scholarly journals RECONSTRUCTIONS OF HYDROLOGIC VARIABLES IN THE NORTH PLATTE RIVER BASIN

2020 ◽  
Vol 6 (2) ◽  
pp. 59-71
Author(s):  
Sally Rose Anderson ◽  
Amanda Bowen ◽  
Glenn Tootle ◽  
Abdoul Oubeidillah
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Platte River ◽  
The North ◽  
Hydrologic Variability ◽  
Snow Water ◽  
Moisture Sensitive ◽  
Platte River Basin

Reconstructions of hydrologic variables are commonly created using tree-ring chronologies (TRCs) to generate information about historic climate and potential future variability. This study used TRCs to reconstruct annual streamflow, April 1st Snow Water Equivalent (SWE), and soil moisture in the North Platte River Basin (NPRB). Stepwise linear regression was performed to determine which of the 55 moisture sensitive TRCs were the best predictors of hydrologic variation. The regressions explained 63% of the variability in streamflow, 55% of the variability in SWE, and 66% of the variability in soil moisture. This study then maximized the overlapping period of records which resulted in a decrease in the percent of variability explained but indicated that the regression models were stable for long reconstruction periods. This study successfully reconstructed all three hydrologic variables for NPRB to 1438 or earlier. Temporal wet and dry periods for streamflow and SWE were closely aligned while soil moisture did not follow similar temporal patterns. This was likely due to a natural “lag” between soil moisture and streamflow / SWE given soil moisture tends to retain antecedent signals. The availability of reconstructed hydrologic data in NPRB allows for a better understanding of the long-term hydrologic variability in the region.

scholarly journals IMPACTS OF BEETLE KILL ON MODELED STREAMFLOW RESPONSE IN THE NORTH PLATTE RIVER BASIN

2020 ◽  
Vol 6 (3) ◽  
pp. 27-39
Author(s):  
Abdoul Oubeidillah ◽  
Glenn Tootle ◽  
Venkat Lakshmi
Keyword(s):  
Land Cover ◽  
River Basin ◽  
Hydrologic Model ◽  
Tree Cover ◽  
Infiltration Capacity ◽  
Platte River ◽  
The North ◽  
Hydrologic Impacts ◽  
Streamflow Response ◽  
Platte River Basin

A beetle epidemic across the western United States has resulted in the death of millions of acres of forests. This beetle outbreak, referred to as “beetle kill”, has caused many to believe that such dramatic changes in land cover could potentially alter the hydrology of the impacted regions. One of the most important hydrological processes that beetle kill has the potential to impact is streamflow. This research evaluates the hydrologic impacts on streamflow from land cover change due to beetle kill in the North Platte River Basin (NPRB) (Colorado and Wyoming, USA) by utilizing the Variable Infiltration Capacity (VIC) hydrologic model. Utilizing the National Agricultural Imagery Program (NAIP) dataset from 2005 / 2006 (onset of “beetle kill”) to more current conditions (2009), a decrease in tree cover of 16% to 40% was estimated. This decrease in tree cover was applied to VIC modeled streamflow from 1950 to 2000. The VIC model predicted a minimal increase in streamflow of approximately 5% which was not statistically significant.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Snowpack variability and trends at long-term stations in northern Colorado, USA

2015 ◽  
Vol 371 ◽  
pp. 131-136 ◽  
Author(s):  
S. R. Fassnacht ◽  
M. Hultstrand
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Entire Period ◽  
Northern Colorado ◽  
Snow Water ◽  
Highly Correlated ◽  
The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

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