scholarly journals Diagnosis of future changes in hydrology for a Canadian Rocky Mountain headwater basin

2020 ◽  
Author(s):  
Xing Fang ◽  
John W. Pomeroy
Keyword(s):  
Climate Change ◽  
Meteorological Data ◽  
Wrf Model ◽  
Rocky Mountain ◽  
Mapping Method ◽  
Blowing Snow ◽  
Runoff Generation ◽  
Forest Canopies ◽  
Near Surface ◽  
Snow Season

Abstract. Climate change is anticipated to have impacts on the water resources of the Saskatchewan River, which originates in the Canadian Rocky Mountains. To better understand the climate change impacts in the mountain headwaters of this basin, a physically based hydrological model was developed for this basin using the Cold Regions Hydrological Modelling platform (CRHM) for Marmot Creek Research Basin (~9.4 km2), located in the Front Ranges of the Canadian Rocky Mountain. Marmot Creek is composed of ecozones ranging from montane forests to alpine tundra and alpine exposed rock and includes both large and small clearcuts. The model included blowing and intercepted snow redistribution, sublimation, energy-balance snowmelt, slope and canopy effects on melt, Penman-Monteith evapotranspiration, infiltration to frozen and unfrozen soils, hillslope hydrology, streamflow routing and groundwater components and was parameterised without calibration from streamflow. Near-surface outputs from the 4-km Weather Research and Forecasting (WRF) model were bias corrected using the quantile delta mapping method with respect to meteorological data from five stations located from montane forest to mountaintop during October 2005–September 2013. The bias corrected WRF outputs during current period (CTRL, 2005–2013) and future period (PGW, 2091–2099) were used to drive model simulations to assess changes in Marmot Creek's hydrology. Under a "business as usual" forcing scenario: representative concentration pathway 8.5 (RCP8.5) in PGW, the basin warms up by 4.7 °C and receives 16 % more precipitation, which leads to a 40 mm decline in seasonal peak snowpack, 84 mm decrease in snowmelt volume, 0.2 mm day-1 slower melt rate, and 49 days shorter snowcover duration. The alpine snow season will be shortened by almost one and half month, but at some lower elevations there are large decreases in peak snowpack (~45 %) as well as a shorter snow season. Losses of peak snowpack will be much greater in clearcuts than under forest canopies. In alpine and treeline ecozones blowing snow transport and sublimation will be suppressed by higher threshold wind speeds for transport, in forest canopies sublimation losses from intercepted snow will decrease due to faster unloading and drip, and for all ecozones, evapotranspiration will increase due to longer snow-free seasons and more rainfall. Runoff will begin earlier in all ecozones, but, as result of variability in surface and subsurface hydrology, forested and alpine ecozones generate larger runoff volumes, ranging from 12 % to 25 %, whereas the treeline ecozone has a small (2 %) decrease in runoff volume due to decreased melt volumes from smaller snowdrifts. The shift in timing in streamflow is notable, with 236 % higher flows in spring months and 12 % lower flows in summer and 13 % higher flows in early fall. Overall, Marmot Creek basin annual streamflow discharge will increase by 18 % with PGW without a change in its streamflow generation efficiency, despite the basin shifting from snowmelt runoff towards rainfall-dominated runoff generation.

scholarly journals Diagnosis of future changes in hydrology for a Canadian Rockies headwater basin

2020 ◽  
Vol 24 (5) ◽  
pp. 2731-2754 ◽  
Author(s):  
Xing Fang ◽  
John W. Pomeroy
Keyword(s):  
Climate Change ◽  
Meteorological Data ◽  
Mapping Method ◽  
Mountain Range ◽  
Blowing Snow ◽  
Runoff Generation ◽  
Montane Forests ◽  
Canadian Rockies ◽  
Near Surface ◽  
Snow Season

Abstract. Climate change is anticipated to impact the hydrology of the Saskatchewan River, which originates in the Canadian Rockies mountain range. To better understand the climate change impacts in the mountain headwaters of this basin, a physically based hydrological model was developed for this basin using the Cold Regions Hydrological Modelling platform (CRHM) for Marmot Creek Research Basin (∼9.4 km2), located in the Front Ranges of the Canadian Rockies. Marmot Creek is composed of ecozones ranging from montane forests to alpine tundra and alpine exposed rock and includes both large and small clearcuts. The model included blowing and intercepted snow redistribution, sublimation, energy-balance snowmelt, slope and canopy effects on melt, Penman–Monteith evapotranspiration, infiltration to frozen and unfrozen soils, hillslope hydrology, streamflow routing, and groundwater components and was parameterised without calibration from streamflow. Near-surface outputs from the 4 km Weather Research and Forecasting (WRF) model were bias-corrected using the quantile delta mapping method with respect to meteorological data from five stations located from low-elevation montane forests to alpine ridgetops and running over October 2005–September 2013. The bias-corrected WRF outputs during a current period (2005–2013) and a future pseudo global warming period (PGW, 2091–2099) were used to drive model simulations to assess changes in Marmot Creek's hydrology. Under a “business-as-usual” forcing scenario, Representative Concentration Pathway 8.5 (RCP8.5) in PGW, the basin will warm up by 4.7 ∘C and receive 16 % more precipitation, which will lead to a 40 mm decline in seasonal peak snowpack, 84 mm decrease in snowmelt volume, 0.2 mm d−1 slower melt rate, and 49 d shorter snow-cover duration. The alpine snow season will be shortened by almost 1.5 months, but at some lower elevations there will be large decreases in peak snowpack (∼45 %) in addition to a shorter snow season. Declines in the peak snowpack will be much greater in clearcuts than under mature forest canopies. In alpine and treeline ecozones, blowing snow transport and sublimation will be suppressed by higher-threshold wind speeds for transport, in forest ecozones, sublimation losses from intercepted snow will decrease due to faster unloading and drip, and throughout the basin, evapotranspiration will increase due to a longer snow-free season and more rainfall. Runoff will begin earlier in all ecozones, but, as a result of variability in surface and subsurface hydrology, forested and alpine ecozones will generate the greatest runoff volumetric increases, ranging from 12 % to 25 %, whereas the treeline ecozone will have a small (2 %) decrease in runoff volume due to decreased melt volumes from smaller snowdrifts. The shift in timing in streamflow will be notable, with 236 % higher flows in spring months and 12 % lower flows in summer and 13 % higher flows in early fall. Overall, Marmot Creek's annual streamflow discharge will increase by 18 % with PGW, without a change in its streamflow generation efficiency, despite its basin shifting from primarily snowmelt runoff towards rainfall-dominated runoff generation.

scholarly journals BAYWRF: a high-resolution present-day climatological atmospheric dataset for Bavaria

2020 ◽  
Vol 12 (4) ◽  
pp. 3097-3112
Author(s):  
Emily Collier ◽  
Thomas Mölg
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Large Scale ◽  
Wrf Model ◽  
Atmospheric Model ◽  
Climate Impact ◽  
Reanalysis Data ◽  
Near Surface ◽  
Local Climate ◽  
The Impact

Abstract. Climate impact assessments require information about climate change at regional and ideally also local scales. In dendroecological studies, this information has traditionally been obtained using statistical methods, which preclude the linkage of local climate changes to large-scale drivers in a process-based way. As part of recent efforts to investigate the impact of climate change on forest ecosystems in Bavaria, Germany, we developed a high-resolution atmospheric modelling dataset, BAYWRF, for this region over the thirty-year period of September 1987 to August 2018. The atmospheric model employed in this study, the Weather Research and Forecasting (WRF) model, was configured with two nested domains of 7.5 and 1.5 km grid spacing centred over Bavaria and forced at the outer lateral boundaries by ERA5 reanalysis data. Using an extensive network of observational data, we evaluate (i) the impact of using grid analysis nudging for a single-year simulation of the period of September 2017 to August 2018 and (ii) the full BAYWRF dataset generated using nudging. The evaluation shows that the model represents variability in near-surface meteorological conditions generally well, although there are both seasonal and spatial biases in the dataset that interested users should take into account. BAYWRF provides a unique and valuable tool for investigating climate change in Bavaria with high interdisciplinary relevance. Data from the finest-resolution WRF domain are available for download at daily temporal resolution from a public repository at the Open Science Framework (Collier, 2020; https://doi.org/10.17605/OSF.IO/AQ58B).

scholarly journals Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)

2020 ◽  
Vol 14 (12) ◽  
pp. 4553-4579
Author(s):  
François Tuzet ◽  
Marie Dumont ◽  
Ghislain Picard ◽  
Maxim Lamare ◽  
Didier Voisin ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Meteorological Data ◽  
Measurement Techniques ◽  
Temporal Variations ◽  
Surface Energy Budget ◽  
Near Surface ◽  
French Alps ◽  
Snow Season ◽  
Snow Metamorphism ◽  
The Impact

Abstract. The presence of light-absorbing particles (LAPs) in snow leads to a decrease in short-wave albedo affecting the surface energy budget. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps and an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism. First, a unique dataset collected at Col du Lautaret (2058 m a.s.l., above sea level, French Alps) for the two snow seasons 2016–2017 and 2017–2018 is presented. This dataset consists of spectral albedo measurements, vertical profiles of snow specific surface area (SSA), density and concentrations of different LAP species. Spectral albedos are processed to estimate SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of LAP concentrations and SSA measurements. Our dataset highlights, among others, large discrepancies between two measurement techniques of black carbon (BC) concentrations in snow (namely thermal-optical and laser-induced incandescence). Second, we present ensemble snowpack simulations of the multi-physics version of the detailed snowpack model Crocus, forced with in situ meteorological data, as well as dust and BC deposition fluxes from an atmospheric model. The temporal variations of near-surface LAP concentrations and SSA are most of the time correctly simulated. The simulated seasonal radiative forcing of LAPs is 33 % higher for the 2017–2018 snow season than for the 2016–2017 one, highlighting a strong variability between these two seasons. However, the shortening of the snow season caused by LAPs is similar with 10 ± 5 and 11 ± 1 d for the first and the second snow seasons, respectively. This counter-intuitive result is attributed to two small snowfalls at the end of the first season and highlights the importance in accounting for meteorological conditions to correctly predict the impact of LAPs. The strong variability of season shortening caused by LAPs in the multi-physics ensemble for the first season (10 ± 5 d) also points out the sensitivity of model-based estimations of LAP impact on modelling uncertainties of other processes. Finally, the indirect impact of LAPs (i.e. the enhancement of energy absorption due to the acceleration of the metamorphism by LAPs) is negligible for the 2 years considered here, which is contrary to what was found in previous studies for other sites.

scholarly journals How does bias correction of regional climate model precipitation affect modelled runoff?

2015 ◽  
Vol 19 (2) ◽  
pp. 711-728 ◽  
Author(s):  
J. Teng ◽  
N. J. Potter ◽  
F. H. S. Chiew ◽  
L. Zhang ◽  
B. Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Mapping Method ◽  
Future Climate Change ◽  
Runoff Generation ◽  
Simulated Precipitation

Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf with Tower Observations

2016 ◽  
Vol 55 (11) ◽  
pp. 2349-2367 ◽  
Author(s):  
Jonathan D. Wille ◽  
David H. Bromwich ◽  
Melissa A. Nigro ◽  
John J. Cassano ◽  
Marian Mateling ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Meteorological Data ◽  
Wrf Model ◽  
Blowing Snow ◽  
Ice Shelf ◽  
Ross Ice Shelf ◽  
Numerical Predictions ◽  
Flight Operations ◽  
Pbl Scheme

AbstractFlight operations in Antarctica rely on accurate weather forecasts aided by the numerical predictions primarily produced by the Antarctic Mesoscale Prediction System (AMPS) that employs the polar version of the Weather Research and Forecasting (Polar WRF) Model. To improve the performance of the model’s Mellor–Yamada–Janjić (MYJ) planetary boundary layer (PBL) scheme, this study examines 1.5 yr of meteorological data provided by the 30-m Alexander Tall Tower! (ATT) automatic weather station on the western Ross Ice Shelf from March 2011 to July 2012. Processed ATT observations at 10-min intervals from the multiple observational levels are compared with the 5-km-resolution AMPS forecasts run daily at 0000 and 1200 UTC. The ATT comparison shows that AMPS has fundamental issues with moisture and handling stability as a function of wind speed. AMPS has a 10-percentage-point (i.e., RH unit) relative humidity dry bias year-round that is highest when katabatic winds from the Byrd and Mulock Glaciers exceed 15 m s−1. This is likely due to nonlocal effects such as errors in the moisture content of the katabatic flow and AMPS not parameterizing the sublimation from blowing snow. AMPS consistently overestimates the wind speed at the ATT by 1–2 m s−1, in agreement with previous studies that attribute the high wind speed bias to the MYJ scheme. This leads to reduced stability in the simulated PBL, thus affecting the model’s ability to properly simulate the transfer of heat and momentum throughout the PBL.

scholarly journals Impacts of Land Cover Change on the Near-Surface Temperature in the North China Plain

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Ruijie Qu ◽  
Xiaolin Cui ◽  
Haiming Yan ◽  
Enjun Ma ◽  
Jinyan Zhan
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
North China Plain ◽  
North China ◽  
Wrf Model ◽  
Near Surface ◽  
The North ◽  
The North China Plain

This study first tested and verified the ability of the Weather Research and Forecasting (WRF) model to simulate the near-surface temperature in the North China Plain. Then the static land cover data in the WRF were replaced, and thereafter the modified WRF model was used to explore the impacts of land cover change on the near-surface temperature in the North China Plain in year 1992 and year 2005. The results indicated that the land cover change in the North China Plain, which was characterized by the regional urbanization, had led to significant changes in the near-surface temperature, increasing the regional near-surface temperature by 0.03°C/year on average. The spatial pattern of the climate change basically corresponded to that of the land cover change; for example, the temperature increased most significantly in the regions mainly consisting of cities and built-up area. Besides, there were some variations in the degree and range of influence of the land cover change on the temperature among seasons. The result can provide important theoretical support for the adaptation to climate change, scientific land cover change management, and land use planning.

scholarly journals How does bias correction of RCM precipitation affect modelled runoff?

2014 ◽  
Vol 11 (9) ◽  
pp. 10683-10724
Author(s):  
J. Teng ◽  
N. J. Potter ◽  
F. H. S. Chiew ◽  
L. Zhang ◽  
J. Vaze ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Mapping Method ◽  
Future Climate Change ◽  
Runoff Generation ◽  
Simulated Precipitation ◽  
The Difference

Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the difference between the tested methods is small in the modelling experiments here (and as reported in the literature), mainly because of the substantial corrections required and inconsistent errors over time (non-stationarity). The errors remaining in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitation of RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

scholarly journals Climate Variability Since 1970 and Farmers’ Observations in Northern Ghana

2016 ◽  
Vol 5 (2) ◽  
pp. 41 ◽  
Author(s):  
Emmanuel Nyadzi
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Water Conservation ◽  
Crop Production ◽  
Meteorological Data ◽  
Northern Region ◽  
Northern Ghana ◽  
Climate Variability And Change ◽  
Urban Centers ◽  
Access To Credit

<p>The study examines how farmers’ observations of climate variability and change correspond with 42 years (1970-2011) meteorological data of temperature and rainfall. It shows how farmers in the Northern Region of Ghana adjust to the changing climate and explore the various obstacles that hinder the implementation of their adaptation strategies. With the help of an extension officer, 200 farmers from 20 communities were randomly selected based on their farming records. Temperatures over the last four decades (1970-2009) increased at a rate of 0.04 (± 0.41) ˚C and 0.3(± 0.13)˚C from 2010-2011 which is consistent to the farmers (82.5%) observations. Rainfall within the districts are characterised by inter-annual and monthly variability. It experienced an increased rate of 0.66 (± 8.30) mm from 1970-2009, which was inconsistent with the farmers (81.5%) observation. It however decreased from 2010-2011 at a huge rate of -22.49 (±15.90) mm which probably was the reason majority of the respondents claim rainfall was decreasing. Only 64.5% of the respondents had adjusted their farming activities because of climate variability and change. They apply fertilizers and pesticides, practice soil and water conservation, and irrigation for communities close to dams. Respondents desire to continue their current adaptation methods but may in the future consider changing crop variety, water-harvesting techniques, change crop production to livestock keeping, and possibly migrate to urban centers. Lack of climate change education, low access to credit and agricultural inputs are some militating factors crippling the farmers’ effort to adapt to climate change.</p>

scholarly journals Response of LUCC on Runoff Generation Process in Middle Yellow River Basin: The Gushanchuan Basin

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1237 ◽  
Author(s):  
Caihong Hu ◽  
Li Zhang ◽  
Qiang Wu ◽  
Shan-e-hyder Soomro ◽  
Shengqi Jian
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Yellow River ◽  
Trend Test ◽  
Generation Process ◽  
Runoff Generation ◽  
Contribution Rate ◽  
Runoff Reduction ◽  
Runoff Change ◽  
Significant Downward Trend

Runoff reduction in most river basins in China has become a hotpot in recent years. The Gushanchuan river, a primary tributary of the middle Yellow river, Northern China, showed a significant downward trend in the last century. Little is known regarding the relative contributions of changing environment to the observed hydrological trends and response on the runoff generation process in its watershed. On the basis of observed hydrological and meteorological data from 1965–2010, the Mann-Kendall trend test and climate elasticity method were used to distinguish the effects of climate change and human activities on runoff in the Gushanchuan basin. The results indicate that the runoff in the Gushanchuan Basin has experienced significant declines as large as 77% from 1965 to 2010, and a mutation point occurred around 1997; the contribution rate of climate change to runoff change is 12.9–15.1%, and the contribution rate of human activities to runoff change is 84.9–87.1%. Then we divided long-term data sequence into two stages around the mutation point, and analyzed runoff generation mechanisms based on land use and cover changes (LUCC). We found that the floods in the Gushanchuan Basin were still dominated by Excess-infiltration runoff, but the proportion in 1965–1997 and 1998–2010 decreased gradually (68.46% and 45.83% in turn). The proportion of Excess-storage runoff and Mixed runoff has increased, which means that the runoff is made up of more runoff components. The variation law of the LUCC indicates that the forest area increased by 49.61%, the confluence time increased by 50.42%, and the water storage capacity of the watershed increased by 30.35%.

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

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