Advertisement

SN Applied Sciences

, 1:1698 | Cite as

Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation

  • Nagaveni Chokkavarapu
  • Venkata Ravibabu MandlaEmail author
Review Paper
Part of the following topical collections:
  1. Earth and Environmental Sciences: Meteorological Extremes

Abstract

Water resources are naturally influenced by weather, topography, geology and environment. These factors cause difficulties in evaluating future water resources under changing climate. The Intergovernmental Panel on Climate Change (IPCC) reported that the increasing greenhouse gases which can cause sea level rise increase frequency of storms, heavy rainfall events and droughts. In order to quantify the future change of the hydrological cycle rainfall, different modeling approaches like the use of global climate models, regional climate models and hydrological models (for local scale about 200 km2) are being used. This paper aims to investigate the challenges in modeling because of different parameter or variable or model conceptualization uncertainties using different scenarios, downscaling methods and climatic variables. This work helps to quantify future water resources to maintain quality of water to support aquatic life, agriculture and industrial needs. Challenges in climate change impact analysis in the present research and knowledge gaps are also discussed.

Keywords

Global climate models Regional climate models Hydrological models Downscaling Climate change impact Ensemble models 

1 Introduction

Water is essential for the survival of human beings. Water resources play an important role in the economy of a country. However, water resources are unevenly distributed spatially and temporally and they are under pressure due to anthropogenic activities and natural variability. Natural variability of water resources can be attributed to weather, topography, geology and environment. Man-made pressures on water resources are increasing mainly as a result of urbanization, population growth, growing competition for water and pollution. These changes are aggravated by climate change and variations in natural conditions. The depletion of water resources in many parts of the world is a major environmental problem of this century. Because of human influence and natural variability, it has always been a challenge for researchers and planners to assess the water resources with minimum uncertainty under continuous perturbed climatic conditions.

Global climate changes influence hydrological and meteorological characteristics at spatial and temporal scales [66]. According to Intergovernmental Panel on Climate Change [52], climate change is not only due to natural phenomenon variability but also as a result of global warming. The changing climate with high influence of global warming has significant impact on the hydrological cycle [6], which further alters precipitation, runoff, evapotranspiration, etc., changing entire water body characteristics [67] and water quality [137]. River basins are often influenced by increasing population and land use patterns along with climate change. These factors are responsible for uncertainty in the hydrological response estimation. Under perturbed climatic scenario, the increasing temperature and changing rainfall patterns affect hydrological responses. The impacts of climate change are major consideration for integrated assessment of water resources. Simulation models with future projections of hydrological variables like GCM, RCM, and hydrological models are being used for future planning and management in order to combat increasing water demand and scarcity. Integration of models considers feedback between the climate and hydrological system. Another emerging field of projection of future predictions is the multi-model ensembles (MMEs) [9]. Multi-model ensembles give more reliable outputs concerning future runoff in the basins. Apart from climate change, some major factors like land use, land cover and slope along the river basins govern runoff. Evaporation plays a major role in forests along the river banks in deciding the runoff [9]. Simulation models require much advancement in order to predict regional precipitation, runoff and evaporation accurately. According to [55] report, the planets climates are expected to change in such a way that some regions are likely to experience greater amounts of precipitation, while other regions experience lower amounts of precipitation. Model selection to evaluate future climate change considers the fact that increasing greenhouse gases plays a major role. Future climate changes due to rising greenhouse gases are expected to increase both the intensity and frequency of precipitation in central India from 2070 to 2099 [93]. Several studies reveal the impact of future climate change extremes over India in terms of precipitation such as increased (floods) or scarcity (drought) or number of wet and dry days. Such extremes are expected to have serious impact on food, economy, life and property. For India, which is an agro-based country, such changes are likely to have a serious impact.

The analysis of climate impact can be categorized into the following groups:
  1. (a)

    Study of historical trends of stream flow/runoff water with seasonal variations daily/monthly/seasonally

     
  2. (b)

    Using general circulation models (GCMs)

     
  3. (c)

    Downscaling GCM output to reduce uncertainty

     
  4. (d)

    Suitable regional climate models

     
  5. (e)

    Local hydrological models

     

2 General circulation models

To simulate the present climate and to predict future climate change, general circulation models (GCMs) have been developed. The output data of GCM are too coarse to use in hydrological modeling [15] directly. Instead of using single GCM, combinations of multiple GCMs [1] and scenarios reduce uncertainty associated with results [25, 122, 128]. The future climatic series are generally drawn from GCMs, which simulate global and regional climate system [53].

GCMs are considered as major source to explore complexity of climate and to give quantitative measures of future climate change [52]. As GCMs are with large grid size (grid-cell resolutions of approximately 250 and 600 km), it would be difficult to predict variations in weather variables like local variance, persistence, frequencies, topography, etc., because of which GCMs do not accurately define local phenomenon; hence, results are often downscaled to use with hydrological models to assess impacts of projected climate changes [22]. As the future climate cannot be known with certainty, climate change projections must be used to assess the possible future climate trends. Precipitation is inherently difficult to model with climate variables, as local topographic variables have greater control on precipitation occurrence. The highest source of uncertainty in climate impact [50] studies comes from differences between projections among GCMs, followed by differences among emissions scenarios [15, 76, 128]. In many studies, the source of uncertainty is calculated separately (GCMs, RCMs, downscaling methods, emission scenarios and hydrological models). Downscaling bridges the gap between GCM and fine-resolution hydrological models at local scale [104]. One-way coupling approach is useful to evaluate future changes in precipitation [98], evaporation and runoff [16], which integrates GCM scenario outputs with hydrological models through statistical downscaling methods. Using sensitivity analysis, parameters are identified which influence model validation [29] based on observed variables and weather variables at each location. Multi-model approach using mean of ensembles gives better assessment of possible future climate change.

3 Different methods of downscaling

Different methods are used to downscale the output data from GCM model to match with hydrological data and surface variables (Fig. 1). The common methods of downscaling are (1) delta/ratio methods, (2) stochastic/statistical downscaling and (3) dynamic downscaling or nested models.
Fig. 1

Schematics representing three possible methods to use GCM-simulated temperature for hydrological modeling: a direct use of GCM temperature, b delta-change approach and c scaling approach.

Source: [111]

While studying climate change impact, if only one downscaling method is chosen then results must be calibrated carefully. The specific drawback of each downscaling method results from climate projection scenario (Table 1).
Table 1

Comparative study of statistical and dynamical downscaling techniques [125]

 

Statistical downscaling

Dynamical downscaling

Advantages

Comparatively cheap and computationally efficient

Can provide point-scale climatic variables from GCM-scale output

Can be used to derive variables not available from RCMs

Easily transferable to other regions

Based on standard and accepted statistical procedures

Able to directly incorporate observations into method

Produces responses based on physically consistent processes

Produces finer resolution information from GCM-scale output that can resolve atmospheric processes on a smaller scale

Disadvantages

Require long and reliable observed historical data series for calibration

Dependent upon choice of predictors

Non-stationarity in the predictor–predictand relationship

Climate system feedbacks not included

Dependent on GCM boundary forcing; affected by biases in underlying GCM

Domain size, climatic region and season affect downscaling skill

Computationally intensive

Limited number of scenario ensembles available

Strongly dependent on GCM boundary forcing

Precipitation and stream flow models need very fine-scale parameters from GCM downscaling [111] output data. Statistical downscaling and dynamic downscaling methods are dependent on GCM boundary forcing, domain size and location. As uncertainties can propagate through the chain of GCM, RCM, downscaling, hydrological modeling, emission scenario, it is still a major obstacle to quantify and reduce the uncertainty associated with each source [111, 112]. The hydrological responses such as stream flow [76], surface runoff and evapotranspiration change seasonally [16] with climatic change [108]. The dynamic downscaling approach cannot be directly used due to uncertainties between the grid-scale and point-scale observed data. RCM data can be corrected using downscaling method considering that present-day climate remains invariant in future climate projections [4]. GCMs’ outputs are used as predictors to estimate impact of climate change on regional precipitation using a statistical downscaling method [18, 21]. The main demands on large-scale variables are:
  1. 1.

    Reliably simulated by GCM

     
  2. 2.

    Readily available from archives of GCMs’ output

     
  3. 3.

    Strongly correlated with the surface variables of interest

     
  4. 4.

    Carry climate change information. [126]

     

Climate change impact on future water resource studies allows the prediction of present and future climate and estimates the future discharges in river basins [2]. A bias correction technique applied to GCM-downscaled output data helps remove bias between simulated and observed results. While predicting future climate variables [33, 76] like the winter precipitation in north India, the model produced near real-time observations [114]. Regression-based statistical downscaling methods establish a statistical relationship between GCM outcome, climate variables and local surface variables depending on the historical record. Delta method has an impact on statistical properties like mean and variance of temperature and precipitation. Sometimes, the final output is biased when corrections are made to variables [18]. Bias corrections in RCM’s output before using it as input in hydrological model is recommended as RCMs are reported to have inherent systematic errors due to imperfect conceptualization. The choice of model has a significant impact on climate change response on hydrological variables [36]. Water bodies are sensitive to climate change impact as base flow is influenced by temperature and precipitation [83] which cause change in seasonal low flows and high flows [20, 135].

4 Regional climate models (RCMs)

RCM (approximately 25–50 km) is better suited for areas with complex physiographical features because of its higher resolution as compared to GCM. Growing greenhouse gases and aerosols indicate increase in precipitation and temperature by the end of the twenty-first century [99]. RCM outputs are being used for hydrological analysis like effects of climate change on ground water [118], runoff estimation, flood risk assessment, precipitation [85], global warming [4], land use–land cover change [106] and evaporation calculations. The use of RCM output is being increased due to availability of large number of models and the capability of the model to represent the local climate [33]. The RCM-projected future climate changes are further influenced by human-induced climate change at regional level, and hence it needs further investigation of results [13].

RCM predictions over India often show over estimates in Western Ghats due to dry bias [26, 86] as the atmospheric moisture is removed by the warm ocean currents.

5 Hydrological models

Hydrological models predict future changes of total water yield in a basin including stream flow/runoff using historical data of temperature and precipitation with respect to greenhouse gases, precipitation, land use and land cover. Present hydrological models are being improved according to IPCCs Vth Report based on new emission scenarios, i.e., the Representative Concentration Pathway (RCP) [119, 131]. Simulation models need to improve their interconnectivities of monsoonal climate, extreme events of weather (floods and droughts), temporal and spatial variations in rainfall, increase in human population demands and scarcities in the future (Fig. 2).
Fig. 2

Scheme of the climate variable transfer from global to catchment scale

Basic models used for hydrological studies are
  1. (a)

    Statistical hydrological models

     
  2. (b)

    Physical hydrological models.

     

At local scale (about 10,000 km2) to sub-basin level, physical-based hydrological models are used for climate change impact studies. For good performance of hydrological models, downscaled GCM data with more than one statistical method [17] must be used. Many hydrological predictions do not allow direct feeding of GCM output into a calibration-free hydrological model [30]. It is evident that atmospheric mechanisms influence soil moisture [10] which is one of the variables of hydrology at regional level [116].

6 Parameters used for future climate projections

6.1 Rainfall–runoff

Rainfall, soil moisture and land cover are important factors to predict runoff within a watershed. Future water fluxes can be calculated using rainfall–runoff models to analyze climate changes using satellite climate data as weather input [23]. In order to understand catchment hydrology, there is a need to estimate seasonal and annual values of rainfall, surface runoff, evaporation and recharge rates. Climate change may impact surface and groundwater resources [8] like severe floods, precipitation and temperature, surface runoff and base flow of catchment which affect ecosystem evapotranspiration, soil infiltration capacity and surface and subsurface flow regimes [92]. Future projected precipitation changes influence surface runoff and evapotranspiration, which differ across the globe and are expected to have implications on freshwater ecosystems [22], yet there is a high-level agreement in terms of the magnitude and intensity of the change in flow in many regions [6]. Application of the same model in different regions for future runoff estimation gives different results [5] due to seasonal and inter-annual variations within the catchment [97] while average flows and the extreme events are affected by climate change [134]. The runoff is sensitive to temperature and rainfall as it shows different runoff amounts in arid and humid regions. This sensitivity also changes with the type of management practice. While considering precipitation changes with respect to climate change, there is a need to consider plant water use efficiency [61] because different factors show varying feedback effects to changing climate [109]. Macroscale and semi-distributed monthly water balance models are good at predicting the magnitude, timing of runoff and condition of water resources to simulate and predict the hydrological processes [43] like monthly runoff and soil moisture of basins in different climatic conditions.

6.2 Potential evaporation (PE)

Potential evaporation [5] is an important component in the water budget of the catchment and input to the rainfall–runoff model. Slow increase in PE, due to increasing temperature [23], has an impact on annual hydrological cycle [5]. Seasonal variability accounts for major impact on agriculture [58]. Impact of climate change on global water resources gives an overview about future water availability with increasing population [101]. Delayed beginning of rainy season and decrease in rainfall during rainy season may decrease annual discharge over long term due to an increase in potential evapotranspiration [97]. Rate of evaporation depends on total incoming net radiation, amount of rainfall, wind speed and direction and vapor pressure [71]. Changing climate scenarios of RCP are likely to predict impact of CO2 on global climate, but it does not consider local climate feedback at global scale, such as plant physiology, number and spread area influence on local climate. India, which is a vast geographical region with varying climate, topography and geology, is also sensitive to greenhouse effect and requires very detailed knowledge on precipitation pattern and influencing parameters so as to incorporate in model structure to reduce bias and uncertainty. Northeastern states and south peninsular India are expected to experience increase in precipitation during summer and reduction in monsoon precipitation under global warming conditions [129].

6.3 Greenhouse gases and CO2

Water scarcity severely affects food production and economic stability of any country. Global hydrological models (GHMs) forced with GCM and latest greenhouse gas concentration scenarios (RCP) provide future predicted changes of water bodies. According to RCP projections, an increment in temperature by 2 °C above the present state would affect nearly 15% of the global population through water scarcity [101]. Increasing temperatures across globe have considerable influence on cropping pattern [115] and changes in carbon balance of an ecosystem [100]. Global warming has a significant impact on water systems due to increasing troposphere temperature [120] and changes in extreme precipitation [7]. Climate changes are expected to increase climate-related risks to ecosystems due to increased heat, flooding, drought on natural and managed ecosystems like pest and disease prevalence, invasion of non-native species [48], ecosystem disturbances, land use change and landscape fragmentation [91]. Runoff is important in an ecosystem, as transpiration and interception processes are influenced by biological processes and affected by CO2 concentrations and climate [38]. Vegetation interactions with CO2 increase, and climate change magnitude and intensity coupled with seasonal timings have a significant influence on plant physiology [46]. Amount of carbon storage capacity varies with changing CO2 and temperature [37].

6.4 Land cover and population growth

Climate change is associated with changes in temperature and precipitation intensity which further affect the hydrology of the region and exaggerated by land use change in the form of soil compaction and more impervious surface coverage. The impacts of climate change are more significant than LULC change in determining the basin hydrological response [65], but it is important to consider land use–land cover changes and associated flood and drought patterns when developing water resource management plans. Land use changes are intensified because of rapidly increasing population, agricultural production, deforestation, reclamation of wet lands, etc. These changes locally demand more water, while climate change results in low or high precipitation and increasing temperature and evaporation rates which leads to scarcity of water. Future water stress is calculated by the ratio of water use to availability [47] which can be attributed to economic growth, lifestyle changes and technological developments [101, 123]. The total amount of water and future water demand should be considered while modeling future hydrological responses to climate change [66]. The relative impacts of climate [103] and water withdrawals [24] need to be given priority for better water management planning. The winter and summer seasonal changes cause water stress [138] in the catchment [24]. Future change in rainfall and population also leads to change in land use–land cover, further increasing soil degradation. Watershed base flow is affected by topography and geomorphology which influence the water storage properties and water transmission within a catchment [88]. Even in regions characterized by relatively low-intensity land use change, yet due to climate change, there have been clear reductions in base flow quantity and quality. Reduced base flows contribute to impairments known to affect fish, invertebrates and algal assemblages [96].

6.5 Flood

To investigate the flood frequency and re-occurrence, weather generator [69] can be used to generate time series of precipitation and temperature for long periods. Rainfall–runoff models predict future stream flow extremes and future flood risk assessment from climatic projections [124]. The model simulations show variations in both seasonal precipitation and stream flow which can be attributed to human-induced climate change and natural inter-variability of climate because future projections do not consider vulnerability of risk components which may change over time. Flood risk is expected to continue increasing due to increasing population, land use–land cover change, socioeconomic development and climate change in the coming decades [54, 121]. Increasing global sea levels (0.26–0.82 m) by 2100 results in more severe coastal floods [55]. GCM’s output data are of relatively low spatial resolution, yet it enables large-scale analysis of floods in countries with data scarcity. Availability of continental and global-scale land use–land cover data supports large-scale forecasting of land use change dynamics that include the uncertainties associated with changing climate. Highly populated coastal areas are more prone to floods due to land use change, deforestation, extension of urban areas [32] and land subsidence causing hydrological changes [79]. Advances in mapping the flood hazard are possible with flood models integrated with suitable high-resolution topography [133]. Socioeconomic flood risk assessment based on an ensemble of the latest regional climate scenarios [3] and combining flood inundation maps with information on assets exposure and vulnerability [94] lead to an overall evaluation of the future flood risk and the related uncertainty.

7 General features of impact assessment procedure

Future changes in hydrological extremes like precipitation and temperature are still highly uncertain. According to [54], world climate changes are expected to result in severe rainfall extremes which are likely to differ from region to region. These uncertainties can be addressed using downscaling methods as the propagation of uncertainty through GCM, RCM and hydrological models [41] and identification of the most suitable emission scenarios [62]. In downscaling methods, it is usually assumed that the relationship between local-scale phenomena and regional climate is expected to be the same in the future; calibration of rainfall–runoff models depends on the climatic and temporal variability of model parameters [49]. The intensity of the hydrological response to the simulated climate change varies with the geological conditions of the area such as soils [116] and air temperature [11]. Model performance varies systematically with climatic conditions due to large differences in model structure, but the ensemble mean produces rather robust predictions [42]. Increasing or doubling of the atmospheric carbon dioxide concentration leads to negative impacts on groundwater resources as soil water deficit increases in root zone. Large amount of groundwater withdrawal and seasonal variations have small impact on groundwater. In impact studies, the site-specific models are important as they can be well calibrated with local physical conditions to simulate hydrological responses successfully [51]. Small changes in precipitation show large decrease in runoff due to increasing evapotranspiration. Local area hydrological response varies with geological conditions and climate change. Conversion of land use type results in decrease or increase in seasonal flows, flow intensities, evaporation and runoff. Such analysis must be considered while planning to build adaptation measures to overcome climate change for present and for future sustainable management efforts.

The climate change caused by local conditions on hydrology is less predictable and more diversified due to catchment properties like soil and topography which can add uncertainty to hydrological modeling [60]. Increase in greenhouse gases in atmosphere from the past 50 years results in continental runoff increase, heavy precipitation, flooding and drought [75]. RCMs are more efficient to capture the spatial variability of precipitation distribution at regional scale [70, 122] (Table 2).
Table 2

Literature collection using global, regional and hydrological models with SRES and RCP scenarios

References

Global climate model (GCM)

Regional climate models (RCM)

Hydrological model

IPCC projection

Climatic variable

Suh et al. [107]

HadGEM2-AO

4 RCMs of GRIMs, HadGEM3-RA, RegCM4, SNURCM, WRF

RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5

Surface air temperature

Li et al. [72]

2 GCMs of RMIP III

8 RCMs based on MM5, WRF, RAMS

A1B scenario

Future precipitation change (2041–2060)

Ruosteenoja et al. [98]

CMIP5

RCP 8.5

Monthly, seasonal and annual means of surface air temperature, precipitation and incident solar radiation

Soro et al. [104]

HadGEM2-ES

GR2 M

RCP 4.5, RCP 8.5

Monthly rainfall and temperature

Didovets et al. [29]

7 GCM

7 RCM

Soil and Water Integrated Model (SWIM)

RCP 4.5, RCP 8.5

Seasonal distribution of runoff

Keuler et al. [62]

MPI-ESM-LR, HadGEM2-ES, CNRM-CM5,EC-EARTH

COSMO-CLM

RCP 4.5, RCP 8.5

Climate change

[136]

SMHI-RCA, CNRM-ARPEGE

GBM

RCP 8.5

Climate change, surface temperature

Hosseinzadehtalaei et al. [50]

CMIP5

RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5

Impact of climate change on extreme precipitation

Chilkoti et al. [19]

CORDEX

HYMOD

Representative concentrated pathways (RCP) 4.5

Impact of the future climate on water availability

Karlsson et al. [58]

2 GCMs

HIRHAM5

MIKE SHE

RCP 4.5

Impacts on soil moisture dynamics and evapotranspiration

Lutz et al. [73]

CMIP5

RCP 4.5, RCP 8.5

Surface air temperature, precipitation

Oh et al. [84]

HadGEM2-AO

RegCM4, GRIMs, SNURCM, and WRF

RCP 2.6, 4.5, 6.0, 8.5

Seasonal monthly precipitation

Sunde et al. [108]

CMIP5

SWAT

RCP 2.6, 8.5

Stream flow, evapotranspiration

Brovkin et al. [12]

CMIP5

RCP 2.6, 8.5

Surface annual mean temperature

Jayasankar et al. [57]

CMIP5

RCP 2.6, 8.5

Seasonal cycle of rainfall

8 Ensemble and multiple ensembles

The selection of climate models must be based on the range of changes in climate variables or the ability of climate models to simulate past climate. Ensemble of GCMs and RCMs with transfer of signal change into hydrological models shows different seasonal trends with the same output results [41]. Model behavior and the ability to simulate hydrological processes can vary in different climate regimes [90] as individual models differ regarding magnitudes and trend direction [105]. The result of hydroclimatic ensemble [81] approach is comparable, and future uncertainty can be evaluated. Averaging multi-model RCM ensembles based on different driving GCMs helps to reduce systematic errors. This confirms the importance of multi-model approach [42] to improve the consistency in future climate change projections [28]. Ensemble of hydrological model evaluates the range of climate variables [82] because of different processes of hydrological models. The relative contributions of hydrological models and GCMs to ensemble spread [45] differ spatially across the globe [101]. Ensembles of global-scale hydrological models give relative changes in hydrological indicators with different scenarios of global warming so as to understand the changes in hydrological indicators [117] at catchment level [40] like stream flow [19], spatial and seasonal variations [107]. If models are not calibrated with observed values, the results may be influenced by both model structure and calibration. In agricultural lands, the amount of water availability influences evaporation rate. Hence, soil moisture [113], rainfall [90] and evaporation variables must be included while assessing water demands for irrigation under climate change [78].

An inter-model comparison identified that not all models were consistent in explaining the present climate in terms of the mean, skewness and asymmetry [31]. Assessment of future climate with higher accuracy can be achieved by using ensemble mean [44], and the spread in projections of one single hydrological parameter for each ensemble is usually larger than the difference between the two ensemble medians [40]. The occurrence of time of extreme events during seasonal precipitation can be estimated using changes between precipitation projection model and climate model ensemble [35]. The analysis of individual RCM and its ensemble using the same driving force show different biases and give mixed results of seasonal temperature and precipitation. The population, climate change, expansion of agricultural land and global warming [95] are expected to change the regional climate. In order to assess the future climate change reliably, complete understanding of climate variability and land cover changes in past is required. The production of the ensemble method is affected by simulation skills and projection results of RCMs [72]. Ensemble simulation has been used in both weather forecasting and climate simulation to reduce the uncertainties and therefore improve the reliability of the simulation system. The predicted changes of precipitation are influenced by evapotranspiration and atmospheric moisture [122].

With the help of the ensemble modeling, it has been proven that soil surface temperature [136] and land cover are responsible for precipitation changes. Multi-model and multi-projection approach to create probability distribution functions of future hydrological variables helps to better define the uncertainty linked to future climate. The probability distributions of future hydrological variables allow future impacts to be estimated. It is evident that uncertainty related to multi-model approach is greater than multi-projection approach [76]. Some ensembles incorporate additional processes like cloud formation, radiation balance, etc., in order to improve the climate simulations. These models can assess the potential impacts of future climate change at local scale.

9 Observations

A future projected change in precipitation varies depending on the type of GCMs, greenhouse gas emission scenarios, time horizon and season. Inter-model variability is always greater than inter-scenario variability. For future projections, hydrological model calibration can become a limitation factor. Climate models when combined with satellite observations indicate an increase in atmospheric water by 7% per degree Celsius of surface temperature.

To obtain more accurate and near real-time values, the hydrological response to the future climate including the indirect impact of climate changes like sea level increase, agricultural practices and type of land use, and increased water demand both domestic and agriculture must be considered. Increasing greenhouse gases increase evapotranspiration, thus affecting availability of water and crop yield. Some of the parameters affecting irrigation water requirements are type of crop, soil type, precipitation, temperature, cropping pattern, crop season, intensity of light, etc. However, the best results can be predicted by averaging all the available RCMs, as this technique can counterbalance the errors coming from different models. But different RCMs forced by the same GCM show significant errors which indicate that the processes and parameter selection are important while selecting the model response to the boundary forcing. RCMs are well functional at simulating some basin parameters or variables, but extreme events of rain cannot be significantly simulated.

For smaller regions, local climate mainly depends upon local feedback mechanisms because of which model selection should consider local climate, topography and vegetation conditions of the area. The sensitivity of runoff to climate change has direct influence on long-term soil moisture and basin soil types. As per research, the better method would be to use multi-downscaling techniques so as to consider the error propagation in hydrology. Local precipitation quantities depend on wet/dry day and regional-scale indicators like mean sea level pressure, specific humidity and geo-potential height. Based on the results from different models, it is possible to predict progressive modification over the coming decades in climate variables (Table 3).
Table 3

Supporting review papers on downscaling, RCM and hydrological modeling

Kidson and Thompson [64]

A comparison of statistical and model-based downscaling techniques for estimating local climate variations

Downscaling climate variables using statistical and local area mesoscale modeling (RAMS) for daily and monthly climate variables

Murphy [80]

An evaluation of statistical and dynamical techniques for downscaling local climate

Dynamical and statistical methods are compared in terms of the correlation between the estimated and observed precipitation and temperature monthly

Casanueva et al. [14]

Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative

Comparison of statistical and dynamical downscaling methods on the same grid

Prudhomme et al. [89]

Downscaling of global climate models for flood frequency analysis: where are we now?

A review of the different methodologies suggested in the literature to downscale GCM results at smaller spatial and temporal resolutions

Fowler et al. [34]

Linking climate change modeling to impact studies: recent advances in downscaling techniques for hydrological modeling

This review paper assesses the current downscaling literature, examining new developments in the downscaling field with reference to hydrological impacts

Xu [132]

From GCMs to river flow: a review of downscaling methods and hydrologic modeling approaches

Climatic variable downscaling and the problems related to the practical application of appropriate models in impact studies. Advantages and deficiencies of the various approaches; challenges for the future study of the hydrological impacts of climate change are identified

Graham et al. [41]

Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods—a case study on the Lule River basin

Different regional climate model simulations are used to analyze the affects of climate change impacts on hydrology

Wood et al. [130]

Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs

Downscaling climate model outputs for use in hydrological simulation to estimate each method’s ability to produce precipitation and other variables of hydrology

Langousis et al. [68]

Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results

Relative performance of direct statistical correction of climate model rainfall outputs using nonparametric distribution and statistical downscaling methods in reproducing the historical rainfall statistics at a regional level

Schmidli et al. [102]

Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps

Inter-comparison of daily precipitation statistics as downscaled by six statistical and three dynamical methods

Wilby and Wigley [125]

Downscaling general circulation model output: a review of methods and limitations

Comparison of downscaling tools from GCM output

Wilby et al. [127]

A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado

This study compared three sets of present and future rainfall–runoff scenarios using scenarios developed statistically downscaled GCM output, raw GCM output and raw GCM output corrected for elevation biases

Kauffeldt et al. [59]

Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level

Uncertainty in future hydrological model prediction often due to weather inputs from climate models and model conceptualization. A multi-model system can collectively capture a representative spread of model uncertainty. This paper provides a technical review of 24 large-scale models to provide guidance for model selection

Praskievicz and Chang [87]

A review of hydrological modeling of basin-scale climate change and urban development impacts

Modeling is associated with uncertainty at the basin scale due to latitude, topography, geology and land use. Under scenarios of future climate change, water quality is determined by type of pollutants, surface runoff and urban development. Modeling studies should be based on scenarios constructed based on impacts of climate changes and future basin characteristics

Diaconescu et al. [27]

Evaluation of precipitation indices over North America from various configurations of regional climate models

This study presents the comparison of simulations from two Canadian regional climate models (RCMs) in simulating daily precipitation indices and the standardized precipitation index (SPI)

Giorgi [39]

Regional climate modeling: Status and perspectives

This paper presents a review of regional climate modeling in 1980s applications and possible future directions in RCM research

Jacob et al. [56]

An inter-comparison of regional climate models for Europe: model performance in present-day climate

The analysis of possible regional climate changes over Europe is simulated by 10 regional climate models to investigate possible systematic biases in the models to simulate the long-term mean climate and the inter-annual variability with respect to near-surface air temperature and precipitation during winter and summer

Mearns et al. [74]

Guidelines for use of climate scenarios developed from regional climate model experiments

The purpose of this guidance material is to develop, select and use procedures for evaluating, producing and using high-resolution climate scenarios

Teutschbein and Seibert [110]

Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies

This article reviews applications of regional climate model (RCM) output for hydrological impact studies using single RCM and ensemble RCM

10 Conclusions

To obtain reliable assessment requires the estimates of future climatic trends with perfection at regional and local scale. It is confirmed that modeling can address the climate change and water resource problems. These problems are related to inefficiency of producing reliable parameters by GCMs due to their coarse resolution, downscaling and their limitations and tools used in hydrological modeling. The main uncertainty arises from the variations between GCMs and hydrological modeling in their spatial and temporal resolution. Ensemble and multiple ensembles give reliable mean precipitation and temperature with observed values. Simulations with different greenhouse gas scenarios are likely to produce increasing precipitation over India in near future [93]. The transfer of simulated values across different scales, models and regions is still a challenge. Models simulate catchment characteristics and variables at sub-basin scale. Few studies explain the reason for heavy precipitation as warm climate in central India [63], while few models fail to capture heavy precipitation with warming [77]. Hence, it is necessary to improve the model capacity, bias adjustment and uncertainty assessment while estimating future climate changes at local and regional scale.

The decision-makers make use of simulated future hydrological variables and trends to take long-term decisions based on sensitivity assessment, adaption and vulnerability to changing climatic conditions.

Notes

Acknowledgements

Authors would like to thank anonymous reviewer for their constructive comments to improve the manuscript technically. Also, they would like to thank Dr. Sainu Franco, A/Professor & Head of Civil Engineering, Providence College of Engineering, India, for reviewing our manuscript toward language corrections. Authors are grateful to Mr. P.V.S. Sylesh and Mr. Pragadeesh Kumar M, Research Scholars, NIT Warangal, TS, India, for their contribution while working on this paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. 1.
    Acharya N, Shrivastava NA, Panigrahi BK, Mohanty UC (2014) Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine. Clim Dyn 43(5–6):1303–1310.  https://doi.org/10.1007/s00382-013-1942-2 CrossRefGoogle Scholar
  2. 2.
    Akhtar M, Ahmad N, Booij MJ (2008) The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios. J Hydrol 355:148–163.  https://doi.org/10.1016/j.jhydrol.2008.03.015 CrossRefGoogle Scholar
  3. 3.
    Alfieri L, Feyen L, Dottori F, Bianchi A (2015) Ensemble flood risk assessment in Europe under high end climate scenarios. Glob Environ Change 35:199–212.  https://doi.org/10.1016/j.gloenvcha.2015.09.004 CrossRefGoogle Scholar
  4. 4.
    Anderson L, Samuelsson P, Kjellström E (2011) Assessment of climate change impact on water resources in the Pungwe river basin. Tellus A 63:138–157CrossRefGoogle Scholar
  5. 5.
    Ardoin-Bardin S, Dezetter A, Servat E, Paturel JE, Mahe G, Niel H, Dieulin C (2009) Using general circulation model outputs to assess impacts of climate change on runoff for large hydrological catchments in West Africa. Hydrol Sci J 54:77–89CrossRefGoogle Scholar
  6. 6.
    Arnell NW, Gosling SN (2013) The impacts of climate change on river flow regimes at the global scale. J Hydrol 486:351–364CrossRefGoogle Scholar
  7. 7.
    Asong ZE, Khaliq MN, Wheater HS (2016) Projected changes in precipitation and temperature over the Canadian Prairie Provinces using the Generalized Linear Model statistical downscaling approach. J Hydrol 539:429–446.  https://doi.org/10.1016/j.jhydrol.2016.05.044 CrossRefGoogle Scholar
  8. 8.
    Awotwi A, Kumi M, Jansson PE, Yeboah F, Nti IK (2015) Predicting hydrological response to climate change in the White Volta Catchment, West Africa. Earth Sci Clim Change 6:1–2.  https://doi.org/10.4172/2157-7617.1000249 CrossRefGoogle Scholar
  9. 9.
    Bhatt D, Mall RK (2015) Surface water resources, climate change and simulation modeling. Aquat Procedia 4:730–738.  https://doi.org/10.1016/j.aqpro.2015.02.094 CrossRefGoogle Scholar
  10. 10.
    Berg A, Sheffield J, Milly PC (2017) Divergent surface and total soil moisture projections under global warming. Geophys Res Lett 44(1):236–244CrossRefGoogle Scholar
  11. 11.
    Blöschl G, Montanari A (2010) Climate change impacts—throwing the dice. Hydrol Process 24:374–381.  https://doi.org/10.1002/hyp.7574 CrossRefGoogle Scholar
  12. 12.
    Brovkin V, Boysen L, Arora VK, Boisier JP, Cadule P, Chini L, Claussen M, Friedlingstein P, Gayler V, Van Den Hurk BJJM, Hurtt GC (2013) Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in CMIP5 projections for the twenty-first century. J Clim 26(18):6859–6881CrossRefGoogle Scholar
  13. 13.
    Campbell JD, Taylor MA, Stephenson TS, Watson RA, Whyte FS (2011) Future climate of the Caribbean from a regional climate model. Int J Climatol 31:1866–1878.  https://doi.org/10.1002/joc.2200 CrossRefGoogle Scholar
  14. 14.
    Casanueva A, Herrera S, Fernández J, Gutiérrez JM (2016) Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative. Clim Change 137(3–4):411–426CrossRefGoogle Scholar
  15. 15.
    Chen J, Brissette FP, Leconte R (2011) Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J Hydrol 401:190–202.  https://doi.org/10.1016/j.jhydrol.2011.02.020 CrossRefGoogle Scholar
  16. 16.
    Chen J, Brissette FP, Poulin A, Leconte R (2011) Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resour Res 47:W12509.  https://doi.org/10.1029/2011WR010602 CrossRefGoogle Scholar
  17. 17.
    Chen H, Xiang T, Zhou X, Xu CY (2012) Impacts of climate change on the Qingjiang Watershed’s runoff change trend in China. Stoch Environ Res Risk Assess 26:847–858.  https://doi.org/10.1007/s00477-011-0524-2 CrossRefGoogle Scholar
  18. 18.
    Chen H, Xu CY, Guo S (2012) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrol 434:36–45.  https://doi.org/10.1016/j.jhydrol.2012.02.040 CrossRefGoogle Scholar
  19. 19.
    Chilkoti V, Bolisetti T, Balachandar R (2017) Climate change impact assessment on hydropower generation using multi-model climate ensemble. Renew Energy 109:510–517.  https://doi.org/10.1016/j.renene.2017.02.041 CrossRefGoogle Scholar
  20. 20.
    Choi W, Rasmussen PF, Moore AR, Kim SJ (2009) Simulating streamflow response to climate scenarios in central Canada using a simple statistical downscaling method. Clim Res 40(1):89–102.  https://doi.org/10.3354/cr00826 CrossRefGoogle Scholar
  21. 21.
    Chu JL, Yu PS (2010) A study of the impact of climate change on local precipitation using statistical downscaling. J Geophys Res Atmos 115:D10105.  https://doi.org/10.1029/2009JD012357 CrossRefGoogle Scholar
  22. 22.
    Cisneros BJ, Oki T, Arnell NW, Benito G, Cogley JG, Döll P, Jiang T, Mwakalila SS (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  23. 23.
    Cleridou N, Benas N, Matsoukas C, Croke B, Vardavas I (2014) Water resources of Cyprus under changing climatic conditions: modelling approach, validation and limitations. Environ Model Softw 60:202–218.  https://doi.org/10.1016/j.envsoft.2014.06.008 CrossRefGoogle Scholar
  24. 24.
    Collet L, Ruelland D, Borrell-Estupina V, Servat E (2014) Assessing the long-term impact of climatic variability and human activities on the water resources of a meso-scale Mediterranean catchment. Hydrol Sci J 59(8):1457–1469.  https://doi.org/10.1080/02626667.2013.842073 CrossRefGoogle Scholar
  25. 25.
    Crosbie RS, Dawes WR, Charles SP, Mpelasoka FS, Aryal S, Barron O, Summerell GK (2011) Differences in future recharge estimates due to GCMs, downscaling methods and hydrological models. Geophys Res Lett 38(11):L11406.  https://doi.org/10.1029/2011GL047657 CrossRefGoogle Scholar
  26. 26.
    Dash SK, Mamgain A, Pattnayak KC, Giorgi F (2013) Spatial and temporal variations in Indian summer monsoon rainfall and temperature: an analysis based on RegCM3 simulations. Pure Appl Geophys 170(4):655–674CrossRefGoogle Scholar
  27. 27.
    Diaconescu EP, Gachon P, Laprise R, Scinocca JF (2016) Evaluation of precipitation indices over North America from various configurations of regional climate models. Atmos Ocean 54(4):418–439CrossRefGoogle Scholar
  28. 28.
    Diallo I, Sylla MB, Giorgi F, Gaye AT, Camara M (2012) Multimodel GCM-RCM ensemble-based projections of temperature and precipitation over West Africa for the early 21st century. Int J Geophys.  https://doi.org/10.1155/2012/972896 CrossRefGoogle Scholar
  29. 29.
    Didovets I, Lobanova A, Bronstert A, Snizhko S, Maule CF, Krysanova V (2017) Assessment of climate change impacts on water resources in three representative Ukrainian catchments using eco-hydrological modelling. Water 9(3):204.  https://doi.org/10.3390/w9030204 CrossRefGoogle Scholar
  30. 30.
    Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K, Liebert J (2012) HESS Opinions” Should we apply bias correction to global and regional climate model data”. Hydrol Earth Syst Sci 16(9):3391–3404.  https://doi.org/10.5194/hess-16-3391-2012 CrossRefGoogle Scholar
  31. 31.
    El Kenawy AM, McCabe MF (2017) Future projections of synoptic weather types over the Arabian Peninsula during the twenty-first century using an ensemble of CMIP5 models. Theor Appl Climatol 130(1):173–189CrossRefGoogle Scholar
  32. 32.
    Elmer F, HoymannJ Düthmann D, Vorogushyn S, Kreibich H (2012) Drivers of flood risk change in residential areas. Nat Hazards Earth Syst Sci 12:1641–1657.  https://doi.org/10.5194/nhess-12-1641-2012 CrossRefGoogle Scholar
  33. 33.
    Fiseha BM, Setegn SG, Melesse AM, Volpi E, Fiori A (2014) Impact of climate change on the hydrology of upper Tiber River Basin using bias corrected regional climate model. Water Resour Manag 28(5):1327–1343.  https://doi.org/10.1007/s11269-014-0546-x CrossRefGoogle Scholar
  34. 34.
    Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578CrossRefGoogle Scholar
  35. 35.
    Fowler HJ, Wilby RL (2010) Detecting changes in seasonal precipitation extremes using regional climate model projections: Implications for managing fluvial flood risk. Water Resour Res 46(3):3.  https://doi.org/10.1029/2008wr007636 CrossRefGoogle Scholar
  36. 36.
    Frei C, Christensen JH, Déqué M, Jacob D, Jones RG, Vidale PL (2003) Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. J Geophys Res Atmos 108(D3):4124.  https://doi.org/10.1029/2002JD002287 CrossRefGoogle Scholar
  37. 37.
    Friedlingstein P, Meinshausen M, Arora VK, Jones CD, Anav A, Liddicoat SK, Knutti R (2014) Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J Clim 27(2):511–526.  https://doi.org/10.1175/JCLI-D-12-00579.1 CrossRefGoogle Scholar
  38. 38.
    Gerten D, Schaphoff S, Haberlandt U, Lucht W, Sitch S (2004) Terrestrial vegetation and water balance—hydrological evaluation of a dynamic global vegetation model. J Hydrol 286(1):249–270.  https://doi.org/10.1016/j.jhydrol.2003.09.029 CrossRefGoogle Scholar
  39. 39.
    Giorgi F (2006) Regional climate modeling: status and perspectives. J Phys IV 139:101–118Google Scholar
  40. 40.
    Gosling SN, Zaherpour J, Mount NJ, Hattermann FF, Dankers R, Arheimer B, Breuer L, Ding J, Haddeland I, Kumar R, Kundu D (2017) A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 C, 2 C and 3 C. Clim Change 141(3):577–595.  https://doi.org/10.1007/s10584-016-1773-3 CrossRefGoogle Scholar
  41. 41.
    Graham LP, Andréasson J, Carlsson B (2007) Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods—a case study on the Lule River basin. Clim Change 81:293–307.  https://doi.org/10.1007/s10584-006-9215-2 CrossRefGoogle Scholar
  42. 42.
    Gudmundsson L, Wagener T, Tallaksen LM, Engeland K (2012) Evaluation of nine large-scale hydrological models with respect to the seasonal runoff climatology in Europe. Water Resour Res 48(11):W11504.  https://doi.org/10.1029/2011WR010911 CrossRefGoogle Scholar
  43. 43.
    Guo S, Wang J, Xiong L, Ying A, Li D (2002) A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China. J Hydrol 268:1–15CrossRefGoogle Scholar
  44. 44.
    Haddeland I, Clark DB, Franssen W, Ludwig F, Frank V, Arnell NW, Bertrand N, Best M, Well SF, Gerten D, GomeS S, Gosling SN, Hagemann S, Hanasaki N, Harding R, Heinke J, Kabat P, KoiraLA S, Oki T, Polcher J, Stacke T, Viterbo P, Weedon GP, Yeh P (2011) Multi-model estimate of the global terrestrial water balance: setup and first results. J Hydrometeorol 12:869–884.  https://doi.org/10.1175/2011JHM1324.1 CrossRefGoogle Scholar
  45. 45.
    Hagemann S, Chen C, Clark D, Folwell S, Gosling SN, Haddeland I, Hannasaki N, Heinke J, Ludwig F, Voss F, Wiltshire A (2013) Climate change impact on available water resources obtained using multiple global climate and hydrology models. Earth Syst Dyn 4:129–144.  https://doi.org/10.5194/esd-4-129-2013 CrossRefGoogle Scholar
  46. 46.
    Hashimoto H, Nemani RR, Bala G, Cao L, Michaelis AR, Ganguly S, Wang W, Milesi C, Eastman R, Lee T, Myneni R (2019) Constraints to vegetation growth reduced by region-specific changes in seasonal climate. Climate 7(2):27(1)–27(24)CrossRefGoogle Scholar
  47. 47.
    Hejazi M, Edmonds J, Clarke L, Kyle P, Davies E, Chaturvedi V, Wise M, Patel P, Eom J, Calvin K, Moss R (2014) Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework. Technol Forecast Soc Change 81:205–226.  https://doi.org/10.1016/j.techfore.2013.05.006 CrossRefGoogle Scholar
  48. 48.
    Hellmann JJ, Byers JE, Bierwagen BG, Dukes JS (2008) Five potential consequences of climate change for invasive species. Conserv Biol 22(3):534–543.  https://doi.org/10.1111/j.1523-1739.2008.00951.x CrossRefGoogle Scholar
  49. 49.
    Honti M, Scheidegger A, Stamm C (2014) The importance of hydrological uncertainty assessment methods in climate change impact studies. Hydrol Earth Syst Sci 18(8):3301–3317.  https://doi.org/10.5194/hess-18-3301-2014 CrossRefGoogle Scholar
  50. 50.
    Hosseinzadehtalaei P, Tabari H, Willems P (2017) Uncertainty assessment for climate change impact on intense precipitation: how many model runs do we need. Int J Climatol.  https://doi.org/10.1002/joc.5069 CrossRefGoogle Scholar
  51. 51.
    Huang S, Kumar R, Flörke M, Yang T, Hundecha Y, Kraft P, Gao C, Gelfan A, Liersch S, Lobanova A, Strauch M (2017) Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide. Clim Change 141(3):381–397.  https://doi.org/10.1007/s10584-016-1841-8 CrossRefGoogle Scholar
  52. 52.
    IPCC (2001) Climate change 2001: Impacts, adaptation and vulnerability. In: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Contribution of Working Group II to the Third Assessment Report of the IPCC. Cambridge University Press, CambridgeGoogle Scholar
  53. 53.
    IPCC (2007) Climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of Working Group II to the Fourth Assessment Report of the IPCC. Cambridge University Press, CambridgeGoogle Scholar
  54. 54.
    IPCC, Yuvi K, Field CB, Barros V, Stocker TF, Dahe Q, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM (eds) (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge.  https://doi.org/10.1017/CBO9781139177245 CrossRefGoogle Scholar
  55. 55.
    IPCC (2013) Climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  56. 56.
    Jacob D, Bärring L, Christensen OB, Christensen JH, de Castro M, Deque M, Giorgi F, Hagemann S, Hirschi M, Jones R, Kjellström E (2007) An inter-comparison of regional climate models for Europe: model performance in present-day climate. Clim Change 81:31–52CrossRefGoogle Scholar
  57. 57.
    Jayasankar CB, Surendran S, Rajendran K (2015) Robust signals of future projections of Indian summer monsoon rainfall by IPCC AR5 climate models: role of seasonal cycle and interannual variability. Geophys Res Lett 42(9):3513–3520CrossRefGoogle Scholar
  58. 58.
    Karlsson IB, Sonnenborg TO, Seaby LP, Jensen KH, Refsgaard JC (2015) Effect of a high-end CO2-emission scenario on hydrology. Clim Res 64(1):39–54.  https://doi.org/10.3354/cr01265 CrossRefGoogle Scholar
  59. 59.
    Kauffeldt A, Wetterhall F, Pappenberger F, Salamon P, Thielen J (2016) Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level. Environ Model Softw 75:68–76CrossRefGoogle Scholar
  60. 60.
    Kay AL, Davies HN, Bell VA, Jones RG (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim Change 92(1):41–63.  https://doi.org/10.1007/s10584-008-9471-4 CrossRefGoogle Scholar
  61. 61.
    Keenan TF, Hollinger DY, Bohrer G, Dragoni D, Munger JW, Schmid HP, Richardson AD (2013) Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499(7458):324.  https://doi.org/10.1038/nature12291 CrossRefGoogle Scholar
  62. 62.
    Keuler K, Radtke K, Kotlarski S, Lüthi D (2016) Regional climate change over Europe in COSMO-CLM: influence of emission scenario and driving global model. Meteorol Z.  https://doi.org/10.1127/metz/2016/0662 CrossRefGoogle Scholar
  63. 63.
    Kharin VV, Zwiers FW, Zhang X, Wehner M (2013) Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim Change 119(2):345–357CrossRefGoogle Scholar
  64. 64.
    Kidson JW, Thompson CS (1998) A comparison of statistical and model-based downscaling techniques for estimating local climate variations. J Clim 11(4):735–753CrossRefGoogle Scholar
  65. 65.
    Kim J, Choi J, Choi C, Park S (2013) Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Sci Total Environ 452:181–195.  https://doi.org/10.1016/j.scitotenv.2013.02.005 CrossRefGoogle Scholar
  66. 66.
    Kim S, Kim BS, Jun H, Kim HS (2014) Assessment of future water resources and water scarcity considering the factors of climate change and social–environmental change in Han River basin, Korea. Stoch Environ Res Risk Assess 28(8):1999–2014.  https://doi.org/10.1007/s00477-014-0924-1 CrossRefGoogle Scholar
  67. 67.
    Kusangaya S, Warburton ML, Van Garderen EA, Jewitt GP (2014) Impacts of climate change on water resources in southern Africa: a review. Phys Chem Earth Parts A/B/C 67:47–54CrossRefGoogle Scholar
  68. 68.
    Langousis A, Mamalakis A, Deidda R, Marrocu M (2016) Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results. Water Resour Res 52(1):471–494CrossRefGoogle Scholar
  69. 69.
    Leander R, Buishand TA, van den Hurk BJ, de Wit MJ (2008) Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output. J Hydrol 351:331–343.  https://doi.org/10.1016/j.jhydrol.2007.12.020 CrossRefGoogle Scholar
  70. 70.
    Lee JW, Hong SY, Chang EC, Suh MS, Kang HS (2014) Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP. Clim Dyn 42(3–4):733–747.  https://doi.org/10.1007/s00382-013-1841-6 CrossRefGoogle Scholar
  71. 71.
    Lemordant L, Gentine P, Swann AS, Cook BI, Scheff J (2018) Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proc Natl Acad Sci 115(16):4093–4098.  https://doi.org/10.1073/pnas.1720712115 CrossRefGoogle Scholar
  72. 72.
    Li Q, Wang S, Lee DK, Tang J, Niu X, Hui P, Gutowski WJ, Dairaku K, McGregor JL, Katzfey J, Gao X (2016) Building Asian climate change scenario by multi-regional climate models ensemble. Part II: mean precipitation. Int J Climatol 36(13):4253–4264.  https://doi.org/10.1002/joc.4633 CrossRefGoogle Scholar
  73. 73.
    Lutz AF, Maat HW, Biemans H, Shrestha AB, Wester P, Immerzeel WW (2016) Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach. Int J Climatol 36(12):3988–4005CrossRefGoogle Scholar
  74. 74.
    Mearns LO, Giorgi F, Whetton P, Pabon D, Hulme M, Lal M (2003) Guidelines for use of climate scenarios developed from regional climate model experiments. IPCCGoogle Scholar
  75. 75.
    Milly PC, Dunne KA (2016) Potential evapotranspiration and continental drying. Nat Clim Change 6(10):946CrossRefGoogle Scholar
  76. 76.
    Minville M, Brissette F, Leconte R (2008) Uncertainty of the impact of climate change on the hydrology of a nordic watershed. J Hydrol 358:70–83.  https://doi.org/10.1016/j.jhydrol.2008.05.033 CrossRefGoogle Scholar
  77. 77.
    Mishra V, Kumar D, Ganguly AR, Sanjay J, Mujumdar M, Krishnan R, Shah RD (2014) Reliability of regional and global climate models to simulate precipitation extremes over India. J Geophys Res 119(15):9301–9323Google Scholar
  78. 78.
    Mondal A, Mujumdar PP (2015) Regional hydrological impacts of climate change: implications for water management in India. Proc Int Assoc Hydrol Sci 366:34–43.  https://doi.org/10.5194/piahs-366-34-2015 CrossRefGoogle Scholar
  79. 79.
    Muis S, Güneralp B, Jongman B, Aerts JC, Ward PJ (2015) Flood risk and adaptation strategies under climate change and urban expansion: a probabilistic analysis using global data. Sci Total Environ 538:445–457.  https://doi.org/10.1016/j.scitotenv.2015.08.068 CrossRefGoogle Scholar
  80. 80.
    Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12(8):2256–2284CrossRefGoogle Scholar
  81. 81.
    Najafi MR, Moradkhani H (2014) A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes. Hydrol Process 28(26):6292–6308.  https://doi.org/10.1002/hyp.10113 CrossRefGoogle Scholar
  82. 82.
    Najafi MR, Moradkhani H (2015) Multi-model ensemble analysis of runoff extremes for climate change impact assessments. J Hydrol 525:352–361.  https://doi.org/10.1016/j.jhydrol.2015.03.045 CrossRefGoogle Scholar
  83. 83.
    Nyenje PM, Batelaan O (2009) Estimating the effects of climate change on groundwater recharge and baseflow in the upper Ssezibwa catchment, Uganda. Hydrol Sci J 54(4):713–726.  https://doi.org/10.1623/hysj.54.4.713 CrossRefGoogle Scholar
  84. 84.
    Oh SG, Suh MS, Lee YS, Ahn JB, Cha DH, Lee DK, Hong SY, Min SK, Park SC, Kang HS (2016) Projections of high resolution climate changes for South Korea using multiple-regional climate models based on four RCP scenarios. Part 2: precipitation. Asia-Pac J Atmos Sci 52(2):171–189CrossRefGoogle Scholar
  85. 85.
    Park C, Min SK, Lee D, Cha DH, Suh MS, Kang HS, Hong SY, Lee DK, Baek HJ, Boo KO, Kwon WT (2016) Evaluation of multiple regional climate models for summer climate extremes over East Asia. Clim Dyn 46(7–8):2469–2486.  https://doi.org/10.1007/s00382-015-2713-z CrossRefGoogle Scholar
  86. 86.
    Pattnayak KC, Panda SK, Dash SK (2013) Comparative study of regional rainfall characteristics simulated by RegCM3 and recorded by IMD. Glob Planet Change 106:111–122CrossRefGoogle Scholar
  87. 87.
    Praskievicz S, Chang H (2009) A review of hydrological modelling of basin-scale climate change and urban development impacts. Prog Phys Geogr 33(5):650–671CrossRefGoogle Scholar
  88. 88.
    Price K (2011) Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: a review. Prog Phys Geogr 35(4):465–492.  https://doi.org/10.1177/0309133311402714 CrossRefGoogle Scholar
  89. 89.
    Prudhomme C, Reynard N, Crooks S (2002) Downscaling of global climate models for flood frequency analysis: where are we now? Hydrol Process 16(6):1137–1150CrossRefGoogle Scholar
  90. 90.
    Prudhomme C, Parry S, Hannaford J, Clark DB, Hagemann S, Voss F (2011) How well do large-scale models reproduce regional hydrological extremes in Europe. J Hydrometeorol 12:1181–1204.  https://doi.org/10.1175/2011JHM1387.1 CrossRefGoogle Scholar
  91. 91.
    Pryor SC, Scavia D, Downer C, Gaden M, Iverson L, Nordstrom R, Patz J, Robertson GP (2014) Midwest. Climate change impacts in the United States: the third national climate assessment. U.S. Global Change Research Program, pp 418–440.  https://doi.org/10.7930/j0j1012n
  92. 92.
    Qi S, Sun G, Wang Y, McNulty SG, Myers JM (2009) Streamflow response to climate and landuse changes in a coastal watershed in North Carolina. Trans ASABE 52(3):739–749.  https://doi.org/10.13031/2013.27395 CrossRefGoogle Scholar
  93. 93.
    Rai P, Choudhary A, Dimri AP (2019) Future precipitation extremes over India from the CORDEX-South Asia experiments. Theor Appl Climatol 137:2961.  https://doi.org/10.1007/s00704-019-02784-1 CrossRefGoogle Scholar
  94. 94.
    Rojas R, Feyen L, Watkiss P (2013) Climate change and river floods in the European Union: socio-economic consequences and the costs and benefits of adaptation. Glob Environ Change 23(6):1737–1751.  https://doi.org/10.1016/j.gloenvcha.2013.08.006 CrossRefGoogle Scholar
  95. 95.
    Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N, Neumann K (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci 111(9):3268–3273.  https://doi.org/10.1073/pnas.1222463110 CrossRefGoogle Scholar
  96. 96.
    Roy AH, Rosemond AD, Paul MJ, Leigh DS, Wallace JB (2003) Stream macroinvertebrate response to catchment urbanisation (Georgia, USA). Freshw Biol 48(2):329–346.  https://doi.org/10.1046/j.1365-2427.2003.00979.x CrossRefGoogle Scholar
  97. 97.
    Ruelland D, Ardoin-Bardin S, Collet L, Roucou P (2012) Simulating future trends in hydrological regime of a large Sudano-Sahelian catchment under climate change. J Hydrol 424:207–216.  https://doi.org/10.1016/j.jhydrol.2012.01.002 CrossRefGoogle Scholar
  98. 98.
    Ruosteenoja K, Jylhä K, Kämäräinen M (2016) Climate projections for Finland under the RCP forcing scenarios. Geophysica 51:17–50Google Scholar
  99. 99.
    RupKumar K, Sahai AK, Krishna Kumar K, Patwardhan SK, Mishra PK, Revadekar JV, Pant GB (2006) High-resolution climate change scenarios for India for the 21st century. Curr Sci 90:334–345Google Scholar
  100. 100.
    Scholze M, Knorr W, Arnell NW, Prentice IC (2006) A climate-change risk analysis for world ecosystems. Proc Natl Acad Sci 103(35):13116–13120.  https://doi.org/10.1073/pnas.0601816103 CrossRefGoogle Scholar
  101. 101.
    Schewe J, Heinke J, Gerten D, Haddeland I, Arnell NW, Clark DB, Gosling SN (2014) Multimodel assessment of water scarcity under climate change. Proc Natl Acad Sci 111:3245–3250CrossRefGoogle Scholar
  102. 102.
    Schmidli J, Goodess CM, Frei C, Haylock MR, Hundecha Y, Ribalaygua J, Schmith T (2007) Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps. J Geophys Res Atmos 112:D04105.  https://doi.org/10.1029/2005JD007026
  103. 103.
    Scott MJ, Daly DS, Hejazi MI, Kyle GP, Liu L, McJeon HC, Mundra A, Patel PL, Rice JS, Voisin N (2016) Sensitivity of future US Water shortages to socioeconomic and climate drivers: a case study in Georgia using an integrated human-earth system modeling framework. Clim Change 136(2):233–246.  https://doi.org/10.1007/s10584-016-1602-8 CrossRefGoogle Scholar
  104. 104.
    Soro GE, Yao AB, Kouame YM, Bi TAG (2017) Climate change and its impacts on water resources in the Bandama Basin, Côte D’ivoire. Hydrology 4:18CrossRefGoogle Scholar
  105. 105.
    Stahl K, Tallaksen LM, Hannaford J, van Lanen HAJ (2012) Filling the white space on maps of European runoff trends: estimates from a multi-model ensemble. Hydrol Earth Syst Sci 16:2035–2047CrossRefGoogle Scholar
  106. 106.
    Suh MS, Lee DK (2004) Impacts of land use/cover changes on surface climate over east Asia for extreme climate cases using RegCM2. J Geophys Res 109:D02108.  https://doi.org/10.1029/2003JD003681 CrossRefGoogle Scholar
  107. 107.
    Suh MS, Oh SG, Lee YS, Ahn JB, Cha DH, Lee DK, Hong SY, Min SK, Park SC, Kang HS (2016) Projections of high resolution climate changes for South Korea using multiple-regional climate models based on four RCP scenarios. Part 1: surface air temperature. Asia-Pac J Atmos Sci 52:151–169.  https://doi.org/10.1007/s13143-016-0017-9 CrossRefGoogle Scholar
  108. 108.
    Sunde MG, He HS, Hubbart JA, Urban MA (2017) Integrating downscaled CMIP5 data with a physically based hydrologic model to estimate potential climate change impacts on streamflow processes in a mixed-use watershed. Hydrol Process 31(9):1790–1803.  https://doi.org/10.1002/hyp.11150 CrossRefGoogle Scholar
  109. 109.
    Swann AL, Hoffman FM, Koven CD, Randerson JT (2016) Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc Natl Acad Sci 113(36):10019–10024CrossRefGoogle Scholar
  110. 110.
    Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geography Compass 4(7):834–860CrossRefGoogle Scholar
  111. 111.
    Teutschbein C, Wetterhall F, Seibert J (2011) Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale. Clim Dyn 37:2087–2105CrossRefGoogle Scholar
  112. 112.
    Teutschbein C (2013) Hydrological modeling for climate change impact assessment: transferring large-scale information from global climate models to the catchment scale. Geogr Compass 4:834–860CrossRefGoogle Scholar
  113. 113.
    Thober S, Kumar R, Sheffield J, Mai J, Schäfer D, Samaniego L (2015) Seasonal soil moisture drought prediction over Europe using the North American Multi-Model Ensemble (NMME). J Hydrometeorol 16(6):2329–2344.  https://doi.org/10.1175/JHM-D-15-0053.1 CrossRefGoogle Scholar
  114. 114.
    Tiwari PR, Kar SC, Mohanty UC, Dey S, Sinha P, Shekhar MS, Sokhi RS (2019) Comparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north India. Int J Climatol 39(3):1504–1516CrossRefGoogle Scholar
  115. 115.
    Vaghefi SA, Mousavi SJ, Abbaspour KC, Srinivasan R, Arnold JR (2015) Integration of hydrologic and water allocation models in basin-scale water resources management considering crop pattern and climate change: Karkheh River Basin in Iran. Reg Environ Change 15(3):475–484.  https://doi.org/10.1007/s10113-013-0573-9 CrossRefGoogle Scholar
  116. 116.
    Van den Hurk BJ, van Meijgaard E (2010) Diagnosing land-atmosphere interaction from a regional climate model simulation over West Africa. J Hydrol 11:467–481Google Scholar
  117. 117.
    Van Huijgevoort MHJ, Hazenberg P, Van Lanen HAJ, Teuling AJ, Clark DB, Folwell S, Gosling SN, Hanasaki N, Heinke J, Koirala S, Stacke T (2013) Global multimodel analysis of drought in runoff for the second half of the twentieth century. J Hydrometeorol 14(5):1535–1552.  https://doi.org/10.1175/JHM-D-12-0186.1 CrossRefGoogle Scholar
  118. 118.
    Van Roosmalen L, Christensen BS, Sonnenborg TO (2007) Regional differences in climate change impacts on groundwater and stream discharge in Denmark. Vadose Zone J 6:554–571CrossRefGoogle Scholar
  119. 119.
    Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T (2011) The representative concentration pathways: an overview. Clim Change 109(1–2):5–31.  https://doi.org/10.1007/s10584-011-0148-z CrossRefGoogle Scholar
  120. 120.
    Vicuna S, Dracup JA (2007) The evolution of climate change impact studies on hydrology and water resources in California. Clim Change 82:327–350CrossRefGoogle Scholar
  121. 121.
    Visser H, Petersen AC, Ligtvoet W (2014) On the relation between weather-related disaster impacts, vulnerability and climate change. Clim Change 125(3–4):461–477.  https://doi.org/10.1007/s10584-014-1179-z CrossRefGoogle Scholar
  122. 122.
    Wang G, Yu M, Xue Y (2015) Modeling the potential contribution of land cover changes to the late twentieth century Sahel drought using a regional climate model: impact of lateral boundary conditions. Clim Dyn.  https://doi.org/10.1007/s00382-015-2812-x CrossRefGoogle Scholar
  123. 123.
    Wantzen KM, Ballouche A, Longuet I, Bao I, Bocoum H, Cisse L, Chauhan M, Girard P, Gopal B, Kane A, Marchese MR, Prakash N, Paulo T, Zalewski M (2016) River Culture: an eco-social approach to mitigate the biological and cultural diversity crisis in riverscapes. Ecohydrol Hydrobiol 16(1):7–18CrossRefGoogle Scholar
  124. 124.
    Ward PJ, Pelt SV, Keizer OD, Aerts JCJH, Beersma JJ, Hurk BJJM, Linde AT (2014) Including climate change projections in probabilistic flood risk assessment. J Flood Risk 7:141–151.  https://doi.org/10.1111/jfr3.12029 CrossRefGoogle Scholar
  125. 125.
    Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21(4):530–548CrossRefGoogle Scholar
  126. 126.
    Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34(11):2995–3008CrossRefGoogle Scholar
  127. 127.
    Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol 225(1):67–91CrossRefGoogle Scholar
  128. 128.
    Wilby RL (2006) When and where might climate change be detectable in UK river flows. Geophys Res Lett 33:L19407.  https://doi.org/10.1029/2006GL027552
  129. 129.
    Woo S, Singh GP, Oh JH, Lee KM (2019) Projection of seasonal summer precipitation over Indian sub-continent with a high-resolution AGCM based on the RCP scenarios. Meteorol Atmos Phys 131(4):897–916CrossRefGoogle Scholar
  130. 130.
    Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62(1):189–216CrossRefGoogle Scholar
  131. 131.
    Xin X, Zhang L, Zhang J, Wu T, Fang Y (2013) Climate change projections over East Asia with BCC_CSM1. 1 climate model under RCP scenarios. J Meteorol Soc Jpn Ser II 91(4):413–429.  https://doi.org/10.2151/jmsj.2013-401 CrossRefGoogle Scholar
  132. 132.
    Xu CY (1999) From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches. Prog Phys Geogr 23(2):229–249CrossRefGoogle Scholar
  133. 133.
    Yan K, Di Baldassarre G, Solomatine DP, Schumann GJP (2015) A review of low-cost space-borne data for flood modelling: topography, flood extent and water level. Hydrol Process 29(15):3368–3387CrossRefGoogle Scholar
  134. 134.
    Yan D, Werners SE, Ludwig F, Huang HQ (2015) Hydrological response to climate change: the Pearl River, China under different RCP scenarios. J Hydrol Reg Stud 4:228–245.  https://doi.org/10.1016/j.ejrh.2015.06.006 CrossRefGoogle Scholar
  135. 135.
    Yang T, Xu CY, Shao Q, Chen X, Lu GH, Hao ZC (2010) Temporal and spatial patterns of low-flow changes in the Yellow River in the last half century. Stoch Environ Res Risk Assess 24(2):297–309.  https://doi.org/10.1007/s00477-009-0318-y CrossRefGoogle Scholar
  136. 136.
    Zaman AM, Molla MK, Pervin IA, Rahman SM, Haider AS, Ludwig F, Franssen W (2016) Impacts on river systems under 2° Bangladesh Case Study. Clim Serv, C warming.  https://doi.org/10.1016/j.cliser.2016.10.002 CrossRefGoogle Scholar
  137. 137.
    Zalewski M (2000) Ecohydrology—the scientific background to use ecosystem properties as management tools toward sustainability of water resources. Ecol Eng 16:1–8CrossRefGoogle Scholar
  138. 138.
    Zalewski M, McClain M, Eslamian S (2016) New challenges and dimensions of ecohydrology—enhancement of catchments sustainability potential. Ecohydrol Hydrobiol 16:1–3CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nagaveni Chokkavarapu
    • 1
  • Venkata Ravibabu Mandla
    • 2
    Email author
  1. 1.Allianza Techno ConsultancyHyderabadIndia
  2. 2.CGARD, School of Science, Technology and Knowledge Systems, National Institute of Rural Development and Panchayati Raj (NIRDPR), Ministry of Rural DevelopmentGovt of IndiaHyderabadIndia

Personalised recommendations