scholarly journals Assessing hydrological sensitivity of grassland basins in the Canadian Prairies to climate using a basin classification–based virtual modelling approach

2021 ◽  
Author(s):  
Christopher Spence ◽  
Zhihua He ◽  
Kevin R. Shook ◽  
Balew A. Mekonnen ◽  
John W. Pomeroy ◽  
...  
Keyword(s):  
Climate Change ◽  
Snow Water Equivalent ◽  
Regional Scale ◽  
High Elevation ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Modeling Framework ◽  
Canadian Prairies ◽  
Hydrological Sensitivity ◽  
Virtual Modeling

Abstract. Significant challenges from changes in climate and land-use face sustainable water use in the Canadian Prairies ecozone. The region has experienced significant warming since the mid 20th Century, and continued warming of an additional 2 °C by 2050 is expected. This paper aims to enhance understanding of climate controls on Prairie basin hydrology through numerical model experiments. It approaches this by developing a basin classification–based virtual modeling framework for a portion of the Prairie region, and applying the modelling framework to investigate the hydrological sensitivity of one Prairie basin class (High Elevation Grasslands) to changes in climate. High Elevation Grasslands dominate much of central and southern Alberta and parts of southwestern Saskatchewan with outliers in eastern Saskatchewan and western Manitoba. The experiments revealed that High Elevation Grasslands snowpacks are highly sensitive to changes in climate, but that this varies geographically. Spring maximum snow water equivalent in grasslands decreases 8% per degree °C of warming. Climate scenario simulations indicated a 2 °C increase in temperature requires at least an increase of 20% in mean annual precipitation for there to be enough additional snowfall to compensate for enhanced melt losses. The sensitivity in runoff is less linear and varies substantially across the study domain; simulations using 6 °C of warming and a 30% increase in mean annual precipitation yields simulated decreases in annual runoff of 40% in climates of the western Prairie but 55% increases in climates of eastern portions. These results can be used to identify those areas of the region that are most sensitive to climate change, and highlight focus areas for monitoring and adaptation. The results also demonstrate how a basin classification–based virtual modeling framework can be applied to evaluate regional scale impacts of climate change with relatively high spatial resolution, in a robust, effective and efficient manner.

scholarly journals Climate change impacts on maritime mountain snowpack in the Oregon Cascades

2012 ◽  
Vol 9 (11) ◽  
pp. 13037-13081 ◽  
Author(s):  
E. Sproles ◽  
A. Nolin ◽  
K. Rittger ◽  
T. Painter
Keyword(s):  
Climate Change ◽  
Snow Water Equivalent ◽  
Regional Scale ◽  
Climate Change Impacts ◽  
Snow Accumulation ◽  
Distributed Model ◽  
Modeling Framework ◽  
Highly Sensitive ◽  
Snow Cover Extent ◽  
Snow Water

Abstract. Globally maritime snow comprises 10% of seasonal snow and is considered highly sensitive to changes in temperature. This study investigates the effect of climate change on maritime mountain snowpack in the McKenzie River Basin (MRB) in the Cascades Mountains of Oregon, USA. Melt water from the MRB's snowpack provides critical water supply for agriculture, ecosystems, and municipalities throughout the region especially in summer when water demand is high. Because maritime snow commonly falls at temperatures close to 0 °C, accumulation of snow versus rainfall is highly sensitive to temperature increases. Analyses of current climate and projected climate change impacts show rising temperatures in the region. To better understand the sensitivity of snow accumulation to increased temperatures, we modeled the spatial distribution of snow water equivalent (SWE) in the MRB for the period of 1989–2009 with the SnowModel spatially distributed model. Simulations were evaluated using point-based measurements of SWE, precipitation, and temperature that showed Nash-Sutcliffe Efficiency coefficients of 0.83, 0.97, and 0.80, respectively. Spatial accuracy was shown to be 82% using snow cover extent from the Landsat Thematic Mapper. The validated model was used to evaluate the sensitivity of snowpack to projected temperature increases and variability in precipitation, and how changes were expressed in the spatial and temporal distribution of SWE. Results show that a 2 °C increase in temperature would shift peak snowpack 12 days earlier and decrease basin-wide volumetric snow water storage by 56%. Snowpack between the elevations of 1000 and 1800 m is the most sensitive to increases in temperature. Upper elevations were also affected, but to a lesser degree. Temperature increases are the primary driver of diminished snowpack accumulation, however variability in precipitation produce discernible changes in the timing and volumetric storage of snowpack. This regional scale study serves as a case study, providing a modeling framework to better understand the impacts of climate change in similar maritime regions of the world.

Net Primary Production in the Shortgrass Steppe

2008 ◽  
Author(s):  
William K. Lauenroth ◽  
Daniel G. Milchunas
Keyword(s):  
Global Change ◽  
Primary Production ◽  
Net Primary Production ◽  
Regional Scale ◽  
Ecosystem Dynamics ◽  
Shortgrass Steppe ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Spatial And Temporal Patterns ◽  
Temporal And Spatial

Net primary production (NPP), the amount of carbon or energy fixed by green plants in excess of their respiratory needs, is the fundamental quantity upon which all heterotrophs and the ecosystem processes they are associated with depend. Understanding NPP is therefore a prerequisite to understanding ecosystem dynamics. Our objectives for this chapter are to describe the current state of our knowledge about the temporal and spatial patterns of NPP in the shortgrass steppe, to evaluate the important variables that control NPP, and to discuss the future of NPP in the shortgrass steppe given current hypotheses about global change. Most of the data available for NPP in the shortgrass steppe are for aboveground net primary production (ANPP), so most of our presentation will focus on ANPP and we will deal with belowground net primary production (BNPP) as a separate topic. Furthermore, our treatment of NPP in this chapter will ignore the effects of herbivory, which will be covered in detail in chapter 16. Our approach will be to start with a regional-scale view of ANPP in shortgrass ecosystems and work toward a site-scale view. We will begin by briefly placing ANPP in the shortgrass steppe in its larger context of the central North American grassland region. We will then describe the regional-scale patterns and controls on ANPP, and then move to the site-scale patterns and controls on ANPP. At the site scale, we will describe both temporal and spatial dynamics, and controls on ANPP as well as BNPP. We will then discuss relationships between spatial and temporal patterns in ANPP and end the chapter with a short, speculative section on how future global change may influence NPP in the shortgrass steppe. Temperate grasslands in central North America are found over a range of mean annual precipitation from 200 to 1200 mm.y–1 and mean annual temperatures from 0 to 20 oC (Lauenroth et al., 1999). The widely cited relationship between mean annual precipitation and average annual ANPP allows us to convert the precipitation gradient into a production gradient (Lauenroth, 1979; Lauenroth et al., 1999; Noy-Meir, 1973; Rutherford, 1980; Sala et al., 1988b).

Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

2015 ◽  
Vol 95 (1) ◽  
pp. 49-61 ◽  
Author(s):  
Ted Huffman ◽  
Budong Qian ◽  
Reinder De Jong ◽  
Jiangui Liu ◽  
Hong Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Spring Wheat ◽  
Input Data ◽  
Crop Yields ◽  
Regional Scale ◽  
Climate Change Impacts ◽  
Crop Productivity ◽  
Canadian Prairies ◽  
Crop Models

Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. and Yang, J. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49–61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERES–Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regions where crop yields were reported. A comparison between the aggregated and the reported yields was performed to examine the potential of using dynamic crop models with individual soils in a region for the simulation of regional crop yields. The regionally aggregated simulated yields demonstrated reasonable agreement with the reported data, with a correlation coefficient of 0.71 and a root-mean-square error of 266 kg ha−1 (i.e., 15% of the average yield) over 40 regions on the Canadian prairies. Our conclusion is that aggregating simulated crop yields on individual soils with a crop model can be reliable for the estimation of regional crop yields. This demonstrated its potential as a useful approach for using crop models to assess climate change impacts on regional crop productivity.

scholarly journals Climatic Rather than Edaphic Variables Determine Leaf C, N, P Stoichiometry of Deciduous Quercus Species

2021 ◽  
Author(s):  
Yutong Lin ◽  
Yuan Lai ◽  
Songbo Tang ◽  
Zhangfen Qin ◽  
Jianfeng Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Distribution Range ◽  
Climatic Variables ◽  
Annual Precipitation ◽  
Maximum Temperature ◽  
Mean Annual Precipitation ◽  
Elemental Stoichiometry ◽  
Quercus Species ◽  
Edaphic Variables ◽  
Leaf P

Abstract Purpose Leaf elemental stoichiometry is indicative of plant nutrient limitation, community composition, ecosystem function. Understanding the variations of leaf carbon (C), nitrogen (N), and phosphorus (P) stoichiometry at genus-level across large geographic regions and identifying their driving factors are important to predict species’ distribution range shifts affected by climate change.MethodsHere, we determined the patterns of leaf concentrations ([ ]) and ratios ( / ) of C, N, P of five deciduous oaks species (Quercus) across China covering ~ 20 latitude (~21–41˚ N) and longitude (~99–119˚ E) degrees, and detected their relationships with climatic, edaphic variables. ResultsLeaf [C], [N] and N/P, C/P significantly increased, while leaf [P] and C/N decreased with the increasing latitude. Leaf stoichiometry except for leaf [C] had no significant trends along the longitude. Climatic variables, i.e. mean annual temperature, mean annual precipitation, the maximum temperature of the warmest month, temperature seasonality, aridity index, and the potential evapo-transpiration were the determinants of the geographic patterns of leaf C, N, P stoichiometry. The mean annual precipitation and the maximum temperature of the warmest month indirectly regulated leaf C/N, C/P and N/P via altering leaf [P]. Edaphic variables had non-significant effects on leaf C, N, and P stoichiometry at the broad geographic range.ConclusionsClimatic variables have more important effects than edaphic properties on leaf C, N, P stoichiometry of the studied deciduous Quercus species, which imply the ongoing climate change will alter nutrient strategies and potentially shift the distribution range of this eurytopic species.

scholarly journals Modeling of the additive biomass structure of Pinus L. stands in climatic gradients of Eurasia

2018 ◽  
Author(s):  
В.А. Усольцев ◽  
И.С. Цепордей ◽  
А.А. Осмирко ◽  
В.Ф. Ковязин ◽  
В.П. Часовских ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Cover ◽  
Forest Biomass ◽  
Component Composition ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Simultaneous Increase ◽  
Biomass Equation ◽  
Additive Structure ◽  
Biomass Structure

Биомасса лесов является ключевой экосистемной составляющей и важным компонентом глобального углеродного цикла. Разработка моделей биомассы, чувствительных к изменению климата, ведется сегодня на уровнях как древостоев, так и модельных деревьев. Однако все текущие исследования подобного рода выполняются в пределах ограниченных экорегионов. Сформированная авторами база данных о биомассе насаждений подрода Pinus L., произрастающего в Евразии, в количестве 2460 пробных площадей использована в качестве основы для выявления трансконтинентальных закономерностей. Предпринята первая попытка разработать гармонизированную по структуре биомассы модель аддитивной по фракционному составу биомассы насаждений двухвойных сосен, изменяющейся по трансевразийским гидротермическим градиентам, а именно, по среднегодовым осадкам и средней январской температуре воздуха. Гармонизация обеспечена аддитивностью фракционного состава, когда суммарная биомасса стволов, ветвей, хвои и корней, полученная по «фракционным» уравнениям, равняется значению биомассы, полученной по общему уравнению. Показано, что в холодных климатических поясах увеличение осадков приводит к снижению биомассы большинства фракций, а в теплых – к ее увеличению. Соответственно во влагообеспеченных районах повышение температуры вызывает увеличение биомассы, а в засушливых – ее снижение. Геометрическая интерпретация полученной модели представлена «пропеллеро-образной» поверхностью, что согласуется с аналогичными закономерностями, ранее установленными в России на локальном и региональном уровнях. Предложенная модель аддитивной структуры биомассы сосновых древостоев дает возможность прогнозировать изменение структуры биомассы, связанное с одновременным повышением или понижением температуры января и годичных осадков. Forest biomass is a key ecosystem part and an important component of the global carbon cycle. Modelling of biomass, sensitive to climate change, is fulfiled up-to-date at levels as forest stands and sample trees. However, all current studies of this matter are carried out within limited ecoregions. The database on forest biomass of the subgenus Pinus L. in Eurasia in a number of 2460 sample plots compiled by the authors is the basis for revealing transcontinental regularities. The first attempt is made to develop a biomass structure model harmonized by means of additive component composition algorithm describing biomass change in trans-Eurasian hydrothermal gradients, namely, mean annual precipitation and mean January air temperature. Additivity of biomass component composition means that the total of biomass components (stems, branches, foliage, roots) derived from component equations is equal to the result obtained using the common biomass equation. It is stated that in cold climatic zones any increase in precipitation leads to corresponding decrease in the biomass values, but in warm zones – to its increase. In wet areas, the rise in temperature causes an increase of biomass values, but in arid areas – their reductions. Geometric view of this model represented by a «propeller-shaped» surface is consistent with the results, formerly revealed by the other authors in Russia on local and regional levels. The proposed transcontinental model of additive structure of forest biomass gives a possibility to predict the change of biomass structure in relation to simultaneous increase or decrease of January temperature and annual precipitation. The development of such models for basic forest-forming species grown in Eurasia enables to forecast any changes in the biological productivity of forest cover of Eurasia in relation to climate change.

Using a water budget model to anticipate the impact of climate change on groundwater recharge at the regional scale in cold and humid climates - example of southern Quebec (Canada)

2021 ◽  
Author(s):  
Emmanuel Dubois ◽  
Marie Larocque ◽  
Sylvain Gagné
Keyword(s):  
Climate Change ◽  
Groundwater Recharge ◽  
Water Budget ◽  
Regional Scale ◽  
Growing Season ◽  
Annual Precipitation ◽  
Climate Scenarios ◽  
Budget Model ◽  
Impact Of Climate Change ◽  
The Impact

<p>In cold and humid climates, rivers and superficial water bodies are often fed by groundwater with relatively constant inflows that are most visible during the summer (limited net precipitation) and the winter (limited runoff and infiltration). The harsh winter – short growing season succession could be drastically affected by climate change. Although water is abundant, extreme low flows are expected in the near future, most likely due to warmer summer temperatures, increased summer PET and possible lower summer precipitation. It is thus crucial to provide stakeholders with scenarios of future groundwater recharge (GWR) to anticipate the impacts of climate change on groundwater resources at the regional scale. This study aims to test the contributions of a superficial water budget model to estimate the impact of climate change on the regional GWR. The methodology is tested in a forested and agricultural region of southern Quebec, located between the St. Lawrence River and the Canada-USA border, and between the Quebec-Ontario border and Quebec City (36,000 km²). Scenarios of GWR for the region are simulated with the HydroBudget model, performing a transient-state spatialized superficial water budget, and 12 climate scenarios (RCP 4.5 and 8.5, 1951-2100 period). The model was previously calibrated in the study area for the 1961-2017 period and provides spatially distributed runoff, actual evapotranspiration, and GWR fluxes at a 500 x 500 m resolution with a monthly time step. Climate scenarios show warming of the annual temperature from +2 to +5°C and up to 20% increase of annual precipitation at the 2100 horizon compared to the 1981-2010 reference period. By the end of the century, the number of days above 0°C could double between November and April, dividing by almost two the quantity of snow during winter. The clear trends of warming temperature leads to a clear actual evapotranspiration (AET) increase while the increasing variability in annual precipitation translates into more variable annual runoff and GWR. Although no annual GWR decrease is simulated, an increase of winter GWR (up to x2) is expected, linked to warmer winters and unfrozen soils, followed by a decrease for the rest of the year, linked to a longer growing season producing higher AET rates. Although simple in its simulation process, the use of a superficial water budget model simulating soil frost provides new insights into the possible future trends in the different hydrologic variables based on a robust understanding of past condition. Aside from providing scenarios of spatialized GWR (also runoff and AET) at the 2100 horizon for a large region, this study shows that a simple water budget model is an appropriate and affordable tool to provide stakeholders with useful data for water management in a changing climate.</p>

scholarly journals Temporal and Spatial Characteristics of Precipitation and Temperature in Punjab, Pakistan

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1916 ◽  
Author(s):  
Nawaz ◽  
Li ◽  
Chen ◽  
Guo ◽  
Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Climatic Variability ◽  
Regional Scale ◽  
Elevation Gradient ◽  
High Elevation ◽  
Maximum Temperature ◽  
Moderate Increase ◽  
Study Region ◽  
Lower Elevation ◽  
Precipitation And Temperature

Identifying the changes in precipitation and temperature at a regional scale is of great importance for the quantification of climate change. This research investigates the changes in precipitation and surface air temperature indices in the seven irrigation zones of Punjab Province during the last 50 years; this province is a very important region in Pakistan in terms of agriculture and irrigated farming. The reliability of the data was examined using double mass curve and autocorrelation analysis. The magnitude and significance of the precipitation and temperature were visualized by various statistical methods. The stations’ trends were spatially distributed to better understand climatic variability across the elevation gradient of the study region. The results showed a significant warming trend in annual Tmin (minimum temperature) and Tmean (mean temperature) in different irrigation zones. However, Tmax (maximum temperature) had insignificant variations except in the high elevation Thal zone. Moreover, the rate of Tmin increased faster than that of Tmax, resulting in a reduction in the diurnal temperature range (DTR). On a seasonal scale, warming was more pronounced during spring, followed by that in winter and autumn. However, the summer season exhibited insignificant negative trends in most of the zones and gauges, except in the higher-altitude Thal zone. Overall, Bahawalpur and Faisalabad are the zones most vulnerable to warming annually and in the spring, respectively. Furthermore, the elevation-dependent trend (EDT) indicated larger increments in Tmax for higher-elevation (above 500 m a.s.l.) stations, compared to the lower-elevation ones, on both annual and seasonal scales. In contrast, the Tmin showed opposite trends at higher- and lower-elevation stations, while a moderate increase was witnessed in Tmean trends from lower to higher altitude over the study region. An increasing trend in DTR was observed at higher elevation, while a decreasing trend was noticed at the lower-elevation stations. The analysis of precipitation data indicated wide variability over the entire region during the study period. Most previous studies reported no change or a decreasing trend in precipitation in this region. Conversely, our findings indicated the cumulative increase in annual and autumn precipitation amounts at zonal and regional level. However, EDT analysis identified the decrease in precipitation amounts at higher elevation (above 1000 m a.s.l.) and increase at the lower-elevation stations. Overall, our findings revealed unprecedented evidence of regional climate change from the perspectives of seasonal warming and variations in precipitation and temperature extremes (Tmax and Tmin) particularly at higher-elevation sites, resulting in a variability of the DTR, which could have a significant influence on water resources and on the phenology of vegetation and crops at zonal and station level in Punjab.

Stable isotope ecology of the koala (Phascolarctos cinereus)

2016 ◽  
Vol 64 (5) ◽  
pp. 353 ◽  
Author(s):  
L. R. G. DeSantis ◽  
C. Hedberg
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Oxygen Isotope ◽  
Tooth Enamel ◽  
Annual Precipitation ◽  
Model Organisms ◽  
Maximum Temperature ◽  
Mean Annual Precipitation ◽  
Drinking Behaviour ◽  
Climate Variables

Australia has undergone significant climate change, both today and in the past. Koalas, due to their restricted diet of predominantly eucalyptus leaves and limited drinking behaviour may serve as model organisms for assessing past climate change via stable isotopes of tooth enamel. Here, we assess whether stable carbon and oxygen isotopes from tooth enamel record known climate variables, including proxies of relative aridity (e.g. mean annual precipitation, mean annual maximum temperature, and relative humidity). The results demonstrate significant negative relationships between oxygen isotope values and both relative humidity and mean annual precipitation, proxies for relative aridity. The best model for predicting enamel oxygen isotope values incorporates mean annual precipitation and modelled oxygen isotope values of local precipitation. These data and the absence of any relationship between modelled oxygen isotope precipitation values, independently, suggest that koalas do not track local precipitation values but instead record relative aridity. The lack of significant relationships between carbon isotopes and climate variables suggests that koalas may instead be tracking the density of forests and/or their location in the canopy. Collectively, these data suggest that koalas are model organisms for assessing relative aridity over time – much like kangaroos.

scholarly journals The Effects of Climate Change on Seasonal Snowpack and the Hydrology of the Northeastern and Upper Midwest United States

2016 ◽  
Vol 29 (18) ◽  
pp. 6527-6541 ◽  
Author(s):  
Eleonora M. C. Demaria ◽  
Joshua K. Roundy ◽  
Sungwook Wi ◽  
Richard N. Palmer
Keyword(s):  
Climate Change ◽  
United States ◽  
Snow Cover ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Early Spring ◽  
Climate Projections ◽  
Upper Midwest ◽  
Midwest United States

Abstract The potential effects of climate change on the snowpack of the northeastern and upper Midwest United States are assessed using statistically downscaled climate projections from an ensemble of 10 climate models and a macroscale hydrological model. Climate simulations for the region indicate warmer-than-normal temperatures and wetter conditions for the snow season (November–April) during the twenty-first century. However, despite projected increases in seasonal precipitation, statistically significant negative trends in snow water equivalent (SWE) are found for the region. Snow cover is likely to migrate northward in the future as a result of warmer-than-present air temperatures, with higher loss rates in northern latitudes and at high elevation. Decreases in future (2041–95) snow cover in early spring will likely affect the timing of maximum spring peak streamflow, with earlier peaks predicted in more than 80% of the 124 basins studied.

Temporal-spatial evolution patterns of the annual precipitation considering the climate change conditions in the Sanjiang Plain

2015 ◽  
Vol 7 (1) ◽  
pp. 198-211 ◽  
Author(s):  
Qiang Fu ◽  
Tianxiao Li ◽  
Tienan Li ◽  
Heng Li
Keyword(s):  
Climate Change ◽  
Central Area ◽  
Periodic Variation ◽  
Warming Trend ◽  
Sanjiang Plain ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Trend Test ◽  
Annual Fluctuation ◽  
The Sanjiang Plain

The wavelet theory, Mann-Kendall trend test and ArcGIS spatial analysis theory were used to analyze annual precipitation and mean temperature data that were collected at seven national weather stations in the Sanjiang Plain from 1956 to 2013 to identify the temporal-spatial patterns of annual precipitation changes caused by climate change conditions. The results showed that the climate in the Sanjiang Plain experienced a significant warming trend over the past 50 years, with the temperature increasing by 1.35 °C since the 1960s. Additionally, the precipitation also exhibited certain trend characteristics, which revealed a larger difference in different areas. The annual precipitation exhibited 23-year and 12-year periodic variation characteristics, and the period with above-average annual precipitation levels is expected to continue after 2013. The spatial distributions of the mean annual precipitation for different years were different, whereas the spatial distribution of the multi-year mean precipitation was relatively uniform. The annual variation amplitude of the annual precipitation in the central area was larger than that in the south. The overall inter-annual fluctuation of the annual precipitation was relatively small with a mostly normal distribution. The results can provide guidance for scientific investigations and the reasonable use of rainfall resources in the Sanjiang Plain.

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