Statistical validation of GCM-simulated climates for the U.S. Great Lakes and the C.I.S. Emba and Ural River basins

Many researchers use outputs from large-scale global circulation models of the atmosphere to assess hydrological and other impacts associated with climate change. However, these models cannot capture all climate variations since the physical processes are imperfectly understood and are poorly represented at smaller regional scales. This paper statistically compares model outputs from the global circulation model of the Geophysical Fluid Dynamics Laboratory to historical data for the United States' Laurentian Great Lakes and for the Emba and Ural River basins in the Commonwealth of Independent States (C.I.S.). We use maximum entropy spectral analysis to compare model and data time series, allowing us to both assess statistical predictabilities and to describe the time series in both time and frequency domains. This comparison initiates assessments of the model's representation of the real world and suggests areas of model improvement.     

Statistical validation of GCM-simulated climates for the U.S. Great Lakes and the C.I.S. Emba and Ural River basins

Many researchers use outputs from large-scale global circulation models of the atmosphere to assess hydrological and other impacts associated with climate change. However, these models cannot capture all climate variations since the physical processes are imperfectly understood and are poorly represented at smaller regional scales. This paper statistically compares model outputs from the global circulation model of the Geophysical Fluid Dynamics Laboratory to historical data for the United States' Laurentian Great Lakes and for the Emba and Ural River basins in the Commonwealth of Independent States (C.I.S.). We use maximum entropy spectral analysis to compare model and data time series, allowing us to both assess statistical predictabilities and to describe the time series in both time and frequency domains. This comparison initiates assessments of the model's representation of the real world and suggests areas of model improvement.     

Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin,India with HEC‐HMS and SDSM

Climate change would significantly affect many hydrologic systems, which in turn would affect the water availability, runoff, and the flow in rivers. This study evaluates the impacts of possible future climate change scenarios on the hydrology of the catchment area of the Tunga–Bhadra River, upstream of the Tungabhadra dam. The Hydrologic Engineering Center's Hydrologic Modeling System version 3.4 (HEC‐HMS 3.4) is used for the hydrological modelling of the study area. Linear‐regression‐based Statistical DownScaling Model version 4.2 (SDSM 4.2) is used to downscale the daily maximum and minimum temperature, and daily precipitation in the four sub‐basins of the study area. The large‐scale climate variables for the A2 and B2 scenarios obtained from the Hadley Centre Coupled Model version 3 are used. After model calibration and testing of the downscaling procedure, the hydrological model is run for the three future periods: 2011–2040, 2041–2070, and 2071–2099. The impacts of climate change on the basin hydrology are assessed by comparing the present and future streamflow and the evapotranspiration estimates. Results of the water balance study suggest increasing precipitation and runoff and decreasing actual evapotranspiration losses over the sub‐basins in the study area. Copyright © 2012 John Wiley & Sons, Ltd.     

Integrating downscaled CMIP5 data with a physically based hydrologic model to estimate potential climate change impacts on streamflow processes in a mixed‐use watershed

Climatic changes have altered surface water regimes worldwide, and climate projections suggest that such alterations will continue. To inform management decisions, climate projections must be paired with hydrologic models to develop quantitative estimates of watershed scale water regime changes. Such modeling approaches often involve downscaling climate model outputs, which are generally presented at coarse spatial scales. In this study, Coupled Model Intercomparison Project Phase 5 climate model projections were analyzed to determine models representing severe and conservative climate scenarios for the study watershed. Based on temperature and precipitation projections, output from GFDL‐ESM2G (representative concentration pathway 2.6) and MIROC‐ESM (representative concentration pathway 8.5) were selected to represent conservative (Δ_C) and severe (Δ_S) change scenarios, respectively. Climate data were used as forcing for the soil and water assessment tool to analyze the potential effects of climate change on hydrologic processes in a mixed‐use watershed in central Missouri, USA. Results showed annual streamflow decreases ranging from ?5.9% to ?26.8% and evapotranspiration (ET) increases ranging from +7.2% to +19.4%. During the mid‐21st century, sizeable decreases to summer streamflow were observed under both scenarios, along with large increases of fall, spring, and summer ET under Δ_S. During the late 21st century period, large decreases of summer streamflow under both scenarios, and large increases to spring (Δ_S), fall (Δ_S) and summer (Δ_C) ET were observed. This study demonstrated the sensitivity of a Midwestern watershed to future climatic changes utilizing projections from Coupled Model Intercomparison Project Phase 5 models and presented an approach that used multiple climate model outputs to characterize potential watershed scale climate impacts.     

A temperature‐precipitation‐based model of thirty‐year mean snowpack accumulation and melt in Oregon,USA

High‐resolution, spatially extensive climate grids can be useful in regional hydrologic applications. However, in regions where precipitation is dominated by snow, snowmelt models are often used to account for timing and magnitude of water delivery. We developed an empirical, nonlinear model to estimate 30‐year means of monthly snowpack and snowmelt throughout Oregon. Precipitation and temperature for the period 1971–2000, derived from 400‐m resolution PRISM data, and potential evapotranspiration (estimated from temperature and day length) drive the model. The model was calibrated using mean monthly data from 45 SNOTEL sites and accurately estimated snowpack at 25 validation sites: R² = 0·76, Nash‐Sutcliffe Efficiency (NSE) = 0·80. Calibrating it with data from all 70 SNOTEL sites gave somewhat better results (R² = 0·84, NSE = 0·85). We separately applied the model to SNOTEL stations located < 200 and ≥ 200 km from the Oregon coast, since they have different climatic conditions. The model performed equally well for both areas. We used the model to modify moisture surplus (precipitation minus potential evapotranspiration) to account for snowpack accumulation and snowmelt. The resulting values accurately reflect the shape and magnitude of runoff at a snow‐dominated basin, with low winter values and a June peak. Our findings suggest that the model is robust with respect to different climatic conditions, and that it can be used to estimate potential runoff in snow‐dominated basins. The model may allow high‐resolution, regional hydrologic comparisons to be made across basins that are differentially affected by snowpack, and may prove useful for investigating regional hydrologic response to climate change. Published in 2011 by John Wiley & Sons, Ltd.     

Understanding the role of hydrologic model structures on evapotranspiration-driven sensitivity

ABSTRACT

Most conceptual hydrological models do not treat vegetation as a dynamic component. This study focuses on understanding the impact of model structural complexity on the sensitivity of hydrologic models to potential evapotranspiration forcing data. To achieve this, two classes of hydrologic models are examined: (1) lumped, conceptual rainfall–runoff models and (2) eco-hydrologic models. A sample of 57 US catchments, covering eight eco-regions, included in the MOPEX dataset is used. While streamflow simulation performance in complex models did not exhibit increased sensitivity to PET, actual evapotranspiration simulation performance showed greater sensitivity in energy-limited catchments. This analysis warns against using over-simplistic PET estimations in energy-limited catchments for eco-hydrologic models and for more complex conceptual hydrologic models. This is particularly true for streamflow-only calibrations that commonly fail to properly constrain physically based parameters. Ultimately, these results have the potential to inform data collection and model selection efforts to yield the greatest benefit.     

Assess hydrological responses to a warming climate at the Lysina Critical Zone Observatory in Central Europe

Climate warming is having profound effects on the hydrological cycle by increasing atmospheric demand, changing water availability, and snow seasonality. Europe suffered three distinct heat waves in 2019, and 11 of the 12 hottest years ever recorded took place in the past two decades, which will potentially change seasonal streamflow patterns and long-term trends. Central Europe exhibited six dry years in a row since 2014. This study uses data from a well-documented headwater catchment in Central Europe (Lysina) to explore hydrological responses to a warming climate. We applied a lumped parameter hydrologic model Brook90 and a distributed model Penn State Integrated Hydrologic Model (PIHM) to simulate long-term hydrological change under future climate scenarios. Both models performed well on historic streamflow and in agreement with each other according to the catchment water budget. In addition, PIHM was able to simulate lateral groundwater redistribution within the catchment validated by the groundwater table dynamics. The long-term trends in runoff and low flow were captured by PIHM only. We applied different EURO-CORDEX models with two emission scenarios (Representative Concentration Pathways RCP 4.5, 8.5) and found significant impacts on runoff and evapotranspiration (ET) for the period of 2071–2100. Results from both models suggested reduced runoff and increased ET, while the monthly distribution of runoff was different. We used this catchment study to understand the importance of subsurface processes in projection of hydrologic response to a warming climate.     

Macroscale hydrological modelling approach for study of large scale hydrologic impacts under climate change in Indian river basins

In climate‐change studies, a macroscale hydrologic model (MHM) operating over large scales can be an important tool in developing consistent hydrological variability estimates over large basins. MHMs, which can operate at coarse grid resolutions of about 1° latitude by longitude, have been used previously to study climate change impacts on the hydrology of continental scale or global river basins. They can provide a connection between global atmospheric models and water resource systems on large spatial scales and long timescales. In this study, the variable infiltration capacity (VIC) MHM is used to study large scale hydrologic impacts of climate change for Indian river basins. Large‐scale changes in runoff, evapotranspiration and soil moisture for India, as well as station‐scale changes in discharges for three major river basins with distinct climatic and geographic characteristics are examined in this study. Climate model projections for meteorological variables (precipitation, temperature and wind speed) from three general circulation models (GCMs) and three emissions scenarios are used to drive the VIC MHM. GCM projections are first interpolated to a 1° by 1° hydrologic model grid and then bias‐corrected using a quantile–quantile mapping. The VIC model is able to reproduce observed statistics for discharges in the Ganga, Narmada and Krishna basins reasonably well, even at the coarse grid resolution employed using a calibration period for years 1965–1970 and testing period from 1971–1973/1974. An increasing trend is projected for summer monsoon surface runoff, evapotranspiration and soil moisture in most central Indian river basins, whereas a decrease in runoff and soil moisture is projected for some regions in southern India, with important differences arising from GCM and scenario variability. Discharge statistics show increases in mid‐flow and low flow at Farakka station on Ganga River, increased high flows at Jamtara station upstream of Narmada, and increased high, mid‐flow and low flow for Vijayawada station on Krishna River in the future. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluating the impacts of climate change and switchgrass production on a semiarid basin

Climate and land use changes greatly modify hydrologic regimes. In this paper, we modelled the impacts of biofuel cultivation in the US Great Plains on a 1061‐km² watershed using the Soil and Water Assessment Tool (SWAT) hydrologic model. The model was calibrated to monthly discharges spanning 2002–2010 and for the winter, spring, and summer seasons. SWAT was then run for a climate‐change‐only scenario using downscaled precipitation and a projected temperature for 16 general circulation model (GCM) runs associated with the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios A2 scenario spanning 2040–2050. SWAT was also run on a climate change plus land use change scenario in which Alamo switchgrass (Panicum virgatum L.) replaced native range grasses, winter wheat, and rye (89% of the basin). For the climate‐change‐only scenario, the GCMs agreed on a monthly temperature increase of 1–2 °C by the 2042–2050 period, but they disagreed on the direction of change in precipitation. For this scenario, decreases in surface runoff during all three seasons and increases in spring and summer evapotranspiration (eT) were driven predominantly by precipitation. Increased summer temperatures also significantly contributed to changes in eT. With the addition of switchgrass, changes in surface runoff are amplified during the winter and summer, and changes in eT are amplified during all three seasons. Depending on the GCM utilized, either climate change or land use change (switchgrass cultivation) was the dominant driver of change in surface runoff while switchgrass cultivation was the major driver of changes in eT. Copyright © 2014 John Wiley & Sons, Ltd.     

Assessing the uncertainties of hydrologic model selection in climate change impact studies

The uncertainties associated with atmosphere‐ocean General Circulation Models (GCMs) and hydrologic models are assessed by means of multi‐modelling and using the statistically downscaled outputs from eight GCM simulations and two emission scenarios. The statistically downscaled atmospheric forcing is used to drive four hydrologic models, three lumped and one distributed, of differing complexity: the Sacramento Soil Moisture Accounting (SAC‐SMA) model, Conceptual HYdrologic MODel (HYMOD), Thornthwaite‐Mather model (TM) and the Precipitation Runoff Modelling System (PRMS). The models are calibrated based on three objective functions to create more plausible models for the study. The hydrologic model simulations are then combined using the Bayesian Model Averaging (BMA) method according to the performance of each models in the observed period, and the total variance of the models. The study is conducted over the rainfall‐dominated Tualatin River Basin (TRB) in Oregon, USA. This study shows that the hydrologic model uncertainty is considerably smaller than GCM uncertainty, except during the dry season, suggesting that the hydrologic model selection‐combination is critical when assessing the hydrologic climate change impact. The implementation of the BMA in analysing the ensemble results is found to be useful in integrating the projected runoff estimations from different models, while enabling to assess the model structural uncertainty. Copyright © 2011 John Wiley & Sons, Ltd.     

Analysis of uncertainties in the hydrological response of a model‐based climate change impact assessment in a subcatchment of the Spree River,Germany

Climate change impact assessments form the basis for the development of suitable climate change adaptation strategies. For this purpose, ensembles consisting of stepwise coupled models are generally used [emission scenario → global circulation model → downscaling approach (DA) → bias correction → impact model (hydrological model)], in which every item is affected by considerable uncertainty. The aim of the current study is (1) to analyse the uncertainty related to the choice of the DA as well as the hydrological model and its parameterization and (2) to evaluate the vulnerability of the studied catchment, a subcatchment of the highly anthropogenically impacted Spree River catchment, to hydrological change. Four different DAs are used to drive four different model configurations of two conceptually different hydrological models (Water Balance Simulation Model developed at ETH Zürich and HBV‐light). In total, 452 simulations are carried out. The results show that all simulations compute an increase in air temperature and potential evapotranspiration. For precipitation, runoff and actual evapotranspiration, opposing trends are computed depending on the DA used to drive the hydrological models. Overall, the largest source of uncertainty can be attributed to the choice of the DA, especially regarding whether it is statistical or dynamical. The choice of the hydrological model and its parameterization is of less importance when long‐term mean annual changes are compared. The large bandwidth at the end of the modelling chain may exacerbate the formulation of suitable climate change adaption strategies on the regional scale. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling basin-scale hydrology in support of physical climate and global biogeochemical studies: An example using the Zambezi River

Human activity is an important agent defining the contemporary hydrologic cycle. We have documented the potential impacts of impoundment, land use change and climate change on the Zambezi River system in southern Africa and found that they can be substantial. A full analysis requires construction and parameterization of a simulation for the entire catchment. This paper develops a strategy for implementing catchment-scale models of the major hydrologic processes operating within the basin. A coherent data set for calibrating the models has also been assembled. The algorithms consist of a Water Balance (WBM) and a Water Transport (WTM) operating at 1/2^o spatial scale and at monthly timesteps. These models transform complex patterns of regional climatology into estimates of soil water, evapotranspiration, runoff, and discharge through rivers of various size. The models are dependent on the characteristics of the terrestrial surface, principally soil texture and land cover. A simulated river network is also required. Additional tabular data sets are essential for model testing and calibration. These include subcatchment areas; observed river discharge at selected points; flooding, storage and loss characteristics of major wetlands; floodwave translation; and, volume, surface area, withdrawal and evaporative losses from impoundments. An important design consideration for the numerous impoundments in the Zambezi requires an understanding of the seasonal variation in discharge, in particular how it might respond to climate and land use change. The research strategy offered here lays a framework for addressing such issues. Although the primary focus of this work is hydrologic, we discuss how the model can be extended to consider constituent transport and biogeochemical cycling issues at the continental scale.     

Improvement of the Noah land surface model for warm season processes: evaluation of water and energy flux simulation

The Noah model is a land surface model of the National Centers for Environmental Prediction. It has been widely used in regional coupled weather and climate models (i.e. Weather Research and Forecasting Model, Eta Mesoscale Model) and global coupled weather and climate models (i.e. National Centers for Environmental Prediction Global Forecast System, Climate Forecast System). Therefore, its continued improvement and development are keys to enhancing our weather and climate forecast ability and water and energy flux simulation accuracy. North American Land Data Assimilation System phase 1 (NLDAS‐1) experiments indicated that the Noah model exhibited substantial bias in latent heat flux, total runoff and land skin temperature during the warm season, and such bias can significantly affect coupled weather and climate models. This paper presents a study to improve the Noah model by adding model parameterization processes such as including seasonal factor on leaf area index and root distribution and selecting optimal model parameters. We compared simulated latent heat flux, mean annual runoff and land skin temperature from the Noah control and test versions with measured latent heat flux, land surface skin temperature, mean annual runoff and satellite‐retrieved land surface skin temperature. The results show that the test version significantly reduces biases in latent heat, total runoff and land skin temperature simulation. The test version has been used for the NLDAS phase 2 (NLDAS‐2) to produce 30‐year water flux, energy flux and state variable products to support the US drought monitor of National Integrated Drought Information System. Copyright © 2012 John Wiley & Sons, Ltd.     

Hydrologic modeling,parameter estimation,and watershed characteristics

A general watershed model represents the runoff phase of the hydrologic cycle by a series of moisture accounting equations. The Stanford Watershed Model uses fixed equations containing variable parameters which are calibrated for a watershed by trial-and-error matching of simulated to recorded flows. Opset was developed to estimate these parameters through a computerized least squares matching. The procedure reduces estimating scatter and provides parameter estimates which were correlated with physical characteristics of the watershed and with watershed changes with urbanization.     

A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes

In this study the impact of climate change on runoff extremes is investigated over the Pacific Northwest (PNW). This paper aims to address the question of how the runoff extremes change in the future compared to the historical time period, investigate the different behaviors of the regional climate models (RCMs) regarding the runoff extremes and assess the seasonal variations of runoff extremes. Hydrologic modeling is performed by the variable infiltration capacity (VIC) model at a 1/8° resolution and the model is driven by climate scenarios provided by the North American Regional Climate Change Assessment Program (NARCCAP) including nine regional climate model (RCM) simulations. Analysis is performed for both the historical (1971–2000) and future (2041–2070) time periods. Downscaling of the climate variables including precipitation, maximum and minimum temperature and wind speed is done using the quantile‐mapping (QM) approach. A spatial hierarchical Bayesian model is then developed to analyse the annual maximum runoff in different seasons for both historical and future time periods. The estimated spatial changes in extreme runoffs over the future period vary depending on the RCM driving the hydrologic model. The hierarchical Bayesian model characterizes the spatial variations in the marginal distributions of the General Extreme Value (GEV) parameters and the corresponding 100‐year return level runoffs. Results show an increase in the 100‐year return level runoffs for most regions in particular over the high elevation areas during winter. The Canadian portions of the study region reflect higher increases during spring. However, reduction of extreme events in several regions is projected during summer. Copyright © 2013 John Wiley & Sons, Ltd.     

Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models

ABSTRACT

Understanding streamflow patterns by incorporating climate signal information can contribute remarkably to the knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM) are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have a substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow.     

On the prediction of the Toce alpine basin floods with distributed hydrologic models

With the objective of improving flood predictions, in recent years sophisticated continuous hydrologic models that include complex land‐surface sub‐models have been developed. This has produced a significant increase in parameterization; consequently, applications of distributed models to ungauged basins lacking specific data from field campaigns may become redundant. The objective of this paper is to produce a parsimonious and robust distributed hydrologic model for flood predictions in Italian alpine basins. Application is made to the Toce basin (area 1534 km²). The Toce basin was a case study of the RAPHAEL European Union research project, during which a comprehensive set of hydrologic, meteorological and physiographic data were collected, including the hydrologic analysis of the 1996–1997 period. Two major floods occurred during this period. We compare the FEST04 event model (which computes rainfall abstraction and antecedent soil moisture conditions through the simple Soil Conservation Service curve number method) and two continuous hydrologic models, SDM and TDM (which differ in soil water balance scheme, and base flow and runoff generation computations). The simple FEST04 event model demonstrated good performance in the prediction of the 1997 flood, but shows limits in the prediction of the long and moderate 1996 flood. More robust predictions are obtained with the parsimonious SDM continuous hydrologic model, which uses a simple one‐layer soil water balance model and an infiltration excess mechanism for runoff generation, and demonstrates good performance in both long‐term runoff modelling and flood predictions. Instead, the use of a more sophisticated continuous hydrologic model, the TDM, that simulates soil moisture dynamics in two layers of soil, and computes runoff and base flow using some TOPMODEL concepts, does not seem to be advantageous for this alpine basin. Copyright © 2006 John Wiley & Sons, Ltd.     

Quantifying the effects of climate variability and human activities on runoff from the Laohahe basin in northern China using three different methods

Much attention has recently been focused on the effects that climate variability and human activities have had on runoff. In this study, these effects are quantified using three methods, namely, multi‐regression, hydrologic sensitivity analysis, and hydrologic model simulation. A conceptual framework is defined to separate the effects. As an example, the change in annual runoff from the semiarid Laohahe basin (18 112 km²) in northern China was investigated. Non‐parametric Mann‐Kendall test, Pettitt test, and precipitation‐runoff double cumulative curve method were adopted to identify the trends and change‐points in the annual runoff from 1964 to 2008 by first dividing the long‐term runoff series into a natural period (1964–1979) and a human‐induced period (1980–2008). Then the three quantifying methods were calibrated and calculated, and they provided consistent estimates of the percentage change in mean annual runoff for the human‐induced period. In 1980–2008, human activities were the main factors that reduced runoff with contributions of 89–93%, while the reduction percentages due to changes in precipitation and potential evapotranspiration only ranged from 7 to 11%. For the various effects at different durations, human activities were the main reasons runoff decreased during the two drier periods of 1980–1989 and 2000–2008. Increased runoff during the wetter period of 1990–1999 is mainly attributed to climate variability. This study quantitatively separates the effects of climate variability and human activities on runoff, which can serve as a reference for regional water resources assessment and management. Copyright © 2011 John Wiley & Sons, Ltd.     

Regionalization of hydrologic parameters of Nash mode

hydrologic models are important tools to estimate runoff from a catchment. Identification of broad based parameters of a hydrologic model for development of direct runoff hydrograph is a key issue for the modelers. Optimization and regionalization of hydrologic parameters for application of Nash’s model is investigated in this paper. Six catchments dominated by hill torrent flows were selected for this purpose. Fifty seven rainfall events were used for regionalization of parameters and about 55 events were used for validation of the results. The hydrologic parameters of the Nash Model, number of linear cascades (N) and storage coefficient (k) were determined using optimization based upon Downhill Simplex method. The data was collected by field measurements and from Water and Power Development Authority (WAPDA) Pakistan. The physical parameters of the catchments were derived from the satellite images of the watersheds with the help of ERDAS software. The performance of the model was assessed by the model efficiency. It is concluded that the conceptual Nash model can simulate direct runoff hydrograph using regional hydrologic parameters with model efficiency of 67%.