsoonal cloud mechanisms, such as shifting down the Lifting Condensation Level (LCL) closer to the Level of Free Convection (LFC) as an effect of continuous moisture supply from the lower levels and rising of Level of Neutral Buoyancy (LNB) near the tropopause region during the monsoonal period. Further influenced by the moisture in the boundary layer, the Precipitable Water (PW) is also reliant on the LCL height. Additionally, it is observed that the short-term zonal disturbances induced by extra-tropical and sub-tropical flows impacts intra-monsoonal weakening and variations. Analysis from several of the convective parameters and indices revealed that deep convective systems might occur throughout the monsoon season, while severe thunderstorms could develop during the pre-monsoon, and thunderstorms are less likely to happen during the withdrawal spell. The data presented here are the first to use continuous Radiosondes observations for quantitative assessments of the nature of the ISM over northern India.

Applying different methods to model dry and wet spells at daily scale in a large range of rainfall regimes across Europe

Abstract. The modeling of the occurrence of a rainfall dry spell and wet spell (ds and ws, respectively) can be jointly conveyed using interarrival times (its). While the modeling has the advantage of requiring a single fitting for the description of all rainfall time characteristics (including wet and dry chains, an extension of the concept of spells), the assumption of the independence and identical distribution of the renewal times it implicitly imposes a memoryless property on the derived ws, which may not be true in some cases. In this study, two different methods for the modeling of rainfall time characteristics at the station scale have been applied: (i) a direct method (DM) that fits the discrete Lerch distribution to it records and that then derives ws and ds (as well as the corresponding chains) from the it distribution and (ii) an indirect method (IM) that fits the Lerch distribution to the ws and ds records separately, relaxing the assumptions of the renewal process. The results of this application over six stations in Europe, characterized by a wide range of rainfall regimes, highlight how the geometric distribution does not always reasonably reproduce the ws frequencies, even when its are modeled well by the Lerch distribution. Improved performances are obtained with the IM thanks to the relaxation of the assumption of the independence and identical distribution of the renewal times. A further improvement of the fittings is obtained when the datasets are separated into two periods, suggesting that the inferences may benefit from accounting for the local seasonality.

Subseasonal Variability of Humid Heat During the South Asian Summer Monsoon

AbstractThe South Asian summer monsoon strongly modulates regional temperature and humidity. While extreme dry heat peaks in the pre‐monsoon season, recent literature suggests that extreme humid heat can continue to build throughout the monsoon season. Here we explore the influence of monsoon onset and subseasonal precipitation variability on the occurrence of extreme wet bulb temperatures (Tw) across South Asia. We find that extreme Tw events often occur on rainy days during the monsoon season. However, the influence of precipitation on Tw varies with the background climatology of surface specific humidity. In climatologically drier areas, positive Tw anomalies tend to occur when precipitation increases due to either early onset or wet spells during the monsoon. In contrast, in climatologically humid areas, positive Tw anomalies occur during periods of suppressed precipitation, including both delayed onset and dry spells during the monsoon.

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies.

Response of lacustrine glacier dynamics to atmospheric forcing in the Cordillera Darwin

Abstract Calving glaciers respond quickly to atmospheric variability through ice dynamic adjustment. Particularly, single weather extremes may cause changes in ice-flow velocity and terminus position. Occasionally, this can lead to substantial event-driven mass loss at the ice front. We examine changes in terminus position, ice-flow velocity, and calving flux at the grounded lacustrine Schiaparelli Glacier in the Cordillera Darwin using geo-referenced time-lapse camera images and remote sensing data (Sentinel-1) from 2015 to 2022. Lake-level records, lake discharge measurements, and a coupled energy and mass balance model provide insight into the subglacial water discharge. We use downscaled reanalysis data (ERA5) to identify climate extremes and track land-falling atmospheric rivers to investigate the ice-dynamic response on possible atmospheric drivers. Meltwater controls seasonal variations in ice-flow velocity, with an efficient subglacial drainage system developing during the warm season and propagating up-glacier. Calving accounts for 4.2% of the ice loss. Throughout the year, warm spells, wet spells, and landfalling atmospheric rivers promote calving. The progressive thinning of the ice destabilizes the terminus position, highlighting the positive feedback between glacier thinning, near-terminus ice-flow acceleration, and calving flux.