Changes in the Timing of Snowmelt and Streamflow in Colorado: A Response to Recent Warming

2010 ◽  
Vol 23 (9) ◽  
pp. 2293-2306 ◽  
Author(s):  
David W. Clow
Keyword(s):  
Linear Regression ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Geographic Region ◽  
Regression Coefficients ◽  
Trend Detection ◽  
Linear Regression Models ◽  
Daily Streamflow ◽  
Air Temperatures ◽  
High Elevations

Abstract Trends in the timing of snowmelt and associated runoff in Colorado were evaluated for the 1978–2007 water years using the regional Kendall test (RKT) on daily snow-water equivalent (SWE) data from snowpack telemetry (SNOTEL) sites and daily streamflow data from headwater streams. The RKT is a robust, nonparametric test that provides an increased power of trend detection by grouping data from multiple sites within a given geographic region. The RKT analyses indicated strong, pervasive trends in snowmelt and streamflow timing, which have shifted toward earlier in the year by a median of 2–3 weeks over the 29-yr study period. In contrast, relatively few statistically significant trends were detected using simple linear regression. RKT analyses also indicated that November–May air temperatures increased by a median of 0.9°C decade−1, while 1 April SWE and maximum SWE declined by a median of 4.1 and 3.6 cm decade−1, respectively. Multiple linear regression models were created, using monthly air temperatures, snowfall, latitude, and elevation as explanatory variables to identify major controlling factors on snowmelt timing. The models accounted for 45% of the variance in snowmelt onset, and 78% of the variance in the snowmelt center of mass (when half the snowpack had melted). Variations in springtime air temperature and SWE explained most of the interannual variability in snowmelt timing. Regression coefficients for air temperature were negative, indicating that warm temperatures promote early melt. Regression coefficients for SWE, latitude, and elevation were positive, indicating that abundant snowfall tends to delay snowmelt, and snowmelt tends to occur later at northern latitudes and high elevations. Results from this study indicate that even the mountains of Colorado, with their high elevations and cold snowpacks, are experiencing substantial shifts in the timing of snowmelt and snowmelt runoff toward earlier in the year.

scholarly journals Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Manickavasagar Kayanan ◽  
Pushpakanthie Wijekoon
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
Prior Information ◽  
Estimation Procedure ◽  
Regression Coefficients ◽  
Linear Regression Models ◽  
Monte Carlo Simulation Study ◽  
Regression Parameters ◽  
Linear Restrictions

Among several variable selection methods, LASSO is the most desirable estimation procedure for handling regularization and variable selection simultaneously in the high-dimensional linear regression models when multicollinearity exists among the predictor variables. Since LASSO is unstable under high multicollinearity, the elastic-net (Enet) estimator has been used to overcome this issue. According to the literature, the estimation of regression parameters can be improved by adding prior information about regression coefficients to the model, which is available in the form of exact or stochastic linear restrictions. In this article, we proposed a stochastic restricted LASSO-type estimator (SRLASSO) by incorporating stochastic linear restrictions. Furthermore, we compared the performance of SRLASSO with LASSO and Enet in root mean square error (RMSE) criterion and mean absolute prediction error (MAPE) criterion based on a Monte Carlo simulation study. Finally, a real-world example was used to demonstrate the performance of SRLASSO.

scholarly journals The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

2018 ◽  
Vol 22 (11) ◽  
pp. 5817-5846 ◽  
Author(s):  
Camila Alvarez-Garreton ◽  
Pablo A. Mendoza ◽  
Juan Pablo Boisier ◽  
Nans Addor ◽  
Mauricio Galleguillos ◽  
...  
Keyword(s):  
Time Series ◽  
Arid Regions ◽  
Snow Water Equivalent ◽  
Potential Evapotranspiration ◽  
Data Sources ◽  
Large Sample ◽  
Daily Streamflow ◽  
Multiple Data ◽  
Diverse Data ◽  
High Elevations

Abstract. We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885.

Five decades of warming: impacts on snow cover in Norway

2017 ◽  
Vol 49 (3) ◽  
pp. 670-688 ◽  
Author(s):  
Jonathan Rizzi ◽  
Irene Brox Nilsen ◽  
James Howard Stagge ◽  
Kjersti Gisnås ◽  
Lena M. Tallaksen
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Land Area ◽  
Air Temperatures ◽  
Northern Latitudes ◽  
Snow Cover Extent ◽  
The World ◽  
Snow Water ◽  
Seasonal Analysis ◽  
High Elevations

Abstract Northern latitudes are experiencing faster warming than other regions in the world, which is partly explained by the snow albedo feedback. In Norway, mean temperatures have been increasing since the 1990s, with 2014 being the warmest year on record, 2.2 °C above normal (1961–1990). At the same time, a concurrent reduction in the land area covered by snow has been reported. In this study, we present a detailed spatial and temporal (monthly and seasonal) analysis of trends and changes in snow indices based on a high resolution (1 km) gridded hydro-meteorological dataset for Norway (seNorge). During the period 1961–2010, snow cover extent (SCE) was found to decrease, notably at the end of the snow season, with a corresponding decrease in snow water equivalent except at high elevations. SCE for all Norway decreased by more than 20,000 km2 (6% of the land area) between the periods 1961–1990 and 1981–2010, mainly north of 63° N. Overall, air temperature increased in all seasons, with the highest increase in spring (particularly in April) and winter. Mean monthly air temperatures were significantly correlated with the monthly SCE, suggesting a positive land–atmosphere feedback enhancing warming in winter and spring.

An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression

2021 ◽  
Author(s):  
Xue Yu ◽  
Yifan Sun ◽  
Hai-Jun Zhou
Keyword(s):  
Linear Regression ◽  
Message Passing ◽  
Greedy Algorithms ◽  
Linear Equations ◽  
Regression Coefficients ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Approximate Message Passing ◽  
Highly Correlated ◽  
Solution Accuracy

Abstract High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients. In this paper, we propose a simple heuristic algorithm to construct sparse high-dimensional linear regression models, which is adapted from the shortest-solution guided decimation algorithm and is referred to as ASSD. This algorithm constructs the support of regression coefficients under the guidance of the least-squares solution of the recursively decimated linear equations, and it applies an early-stopping criterion and a second-stage thresholding procedure to refine this support. Our extensive numerical results demonstrate that ASSD outper-forms LASSO, vector approximate message passing, and two other representative greedy algorithms in solution accuracy and robustness. ASSD is especially suitable for linear regression problems with highly correlated measurement matrices encountered in real-world applications.

AGRICULTURAL METEOROLOGY: CORRELATION OF AIR TEMPERATURES IN CENTRAL AND SOUTHERN ALBERTA AND SASKATCHEWAN WITH LATITUDE, LONGITUDE AND ALTITUDE

1938 ◽  
Vol 16c (1) ◽  
pp. 16-26 ◽  
Author(s):  
J. W. Hopkins
Keyword(s):  
Air Temperature ◽  
Local Variation ◽  
Temperature Gradients ◽  
Regression Coefficients ◽  
Regression Equations ◽  
Partial Regression ◽  
Air Temperatures ◽  
Southern Alberta ◽  
Different Seasons ◽  
Agricultural Meteorology

Linear partial regression coefficients of the 18-year average (1917–34) monthly mean air temperature recorded at 43 points in central and southern Alberta and Saskatchewan on latitude, longitude, and altitude were determined for each month of the year. The three series of coefficients each show an independent seasonal trend. The decrease in air temperature with altitude is greatest in summer and least in winter, whereas the gradient associated with longitude is most pronounced in winter and least in evidence in summer. The influence of latitude is likewise most pronounced in winter, but shows two minima, in spring and autumn respectively. The monthly regression equations account for most of the variance of the station averages, and hence provide a reasonably satisfactory graduation of the climatological temperature gradients characteristic of this area at different seasons of the year.These regression equations could not, however, be applied satisfactorily to the monthly averages for individual years, owing to greater local variation. Additional equations were therefore determined from the records for 1935 at 27 stations in the sub-area bounded by the 50th and 52nd parallels and the 104th and 108th meridians. The results suggest that further additions to the number of stations would still be desirable, and that if this was effected a fairly accurate graduation should be possible within this district, even in individual years.

scholarly journals Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 22 ◽  
Author(s):  
Pierre Perron ◽  
Yohei Yamamoto
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Structural Changes ◽  
Error Variance ◽  
Regression Coefficients ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Finite Sample Properties ◽  
Joint Approach ◽  
Size Distortions

In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power.

scholarly journals UNHEATED ENVIRONMENTS FOR OVERWINTERING NURSERY PLANTS IN CONTAINERS

1980 ◽  
Vol 60 (3) ◽  
pp. 895-902 ◽  
Author(s):  
R. L. DESJARDINS ◽  
CALVIN CHONG
Keyword(s):  
Linear Relationship ◽  
Snow Cover ◽  
Air Temperature ◽  
Plant Protection ◽  
Storage Condition ◽  
Regression Coefficients ◽  
Good Linear ◽  
Plastic Structure ◽  
Good Protection ◽  
Air Temperatures

Nineteen unheated environments, including 12 under plastic structure, 6 under structureless blanket cover, and a control, were monitored and compared for their effectiveness in protecting nursery plants in containers during the winter. A relatively good linear relationship was found between the minimum daily air temperatures outside (control) and the minimum daily temperatures of both air and container within the various environments. From these relationships, regression coefficients were found that could be used for predicting the minimum air and container temperatures within the protected environments using outside air temperature. As indicated by post-winter storage condition of selected cultivars, all environments with snow cover under plastic structure or under blanket cover provided good plant protection, while similar environments without snow cover were less effective. Over-the-canopy protection with Microfoam insulation during the coldest months provided good protection in polyhouse environments without snow cover. Based on the regression coefficients and on the post-winter storage condition of plants, the environments were classified into three major groups: regression coefficients ranging from 0.2 to 0.4 for environments providing good protection; from 0.4 to 0.5 for those providing intermediate protection; and from 0.5 to 0.7 for those providing poor protection. A technique is presented to determine which types of environment are likely to be successful in other locations across Canada.

scholarly journals Multiple Indicators of Extreme Changes in Snow-Dominated Streamflow Regimes, Yakima River Basin Region, USA

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2608
Author(s):  
Anna M. Wagner ◽  
Katrina E. Bennett ◽  
Glen E. Liston ◽  
Christopher A. Hiemstra ◽  
Dan Cooley
Keyword(s):  
River Basin ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Negative Impact ◽  
Clear Trend ◽  
Multiple Indicators ◽  
Air Temperatures ◽  
Yakima River ◽  
Streamflow Trends

Snow plays a major role in the hydrological cycle. Variations in snow duration and timing can have a negative impact on water resources. Excluding predicted changes in snowmelt rates and amounts could result in deleterious infrastructure, military mission, and asset impacts at military bases across the US. A change in snowpack can also lead to water shortages, which in turn can affect the availability of irrigation water. We performed trend analyses of air temperature, snow water equivalent (SWE) at 22 SNOTEL stations, and streamflow extremes for selected rivers in the snow-dependent and heavily irrigated Yakima River Basin (YRB) located in the Pacific Northwest US. There was a clear trend of increasing air temperature in this study area over a 30 year period (water years 1991–2020). All stations indicated an increase in average air temperatures for December (0.97 °C/decade) and January (1.12 °C/decade). There was also an upward trend at most stations in February (0.28 °C/decade). In December–February, the average air temperatures were 0.82 °C/decade. From these trends, we estimate that, by 2060, the average air temperatures for December–February at most (82%) stations will be above freezing. Furthermore, analysis of SWE from selected SNOTEL stations indicated a decreasing trend in historical SWE, and a shift to an earlier peak SWE was also assumed to be occurring due of the shorter snow duration. Decreasing trends in snow duration, rain-on-snow, and snowmelt runoff also resulted from snow modeling simulations of the YRB and the nearby area. We also observed a shift in the timing of snowmelt-driven peak streamflow, as well as a statistically significant increase in winter maximum streamflow and decrease in summer maximum and minimum streamflow trends by 2099. From the streamflow trends and complementary GEV analysis, we show that the YRB basin is a system in transition with earlier peak flows, lower snow-driven maximum streamflow, and higher rainfall-driven summer streamflow. This study highlights the importance of looking at changes in snow across multiple indicators to develop future infrastructure and planning tools to better adapt and mitigate changes in extreme events.

scholarly journals Comparison study on the confidence intervals in linear regression models with restricted parameter space

2019 ◽  
Vol 52 (2) ◽  
pp. 115-127
Author(s):  
XIUQIN BAI ◽  
WEIXING SONG
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Confidence Region ◽  
Estimation Procedure ◽  
Regression Coefficients ◽  
Comparison Study ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Confidence Estimation

This paper proposes an empirical likelihood confidence region for the regression coefficients in linear regression models when the regression coefficients are subjected to some equality constraints. The shape of the confidence set does not depend on the re-parametrization of the regression model induced by the equality constraint. It is shown that the asymptotic coverage rate attains the nominal confidence level and the Bartlett correction can successfully reduce the coverage error rate from $O(n^{-1})$ to $O(n^{-2})$, where n denotes the sample size. Simulation studies are conducted to evaluate the finite sample performance of the proposed empirical likelihood empirical confidence estimation procedure. Finally, a comparison study is conducted to compare the finite sample performance of the proposed and the classical ellipsoidal confidence sets based on normal theory.

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