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Flood is among the deadliest disasters in India, and the frequency of floods and extreme precipitation events is projected to increase under the warming climate. The frequency of floods in India varies geographically as some regions are more prone to floods than the others. The Kerala flood of 2018 caused enormous economic damage, affected millions of people, and resulted in the death of more than 400 people. Here we provide a hydro-climatological perspective on the Kerala flood of 2018. Using the observations and model simulations from the Variable Infiltration Capacity (VIC) model, we show that the 2018 extreme precipitation and runoff conditions that caused flooding were unprecedented in the record of the past 66 years (1951-2017). Our results show that mean monsoon precipitation has significantly declined while air temperature has significantly increased during 1951-2017 in Kerala. The drying and warming trends during the monsoon season resulted in a declined total runoff in large part of the state in the last 66 years. Apart from the mean hydroclimatic conditions, extreme precipitation, and extreme total runoff have also declined from 1951 to 2017. However, 1 and 2-day extreme precipitation and extreme runoff conditions in August 2018 exceeded substantially from the long-term 95th percentiles recorded during 1951-2017. Since there is no increase in mean and extreme precipitation in Kerala over the last six decades, the extreme event during August 2018 is likely to be driven by anomalous atmospheric conditions due to climate variability rather anthropogenic climate warming. The severity of the Kerala flood of 2018 and the damage caused might be affected by several factors including land use/land cover change, antecedent hydrologic conditions, reservoir storage and operations, encroachment of flood plains, and other natural factors. The impacts of key drivers (anthropogenic and natural) on flood severity need to be established to improve our understanding of floods and associated damage.

The sustainability of water resources mainly depends on planning and management of land use; a small change in it may affect water yield largely, as both are linked through relevant hydrological processes, explicitly. However, human activities, especially a significant increase in population, in-migration and accelerated socio-economic activities, are constantly modifying the land use and land cover (LULC) pattern. The impact of such changes in LULC on the hydrological regime of a basin is of widespread concern and a great challenge to the water resource engineers. While studying these impacts, the issue that prevails is the selection of a hydrological model that may be able to accommodate spatial and temporal dynamics of the basin with higher accuracy. Therefore, in the present study, the capabilities of variable infiltration capacity hydrological model to hydrologically simulate the basin under varying LULC scenarios have been investigated. For the present analysis, the Pennar River Basin, Andhra Pradesh, which falls under a water scarce region in India, has been chosen. The water balance components such as runoff potential, evapotranspiration (ET) and baseflow of Pennar Basin have been simulated under different LULC scenarios to study the impact of change on hydrological regime of a basin. Majorly, increase in built-up (13.94% approx.) and decrease in deciduous forest cover (2.44%) are the significant changes observed in the basin during the last three decades. It was found that the impact of LULC change on hydrology is balancing out at basin scale (considering the entire basin, while routing the runoff at the basin outlet). Therefore, an analysis on spatial variation in each of the water balance components considered in the study was done at grid scale. It was observed that the impact of LULC is considerable spatially at grid level, and the maximum increase of 265 mm (1985–2005) and the decrease of 48 mm (1985–1995) in runoff generation at grid were estimated. On the contrary, ET component showed the maximum increase of 400 and decrease of 570 mm under different LULC change scenario. Similarly, in the base flow parameter, an increase of 70 mm and the decrease of 100 mm were observed. It was noticed that the upper basin is showing an increasing trend in almost all hydrological components as compared to the lower basin. Based on this basin scale study, it was concluded that change in the land cover alters the hydrology; however, it needs to be studied at finer spatial scale rather than the entire basin as a whole. The information like the spatial variation in hydrological components may be very useful for local authority and decision-makers to plan mitigation strategies accordingly.

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3–0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10 mm and 8.9 mm (6%, 39.1 mm, 39.1 mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50 mm/15 mm in the near future (2011–2040) for lower and 20 mm/6 mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2.

In this study, a physics-informed machine learning-based surrogate model (SM) for the variable infiltration capacity (VIC) model was developed to improve simulation efficiency in the Yarlung Tsangpo River basin. The approach combines the empirical orthogonal function decomposition of low-fidelity VIC models to extract spatial and temporal features, with machine learning techniques applied to refine temporal feature series. This allows for accurate reconstruction of high-fidelity spatial simulations from the results of the low-fidelity model. Using the SM built from the 1.0°-resolution VIC model as an example, the study highlights the challenges and solutions associated with low-fidelity simulations. The SM significantly improves accuracy, achieving a Kling–Gupta efficiency of 0.88, an Nash–Sutcliffe efficiency of 0.97, and a PBIAS value of −6.21% with reduced computational demands. Additionally, different machine learning methods impact the performance of the SM, with the support vector machine regression model performing best in these methods. SMs from varying low-fidelity resolutions maintain similar accuracy, but higher resolutions notably enhance computational efficiency, reducing time by 86.31% when compared to the high-fidelity VIC model. These findings demonstrate the potential of the SM to enhance VIC model simulations while reducing computational requirements.

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security.

The assessment of land use land cover (LULC) and climate change over the hydrology of a catchment has become inevitable and is an essential aspect to understand the water resources-related problems within the catchment. For large catchments, mesoscale models such as variable infiltration capacity (VIC) model are required for appropriate hydrological assessment. In this study, Ashti Catchment (sub-catchment of Godavari Basin in India) is considered as a case study to evaluate the impacts of LULC changes and rainfall trends on the hydrological variables using VIC model. The land cover data and rainfall trends for 40 years (1971–2010) were used as driving input parameters to simulate the hydrological changes over the Ashti Catchment and the results are compared with observed runoff. The good agreement between observed and simulated streamflows emphasises that the VIC model is able to evaluate the hydrological changes within the major catchment, satisfactorily. Further, the study shows that evapotranspiration is predominantly governed by the vegetation classes. Evapotranspiration is higher for the forest cover as compared to the evapotranspiration for shrubland/grassland, as the trees with deeper roots draws the soil moisture from the deeper soil layers. The results show that the spatial extent of change in rainfall trends is small as compared to the total catchment. The hydrological response of the catchment shows that small changes in monsoon rainfall predominantly contribute to runoff, which results in higher changes in runoff as the potential evapotranspiration within the catchments is achieved. The study also emphasises that the hydrological implications of climate change are not very significant on the Ashti Catchment, during the last 40 years (1971–2010).

To alleviate the computational burden of parameter calibration of the Variable Infiltration Capacity (VIC) model, a stepwise surrogate model (SM) is developed based on AdaBoost. An SM first picks out the parameter sets in the range that the values of objective functions are close to the optimization objectives and then approximates the values of objective functions with these parameter sets. The ɛ-NSGA II (Nondominated Sorting Genetic Algorithm II) algorithm is used to search the optimal solutions of SM. The SM is tested with a case study in the upper Brahmaputra River basin, Tibet Plateau, China. The results show that the stepwise SM performed well with the rate of misclassification less than 2.56% in the global simulation step and the root mean square error less than 0.0056 in the local simulation step. With no large difference in the optimal solutions between VIC and the SM, the SM-based algorithm saves up to 90% time.

This study aimed to statistically and hydrologically assess the performance of the four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT (CMORPH CRT), BLD (CMORPH BLD), CDR (PERSIANN CDR), 3B42 (TMPA 3B42 version 7) over the upper yellow river basins (UYRB) in china during 2001–2012 time period. The performances of the SGPEs were compared with the Chinese Meteorological Administration (CMA) datasets using the hydrologic model called Variable Infiltration Capacity (VIC) which is known as a land surface hydrologic model. Results indicated that irrespective of the slight underestimation in the western mountains and overestimation in the southeast, the four SGPEs could generally captured the spatial distribution of precipitation well. Although 3B42 exhibited a better performance in capturing the spatial distribution of daily average precipitation, BLD agreed best with CMA in the time series of watershed average precipitation, which resulted in BLD having a comparable performance to the CMA in the long-term hydrological simulations. Moreover, the potential for disastrous heavy rain mainly occurs in southeastern corner of the basin, and CRT and BLD comparisons showed to be closer to the CMA in the distribution of extreme precipitation events while 3B42 and CDR overestimated the extreme precipitation especially over the southeast of UYRB region. Therefore, CRT and BLD were able to match the high peak discharges very well for the wet seasons, while 3B42 and CDR overrated the high peak discharges. In addition, the four SGPEs performed well for the 2005 flood event but exhibited poorly when tested for the 2012 flood event. Results indicate that the application of the four SGPEs should be used with caution in simulating massive flood events over UYRB region.

This study introduces a novel sensitivity analysis approach to assess the resilience and susceptibility of hydrologic systems to the stresses of climate change, moving away from conventional top-down methodologies. By exploring the hydrological sensitivity of the upper Kızılırmak River basin using the Variable Infiltration Capacity (VIC) hydrologic model, we employed a sensitivity-based approach as an alternative to the traditional Global Climate Model (GCM)-based methods, providing more insightful information for water managers. Considering the consistent projections of increasing temperature over this region in GCMs, the hydrologic system was perturbed to examine gradients of a more challenging climate characterized by warming and drying conditions. The sensitivity of streamflow, snow water equivalent, and evapotranspiration to temperature (T) and precipitation (P) variations under each perturbation or “reference” climate was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. As the reference climates become drier, streamflow sensitivity to P increases, indicating that meteorological drought impacts on water availability could be exacerbated. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: (1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, (2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and (3) streamflow sensitivity to hydroclimatic variability will increase, meaning that there will be more extreme impacts to water availability. Water managers will need to plan for a larger set of extreme conditions.

Ethiopia is endowed with huge water potential but uneven distribution in rainfall, increasing demand, and recurrence of droughts have resulted in water scarcity in many parts of the country. The literature lacks spatial water resources assessment at a national scale, covering all the river basins. Large uncertainty in water resource potential is also reported in the literature. The present study utilizes the physics-based, semi-distributed Variable Infiltration Capacity (VIC) model to estimate the hydrological fluxes at national and river basin scales for the entire country of Ethiopia. The model is set up at a grid cell size of 0.05° × 0.05°, and hydrological simulation is carried out for a period spanning 16 years (1998–2013) at a daily time-step. As the model works on each grid cell independently, a geospatial data-driven approach is used for generating the model inputs, performing analysis, and presenting the outputs. The average annual runoff-depth for Ethiopia is estimated as 177 mm, while the average annual evapotranspiration and baseflow are estimated as 737 mm and 27 mm, respectively. Substantial variability in the spatial and temporal distribution of these hydrological fluxes is observed across the country. The runoff coefficient varies from 0.282 (Denakil river basin) to 0.037 (Ogaden river basin), with the country average being ~ 19%. While the spatial pattern of simulated annual runoff is as expected but modeled estimate (present study) is significantly higher than the existing national estimate. The present study underscores the advantage of the geospatial data-driven distributed hydrological modeling approach in assessing the water resource potential in a data-scarce country like Ethiopia. The results will be useful for sustainable development and management of water resources.