On the Hydrologic Adjustment of Climate-Model Projections: The Potential Pitfall of Potential Evapotranspiration

2011 ◽  
Vol 15 (1) ◽  
pp. 1-14 ◽  
Author(s):  
P. C. D. Milly ◽  
Krista A. Dunne
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Climate Change Impacts ◽  
Hydrologic Model ◽  
Energy Budgets ◽  
Drought Indices ◽  
Hydrologic Models ◽  
Runoff Change

Abstract Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement (“downscaling”), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median −11%) caused by the hydrologic model’s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen–Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water.

Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts

2015 ◽  
Vol 16 (2) ◽  
pp. 762-780 ◽  
Author(s):  
Pablo A. Mendoza ◽  
Martyn P. Clark ◽  
Naoki Mizukami ◽  
Andrew J. Newman ◽  
Michael Barlage ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Change Scenario ◽  
Hydrologic Models ◽  
Climate System Model

Abstract The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

scholarly journals Climate change impacts on runoff in West Africa: a review

2014 ◽  
Vol 18 (7) ◽  
pp. 2789-2801 ◽  
Author(s):  
P. Roudier ◽  
A. Ducharne ◽  
L. Feyen
Keyword(s):  
Climate Change ◽  
West Africa ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Climate Change Impacts ◽  
Future Research ◽  
Negative Change ◽  
Lack Of Information ◽  
Runoff Change ◽  
Interannual Fluctuations

Abstract. This review summarizes the impacts of climate change on runoff in West Africa, assesses the uncertainty in the projections and describes future research needs for the region. To do so, we constitute a meta-database made of 19 studies and 301 future runoff change values. The future tendency in streamflow developments is overall very uncertain (median of the 301 points is 0% and mean +5.2%), except for (i) the Gambia River, which exhibits a significant negative change (median = −4.5%), and (ii) the Sassandra and the Niger rivers, where the change is positive (+14.4% and +6.1%). A correlation analysis revealed that runoff changes are tightly linked to changes in rainfall (R = 0.49), and to a smaller extent also to changes in potential evapotranspiration. Other parameters than climate – such as the carbon effect on plant water efficiency, land use dynamics or water withdrawals – could also significantly impact on runoff, but they generally do not offset the effects of climate change. In view of the potential changes, the large uncertainty therein and the high vulnerability of the region to such changes, there is an urgent need for integrated studies that quantify the potential effects of these processes on water resources in West Africa and for more accuracy in climate models rainfall projections. We especially underline the lack of information concerning projections of future floods and droughts, and of interannual fluctuations in streamflow.

scholarly journals Producing hydrologic scenarios from raw climate model outputs using an asynchronous modelling framework

2021 ◽  
Author(s):  
Simon Ricard ◽  
Philippe Lucas-Picher ◽  
François Anctil
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Water Discharge ◽  
Climate Change Impacts ◽  
Climate Variables ◽  
Post Processing ◽  
Hydrologic Models ◽  
Simulated Climate ◽  
Meteorological Observations ◽  
The Impact

Abstract. Statistical post-processing of climate model outputs is a common hydroclimatic modelling practice aiming to produce climate scenarios that better fit in-situ observations and to produce reliable stream flows forcing calibrated hydrologic models. Such practice is however criticized for disrupting the physical consistency between simulated climate variables and affecting the trends in climate change signals imbedded within raw climate simulations. It also requires abundant good-quality meteorological observations, which are not available for many regions in the world. A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations, nor for statistical post-processing of climate model outputs, nor for calibrating hydrologic models. By combining asynchronous hydroclimatic modelling, an alternative framework designed to construct hydrologic scenarios without resorting to meteorological observations, and quantile perturbation applied to streamflow observations, the proposed workflow produces sound and plausible hydrologic scenarios considering: (1) they preserve trends and physical consistency between simulated climate variables, (2) are implemented from a modelling cascades despite observation scarcity, and (3) support the participation of end-users in producing and interpreting climate change impacts on water resources. The proposed modelling workflow is implemented over four subcatchments of the Chaudière River, Canada, using 9 North American CORDEX simulations and a pool of lumped conceptual hydrologic models. Forced with raw climate model outputs, hydrologic models are calibrated over the reference period according to a calibration metric designed to function with temporally uncorrelated observed and simulated streamflow values. Perturbation factors are defined by relating each simulated streamflow quantiles over both reference and future periods. Hydrologic scenarios are finally produced by applying perturbation factors to available streamflow observations.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

scholarly journals Uncertainties in Riverine and Coastal Flood Impacts under Climate Change

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1774
Author(s):  
Shuyi Wang ◽  
Mohammad Reza Najafi ◽  
Alex J. Cannon ◽  
Amir Ali Khan
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Block Design ◽  
Coastal Flooding ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Storm Events ◽  
Idf Curves

Climate change can affect different drivers of flooding in low-lying coastal areas of the world, challenging the design and planning of communities and infrastructure. The concurrent occurrence of multiple flood drivers such as high river flows and extreme sea levels can aggravate such impacts and result in catastrophic damages. In this study, the individual and compound effects of riverine and coastal flooding are investigated at Stephenville Crossing located in the coastal-estuarine region of Newfoundland and Labrador (NL), Canada. The impacts of climate change on flood extents and depths and the uncertainties associated with temporal patterns of storms, intensity–duration–frequency (IDF) projections, spatial resolution, and emission scenarios are assessed. A hydrologic model and a 2D hydraulic model are set up and calibrated to simulate the flood inundation for the historical (1976–2005) as well as the near future (2041–2070) and far future (2071–2100) periods under Representative Concentration Pathways (RCPs) 4.5 and 8.5. Future storm events are generated based on projected IDF curves from convection-permitting Weather Research and Forecasting (WRF) climate model simulations, using SCS, Huff, and alternating block design storm methods. The results are compared with simulations based on projected IDF curves derived from statistically downscaled Global Climate Models (GCMs). Both drivers of flooding are projected to intensify in the future, resulting in higher risks of flooding in the study area. Compound riverine and coastal flooding results in more severe inundation, affecting the communities on the coastline and the estuary area. Results show that the uncertainties associated with storm hyetographs are considerable, which indicate the importance of accurate representation of storm patterns. Further, simulations based on projected WRF-IDF curves show higher risks of flooding compared to the ones associated with GCM-IDFs.

scholarly journals Future directions for hydropedology: quantifying impacts of global change on land use

2009 ◽  
Vol 13 (8) ◽  
pp. 1427-1438 ◽  
Author(s):  
M. J. Vepraskas ◽  
J. L. Heitman ◽  
R. E. Austin
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Water Table ◽  
Climate Change Impacts ◽  
Hydrologic Model ◽  
Soil Survey ◽  
Septic Systems ◽  
Hydrologic Models ◽  
Similar Work ◽  
Table Data

Abstract. Hydropedology is well positioned to address contemporary issues resulting from climate change. We propose a six-step process by which digital, field-scale maps will be produced to show where climate change impacts will be greatest for two land uses: a) home sites using septic systems, and b) wetlands. State and federal laws have defined critical water table levels that can be used to determine where septic systems will function well or fail, and where wetlands are likely to occur. Hydrologic models along with historic rainfall and temperature data can be used to compute long records of water table data. However, it is difficult to extrapolate such data across land regions, because too little work has been done to test different ways for doing this reliably. The modeled water table data can be used to define soil drainage classes for individual mapping units, and the drainage classes used to extrapolate the data regionally using existing digital soil survey maps. Estimates of changes in precipitation and temperature can also be input into the models to compute changes to water table levels and drainage classes. To do this effectively, more work needs to be done on developing daily climate files from the monthly climate change predictions. Technology currently exists to use the NRCS Soil Survey Geographic (SSURGO) Database with hydrologic model predictions to develop maps within a GIS that show climate change impacts on septic system performance and wetland boundaries. By using these maps, planners will have the option to scale back development in sensitive areas, or simply monitor the water quality of these areas for pathogenic organisms. The calibrated models and prediction maps should be useful throughout the Coastal Plain region. Similar work for other climate-change and land-use issues can be a valuable contribution from hydropedologists.

scholarly journals Evaluation of drought indices at interannual to climate change timescales: a case study over the Amazon and Mississippi river basins

2012 ◽  
Vol 9 (11) ◽  
pp. 13231-13249 ◽  
Author(s):  
E. Joetzjer ◽  
H. Douville ◽  
C. Delire ◽  
P. Ciais ◽  
B. Decharme ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
River Discharge ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Global Climate Model ◽  
Meteorological Drought ◽  
River Basins ◽  
Drought Indices ◽  
The Impact

Abstract. The present study compares three meteorological drought indices (scPDSI, SPI and SPEI respectively) and their ability to account for the variations of annual mean river discharge on both interannual and climate change timescales. The Standardized Runoff Index (SRI) is used as a proxy of river discharge. The Mississippi and Amazon river basins provide two contrasted testbeds for this analysis. All meteorological drought indices are derived from monthly 2-meter temperature and/or precipitation, using either gridded observations or outputs of a global climate model. The SPI based solely on precipitation is not outperformed by the SPEI (accounting for potential evapotranspiration) and the scPDSI (based on a simplified water balance) at detecting interannual SRI variations. Under increasing concentrations of greenhouse gases, the simulated response of the areal fraction in drought is highly index-dependent, suggesting that more physical water balance models are needed to account for the impact of global warming on hydrological droughts.

scholarly journals Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

2015 ◽  
Vol 12 (3) ◽  
pp. 2657-2706 ◽  
Author(s):  
T. Olsson ◽  
J. Jakkila ◽  
N. Veijalainen ◽  
L. Backman ◽  
J. Kaurola ◽  
...  
Keyword(s):  
Climate Change ◽  
Seasonal Changes ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Catchment Scale ◽  
Temperature And Precipitation

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional Climate Models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction of RCM temperature and precipitation for Finland is carried out using different versions of distribution based scaling (DBS) method. The DBS adjusted RCM data is used as input of a hydrological model to simulate changes in discharges in four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period (1961–2000) and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the SD of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

Spatiotemporal Changes in Headwater Flow Contributions to Major Rivers of Germany under Changing Climate

2020 ◽  
Author(s):  
Akash Koppa ◽  
Thomas Remke ◽  
Stephan Thober ◽  
Oldrich Rakovec ◽  
Sebastian Müller ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Soil Moisture ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Model Ensemble ◽  
Hydrologic Models ◽  
River Temperature

<p>Headwater systems are a major source of water, sediments, and nutrients (including nitrogen and carbon di-oxide) for downstream aquatic, riparian, and inland ecosystems. As precipitation changes are expected to exhibit considerable spatial variability in the future, we hypothesize that headwater contribution to major rivers will also change significantly. Quantifying these changes is essential for developing effective adaptation and mitigation strategies against climate change. However, the lack of hydrologic projections at high resolutions over large domains have hindered attempts to quantify climate change impacts on headwater systems.</p><p>Here, we overcome this challenge by developing an ensemble of hydrologic projections at an unprecedented resolution (1km) for Germany. These high resolution projections are developed within the framework of the Helmholtz Climate Initiative (https://www.helmholtz.de/en/current-topics/the-initiative/climate-research/). Our modeling chain consists of the following four components:</p><p><strong>Climate Modeling:</strong> We statistically downscale and bias-adjust climate change scenarios from three representative concentration pathway (RCP) scenarios derived from the EURO-CORDEX ensemble - 2.6, 4.5, and 8.5 to a horizontal resolution of 1km over Germany (i.e, a total of 75 ensemble members). The EURO-CORDEX ensemble is generated by dynamically downscaling CMIP-5 general circulation models (GCM) using regional climate models (RCMs). <strong>Hydrologic Modeling:</strong> To account for model structure uncertainty, the climate model projections are used as forcings for three spatially distributed hydrologic models - a) the mesocale Hydrologic model (mHM), b) Noah-MP, and c) HTESSEL. The outputs that will be generated in the study are soil moisture, evapotranspiration, snow water equivalent, and runoff. <strong>Streamflow Routing:</strong> To minimize uncertainty from river routing schemes, we use the multiscale routing model (mRM v1.0) to route runoff from all the three models. <strong>River Temperature Modeling:</strong> A novel river temperature model is used to quantify the changes in river temperature due to anthropogenic warming.</p><p>The 225-member ensemble streamflow outputs (75 climate model members and 3 hydrologic models) are used to quantify the changes in the contribution of headwater watersheds to all the major rivers in Germany. Finally, we analyze changes in soil moisture, snow melt, and river temperature and their implications for headwater contributions. Previously, a high-resolution (5km) multi-model ensemble for climate change projections has been created within the EDgE project<strong><sup>1,2,3,4</sup></strong>. The newly created projections in this project will be compared against those created in the EDgE project.  The ensemble used in this project will profit from the higher resolution of the regional climate models that provide a more detailed land orography.</p><p><strong>References</strong></p><p><strong>[1] </strong>Marx,<em> A. et al. (2018). Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 C. Hydrology and Earth System Sciences, 22(2), 1017-1032.</em></p><p><strong>[2]</strong><em> Samaniego, L. et al. (2019). Hydrological forecasts and projections for improved decision-making in the water sector in Europe. Bulletin of the American Meteorological Society.</em></p><p><strong>[3]</strong> Samaniego,<em> L. and Thober, S., et al. (2018). Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change, 8(5), 421.</em></p><p><strong>[4]</strong> Thober,<em> S. et al. (2018). Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003.</em></p><p> </p><p> </p><p> </p>

Some Pitfalls in Statistical Downscaling of Future Climate

2018 ◽  
Vol 99 (4) ◽  
pp. 791-803 ◽  
Author(s):  
John R. Lanzante ◽  
Keith W. Dixon ◽  
Mary Jo Nath ◽  
Carolyn E. Whitlock ◽  
Dennis Adams-Smith
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Added Value ◽  
Historical Period ◽  
Perfect Model

AbstractStatistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.

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