Climate change and effectiveness of dams in flood mitigation in India

Climate change and effectiveness of dams in flood mitigation in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Climate change and effectiveness of dams in flood mitigation in India Urmin Vegad, Vimal Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6258710/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract India is the third largest dam-building nation in the world. Dams are critical in irrigation, hydropower generation, and flood control. Observational evidence suggests that dams mitigated and caused floods in several river basins in India. However, the effectiveness of dams in flood mitigation and associated driving factors remain unexplored under the current and projected future climate. Using in-situ and satellite observations and simulations from hydrological and hydrodynamic models for 178 major dams, we show that major dams' effectiveness in flood mitigation in India depends more on the antecedent reservoir storage than precipitation variability in the river basins upstream of dams. Most floods in dams' downstream regions occur when reservoirs exceed 90% of their full capacity. Dams are more effective in mitigating floods in July than in the later part of the summer monsoon season. The number of days with reservoir storage over 90% of total capacity is projected to rise considerably under the warming climate, limiting dams' effectiveness in mitigating floods. The duration with reservoir storage exceeding 90% of the total capacity is projected to rise from ~ 6 days at 1°C to 23 at 3°C global warming levels, highlighting the need for climate change mitigation. The substantial rise in compound extremes of high inflow and high antecedent storage in the future under the warming climate will limit the effectiveness of dams in mitigating floods in India. Our findings highlight the need for more advanced approaches for dam operations integrated with early warning of extreme inflow and reservoir storage in India. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Hydrology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction India is the third-largest dam-building country, with over 6,000 completed large dams serving multiple purposes including irrigation, flood control, hydroelectric power generation, drinking water supply, and fish production 1 , 2 . The number of large dams in India has increased significantly from 1,106 in 1970 to over 6,000 in the last 50 years. Notably, more than 2,500 large dams were built in just two decades during 1971–1990 (NRLD, 2023). This surge in dam construction was driven by the increasing irrigation water demands and hydropower production. The primary role of dams in India is to provide water for irrigation, generate hydropower, and flood control. Currently, there are 150 major irrigation projects with 16.33 million hectares of culturable command area 3 . Dam construction has contributed substantially to hydropower production, which accounts for the second-largest share (13%) of the total energy produced in India 4 . The role of dams for irrigation water supply and hydropower generation has been well acknowledged, however, their effectiveness for flood control remains debatable 5 . Dams are one of the several stressors affecting river flow during different seasons 6 . For instance, dams store water during the water surplus season and release water during the dry season 7 . Therefore, dams can considerably influence streamflow variability and are among the most effective measures to mitigate the impacts of droughts 8 . In addition, dams control floods in downstream regions by storing the high inflow. Water release from the dams in a controlled manner can help in reducing the downstream peak flow and mitigate the flood risk 9 , 10 by lowering the flood peak and delaying the time to peak 11 , 12 . Thus, dams can considerably reduce the flood exposure and flood risk in the downstream regions 13 . The increased inflow variability under the warming climate makes dam operations and decision-making more complex and challenging. Palmer et al. reported that basins impacted by dams are more likely to require management interventions such as sediment management, dam retrofitting or removal, floodplain restoration, or ecosystem restoration to mitigate the climate change impacts compared to free-flowing river basins 14 . Poor decision-making under critical conditions can exacerbate flood events 15 . Several regions in India faced dam-induced floods in the recent past 16 . The dam-induced floods are more sudden and intense, leaving a more destructive impact on lives and properties 17 . Despite the mitigation measures such as constructing flood control infrastructures, implementing land use regulations, and enhancing early warning systems the losses that occurred from floods have increased during the past few decades 18 . Examining the impacts of streamflow variability on dam storage is crucial for making informed decisions during floods. While dams play a crucial role in regulating river flows and mitigating downstream flooding, their effectiveness is increasingly being challenged by climate change-induced alterations in hydrology 19 . The vast network of large dams in India holds great potential to control and alter the flow in downstream. However, there remains a critical gap in understanding how the warming climate will impact dam storage dynamics and the effectiveness of dams to attenuate extreme inflows. The major scientific questions we address are: 1) To what extent do dams control high flow conditions during the summer monsoon season in India? and 2) What role do catchment precipitation and antecedent dam storage play in reducing high flow in the downstream regions? 3) How will the dam storage dynamics and flood risk change under the projected future climate? To address these questions, we use the H08 20 land-surface model combined with the CaMa-Flood 21 hydrodynamic model to simulate the historical and future dam dynamics in India and its role on floods in downstream regions. Results Influence of dams on floods in India First, we examined the role of dams in flooding in the downstream regions based on the observational datasets. Dams played a crucial role in either mitigating or intensifying downstream flooding (Fig. 1). For instance, the Tehri Dam on the Ganga River reduced flooding in the downstream in 2010, 2011, and 2013 22,23 . Several large dams, including Nagarjuna Sagar, Krishna Raja Sagar, and Ukai played a major role in reducing the flood risk in the downstream regions 24–26 . During the August 2019 flood in the Sutlej River, the Bhakra Dam attenuated 82.33% of the inflow, while the Pong Dam maintained zero outflow throughout the event 27 . However, during the August 2023 floods in Punjab, sudden and massive water releases from the Bhakra and Pong dams, both of which had already exceeded their full reservoir levels, contributed to the flooding in the downstream regions 28 . In addition to large releases, high reservoir levels prior to extreme high inflow conditions can exacerbate downstream flooding. For example, during the September 2023, Sardar Sarovar Dam's storage level remained close to the full reservoir level (FRL) despite forecasts of high inflows and extreme rainfall in the upstream catchment 29 . This resulted in a high outflow from the dam, leading to flooding in Bharuch city. Similarly, in 2015, a high discharge from the Chembarambakkam Dam into the Adyar River led to severe flooding in Chennai. In contrast, in 2023, proactive dam management significantly reduced storage levels ahead of widespread rainfall, effectively preventing a similar disaster 30 [Fig. 1]. Next, we use the model simulations to examine the influence of dams on the frequency of floods (>99 th percentile streamflow) in their downstream regions under the NAT and DAM scenarios. We estimated the flood frequency under the NAT and DAM scenarios in the 30-year (1991-2020) period (Fig. 2a, b). In the NAT scenario, the flood frequency is notably higher for the dams located in central and southern India than in northern India (Fig. 2a). More than 60% of the dams across India showed flood frequency between 20 and 40, with median frequency of 35 during the 30-year period (Fig. 2d). However, in the DAM scenario, dams effectively reduce the flood frequency compared to the NAT scenario (Fig. 2a, b). For instance, the flood frequency is less than 30 for more than 80% of the dams across India, with a median of 13 floods/30-year under the DAM scenario (Fig. 2e). The effectiveness of dams to capture high inflows and reduce downstream flooding depends on the available reservoir storage capacity of the dams 31 . Thus, we analysed the mean dam storage one day prior to floods in the DAM scenario to examine the antecedent reservoir storage (Fig. 2c). High antecedent reservoir storage (median 94%) [Fig. 2f] prior to floods suggests that when floods occur downstream of dams, most reservoirs are already near their full capacity, limiting their ability to mitigate floods. The difference in the flood frequency under the NAT and DAM scenarios highlights that dam considerably attenuate floods in the downstream region. However, the high antecedent reservoir storage levels before floods remain the key driver in limiting the dam’s ability in mitigating floods in the downstream regions. Floods in India predominantly occur during the summer monsoon season 32 . Therefore, we examined the changes in the high flow magnitude (exceeding the 99 th percentile) downstream of the dams under the DAM and NAT scenarios during the summer monsoon (Fig. S3a). We find that all the dams are effective in reducing high flows during the summer monsoon. Dams with lesser reservoir storage capacity (1000 MCM) can reduce the magnitude of high flows by more than 47% (median change). Further, we examined the intraseasonal variability in high-flow attenuation within the monsoon months (June-September) [Fig. S4b-e]. We hypothesize that the influence of dams in modulating high flow is higher during the early monsoon season (June and July) due to lower initial dam storage levels. Our results show that the highest reduction in high-flow magnitude occurs in July (Fig. S3c), aligning with the period when dams begin to reach higher storage levels. The high flow attenuation persists through August and September (Fig. S3d, e) but with relatively lower reductions compared to July. In June, only a few dams contribute to high-flow attenuation (Fig. S3b), whereas from July onwards, nearly all dams play a role in regulating downstream flows. We also estimated changes in the flows using three different thresholds (exceeding 90th, 95th and 99.9th percentiles) during the summer monsoon season and for monsoon months individually (Fig. S4). We find that majority of dams do not experience high flows exceeding the 99.9th percentile in June (Fig. S4l), as major floods in India occur mostly in August and September 33 . June being the onset month of the Indian summer monsoon, the frequency and intensity of extreme precipitation is relatively lesser 34 . In addition, the absence of wet antecedent conditions does not create favorable conditions for riverine floods in June 35 . Lowering the threshold for high-flow conditions (from the 99.9th to the 90th percentile) increased the sample size of high-flow events, even during the June–July period (Fig. S4). As a result, several dams attenuated high flows exceeding the 90th and 95th percentiles in June, while their influence reduced during the August-September. This suggests that low antecedent dam storage leads to greater modulation of high-flow events in the early monsoon season. Moreover, large dams are more effective than small dams in reducing high-flow peaks during the onset period (Fig. S3b). Linkage of catchment rainfall and antecedent reservoir storage with dam-induced changes We examined the relationship of changes in high flows with annual precipitation in the upstream catchment and antecedent dam storage for all the dams to understand how upstream hydrology influences downstream flow regulation (Fig. 3a, b). From the relationship between change (%) in the high flow and antecedent dam storage (Fig. S6) for four dams (Rengali, Mettur, Konar and Sriram Sagar) located in different climatic and geographical settings, we find that the median decrease in the high flows due to dams was less in the years when higher rainfall in the upstream catchment was recorded. In addition, we note a considerable role of the antecedent reservoir storage on the downstream flow (Fig. S5). High flows showed minimal change (< 10%) in magnitude when dam storage was above 90% of total capacity, with most flow reductions over 10% occurring in July and August, as dams typically reached full capacity by August or September (Fig. S5c, d). Therefore, the relationship (based on correlation coefficient) was estimated only for instances where a decrease in flow greater than 10% was observed. We observed a high correlation between the change in high flows with antecedent dam storage than annual precipitation in the upstream catchment for all the dams (Fig. 3). The median correlation coefficient for all the dams exceeded 0.4 when correlated with both antecedent reservoir storage and annual upstream precipitation. However, dams located in arid and temperate climate zones exhibited a stronger relationship with antecedent reservoir storage, with a median correlation exceeding 0.9. In contrast, correlation between annual upstream precipitation and the median decrease in high flows was observed more than 0.5 for 57% of dams. Overall, our results highlight that the antecedent reservoir storage plays a more important role than precipitation variability in the catchments upstream of the dams. Therefore, maintaining the buffer reservoir storage that can accommodate high inflow is critical for the mitigation of flooding in the downstream regions Influence of dams in reducing future food risk We estimated the projected change in the number of days when dams remain above 90% of their live storage capacity under different global warming scenarios (1.0°C to 3.0°C) compared to the historical period (1995–2014) [Fig. 4]. We selected the 90% storage threshold based on the observation that dams lead to minimal change in the high flow in the downstream regions highlighting their ineffectiveness in mitigating floods. An increase in the number of days when dams remain near their full capacity is projected in the future at all the warming levels. Moreover, the duration in which dams can be ineffective increases with the increase in the global warming level. The distribution shows a systematic increase in the median number of days, from 6.67 days at 1.0°C warming to 23.26 days at 3.0°C warming (Fig. 4f). This trend suggests that climate change-induced shifts in hydrology will likely lead to more prolonged high-storage conditions in dams, increasing the flood risk in downstream areas. Next, we estimated the changes in the projected frequency of the compound events of high inflow (exceeding 99 th percentile) and nearly full (>90%) dam storage (Fig. 5). The frequency of the compound events is projected to rise across many dams under the warming climate. For instance, the median frequency across India remains relatively low at 0.24 per year at 1.5°C global warming level but projected to increase by threefold to 0.61 per year at 3.0°C warming. This suggests that under the higher global warming levels, dams will likely experience more frequent compound events of high inflow and high antecedent storage. These findings highlight a pressing need for adaptive dam management strategies to accommodate extreme inflows and mitigate downstream flooding. Discussion Globally, India is among the largely affected areas under the impact of dams 36 . The country's extensive network of dams plays a crucial role in water and flood management 37 . Several studies have examined the role of dams on the downstream hydrological regime on a regional or global scale 13 , 38 . We investigated the influence of dams in India on downstream river discharge, analyzing the changes induced in floods causing high flows. The combined modelling framework based on the H08-CaMa Flood model was used to estimate the dam-induced hydrological changes downstream. The hydrological modelling framework performed well against the observed flow and dam storage. The role of 178 major dams was considered in simulating the dam dynamics for historical and future climate. Dams potentially reduce the high flows during the onset of the summer monsoon period owing to the dam filling, and the attenuation capacity decreases as the monsoon progresses. The storage capacity of a dam before extreme weather events plays a critical role in determining its ability to mitigate flood risks 39 . The reduction of downstream high flows by dams shows a strong linkage with antecedent dam storage and a moderate to strong correlation between peak flow reduction and rainfall in the upstream catchment. For instance, Zhou reported a strong association between the decrease in flood risk and the flood control capacity of dams 40 . Under the warming climate, downstream regions of dams are projected to experience increased flooding attributed to prolonged high storage levels and increased high flow frequency. Dams are projected to experience at high storage levels for a prolonged period. Non-availability of adequate buffer storage in reservoirs during the summer monsoon season results in an increased risk of flooding in the downstream 41 . For instance, in the year 2024 monsoon, Krishna Raja Sagar dam on Cauvery River remained full for a record 172 days 42 . Furthermore, the dams are projected to receive highly variable and extreme inflows attributed to the precipitation changes. The projected increase in the dam inflow magnitude and variability have been reported for different dams across the world 43 – 45 . The combination of extreme inflow and high storage level conditions is likely to result in more intense downstream flooding 46 . We find a significant increase in the flood frequency under warming climate in the dam downstream when dams are ineffective in reducing the magnitude attributed to the full antecedent dam storage conditions. For instance, Lee and You estimated the risk of dam overtopping under climate change for Shihmen Reservoir in Taiwan and found an increasing trend 47 . Projected increase inflow can further reduce the storage capacity due to sedimentation, which not only reduces the life span of the dam but potentially increases the overtopping risk 48 . With decreasing storage capacity owing to sedimentation, India is one of the most vulnerable countries 49 . Lempérière reported that climate change can significantly increase the need for flood mitigation in many countries, necessitating the repurposing of existing or new dams for flood control 50 . In recent years, the operational flexibility of dams has demonstrated positive impacts under extreme climate scenarios 51 . In an agriculture-dominant country like India, dams primarily designed for hydropower or irrigation can also be utilised for efficient flood mitigation. Furthermore, dam inflow forecast plays a major role in maintaining a safe storage level during the summer monsoon period to prevent flooding downstream and meeting the required demand during the dry period 52 . Effective reservoir management, incorporating advanced inflow forecasting and adaptive operational strategies, is crucial to mitigating downstream flood risks under a warming climate, especially in highly vulnerable regions like India. Methods Data We used gridded precipitation and temperature observations from the India Meteorological Department (IMD) for the period 1901–2020. The precipitation data is compiled from daily records collected from 6955 rain gauge stations in India. The gridded precipitation captures the spatial variability of rainfall across India including the orographic precipitation dominated regions of the western Ghats and foothills of the Himalayas 53 . Gridded daily precipitation from IMD has been widely used for hydrological modelling applications in India 54 – 56 . Gridded maximum and minimum temperatures data from 395 stations are developed at 1 o resolution grids using a modified version of Shepard’s angular distance weighting algorithm 57 . We regridded the 1° gridded temperature data to 0.25° using bilinear interpolation, accounting for the lapse rate, to ensure consistency with the gridded precipitation data. IMD's observational network only covers the Indian region, therefore, meteorological data for the region outside India were obtained from Princeton University 58 . Gridded datasets from Sheffield et al. show a good agreement with IMD observations and have been used in hydrological applications in India 54 , 55 , 58 . The additional meteorological forcing input variables (shortwave and longwave radiation, surface pressure and specific humidity) for the H08 land-surface model were obtained from Kushwaha et al 59 . We obtained observed daily streamflow and dam storage from India Water Resources Information System (IWRIS). In addition, we used the monthly dam storage data from the Global Reservoir Storage (GRS) dataset 60 . The GRS dataset is prepared using multi-source satellite data to estimate storage variations of 7245 global reservoirs from 1999 to 2018. The information regarding other salient features of dams was obtained from the National Register of Large Dams (NRLD). We used daily precipitation, maximum and minimum temperatures from five general circulation models (GCMs; BCC-CSM2-MR, INM-CM5-0, MIROC6, NorESM2-MM, and TaiESM1) that were part of the Coupled Model Intercomparison Project (CMIP)-6 to examine the changes in the inflow and dam storage under the projected future climate. The selected GCMs effectively reproduce the observed seasonal variations in temperature and precipitation across the Indian monsoon region 4 , 61 . We used three scenarios: historical climate (1951–2014), and two shared socioeconomic pathways consisting of a low-emission scenario (SSP1-2.6; 2015–2100) and a high-emission scenario (SSP5-8.5) considering radiative forcing of 2.6 and 8.5 W/m 2 , respectively by the end of 21st century. We bias-corrected the CMIP6 projections for the five GCMs using the method developed by Hempel et al., ensuring the removal of bias while preserving the long-term climate signal 62 . The bias-correction was performed against the observed precipitation and temperature datasets. Hydrological and hydrodynamic models We used the H08 land surface model 20 , 63 and the CaMa-Flood hydrodynamic model 21 to simulate streamflow and dam storage. The H08 model uses daily precipitation, air temperature, short and longwave radiations, wind speed, surface pressure, and specific humidity as input meteorological forcing to calculate runoff based on saturation excess non-linear flow 63 . Soil parameters for the H08 model were obtained from the Harmonized World Soil Database (HWSD). Additionally, albedo and soil type datasets are taken from the Global Soil Wetness Project (GSWP) 64 . We ran the H08 model at 0.25° spatial and daily temporal resolution from 1901 to 2020. We considered five years as the model’s spin up period and the final run for the 1901–2020 was started after the model spin up was over. The runoff generated from the H08 model at 0.25° is used in the CaMa-Flood model to rout streamflow at 0.05° spatial resolution. The CaMa-Flood is a hydrodynamic model, which simulates the river-floodplain dynamics 21 , 65 . Globally, the CaMa-Flood model has been extensively used for simulating streamflow 66 , 67 . The CaMa-Flood simulates river floodplain dynamics by solving shallow water equations of open channel flow while explicitly accounting for backwater effects using the local inertial approximation. The model offers a reservoir operation scheme, which has been used in many global and regional studies 38 , 56 , 68 – 70 . The CaMa-Flood model has been previously used to examine the hydrological changes caused by dams in various basins 71 , 72 . The dam module of the CaMa-Flood has also been extensively used for several other purposes such as flood dynamics downstream of dams and analysing long-term hydrological changes 73 – 75 . We incorporated the influence of 178 major dams (> 3 MCM storage) in India within the CaMa-Flood model based on the availability of storage observations. The model simulations were performed for the two scenarios: natural conditions (NAT) without considering the role of dams on streamflow and with considering the role of dams (DAM) on streamflow. We manually calibrated the H08 model by adjusting the four parameters (single-layer soil depth, gamma, bulk transfer coefficient, and tau) against the daily streamflow observations at gauge stations 63 , 76 , 77 [Fig. S1 ]. We considered 60 gauge stations across 18 river basins to calibrate and evaluate the model. Due to the unavailability of daily observed streamflow data for the three transboundary river basins (Indus, Ganga, and Brahmaputra), we used the observed monthly streamflow for calibration. We used the coefficient of determination (R 2 ), Nash-Sutcliffe efficiency (NSE) 78 and Kling-Gupta Efficiency (KGE) 79 to determine the performance of the model. The model exhibited satisfactory skills (R 2 > 0.6, NSE > 0.5 and KGE > 0.5) for most of gauge stations (Fig. S1 a-c). In addition, we evaluated the model performance in simulating annual maximum flow at different locations using bias in flood peaks and timing error (Fig. S1 d,e). However, we did not perform this analysis for the transboundary rivers (Ganga, Brahmaputra, and Indus) due to the unavailability of daily streamflow observations. While most gauge stations exhibited moderate bias, a few stations in the Cauvery, Sabarmati, Krishna, and Mahi River basins showed a notable dry bias in the simulated mean annual maximum flow. The poor performance of the model at a few gauge stations can be attributed to the limited availability of long-term observations and the considerable influence of human interventions including irrigation, dam storage, and water diversions. Additionally, daily flow observations can be affected by measurement errors, changes in gauge location, and inconsistencies in data recording practices 80 . The mean timing error between the simulated and observed peak streamflow is less than two days at more than 80% of the gauge stations and less than three days for all the stations. Timing errors in the simulated peak flow can largely be attributed to errors in model structure, parameters, and initial conditions 81 , which can be improved through data assimilation 82 . We examined the performance of the models to simulate reservoir storage. Over 70 dams exhibited NSE above 0.5, while more than 95 dams showed KGE above 0.5 in simulating live reservoir storage (Fig. S2a, c). The dams calibrated against the observations from IWRIS exhibited better skills than those calibrated against the GRS dataset (Fig. S2b, d). Li et al. reported a large bias in the GRS dataset for a few Indian dams, which can be attributed to uncertainties such as reservoir sedimentation, reservoir geometry approximation, input data inconsistencies, water area representation, and overestimation 60 . The reservoir operation scheme may vary from one dam to another based on several factors, such as purpose, water demand, and dam location. These operation rules are altered based on the variations in inflow patterns and it highly influences the release from the dams 83 . This study used the generic dam operation scheme available within the CaMa-Flood dam module, as the actual dam operation scheme is not available. Effect of dams and its linkage with catchment precipitation and antecedent dam storage We examined the influence of dams on high flows in the downstream regions by estimating the changes in the frequency of high-flow events considering the influence of dams. To do this, we calculated the frequency of high flows exceeding the 99th percentile threshold over a 30-year period (1991–2020) under the NAT and DAM scenarios. The 99th percentile threshold was determined based on the long-term (1901–2020) streamflow time series under the NAT scenario at each location. Furthermore, we calculated the mean antecedent dam storage one day before a high-flow event in the downstream of a dam under the DAM scenario to assess the ability of dams to reduce the number of high-flow events and to determine the antecedent storage conditions when dams fail to mitigate high flows. In addition, we analysed the changes in the magnitude of high flows under the DAM scenario compared to NAT scenario. For each dam, we estimated the change in downstream and reduction in the number of high-flow events. Additionally, we calculated the changes in high flow magnitudes exceeding 90, 95 and 99.9th percentile thresholds. We accounted for different thresholds to examine how dams influence high-flow events of varying intensity. We also examined the linkage of dam-induced changes in river flows with the precipitation in the upstream catchment and antecedent dam storage. We examined the projected change in duration (number of days) during which dams remain full under different global warming levels. In addition, we estimated the change in the frequency of high inflow events (> 99th percentile) occurring when dam storage exceeds 90% of their capacity, which indicates the number of instances when dams may become ineffective in mitigating downstream floods under different global warming levels. To simulate future dam dynamics under changing climate conditions, we use bias-corrected climate projections from the GCMs to run the H08 and CaMa-Flood models. Declarations Data availability Datasets used in this study will be made available through a public data repository. Code availability The codes that support the findings of this study are available from the author on request. Acknowledgement The authors acknowledge the data availability from the India Meteorological Department (IMD), India-WRIS and CMIP6. 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The News Minute https://www.thenewsminute.com/tamil-nadu/flood-management-at-chembarambakkam-an-important-lesson-learnt-from-2015-deluge (2023). Richter, B. D. & Thomas, G. A. Restoring Environmental Flows by Modifying Dam Operations. Ecol. Soc. 12, (2007). Nanditha, J. S. & Mishra, V. On the need of ensemble flood forecast in India. Water Secur. 12, 100086 (2021). Nanditha, J. S. & Mishra, V. Multiday precipitation is a prominent driver of floods in Indian River Basins. Water Resour. Res. 58, e2022WR032723 (2022). Pattanaik, D. R. & Rajeevan, M. Variability of extreme rainfall events over India during southwest monsoon season. Meteorol. Appl. 17, 88–104 (2010). Nanditha, J. S. & Mishra, V. Wet Antecedent Conditions and High Baseflow Trigger Widespread Floods in Indian Sub-continental River Basins. Authorea Prepr. (2023). Döll, P. & Fiedler, K. Global-scale modeling of groundwater recharge. Hydrol. Earth Syst. Sci. 12, 863–885 (2008). Narayan, P. & Bueno, I. E. 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N., Escuder-Bueno, I. & Basri, H.) 537–550 (Springer Nature, Singapore, 2023). doi: 10.1007/978-981-99-3708-0_36 . Wisser, D., Frolking, S., Hagen, S. & Bierkens, M. F. P. Beyond peak reservoir storage? A global estimate of declining water storage capacity in large reservoirs. Water Resour. Res. 49, 5732–5739 (2013). Lempérière, F. Dams and Floods. Engineering 3, 144–149 (2017). Watts, R. J., Richter, B. D., Opperman, J. J. & Bowmer, K. H. Dam reoperation in an era of climate change. Mar. Freshw. Res. 62, 321–327 (2011). Sankarasubramanian, A., Lall, U., Devineni, N. & Espinueva, S. The Role of Monthly Updated Climate Forecasts in Improving Intraseasonal Water Allocation. J. Appl. Meteorol. Climatol. 48, 1464–1482 (2009). Pai, D. et al. 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 (2014). Chuphal, D. S. & Mishra, V. Reconstructed streamflow for Indian sub-continental river basins, 1951–2021. Zenodo (2023) doi: 10.5281/ZENODO.8004633 . Shah, H. L. & Mishra, V. Uncertainty and bias in satellite-based precipitation estimates over Indian subcontinental basins: Implications for real-time streamflow simulation and flood prediction. J. Hydrometeorol. 17, 615–636 (2016). Vegad, U., Pokhrel, Y. & Mishra, V. Flood risk assessment for Indian sub-continental river basins. Hydrol. Earth Syst. Sci. 28, 1107–1126 (2024). Srivastava, A. K., Rajeevan, M. & Kshirsagar, S. R. Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmospheric Sci. Lett. 10, 249–254 (2009). Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006). Kushwaha, A. P. et al. Multimodel assessment of water budget in Indian sub-continental river basins. J. Hydrol. 603, (2021). Li, Y., Zhao, G., Allen, G. H. & Gao, H. Diminishing storage returns of reservoir construction. Nat. Commun. 14, 3203 (2023). Aadhar, S. & Mishra, V. On the Projected Decline in Droughts Over South Asia in CMIP6 Multimodel Ensemble. J. Geophys. Res. Atmospheres 125, (2020). Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction – the ISI-MIP approach. Earth Syst. Dyn. 4, 219–236 (2013). Hanasaki, N. et al. An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing. Hydrol Earth Syst Sci 12, 1007–1025 (2008). Dirmeyer, P. A. et al. GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface. Bull. Am. Meteorol. Soc. 87, 1381–1398 (2006). Yamazaki, D., Almeida, G. A. M. D. & Bates, P. D. Improving computational efficiency in global river models by implementing the local inertial flow equation and a vector-based river network map. Water Resour. Res. 49, 7221–7235 (2013). Koirala, S., Hirabayashi, Y., Mahendran, R. & Kanae, S. Global assessment of agreement among streamflow projections using CMIP5 model outputs. Environ. Res. Lett. 9, 064017 (2014). Zhao, F. et al. The critical role of the routing scheme in simulating peak river discharge in global hydrological models. Environ. Res. Lett. 12, (2017). Hanazaki, R., Yamazaki, D. & Yoshimura, K. Development of a Reservoir Flood Control Scheme for Global Flood Models. J. Adv. Model. Earth Syst. 14, e2021MS002944 (2022). Mateo, C. M. et al. Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models. Water Resour. Res. 50, 7245–7266 (2014). Singh, R. & Mishra, V. Atmospheric and Land Drivers of Streamflow Flash Droughts in India. J. Geophys. Res. Atmospheres 129, (2024). Dang, H. et al. Hydrologic balance and inundation dynamics of Southeast Asia’s largest inland lake altered by hydropower dams in the Mekong River basin. Sci. Total Environ. 831, 154833 (2022). Shin, S. Investigating the Impact of Manmade Reservoirs on Large-Scale Hydrology and Water Resources Using High-Resolution Modeling . (Michigan State University, 2019). Huang, S. & Hattermann, F. F. Coupling a global hydrodynamic algorithm and a regional hydrological model for large-scale flood inundation simulations. in Hydrology Research vol. 49 438–449 (Nordic Association for Hydrology, 2018). Marthews, T. R. et al. Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations. Hydrol. Earth Syst. Sci. 26, 3151–3175 (2022). Stelly, J. et al. Reconstruction of long-term hydrologic change and typhoon-induced flood events over the entire island of Taiwan. J. Hydrol. Reg. Stud. 53, 101806 (2024). Dangar, S. & Mishra, V. Natural and anthropogenic drivers of the lost groundwater from the Ganga River basin. Environ. Res. Lett. 16, 114009 (2021). Raghav, P. & Eldho, T. I. Investigations on the hydrological impacts of climate change on a river basin using macroscale model H08. J. Earth Syst. Sci. 132, 87 (2023). Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10, 282–290 (1970). Gupta, H. V., Kling, H., Yilmaz, K. K. & Martinez, G. F. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol. 377, 80–91 (2009). Walker, D., Forsythe, N., Parkin, G. & Gowing, J. Filling the observational void: Scientific value and quantitative validation of hydrometeorological data from a community-based monitoring programme. J. Hydrol. 538, 713–725 (2016). Ajami, N. K., Hornberger, G. M. & Sunding, D. L. Sustainable water resource management under hydrological uncertainty. Water Resour. Res. 44, (2008). Prakash, V. & Mishra, V. Soil Moisture and Streamflow Data Assimilation for Streamflow Prediction in the Narmada River Basin. J. Hydrometeorol. 24, 1377–1392 (2023). Feng, M. et al. Identifying changing patterns of reservoir operating rules under various inflow alteration scenarios. Adv. Water Resour. 104, 23–36 (2017). Additional Declarations No competing interests reported. Supplementary Files SIDamPaper.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Editor assigned by journal 20 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 19 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6258710","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433688652,"identity":"5ce28908-f60b-4312-bad6-838c07694606","order_by":0,"name":"Urmin Vegad","email":"","orcid":"","institution":"Indian Institute of Technology (IIT)","correspondingAuthor":false,"prefix":"","firstName":"Urmin","middleName":"","lastName":"Vegad","suffix":""},{"id":433688653,"identity":"b76c1314-c54f-4046-87ef-6ddcb1db25d1","order_by":1,"name":"Vimal Mishra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYFACxgZmICnHxgwTYMajGFmLMRszTClhLRA1iQ1EKQUBg2uHmz8X7rFL72PnP/y6gMFOnoGd9wB+LbcT26RnPEvObWNmZrOewZBs2MDMl4BXi+TsxDZmngPMYC3GPAzMCQzMPAaEtDR/5jlQn84G0VJPWAu/dGKDNM+BwwlALcyPeRgOE6WlDajluCHQYWZAxSAGAS1s0umPgQ6rlpfvPwhkVFTL8/Ofwa8FRbsEA1AxG9HqgYD5AymqR8EoGAWjYOQAANzbMkkk0B2WAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Technology (IIT)","correspondingAuthor":true,"prefix":"","firstName":"Vimal","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2025-03-19 06:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6258710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6258710/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79301210,"identity":"d59c33e5-5380-42ab-bb08-1902c2766075","added_by":"auto","created_at":"2025-03-26 18:54:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":532040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReported Cases Highlighting the Dual Role of Dams in Flood Events.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMap illustrating the reported cases where dams played a major role in either mitigating or causing floods\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6258710/v1/348c862eb4598e0632613782.png"},{"id":79300475,"identity":"13db47dc-1c48-4c7d-99f5-59348a2f5acc","added_by":"auto","created_at":"2025-03-26 18:38:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1121424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in Flood Frequency and Antecedent Dam Storage.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Flood frequency in the downstream of the dams under the NAT scenario for the 30-year period (1991-2020). \u003cstrong\u003eB\u003c/strong\u003e Flood frequency in the downstream of the dams under the DAM scenario for the 30-year period (1991-2020). \u003cstrong\u003eC\u003c/strong\u003eCorresponding antecedent dam storage one day before flood events in the DAM scenario. \u003cstrong\u003eD \u003c/strong\u003eDistribution of flood frequency under the NAT scenario. \u003cstrong\u003eE \u003c/strong\u003eDistribution of flood frequency under the DAM scenario. \u003cstrong\u003eF\u003c/strong\u003eDistribution of the antecedent dam storage one day before flood events in the DAM scenario.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6258710/v1/84958f26bae4475b708ac47c.png"},{"id":79300477,"identity":"ce7f9d9d-f738-4c49-aec2-736f48676f84","added_by":"auto","created_at":"2025-03-26 18:38:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":970288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinkage of catchment rainfall and antecedent reservoir storage with dam-induced changes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Correlation between annual precipitation and the median decrease in high flows for that year within different climate zones. \u003cstrong\u003eB\u003c/strong\u003e Correlation between antecedent dam storage and the decrease in high flows.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6258710/v1/c14c9a7b6e6352c4c35c0118.png"},{"id":79300786,"identity":"0df62967-cc82-4b1f-be3a-f94f1ae69c45","added_by":"auto","created_at":"2025-03-26 18:46:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1598635,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChange in days with high dam storage levels under warming climate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChange in the number of days when dam storage remains full more than 90 percent of their live storage capacity for the selected global warming levels (\u003cstrong\u003eA\u003c/strong\u003e 1.0° C, \u003cstrong\u003eB\u003c/strong\u003e1.5° C, \u003cstrong\u003eC\u003c/strong\u003e 2.0° C, \u003cstrong\u003eD\u003c/strong\u003e 2.5° C, \u003cstrong\u003eE\u003c/strong\u003e 3.0° C). The change is computed against the historical reference period (1995-2014). \u003cstrong\u003eF \u003c/strong\u003eDistribution of change in the number of days when dam storage remains full more than 90 percent of their live storage capacity for the global warming levels shown through \u003cstrong\u003eA\u003c/strong\u003e to \u003cstrong\u003eE.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6258710/v1/58a1676943616d0d56055fa2.png"},{"id":79300481,"identity":"104e9d69-a017-486c-9670-e4c15fe4d533","added_by":"auto","created_at":"2025-03-26 18:38:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1666282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of the compound events of high inflow and nearly full dam storage under warming climate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrequency of high inflow events when dam storage is more than 90 percent of total live storage for the selected global warming levels (\u003cstrong\u003eA\u003c/strong\u003e 1.0° C, \u003cstrong\u003eB\u003c/strong\u003e 1.5° C, \u003cstrong\u003eC\u003c/strong\u003e2.0° C, \u003cstrong\u003eD\u003c/strong\u003e 2.5° C, \u003cstrong\u003eE\u003c/strong\u003e 3.0° C). \u003cstrong\u003eF \u003c/strong\u003eDistribution of frequency of the compound event for the global warming levels shown through \u003cstrong\u003eA\u003c/strong\u003e to \u003cstrong\u003eE.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6258710/v1/fbac882ccb45ea7b6a43a200.png"},{"id":79301365,"identity":"79050e68-e289-4068-9ce9-d43692559b68","added_by":"auto","created_at":"2025-03-26 19:02:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5847475,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6258710/v1/84943927-5bbe-46e6-a46a-ec7149a96bc2.pdf"},{"id":79300792,"identity":"192ac076-e66c-4c47-989b-f11778645fce","added_by":"auto","created_at":"2025-03-26 18:46:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3840811,"visible":true,"origin":"","legend":"","description":"","filename":"SIDamPaper.docx","url":"https://assets-eu.researchsquare.com/files/rs-6258710/v1/01b089df1a0ec2d6762b61a0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate change and effectiveness of dams in flood mitigation in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndia is the third-largest dam-building country, with over 6,000 completed large dams serving multiple purposes including irrigation, flood control, hydroelectric power generation, drinking water supply, and fish production\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The number of large dams in India has increased significantly from 1,106 in 1970 to over 6,000 in the last 50 years. Notably, more than 2,500 large dams were built in just two decades during 1971\u0026ndash;1990 (NRLD, 2023). This surge in dam construction was driven by the increasing irrigation water demands and hydropower production. The primary role of dams in India is to provide water for irrigation, generate hydropower, and flood control. Currently, there are 150 major irrigation projects with 16.33\u0026nbsp;million hectares of culturable command area\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Dam construction has contributed substantially to hydropower production, which accounts for the second-largest share (13%) of the total energy produced in India\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The role of dams for irrigation water supply and hydropower generation has been well acknowledged, however, their effectiveness for flood control remains debatable\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDams are one of the several stressors affecting river flow during different seasons\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. For instance, dams store water during the water surplus season and release water during the dry season\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therefore, dams can considerably influence streamflow variability and are among the most effective measures to mitigate the impacts of droughts\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In addition, dams control floods in downstream regions by storing the high inflow. Water release from the dams in a controlled manner can help in reducing the downstream peak flow and mitigate the flood risk\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e by lowering the flood peak and delaying the time to peak\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Thus, dams can considerably reduce the flood exposure and flood risk in the downstream regions\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe increased inflow variability under the warming climate makes dam operations and decision-making more complex and challenging. Palmer et al. reported that basins impacted by dams are more likely to require management interventions such as sediment management, dam retrofitting or removal, floodplain restoration, or ecosystem restoration to mitigate the climate change impacts compared to free-flowing river basins\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Poor decision-making under critical conditions can exacerbate flood events\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Several regions in India faced dam-induced floods in the recent past\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The dam-induced floods are more sudden and intense, leaving a more destructive impact on lives and properties\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the mitigation measures such as constructing flood control infrastructures, implementing land use regulations, and enhancing early warning systems the losses that occurred from floods have increased during the past few decades\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Examining the impacts of streamflow variability on dam storage is crucial for making informed decisions during floods. While dams play a crucial role in regulating river flows and mitigating downstream flooding, their effectiveness is increasingly being challenged by climate change-induced alterations in hydrology\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The vast network of large dams in India holds great potential to control and alter the flow in downstream. However, there remains a critical gap in understanding how the warming climate will impact dam storage dynamics and the effectiveness of dams to attenuate extreme inflows. The major scientific questions we address are: 1) To what extent do dams control high flow conditions during the summer monsoon season in India? and 2) What role do catchment precipitation and antecedent dam storage play in reducing high flow in the downstream regions? 3) How will the dam storage dynamics and flood risk change under the projected future climate? To address these questions, we use the H08\u003csup\u003e20\u003c/sup\u003e land-surface model combined with the CaMa-Flood\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e hydrodynamic model to simulate the historical and future dam dynamics in India and its role on floods in downstream regions.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eInfluence of dams on floods in India\u003c/h2\u003e\n\u003cp\u003eFirst, we examined the role of dams in flooding in the downstream regions based on the observational datasets. Dams played a crucial role in either mitigating or intensifying downstream flooding (Fig. 1). For instance, the Tehri Dam on the Ganga River reduced flooding in the downstream in 2010, 2011, and 2013\u003csup\u003e22,23\u003c/sup\u003e. Several large dams, including Nagarjuna Sagar, Krishna Raja Sagar, and Ukai played a major role in reducing the flood risk in the downstream regions\u003csup\u003e24\u0026ndash;26\u003c/sup\u003e. During the August 2019 flood in the Sutlej River, the Bhakra Dam attenuated 82.33% of the inflow, while the Pong Dam maintained zero outflow throughout the event\u003csup\u003e27\u003c/sup\u003e. However, during the August 2023 floods in Punjab, sudden and massive water releases from the Bhakra and Pong dams, both of which had already exceeded their full reservoir levels, contributed to the flooding in the downstream regions\u003csup\u003e28\u003c/sup\u003e. In addition to large releases, high reservoir levels prior to extreme high inflow conditions can exacerbate downstream flooding. For example, during the September 2023, Sardar Sarovar Dam\u0026apos;s storage level remained close to the full reservoir level (FRL) despite forecasts of high inflows and extreme rainfall in the upstream catchment\u003csup\u003e29\u003c/sup\u003e. This resulted in a high outflow from the dam, leading to flooding in Bharuch city. Similarly, in 2015, a high discharge from the Chembarambakkam Dam into the Adyar River led to severe flooding in Chennai. In contrast, in 2023, proactive dam management significantly reduced storage levels ahead of widespread rainfall, effectively preventing a similar disaster\u003csup\u003e30\u003c/sup\u003e [Fig. 1].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we use the model simulations to examine the influence of dams on the frequency of floods (\u0026gt;99\u003csup\u003eth\u003c/sup\u003e percentile streamflow) in their downstream regions under the NAT and DAM scenarios. We estimated the flood frequency under the NAT and DAM scenarios in the 30-year (1991-2020) period (Fig. 2a, b). In the NAT scenario, the flood frequency is notably higher for the dams located in central and southern India than in northern India (Fig. 2a). More than 60% of the dams across India showed flood frequency between 20 and 40, with median frequency of 35 during the 30-year period (Fig. 2d). However, in the DAM scenario, dams effectively reduce the flood frequency compared to the NAT scenario (Fig. 2a, b). For instance, the flood frequency is less than 30 for more than 80% of the dams across India, with a median of 13 floods/30-year under the DAM scenario (Fig. 2e). The effectiveness of dams to capture high inflows and reduce downstream flooding depends on the available reservoir storage capacity of the dams\u003csup\u003e31\u003c/sup\u003e. Thus, we analysed the mean dam storage one day prior to floods in the DAM scenario to examine the antecedent reservoir storage (Fig. 2c). High antecedent reservoir storage (median 94%) [Fig. 2f] prior to floods suggests that when floods occur downstream of dams, most reservoirs are already near their full capacity, limiting their ability to mitigate floods. The difference in the flood frequency under the NAT and DAM scenarios highlights that dam considerably attenuate floods in the downstream region. However, the high antecedent reservoir storage levels before floods remain the key driver in limiting the dam\u0026rsquo;s ability in mitigating floods in the downstream regions.\u003c/p\u003e\n\u003cp\u003eFloods in India predominantly occur during the summer monsoon season\u003csup\u003e32\u003c/sup\u003e. Therefore, we examined the changes in the high flow magnitude (exceeding the 99\u003csup\u003eth\u003c/sup\u003e percentile) downstream of the dams under the DAM and NAT scenarios during the summer monsoon (Fig. S3a). We find that all the dams are effective in reducing high flows during the summer monsoon. Dams with lesser reservoir storage capacity (\u0026lt;1000 MCM) can attenuate high flow up to 30-43% (median change), whereas dams with larger reservoirs (\u0026gt;1000 MCM) can reduce the magnitude of high flows by more than 47% (median change). Further, we examined the intraseasonal variability in high-flow attenuation within the monsoon months (June-September) [Fig. S4b-e]. We hypothesize that the influence of dams in modulating high flow is higher during the early monsoon season (June and July) due to lower initial dam storage levels. Our results show that the highest reduction in high-flow magnitude occurs in July (Fig. S3c), aligning with the period when dams begin to reach higher storage levels. The high flow attenuation persists through August and September (Fig. S3d, e) but with relatively lower reductions compared to July. In June, only a few dams contribute to high-flow attenuation (Fig. S3b), whereas from July onwards, nearly all dams play a role in regulating downstream flows.\u003c/p\u003e\n\u003cp\u003eWe also estimated changes in the flows using three different thresholds (exceeding 90th, 95th and 99.9th percentiles) during the summer monsoon season and for monsoon months individually (Fig. S4). We find that majority of dams do not experience high flows exceeding the 99.9th percentile in June (Fig. S4l), as major floods in India occur mostly in August and September\u003csup\u003e33\u003c/sup\u003e. June being the onset month of the Indian summer monsoon, the frequency and intensity of extreme precipitation is relatively lesser\u003csup\u003e34\u003c/sup\u003e. In addition, the absence of wet antecedent conditions does not create favorable conditions for riverine floods in June\u003csup\u003e35\u003c/sup\u003e. Lowering the threshold for high-flow conditions (from the 99.9th to the 90th percentile) increased the sample size of high-flow events, even during the June\u0026ndash;July period (Fig. S4). As a result, several dams attenuated high flows exceeding the 90th and 95th percentiles in June, while their influence reduced during the August-September. This suggests that low antecedent dam storage leads to greater modulation of high-flow events in the early monsoon season. Moreover, large dams are more effective than small dams in reducing high-flow peaks during the onset period (Fig. S3b).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eLinkage of catchment rainfall and antecedent reservoir storage with dam-induced changes\u003c/h2\u003e\n\u003cp\u003eWe examined the relationship of changes in high flows with annual precipitation in the upstream catchment and antecedent dam storage for all the dams to understand how upstream hydrology influences downstream flow regulation (Fig. 3a, b). From the relationship between change (%) in the high flow and antecedent dam storage (Fig. S6) for four dams (Rengali, Mettur, Konar and Sriram Sagar) located in different climatic and geographical settings, we find that the median decrease in the high flows due to dams was less in the years when higher rainfall in the upstream catchment was recorded. In addition, we note a considerable role of the antecedent reservoir storage on the downstream flow (Fig. S5). High flows showed minimal change (\u0026lt; 10%) in magnitude when dam storage was above 90% of total capacity, with most flow reductions over 10% occurring in July and August, as dams typically reached full capacity by August or September (Fig. S5c, d). Therefore, the relationship (based on correlation coefficient) was estimated only for instances where a decrease in flow greater than 10% was observed. We observed a high correlation between the change in high flows with antecedent dam storage than annual precipitation in the upstream catchment for all the dams (Fig. 3). The median correlation coefficient for all the dams exceeded 0.4 when correlated with both antecedent reservoir storage and annual upstream precipitation. However, dams located in arid and temperate climate zones exhibited a stronger relationship with antecedent reservoir storage, with a median correlation exceeding 0.9. In contrast, correlation between annual upstream precipitation and the median decrease in high flows was observed more than 0.5 for 57% of dams. Overall, our results highlight that the antecedent reservoir storage plays a more important role than precipitation variability in the catchments upstream of the dams. Therefore, maintaining the buffer reservoir storage that can accommodate high inflow is critical for the mitigation of flooding in the downstream regions\u003c/p\u003e\n\u003ch2\u003eInfluence of dams in reducing future food risk\u003c/h2\u003e\n\u003cp\u003eWe estimated the projected change in the number of days when dams remain above 90% of their live storage capacity under different global warming scenarios (1.0\u0026deg;C to 3.0\u0026deg;C) compared to the historical period (1995\u0026ndash;2014) [Fig. 4]. We selected the 90% storage threshold based on the observation that dams lead to minimal change in the high flow in the downstream regions highlighting their ineffectiveness in mitigating floods. An increase in the number of days when dams remain near their full capacity is projected in the future at all the warming levels. Moreover, the duration in which dams can be ineffective increases with the increase in the global warming level. The distribution shows a systematic increase in the median number of days, from 6.67 days at 1.0\u0026deg;C warming to 23.26 days at 3.0\u0026deg;C warming (Fig. 4f). This trend suggests that climate change-induced shifts in hydrology will likely lead to more prolonged high-storage conditions in dams, increasing the flood risk in downstream areas.\u003c/p\u003e\n\u003cp\u003eNext, we estimated the changes in the projected frequency of the compound events of high inflow (exceeding 99\u003csup\u003eth\u003c/sup\u003e percentile) and nearly full (\u0026gt;90%) dam storage (Fig. 5). The frequency of the compound events is projected to rise across many dams under the warming climate. For instance, the median frequency across India remains relatively low at 0.24 per year at 1.5\u0026deg;C global warming level but projected to increase by threefold to 0.61 per year at 3.0\u0026deg;C warming. This suggests that under the higher global warming levels, dams will likely experience more frequent compound events of high inflow and high antecedent storage. These findings highlight a pressing need for adaptive dam management strategies to accommodate extreme inflows and mitigate downstream flooding.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlobally, India is among the largely affected areas under the impact of dams\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The country's extensive network of dams plays a crucial role in water and flood management\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Several studies have examined the role of dams on the downstream hydrological regime on a regional or global scale\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. We investigated the influence of dams in India on downstream river discharge, analyzing the changes induced in floods causing high flows. The combined modelling framework based on the H08-CaMa Flood model was used to estimate the dam-induced hydrological changes downstream. The hydrological modelling framework performed well against the observed flow and dam storage. The role of 178 major dams was considered in simulating the dam dynamics for historical and future climate. Dams potentially reduce the high flows during the onset of the summer monsoon period owing to the dam filling, and the attenuation capacity decreases as the monsoon progresses. The storage capacity of a dam before extreme weather events plays a critical role in determining its ability to mitigate flood risks\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The reduction of downstream high flows by dams shows a strong linkage with antecedent dam storage and a moderate to strong correlation between peak flow reduction and rainfall in the upstream catchment. For instance, Zhou reported a strong association between the decrease in flood risk and the flood control capacity of dams\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnder the warming climate, downstream regions of dams are projected to experience increased flooding attributed to prolonged high storage levels and increased high flow frequency. Dams are projected to experience at high storage levels for a prolonged period. Non-availability of adequate buffer storage in reservoirs during the summer monsoon season results in an increased risk of flooding in the downstream\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. For instance, in the year 2024 monsoon, Krishna Raja Sagar dam on Cauvery River remained full for a record 172 days\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Furthermore, the dams are projected to receive highly variable and extreme inflows attributed to the precipitation changes. The projected increase in the dam inflow magnitude and variability have been reported for different dams across the world\u003csup\u003e\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The combination of extreme inflow and high storage level conditions is likely to result in more intense downstream flooding\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. We find a significant increase in the flood frequency under warming climate in the dam downstream when dams are ineffective in reducing the magnitude attributed to the full antecedent dam storage conditions. For instance, Lee and You estimated the risk of dam overtopping under climate change for Shihmen Reservoir in Taiwan and found an increasing trend\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProjected increase inflow can further reduce the storage capacity due to sedimentation, which not only reduces the life span of the dam but potentially increases the overtopping risk\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. With decreasing storage capacity owing to sedimentation, India is one of the most vulnerable countries\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Lemp\u0026eacute;ri\u0026egrave;re reported that climate change can significantly increase the need for flood mitigation in many countries, necessitating the repurposing of existing or new dams for flood control\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In recent years, the operational flexibility of dams has demonstrated positive impacts under extreme climate scenarios\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In an agriculture-dominant country like India, dams primarily designed for hydropower or irrigation can also be utilised for efficient flood mitigation. Furthermore, dam inflow forecast plays a major role in maintaining a safe storage level during the summer monsoon period to prevent flooding downstream and meeting the required demand during the dry period\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Effective reservoir management, incorporating advanced inflow forecasting and adaptive operational strategies, is crucial to mitigating downstream flood risks under a warming climate, especially in highly vulnerable regions like India.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eWe used gridded precipitation and temperature observations from the India Meteorological Department (IMD) for the period 1901\u0026ndash;2020. The precipitation data is compiled from daily records collected from 6955 rain gauge stations in India. The gridded precipitation captures the spatial variability of rainfall across India including the orographic precipitation dominated regions of the western Ghats and foothills of the Himalayas\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Gridded daily precipitation from IMD has been widely used for hydrological modelling applications in India\u003csup\u003e\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGridded maximum and minimum temperatures data from 395 stations are developed at 1\u003csup\u003eo\u003c/sup\u003e resolution grids using a modified version of Shepard\u0026rsquo;s angular distance weighting algorithm\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. We regridded the 1\u0026deg; gridded temperature data to 0.25\u0026deg; using bilinear interpolation, accounting for the lapse rate, to ensure consistency with the gridded precipitation data. IMD's observational network only covers the Indian region, therefore, meteorological data for the region outside India were obtained from Princeton University\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Gridded datasets from Sheffield et al. show a good agreement with IMD observations and have been used in hydrological applications in India\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. The additional meteorological forcing input variables (shortwave and longwave radiation, surface pressure and specific humidity) for the H08 land-surface model were obtained from Kushwaha et al\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe obtained observed daily streamflow and dam storage from India Water Resources Information System (IWRIS). In addition, we used the monthly dam storage data from the Global Reservoir Storage (GRS) dataset\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. The GRS dataset is prepared using multi-source satellite data to estimate storage variations of 7245 global reservoirs from 1999 to 2018. The information regarding other salient features of dams was obtained from the National Register of Large Dams (NRLD).\u003c/p\u003e \u003cp\u003eWe used daily precipitation, maximum and minimum temperatures from five general circulation models (GCMs; BCC-CSM2-MR, INM-CM5-0, MIROC6, NorESM2-MM, and TaiESM1) that were part of the Coupled Model Intercomparison Project (CMIP)-6 to examine the changes in the inflow and dam storage under the projected future climate. The selected GCMs effectively reproduce the observed seasonal variations in temperature and precipitation across the Indian monsoon region\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. We used three scenarios: historical climate (1951\u0026ndash;2014), and two shared socioeconomic pathways consisting of a low-emission scenario (SSP1-2.6; 2015\u0026ndash;2100) and a high-emission scenario (SSP5-8.5) considering radiative forcing of 2.6 and 8.5 W/m\u003csup\u003e2\u003c/sup\u003e, respectively by the end of 21st century. We bias-corrected the CMIP6 projections for the five GCMs using the method developed by Hempel et al., ensuring the removal of bias while preserving the long-term climate signal\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The bias-correction was performed against the observed precipitation and temperature datasets.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHydrological and hydrodynamic models\u003c/h3\u003e\n\u003cp\u003eWe used the H08 land surface model\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e and the CaMa-Flood hydrodynamic model\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e to simulate streamflow and dam storage. The H08 model uses daily precipitation, air temperature, short and longwave radiations, wind speed, surface pressure, and specific humidity as input meteorological forcing to calculate runoff based on saturation excess non-linear flow\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Soil parameters for the H08 model were obtained from the Harmonized World Soil Database (HWSD). Additionally, albedo and soil type datasets are taken from the Global Soil Wetness Project (GSWP)\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We ran the H08 model at 0.25\u0026deg; spatial and daily temporal resolution from 1901 to 2020. We considered five years as the model\u0026rsquo;s spin up period and the final run for the 1901\u0026ndash;2020 was started after the model spin up was over. The runoff generated from the H08 model at 0.25\u0026deg; is used in the CaMa-Flood model to rout streamflow at 0.05\u0026deg; spatial resolution.\u003c/p\u003e \u003cp\u003eThe CaMa-Flood is a hydrodynamic model, which simulates the river-floodplain dynamics\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Globally, the CaMa-Flood model has been extensively used for simulating streamflow\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The CaMa-Flood simulates river floodplain dynamics by solving shallow water equations of open channel flow while explicitly accounting for backwater effects using the local inertial approximation. The model offers a reservoir operation scheme, which has been used in many global and regional studies\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The CaMa-Flood model has been previously used to examine the hydrological changes caused by dams in various basins\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. The dam module of the CaMa-Flood has also been extensively used for several other purposes such as flood dynamics downstream of dams and analysing long-term hydrological changes\u003csup\u003e\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. We incorporated the influence of 178 major dams (\u0026gt;\u0026thinsp;3 MCM storage) in India within the CaMa-Flood model based on the availability of storage observations. The model simulations were performed for the two scenarios: natural conditions (NAT) without considering the role of dams on streamflow and with considering the role of dams (DAM) on streamflow.\u003c/p\u003e \u003cp\u003eWe manually calibrated the H08 model by adjusting the four parameters (single-layer soil depth, gamma, bulk transfer coefficient, and tau) against the daily streamflow observations at gauge stations\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e [Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e]. We considered 60 gauge stations across 18 river basins to calibrate and evaluate the model. Due to the unavailability of daily observed streamflow data for the three transboundary river basins (Indus, Ganga, and Brahmaputra), we used the observed monthly streamflow for calibration. We used the coefficient of determination (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), Nash-Sutcliffe efficiency (NSE)\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e and Kling-Gupta Efficiency (KGE)\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e to determine the performance of the model. The model exhibited satisfactory skills (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.6, NSE\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and KGE\u0026thinsp;\u0026gt;\u0026thinsp;0.5) for most of gauge stations (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea-c). In addition, we evaluated the model performance in simulating annual maximum flow at different locations using bias in flood peaks and timing error (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed,e). However, we did not perform this analysis for the transboundary rivers (Ganga, Brahmaputra, and Indus) due to the unavailability of daily streamflow observations. While most gauge stations exhibited moderate bias, a few stations in the Cauvery, Sabarmati, Krishna, and Mahi River basins showed a notable dry bias in the simulated mean annual maximum flow. The poor performance of the model at a few gauge stations can be attributed to the limited availability of long-term observations and the considerable influence of human interventions including irrigation, dam storage, and water diversions. Additionally, daily flow observations can be affected by measurement errors, changes in gauge location, and inconsistencies in data recording practices\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. The mean timing error between the simulated and observed peak streamflow is less than two days at more than 80% of the gauge stations and less than three days for all the stations. Timing errors in the simulated peak flow can largely be attributed to errors in model structure, parameters, and initial conditions\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, which can be improved through data assimilation\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe examined the performance of the models to simulate reservoir storage. Over 70 dams exhibited NSE above 0.5, while more than 95 dams showed KGE above 0.5 in simulating live reservoir storage (Fig. S2a, c). The dams calibrated against the observations from IWRIS exhibited better skills than those calibrated against the GRS dataset (Fig. S2b, d). Li et al. reported a large bias in the GRS dataset for a few Indian dams, which can be attributed to uncertainties such as reservoir sedimentation, reservoir geometry approximation, input data inconsistencies, water area representation, and overestimation\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. The reservoir operation scheme may vary from one dam to another based on several factors, such as purpose, water demand, and dam location. These operation rules are altered based on the variations in inflow patterns and it highly influences the release from the dams\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. This study used the generic dam operation scheme available within the CaMa-Flood dam module, as the actual dam operation scheme is not available.\u003c/p\u003e\n\u003ch3\u003eEffect of dams and its linkage with catchment precipitation and antecedent dam storage\u003c/h3\u003e\n\u003cp\u003eWe examined the influence of dams on high flows in the downstream regions by estimating the changes in the frequency of high-flow events considering the influence of dams. To do this, we calculated the frequency of high flows exceeding the 99th percentile threshold over a 30-year period (1991\u0026ndash;2020) under the NAT and DAM scenarios. The 99th percentile threshold was determined based on the long-term (1901\u0026ndash;2020) streamflow time series under the NAT scenario at each location. Furthermore, we calculated the mean antecedent dam storage one day before a high-flow event in the downstream of a dam under the DAM scenario to assess the ability of dams to reduce the number of high-flow events and to determine the antecedent storage conditions when dams fail to mitigate high flows. In addition, we analysed the changes in the magnitude of high flows under the DAM scenario compared to NAT scenario. For each dam, we estimated the change in downstream and reduction in the number of high-flow events. Additionally, we calculated the changes in high flow magnitudes exceeding 90, 95 and 99.9th percentile thresholds. We accounted for different thresholds to examine how dams influence high-flow events of varying intensity. We also examined the linkage of dam-induced changes in river flows with the precipitation in the upstream catchment and antecedent dam storage.\u003c/p\u003e \u003cp\u003eWe examined the projected change in duration (number of days) during which dams remain full under different global warming levels. In addition, we estimated the change in the frequency of high inflow events (\u0026gt;\u0026thinsp;99th percentile) occurring when dam storage exceeds 90% of their capacity, which indicates the number of instances when dams may become ineffective in mitigating downstream floods under different global warming levels. To simulate future dam dynamics under changing climate conditions, we use bias-corrected climate projections from the GCMs to run the H08 and CaMa-Flood models.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eDatasets used in this study will be made available through a public data repository.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eThe codes that support the findings of this study are available from the author on request.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors acknowledge the data availability from the India Meteorological Department (IMD), India-WRIS and CMIP6. We acknowledge the Global Reservoir Storage (GRS) dataset availability from Li et al. (2023) [https://zenodo.org/records/7855477].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eV.M. designed the study. U.V. performed the analysis and wrote the initial draft. V.M. and U.V. finalized the draft.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNRLD, C. National register of large dams. Central Water Commission. Ministry of Water Resources, Government of India. (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePradhan, A. \u0026amp; Srinivasan, V. Do dams improve water security in India? A review of post facto assessments. Water Secur. 15, 100112 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIWRIS. command_area_development_programme \u0026mdash; INDIA WRIS WIKI. (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChuphal, D. S. \u0026amp; Mishra, V. 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Water Resour. 104, 23\u0026ndash;36 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6258710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6258710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndia is the third largest dam-building nation in the world. Dams are critical in irrigation, hydropower generation, and flood control. Observational evidence suggests that dams mitigated and caused floods in several river basins in India. However, the effectiveness of dams in flood mitigation and associated driving factors remain unexplored under the current and projected future climate. Using in-situ and satellite observations and simulations from hydrological and hydrodynamic models for 178 major dams, we show that major dams' effectiveness in flood mitigation in India depends more on the antecedent reservoir storage than precipitation variability in the river basins upstream of dams. Most floods in dams' downstream regions occur when reservoirs exceed 90% of their full capacity. Dams are more effective in mitigating floods in July than in the later part of the summer monsoon season. The number of days with reservoir storage over 90% of total capacity is projected to rise considerably under the warming climate, limiting dams' effectiveness in mitigating floods. The duration with reservoir storage exceeding 90% of the total capacity is projected to rise from ~\u0026thinsp;6 days at 1\u0026deg;C to 23 at 3\u0026deg;C global warming levels, highlighting the need for climate change mitigation. The substantial rise in compound extremes of high inflow and high antecedent storage in the future under the warming climate will limit the effectiveness of dams in mitigating floods in India. Our findings highlight the need for more advanced approaches for dam operations integrated with early warning of extreme inflow and reservoir storage in India.\u003c/p\u003e","manuscriptTitle":"Climate change and effectiveness of dams in flood mitigation in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 18:38:33","doi":"10.21203/rs.3.rs-6258710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-12T02:59:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T14:24:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T09:23:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40329558777016918466167136114798649405","date":"2025-03-26T14:19:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315897281630168923790614834974313023847","date":"2025-03-24T18:26:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14304878073536161267949692058305061476","date":"2025-03-24T16:03:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-24T14:40:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-20T23:35:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-20T16:51:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Natural Hazards","date":"2025-03-19T06:50:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66969e84-038a-47af-8914-7a7a46e32b75","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46178529,"name":"Earth and environmental sciences/Climate sciences"},{"id":46178530,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2025-06-30T04:23:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 18:38:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6258710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6258710","identity":"rs-6258710","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}