Variable Infiltration Capacity Model Research Articles

On the occurrence of the observed worst flood in Mahanadi River basin under the warming climate

Floods cause enormous damage to infrastructure and result in the loss of human lives. Most river basin-scale floods in India occur during the summer monsoon season. While extreme precipitation events and floods are projected to increase under the warming climate, it remains unclear how the frequency of the worst flood that occurred during the observed period will change in the future. Using the observations and simulations conducted using the Variable Infiltration Capacity (VIC) model, we identified the worst flood during the observed period of 1971–2012 in the Mahanadi River basin. The observed worst flood at Basantpur occurred in August 2003, exceeding 33,000 m3/s and a return period of 132 years. Kantamal station of the Mahanadi River basin experienced the worst flood in 2008 with a magnitude of 20,000 m3/s and a return period of 64 years. Multi-day extreme precipitation upstream of the gauge stations driven by the moisture transport from the Bay of Bengal caused the observed worst floods in the basin. We used bias-corrected projections from the six global climate models (GCMs) that participated in the coupled model intercomparison project 6 (CMIP6) under the SSP1-2.6 and SSP5-8.5 scenarios to estimate the projected changes in the frequency and magnitude of floods. The frequency of the worst observed floods is projected to rise at both stations under the projected future climate. In addition, the magnitude of floods of 10, 20, and 50-year return periods are projected to rise at both stations in the Mahanadi basin. The projected rise in the frequency of the worst floods and extreme precipitation in the basin will pose challenges for agriculture and infrastructure.

Comprehensive Review: Advancements in Rainfall-Runoff Modelling for Flood Mitigation

Runoff plays an essential part in the hydrological cycle, as it regulates the quantity of water which flows into streams and returns surplus water into the oceans. Runoff modelling may assist in understanding, controlling, and monitoring the quality and amount of water resources. The aim of this article is to discuss various categories of rainfall–runoff models, recent developments, and challenges of rainfall–runoff models in flood prediction in the modern era. Rainfall–runoff models are classified into conceptual, empirical, and physical process-based models depending upon the framework and spatial processing of their algorithms. Well-known runoff models which belong to these categories include the Soil Conservation Service Curve Number (SCS-CN) model, Storm Water Management model (SWMM), Hydrologiska Byråns Vattenbalansavdelning (HBV) model, Soil and Water Assessment Tool (SWAT) model, and the Variable Infiltration Capacity (VIC) model, etc. In addition, the data-driven models such as Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Deep Neural Network (DNN), and Support Vector Machine (SVM) have proven to be better performance solutions in runoff modelling and flood prediction in recent decades. The data-driven models detect the best relationship based on the input data series and the output in order to model the runoff process. Finally, the strengths and downsides of the outlined models in terms of understanding variation in runoff modelling and flood prediction were discussed. The findings of this comprehensive study suggested that hybrid models for runoff modeling and flood prediction should be developed by combining the strengths of traditional models and machine learning methods. This article suggests future research initiatives that could help with filling existing gaps in rainfall–runoff research and will also assist hydrological scientists in selecting appropriate rainfall–runoff models for flood prediction and mitigation based on their benefits and drawbacks.

A two-step calibration framework for hydrological parameter regionalization based on streamflow and remote sensing evapotranspiration

• We proposed a two-step calibration based parameter regionalization method. • This method combines spatial proximity method and RS ET based parameter calibration method. • The proposed regionalization method achieves the overall best model performance. • The accuracy of RS ET is the main factor affecting the performance of the proposed method. Runoff prediction in ungauged catchments is an important and challenging research topic in the field of hydrology. However, traditional parameter regionalization methods may not be applicable in regions with sparsely distributed hydrological stations. Remote sensing (RS) evapotranspiration (ET) products provide valuable information for model parameter estimations. This study aimed to improve the accuracy of hydrological modeling in ungauged catchments by combining the traditional spatial proximity (SP) method with the RS ET-based parameter calibration method to form a two-step calibration-based regionalization method (SP-bcET method using bias-corrected RS ET data and SP-rawET method using raw RS ET data). The proposed regionalization scheme is demonstrated by using the variable infiltration capacity model in the Yangtze River Basin, China, and its performance was evaluated and compared with the SP method and two RS ET-based parameter calibration schemes (bcET method using bias-corrected RS ET data and rawET method using raw RS ET data). The results indicate that the bcET method performs better than the rawET method, the SP method outperforms the RS ET-based parameter calibration method, and the SP-bcET and SP-rawET methods achieve the best overall performance among the five methods. Taking the simulated streamflow at a daily scale in the calibration period as an example, the average Nash-Sutcliffe coefficient of efficiency for the SP, bcET, rawET, SP-bcET, and SP-rawET methods were 0.69, 0.61, 0.56, 0.71, and 0.71, respectively. Moreover, the SP-bcET and SP-rawET methods improved the simulation of the temporal dynamics and spatial variability of soil moisture anomalies. The accuracy of the RS ET data was one of the most dominant factors affecting the performance of the proposed parameter regionalization scheme. The SP-bcET method is expected to perform better than the SP method in warmer catchments or when gauged catchments are sparely distributed. The findings demonstrate the great potential of combining the traditional parameter regionalization and RS ET-based parameter calibration methods in hydrological simulations in ungauged catchments.

Quantifying the effects of human activities and climate variability on runoff changes using variable infiltration capacity model.

Detecting and assessing changes in the hydrologic cycle and its response to a changing environment is essential for maintaining regional ecological security and restoring degraded ecosystems. There is no clear scientific evidence on the effects of human activities and climate variability on runoff and its components in typical arid areas. Therefore, in this study, a heuristic segmentation algorithm, a variable infiltration capacity model (VIC), and remote sensing data to quantify the effects of human activities and climate variability on runoff in the catchment of Lake Ebinur, Xinjiang, China. The results found: (1) The heuristic segmentation algorithm divided the study period into reference period (1964-1985) and two impact periods: I (1986-2000) and II (2001-2017). (2) Cropland and forest land showed an increasing trend, with grassland and barren land accounting for most of the increase. At the same time, the leaf area index (LAI) increased by 0.002 per year during the growing season. (3) Compared with the reference period, runoff depth decreased by 108.80 mm in impact period I due to human activities, but increased by 110.5 mm due to climate variability, resulting in an overall increase in runoff depth of 1.72 mm. Runoff depth increased by 11.10 mm in the impact period II compared to the reference period, with climate variability resulting in an increase of 154.40 mm, but human activities resulted in a decrease of 143.30 mm. Our results shed light on decision-making related to water stress in changing circumstances in arid regions.

Assessment of land use changes in the Verde River basin using two hydrological models

Land use changes can affect hydrological cycle dynamics, mainly groundwater recharge and infiltration-runoff relationships. The objective of this study was to evaluate the application of the Variable Infiltration Capacity (VIC) and Lavras Simulation of Hydrology (LASH) hydrological models and analyse the impacts of land use changes on the hydrological regime in the Verde River basin of southeastern Brazil. The land use change scenarios were developed considering demographic, agricultural and economic indicators as follows: 20% (S1) and 50% (S2) conversion of pasture into eucalyptus; 20% (S3) and 50% (S4) conversion of native forest into pasture; and 20% conversion of pasture into forest (S5). The CNS (Nash-Sutcliffe) values varied from 0.77 to 0.88 for daily and monthly fit during calibration (1993–1999) and validation (2000–2005) periods. The LASH model showed a better capacity to reproduce the regular streamflow regime than the VIC model. The reforestation scenarios (S1, S2, and S5) showed a decrease in total runoff, mainly due to the increase in evapotranspiration and canopy interception, while the deforestation scenarios (S3 and S4) showed the opposite effects. Differences in the structure and representation of hydrological processes in hydrological models must be considered to predict the impacts of land use changes on the hydrology of tropical and subtropical basins.

Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea

There is a large number of grid-based climate datasets available which differ in terms of their data source, estimation procedures, and spatial and temporal resolutions. This study evaluates the performance of diverse meteorological datasets in terms of representing spatio-temporal climate variabilities based on a national-scale domain over South Korea. Eleven precipitation products, including six satellite-based data (CMORPH, MSWEP, MERRA, PERSIANN, TRMM, and TRMM-RT) and five reanalysis-based data (ERA5, JRA-55, CPC-U, NCEP-DOE, and K-Hidra) and four temperature products (MERRA, ERA5, CPC-U, and NCEP-DOE) are investigated. In addition, the hydrological performance of forty-four input combinations of climate datasets are explored by using the Variable Infiltration Capacity (VIC) macroscale model. For this analysis, the VIC model is independently calibrated for each combination of input and the response to each combination is then evaluated with in situ streamflow data. Our results show that the gridded datasets perform differently particularly in representing precipitation variability. When a diverse combination of the datasets are used to represent spatio-temporal variability of streamflow through the hydrological model, K-Hidra and CPC-U performed best for precipitation and temperature, followed by the MERRA and ERA5 datasets, respectively. Lastly, we obtain only marginal improvement in the hydrological performance when utilizing multiple climate datasets after comparing it to a single hydrological simulation with the best performing climate dataset. Overall, our results indicate that the hydrological performance may vary considerably based on the selection of climate datasets, emphasizing the importance of regional evaluation studies for meteorological datasets.

Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information

To minimize systematic differences between soil moisture (SM) time series derived from remote sensing (RS) and land surface model (LSM), RS-based SM climatology information is typically discarded during land data assimilation (DA). However, recent studies have demonstrated that SM climatology estimates provided by L-band microwave RS retrievals can significantly outperform comparable estimates derived from LSMs. Consequently, neglecting a RS-based SM climatology may lead to degraded SM spatial patterns generated by land DA. Here, we propose a climatology-optimized SM DA framework, which first calibrates LSM parameters to leverage the RS SM climatology information. Next, multi-sensor RS SM retrievals are assimilated into the calibrated LSM using Ensemble Kalman Filter (EnKF). This framework is demonstrated using the Variable Infiltration Capacity (VIC) model and RS SM retrievals derived from the L-band Soil Moisture Active Passive (SMAP) and C-band Advanced SCATterometer (ASCAT) sensors. Here, both SMAP and ASCAT SM retrievals are assimilated into the VIC model after calibrating VIC to match spatial variations captured in the SMAP SM climatology. The DA SM results are validated using in-situ SM observations derived from 820 stations. Results show that SMAP-based SM climatology calibration directly improves the quality of SM spatial patterns estimated by VIC. In addition, the SMAP-based calibration also benefits our model-error representation and thereby yields better Kalman gains that improve temporal SM DA accuracy. Relative to typical DA approaches, this newly proposed DA framework improves both spatial (0.26 versus 0.51 (−)) and temporal correlations (0.48 versus 0.52 (−)) versus in-situ SM observations. In addition, SM improvements are effectively propagated into improved streamflow estimates – leading to an average increase of Nash and Sutcliffe coefficient from 0.74 to 0.76 (−). Overall, we demonstrate that RS SM climatology information is valuable for land DA and our climatology-optimized framework successfully retains such information to the benefit of land DA performance.

Spatiotemporal variations in evapotranspiration and transpiration fraction following changes in climate and vegetation in a karst basin of southwest China

Evapotranspiration (ET) is a vital factor in terrestrial water and energy cycles. Exploring the spatiotemporal variations in ET and transpiration (T) can improve our understanding of the role of vegetation restoration in the context of climate change. Karst ecosystems are particularly sensitive and vulnerable to environmental change. In this study, we investigated the spatiotemporal changes of ET, T, and the transpiration fraction (T/ET) in the Longjiang River Basin (LRB) of southwest China from 1987 to 2018 using the variable infiltration capacity (VIC) model. In karst areas, the original third soil layer in the VIC model was modified to an epikarst zone and a reservoir module was integrated with the model. Model validation and evaluation showed that the modified VIC model performed well in fitting the observations of runoff and ET. During the 32-year study period, ET, T, and T/ET increased by 2.30 mm·yr−1, 2.21 mm·yr−1, and 0.0006, respectively, with the significant increase concentrated in areas with increased forestland. High positive correlations between NDVI and ET, T, and T/ET were observed in areas where forestland and grassland increased. Temperature and wind speed were positively correlated with ET, T, and T/ET in areas with low altitude and abundant precipitation, where large areas of cropland are present. There was a positive correlation between precipitation and ET in karst areas, whereas the negative correlation between precipitation and T was mainly occurred in non-karst areas. The scenario analysis based on different land use data revealed that ET and T/ET exhibited significant differences in karst areas, but not in non-karst areas, indicating that karst areas were more affected by ecological restoration. Moreover, T did not show a significant increase after vegetation restoration, suggesting that increased ET was mainly caused by evaporation. This study provides insights into the effects of vegetation restoration and climate change on ET, T, and T/ET under different geological conditions and can help implement appropriate afforestation strategies in different geological settings.

Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient

Abstract. Despite the Variable Infiltration Capacity (VIC) model being used for decades in the hydrology community, there are still model parameters whose sensitivities remain unknown. Additionally, understanding the factors that control spatial variations in parameter sensitivities is crucial given the increasing interest in obtaining spatially coherent parameter fields over large domains. In this study, we investigate the sensitivities of 43 soil, vegetation and snow parameters in the VIC model for 101 catchments spanning the diverse hydroclimates of continental Chile. We implement a hybrid local–global sensitivity analysis approach, using eight model evaluation metrics to quantify sensitivities, with four of them formulated from runoff time series, two characterizing snow processes, and the remaining two based on evaporation processes. Our results confirm an overparameterization for the processes analyzed here, with only 12 (i.e., 28 %) parameters found to be sensitive, distributed among soil (7), vegetation (2) and snow (3) model components. Correlation analyses show that climate variables – in particular, mean annual precipitation and the aridity index – are the main controls on parameter sensitivities. Additionally, our results highlight the influence of the leaf area index on simulated hydrologic processes – regardless of the dominant climate types – and the relevance of hard-coded snow parameters. Based on correlation results and the interpretation of spatial sensitivity patterns, we provide guidance on the most relevant parameters for model calibration according to the target processes and the prevailing climate type. Overall, the results presented here contribute to an improved understanding of model behavior across watersheds with diverse physical characteristics that encompass a wide hydroclimatic gradient from hyperarid to humid systems.

Variability and changes in hydrological drought in the Volta Basin, West Africa

Study regionVolta River Basin, West Africa Study focusHydrological droughts have a wide range of impacts on societies and economic sectors such as agriculture and energy production. Analysis of hydrological droughts helps better plan and manage water resources under global change. This study analyses the spatial and temporal variability of hydrological drought occurrence (duration and severity) in the Volta River basin during 1979–2013. We used the Variable Infiltration Capacity and vector-based routing (RAPID) models and high-resolution forcing data to simulate streamflow for 10300 river reaches. New hydrological insights for the regionDrought duration and severity show high spatial variability and large differences between the three decades (1980 s, 1990 s and 2000 s). Droughts are more severe in larger catchments as the flows are generally higher. The trend analysis shows a general decreasing trend (up to 5% per event) in drought duration in the north-eastern and an increasing trend in southern parts of the basin. Drought severity shows an increasing and decreasing trend (up to ± 20%) in south and north part of the basin, respectively. Daily streamflow has a maximum correlation (up to 0.78) with upstream precipitation for the previous 30-days with a clear signal of propagation from meteorological to hydrological drought with an average lag-time of two weeks. The results indicate the need to consider site-specific and adaptive drought management measures to minimize the impacts.

A Bayesian Hierarchical Framework for Postprocessing Daily Streamflow Simulations across a River Network

Abstract Despite the potential and increasing interest in physically based hydrological models for streamflow forecasting applications, they are constrained in terms of agility to generate ensembles. Hence, we develop and test a Bayesian hierarchical model (BHM) to postprocess physically based hydrologic model simulations at multiple sites on a river network, with the aim to generate probabilistic information (i.e., ensembles) and improve raw model skill. We apply our BHM framework to daily summer (July–August) streamflow simulations at five stations located in the Narmada River basin in central India, forcing the Variable Infiltration Capacity (VIC) model with observed rainfall. In this approach, daily observed streamflow at each station is modeled with a conditionally independent probability density function with time varying distribution parameters, which are modeled as a linear function of potential covariates that include VIC outputs and meteorological variables. Using suitable priors on the parameters, posterior parameters and predictive posterior distributions—and thus ensembles—of daily streamflow are obtained. The best BHM model considers a gamma distribution and uses VIC streamflow and a nonlinear covariate formulated as the product of VIC streamflow and 2-day precipitation spatially averaged across the area between the current and upstream station. The second covariate enables correcting the time delay in flow peaks and nonsystematic biases in VIC streamflow. The results show that the BHM postprocessor increases probabilistic skill in 60% compared to raw VIC simulations, providing reliable ensembles for most sites. This modeling approach can be extended to combine forecasts from multiple sources and provide skillful multimodel ensemble forecasts.

The Role of Groundwater Withdrawals on River Regulation: Example From the Columbia River Basin

AbstractThe Columbia River Basin (CRB) is heavily regulated by more than 250 dams on its river system while depending significantly on groundwater withdrawals in certain sub‐basins. Neglecting groundwater withdrawals in hydrologic models of the basin could result in inaccurate predictions of its water budget and thus mislead water management decisions in the basin. This study aims to understand the impacts of groundwater pumping on the spatiotemporal patterns of modeling regulated streamflow in the CRB using a modified version of the Variable Infiltration Capacity (VIC) model integrated with a water management component that accounts for groundwater withdrawals, irrigation demands, and reservoir operation (VIC‐GIRR). The VIC‐GIRR simulations showed that considering additional groundwater withdrawals would alleviate the stress of irrigation water deficit in the Snake River Basin with an average reduction of 10 km3/year. Such a reduction in water deficit resulted in slight streamflow increase over the CRB with maximum increase up to 40% during dry period in certain locations. We also note that the implementation of groundwater withdrawal does not, however, improve the overall model performance in long‐term averaged streamflow and storage predictions. Our results highlight the efforts needed to examine additional important processes in representing the interactions between water withdrawals and reservoir operations. Such efforts will aid in better simulation of multi‐reservoir system and improve effectiveness for agricultural productivity, power generation, flood control, and navigation purposes.

An Analysis of the Impact of Groundwater Overdraft on Runoff Generation in the North China Plain with a Hydrological Modeling Framework

The long-term overexploitation of groundwater has caused sharp decreases in groundwater table depth and water storage in the agricultural areas of the North China Plain, which has led to obvious changes in the runoff process of the hydrological cycle, affecting the mechanism of runoff generation. Evaluating the impact of groundwater overdraft on runoff generation using hydrological models is the focus of the current work. Herein, a hydrological modeling framework is proposed based on the Variable Infiltration Capacity (VIC) model. The optimal parameters of the VIC model were determined by the synergetic calibration method, combining runoff, evaporation, and water storage levels. Meanwhile, a sliding calibration scheme was employed to explore the implied relationships among runoff coefficient, groundwater exploitation, and model parameters, particularly for the thickness of the second soil layer (i.e., parameter d2), both for the whole period and the sliding window periods. Overall, the VIC model showed good applicability in the southern Haihe river plain, as demonstrated by the low absolute value of the relative error (RE) between the simulated and observed data for runoff and evaporation, with all REs < 8%, as well as large correlation coefficients (CC, all > 0.8). In addition, the CCs between the simulated and the observed data for water storage were all above 0.7. The calibrated optimal parameter d2 increased as the sliding window period increased, and the average d2 gradually increased from 0.372 m to 0.415 m, for which we also found high correlations with both the groundwater table and water storage levels. Additionally, increases in the parameter d2 led to decreases in the runoff coefficient. From 2003 to 2016, the parameter d2 increased from 0.36 m to 0.42 m, and the runoff coefficient decreased by about 0.02.

Climatic Control on Spatial Distribution of Water Storage at the Catchment Scale: A Framework for Unifying Saturation Excess Runoff Models

AbstractThis paper aims to investigate the connection between TOPography‐based hydrological model (TOPMODEL) and Variable Infiltration Capacity (VIC) model through virtual experiments from the perspective of water table and storage at the catchment scale. A simple finite‐difference groundwater flow model was built for a hypothetical catchment forced by a sequence of recharges. A steady‐state water table under a low recharge rate is used as the climatic lower limit, above which the pore space is considered as the maximum storage capacity. When the water table is shallow, the land surface is a good proxy of the water table as assumed in the original TOPMODEL, and the underlying water storage distribution curve is similar as the maximum storage capacity distribution curve. When the water table is deep, the climatic lower limit is a good proxy of water table, and the storage is approximately spatially uniform over the unsaturated area as assumed in the VIC model. The systematic variation of water table and storage distribution potentially provides a framework for unifying the TOPMODEL and VIC model.

Climate change impacts on conventional and flash droughts in the Mekong River Basin

Recent drought events in the Mekong River Basin (MRB) have resulted in devastating environmental and economic losses, and climate change and human-induced alterations have exacerbated drought conditions. Using hydrologic models and multiple climate change scenarios, this study quantified the future climate change impacts on conventional and flash drought conditions in the MRB. The Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models were applied to estimate long-term drought indices for conventional and flash drought conditions over historical and future periods (1966–2099), using two emission scenarios (RCP 4.5 and RCP8.5), and four climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For the conventional drought assessment, monthly scale drought indices were estimated, and pentad-scale (5 days) drought indices were computed for the flash drought evaluations. There were overall increases in droughts from the SWAT model for the conventional drought conditions and overall decreases from the VIC model. For the flash drought conditions, the SWAT-driven drought indices showed overall increases in drought occurrences (up to 165%). On the contrary, the VIC-driven drought indices presented decreases in drought occurrences (up to −44%). The conventional and flash drought evaluations differ between these models as they partition the water budget, specifically soil moisture differently. We conclude that the proposed framework, which includes hydrologic models, various emission scenarios, and projections, allows us to assess the various perspectives on drought conditions. Basin countries have differential impacts, so targeted future adaptation strategy is required.

Evaluation of two new-generation global soil databases for macro-scale hydrological modelling in Norway

Lack of national soil property maps limits the studies of soil moisture (SM) dynamics in Norway. One alternative is to apply the global soil data as input for macro-scale hydrological modelling, but the quality of these data is still unknown. The objectives of this study are 1) to evaluate two recent global soil databases (Wise30sec and SoilGrids) in comparison with data from local soil profiles; 2) to evaluate which database supports better model performance in terms of river discharge and SM for three macro-scale catchments in Norway and 3) to suggest criteria for the selection of soil data for models with different complexity. The global soil databases were evaluated in three steps: 1) the global soil data are compared directly with the Norwegian forest soil profiles; 2) the simulated discharge based on the two global soil databases is compared with observations and 3) the simulated SM is compared with three global SM products. Two hydrological models were applied to simulate discharge and SM: the Soil and Water Integrated Model (SWIM) and the Variable Infiltration Capacity (VIC) model. The comparison with data from local soil profiles shows that SoilGrids has smaller mean errors than Wise30sec, especially for upper soil layers, but both soil databases have large root mean squared errors and poor correlations. SWIM generally performs better in terms of discharge using SoilGrids than using Wise30sec and the simulated SM has higher correlations with the SM products. In contrast, the VIC model is less sensitive to soil input data and the simulated SM using Wise30sec is higher correlated with the SM products than using SoilGrids. Based on the results, we conclude that the global soil databases can provide reasonable soil property information at coarse resolutions and large areas. The selection of soil input data should depend on the characteristics of both models and study areas.

Climate change impacts on concurrences of hydrological droughts and high temperature extremes in a semi-arid river basin of China

Hydrological droughts are natural hazards with devastating impacts on different sectors, which may cause even more adverse impacts if concurring with high temperature extremes. Though many studies have evaluated changes in individual hydrological droughts and high temperature extremes, the impact of climate change on their concurrences (or compound extremes) is still lacking. In this study, we investigated changes in compound hydrological droughts and high temperature extremes (CHDHEs) for the period 1961–2016 in the Luanhe River Basin, a semi-arid basin of China. We employed the Variable Infiltration Capacity (VIC) model to simulate runoff, based on which the temporal and spatial variation of CHDHEs was investigated. Results showed that the frequency of CHDHEs increased by 160% from 1961 to 1988 to the recent period 1989–2016. At the spatial scale, a significant increase in the frequency of CHDHEs was shown over 67.37% of the study area. Temperature was shown to play a significant role in the variation of CHDHEs with an average contribution of 57.57%. Results in this study can provide valuable insights for understanding the risk of compound droughts and high temperature extremes in the Luanhe River Basin under global warming.

Sub‐Seasonal Prediction of Drought and Streamflow Anomalies for Water Management in India

AbstractMeteorological and hydrologic prediction at short to sub‐seasonal scales is essential for reservoir operations to mitigate droughts. We examine the skills in the meteorological forecast from the Subseasonal Experiment (SubX) and Extended Range Forecast System (ERFS) for precipitation, maximum and minimum temperatures at 1, 7, 15, and 30 days lead. We bias‐corrected meteorological forecasts using the multivariate bias correction method for hydrologic prediction. The Variable Infiltration Capacity model was used to simulate total runoff and root‐zone soil moisture for India. We also developed a streamflow forecast for the five major river basins that have large reservoirs. Bias correction of meteorological forecast (precipitation, maximum and minimum temperatures) resulted in a considerable improvement in hydrologic and meteorological forecast skills. The Environmental Modeling Center (EMC) model from the SubX provides either better or equal forecast skills for the raw meteorological forecast compared to ERFS, which is an operational product in India. We examined the forecast skills of the meteorological and hydrological products for the two major droughts that occurred recently. We find that most forecast models effectively captured the onset, peak, and termination of the North Indian drought in 2015–2016 and the South Indian drought in 2016–2017 at a 30‐day lead. Bias correction of the meteorological forecast improved the streamflow forecast for the selected drought event upstream of the major reservoirs. The EMC model showed better forecast skills for the two major droughts than other forecast products. Overall, the SubX products show potential for short‐to‐sub‐seasonal scale hydrologic prediction that can assist water management in India.

Climate change impact assessment in a tropical headwater basin

Changes in precipitation and air temperature may produce different impacts on the hydrological regime, compromising water supply. This study focuses on climate change impacts in the Verde River Basin (VRB), a tropical headwater basin in southeast Brazil, located in the state of Minas Gerais. The Variable Infiltration Capacity model (VIC) was calibrated and validated in the Verde River Basin. The downscaling (Eta Regional Climate Model, at 20-km resolution) of three Global Circulation Models (CanESM2, HadGEM2-ES and MIROC5) were used to drive the VIC for a historical baseline (1961-2005) and three time-slices (2011-2040, 2041-2070 and 2071-2099), under RCPs 4.5 and 8.5 scenarios. The scenarios were used as input in the hydrological model after bias correction. The hydrological model (VIC) showed satisfactory statistical performance in calibration and validation, with CNS varying from 0.77 to 0.85 for daily and monthly discharges; however, it overestimated some peak flows and underestimated the recession flows. Multi-model ensemble means predict increases of the minimum and maximum monthly average temperature for the investigated area at the end of the century. The Eta-CanESM2 indicated greater warming, mainly for RCP8.5 at the end the century, whereas Eta-HadGEM2-ES showed higher reduction in the precipitation for RCP4.5 at the beginning of the century and for RCP8.5 at the end the century, negatively impacting the evapotranspiration and discharge. Among the Regional Climate Models (RCMs), the Eta-MIROC5 showed minor changes in the components of the hydrological cycle. This study suggests that Global Circulation Models represent an additional uncertainty, which should be accounted for in the climate change impact assessment.
 Keywords: climate changes, RCP4.5, RCP8.5, VIC model.

Unraveling human influence on evapotranspiration over East Asian monsoon river basins by using GRACE/GRACE-FO data and land surface models

With intensified human influences on the terrestrial water cycle, evapotranspiration (ET) is changed even at a large river basin scale. However, there is no consensus about the significance of the human-induced ET change across river basins due to the uncertainties from the estimations of both observed and natural ET at a large scale. In this study, the human influence on ET is estimated over seven large river basins in the East Asian monsoon region during 2003–2019, where the actual ET estimations based on the water budget method using the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On terrestrial water storage change and the natural ET estimations based on land surface modeling are compared. The state-of-the-art global reanalyses and the Variable Infiltration Capacity (VIC) land surface model are validated against naturalized runoff from the water resources bulletins, and the VIC model is found to outperform global reanalysis data over most southern river basins. Compared with the selected natural ET model data that has a good performance of naturalized runoff, the annual mean human-induced ET in the East Asian monsoon region is estimated to range from 38 ± 28 to 100 ± 76 mm/year (8 ± 6% to 12 ± 9% of the total ET) during 2003–2019. The reservoir evaporation contributes to 5–18% of the human-induced ET change especially over the Huai river basin. Our results reveal that human-induced ET change is more significant over northern semiarid basins than southern humid basins.